tag:blogger.com,1999:blog-31921840902980163072024-02-20T00:13:49.367+01:00A Structural Realist's Guide to the Universeanti-ism-ism is the one true-ism!Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.comBlogger68125tag:blogger.com,1999:blog-3192184090298016307.post-12980110394041792852014-02-13T00:03:00.003+01:002014-02-13T00:06:54.244+01:00Sane as it ever was: The historical meaning of the crisis In PsychologyHere's my first experiment in self-publishing on figshare!<br />
<br />
Basically it means you can cite the essay as follows:<br />
<br />
Hasselman, Fred (2014): Sane as it ever was: The historical meaning of the crisis In Psychology. figshare.
http://dx.doi.org/10.6084/m9.figshare.930729<br />
<br />
Oh.. and you get this embedded iframe code too, pretty cool so far!<br />
<br />
<br />
<iframe frameborder="0" height="427" src="http://wl.figshare.com/articles/930729/embed?show_title=1" width="568"></iframe>
天Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-9884079057416708332014-01-11T01:32:00.000+01:002014-01-29T15:30:44.928+01:00Time = Money: The Morality of Being Accurate<!DOCTYPE html>
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<h3>A Post-Publication Peer-Review (3PR) of <em>Time, Money, and Morality</em></h3>
<h4>Gino, F., & Mogilner, C. (online, 2013). Time, Money, and Morality. <em>Psychological Science</em>. <a href="http://pss.sagepub.com/lookup/doi/10.1177/0956797613506438">DOI: 10.1177/0956797613506438</a></h4>
<hr/>
<p>File under: </p>
<p><a href="http://hadibeenareviewer.wordpress.com"><strong>HIBAR</strong></a>: <strong>H</strong>ad <strong>I</strong> <strong>B</strong>een <strong>A</strong> <strong>R</strong>eviewer… </p>
<p><strong>3PR</strong>: <strong>P</strong>ost-<strong>P</strong>ublication <strong>P</strong>eer-<strong>R</strong>eview (or: <strong>3</strong>mpirical <strong>P</strong>lausability <strong>R</strong>esuscitation) </p>
<p>Performed by <em>Fred Hasselman</em><br/>
Contact <a href="mailto:me%E2%88%9Efredhasselman%E2%80%A2com">me</a> if you have any questions </p>
<hr/>
<h4>Introduction</h4>
<p>The <em>Time, Money, and Morality</em> article has been HIBAR-ed on Twitter and the Blogosphere (e.g., by <a href="http://rolfzwaan.blogspot.nl/2013/12/time-money-and-morality-and-p-hacking.html">Rolf Zwaan</a> and <a href="http://www1.psych.purdue.edu/%7Egfrancis/Publications/TimeMoneyMorality/">Greg Francis</a> ) and the discussion seems to revolve around the validity of the inferences based p-values close to 0.05 (e.g., they raise suspicions of p-hacking). </p>
<p>In short, the article reports of 4 Experiments testing 2 core postulates: </p>
<ul>
<li>Postulate 1: Priming <code>Money</code> activates self-interest and increases unethical behaviour </li>
<li>Postulate 2: Priming <code>Time</code> activates self-reflection and decreases unethical behaviour<br/></li>
</ul>
<p>Unethical behaviour is operationalised as taking the opportunity to cheat on a task.<br/>
Priming methods vary across experiments, so do the tasks that allow for an opportunity to cheat.<br/>
In Experiment 1 the two postulates are tested, Experiments 2-4 concern an assessment of the role of self-reflection on cheating behaviour and is operationalised differently across experiments. </p>
<h4>Hold on to your P-curves for a moment… Back to the basics!</h4>
<p>In this <strong>P</strong>ost-<strong>P</strong>ublication <strong>P</strong>eer-<strong>R</strong>eview (3PR) I demonstrate that there is indeed some cause for concern about the way these results are presented and interpreted. Was it p-hacking? … I don't know and maybe I don't even care. To me this is an example of <strong>sloppy science</strong>, p-hacked or not, these results were <em>allowed</em> to be published by expert peers. It is more relevant to discuss the broken system of quality control that should have picked up on at least some of the following issues: </p>
<ol>
<li>Important information is missing:
<ul>
<li>in general (e.g., number of subjects per condition, sample size determination)<br/></li>
<li>selectively across experiments (e.g., participants per cell, reporting of effect sizes)<br/></li>
</ul></li>
<li>The analyses used on frequency data are inappropriate<br/></li>
<li>Invalid or biased inferences and oddities:<br/>
<ul>
<li>No adjustments for multiple comparisons<br/></li>
<li>“Marginal significance” shifts ad hoc between <code>0.1 > p > 0.05</code><br/></li>
<li>Obvious intervening/mediator variable is omitted: Accuracy of performance<br/></li>
<li>No explanation of (conflicting) results across experiments (e.g., variation in amount of cheating)</li>
<li>No explanation for failing of random assignment to design levels (<strong>none</strong> of the experiments have equal N samples)</li>
</ul></li>
</ol>
<p>The article under scrutiny is by no means exceptional with respect to such issues, moreover, the way frequency / proportion data are analysed in psychological science is generally awkward and most of the time completely wrong.</p>
<p>I will 3PR the data based on the information in the article and comment on the results: </p>
<p>I. Analysis of Proportion / frequency data<br/>
II. Analysis of Extent of Cheating data<br/>
III. HAPPE-ing: <strong>H</strong>ypothesing <strong>A</strong>fter <strong>P</strong>ost <strong>P</strong>ublication <strong>E</strong>valuation </p>
<p>The R code used to generate the results (and this page) is available in <a href="http://fredhasselman.com/main/wp-content/creations/markdown/HIBAR_timemoneymorality_fin.Rmd">this Markdown</a> file, and <a href="http://fredhasselman.com/?p=303">this post</a> explains how to post to a WordPress blog.</p>
<hr/>
<h2>I. Analysis of proportion / frequency data</h2>
<p>Some concerns can be raised about the significant differences between various conditions in proportion <code>Cheating</code> reported in the 4 experiments.<br/>
First and foremost, no corrections for multiple comparisons are conducted, should one do so, just 2 significant proportion differences remain:<br/>
<code>Money</code> vs. <code>Time</code> in experiment 1 & 4. In Experiment 3, the sample difference <code>No Mirror: Money - Time</code> was marginally significant in the 2<sup>nd</sup> significant digit (original: <code>p = 0.015</code>, adjusted \( \alpha \) = 0.013, Bonferroni). </p>
<p>Second, no continuity correction is applied, these proportions are calculated from discrete numbers (participants). If a continuity correction is applied, 2-3 significant differences remain, depending on the \( \alpha \)-level chosen: </p>
<table><thead>
<tr>
<th align="center">Exp.</th>
<th align="left">Contrast</th>
<th align="right">Published</th>
<th align="center">Continuity corrected</th>
<th align="center">Bonferroni adjusted</th>
</tr>
</thead><tbody>
<tr>
<td align="center">1</td>
<td align="left">Money-Time</td>
<td align="right"><strong><.001</strong></td>
<td align="center"><strong>4 × 10<sup>-4</sup></strong></td>
<td align="center"><strong>< 0.0167</strong></td>
</tr>
<tr>
<td align="center">1</td>
<td align="left">Money-Ctrl</td>
<td align="right"><strong><.05</strong></td>
<td align="center">0.0894</td>
<td align="center">> 0.0167</td>
</tr>
<tr>
<td align="center">1</td>
<td align="left">Time-Ctrl</td>
<td align="right"><strong><.05</strong></td>
<td align="center">0.0836</td>
<td align="center">> 0.0167</td>
</tr>
<tr>
<td align="center"></td>
<td align="left"></td>
<td align="right"></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center">2</td>
<td align="left">Int: Money-Time</td>
<td align="right"><strong><.01</strong></td>
<td align="center">0.1493</td>
<td align="center"><em>~ 0.0125</em></td>
</tr>
<tr>
<td align="center">2</td>
<td align="left">Per: Money-Time</td>
<td align="right">>.05</td>
<td align="center">1</td>
<td align="center">> 0.0125</td>
</tr>
<tr>
<td align="center">2</td>
<td align="left">Money: Int-Per</td>
<td align="right"><strong><.03</strong></td>
<td align="center">0.0856</td>
<td align="center">> 0.0125</td>
</tr>
<tr>
<td align="center">2</td>
<td align="left">Time: Int-Per</td>
<td align="right">>.05</td>
<td align="center">1</td>
<td align="center">> 0.0125</td>
</tr>
<tr>
<td align="center"></td>
<td align="left"></td>
<td align="right"></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center">3</td>
<td align="left">Mir: Money-Time</td>
<td align="right">>.05</td>
<td align="center">0.7996</td>
<td align="center">> 0.0125</td>
</tr>
<tr>
<td align="center">3</td>
<td align="left">NoM: Money-Time</td>
<td align="right"><strong><.003</strong></td>
<td align="center"><strong>0.0293</strong></td>
<td align="center"><em>~ 0.0125</em></td>
</tr>
<tr>
<td align="center">3</td>
<td align="left">Money: Mir-NoM</td>
<td align="right">>.05</td>
<td align="center">0.0537</td>
<td align="center">> 0.0125</td>
</tr>
<tr>
<td align="center">3</td>
<td align="left">Time: Mir-NoM</td>
<td align="right">>.05</td>
<td align="center">1</td>
<td align="center">> 0.0125</td>
</tr>
<tr>
<td align="center"></td>
<td align="left"></td>
<td align="right"></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center">4</td>
<td align="left">Money-Time</td>
<td align="right"><strong><.001</strong></td>
<td align="center"><strong>10<sup>-4</sup></strong></td>
<td align="center"><strong>< 0.0167</strong></td>
</tr>
<tr>
<td align="center">4</td>
<td align="left">Money-Ctrl</td>
<td align="right"><strong><.05</strong></td>
<td align="center">0.0522</td>
<td align="center">> 0.0167</td>
</tr>
<tr>
<td align="center">4</td>
<td align="left">Time-Ctrl</td>
<td align="right"><strong><.05</strong></td>
<td align="center">0.0752</td>
<td align="center">> 0.0167</td>
</tr>
<tr>
<td align="center"></td>
<td align="left"></td>
<td align="right"></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center"></td>
<td align="left"></td>
<td align="right"></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center"></td>
<td align="left"><strong>Number sig. results</strong></td>
<td align="right"><strong>9</strong></td>
<td align="center"><strong>3</strong></td>
<td align="center"><em>Original:</em> <strong>4</strong>, <em>Continuity:</em> <strong>2</strong></td>
</tr>
</tbody></table>
<p>This calls for a more appropriate analysis of frequency data: </p>
<ol>
<li>Log-linear analysis of observed cell frequencies<br/></li>
<li>Exact odds ratios of 2x2 sub-tables to test hypotheses using Effect Size CIs<br/></li>
</ol>
<p>(<code>Cheating</code> can be considered a dichotomous response, so logistic regression could also be used, see III. HAPPE-ing)</p>
<blockquote>
<p><strong>Note:</strong><br/>
Experiment 2 & 3 do not list <em>n</em> per condition, the most likely values for <em>n</em> (1. closest to an integer value; 2. as equal as possible; 3. Add to total N) are assumed: </p>
<p><em>Experiment 2</em> </p>
<table><thead>
<tr>
<th align="center">Prime</th>
<th align="center">Assessment</th>
<th align="left">Ncond * %Cheat = Ncheat (deviation)</th>
</tr>
</thead><tbody>
<tr>
<td align="center">Money</td>
<td align="center">Personality</td>
<td align="left">36 <code>*</code> 0.2778 <code>=</code> 10.0008 (8 × 10<sup>-4</sup>)</td>
</tr>
<tr>
<td align="center">Time</td>
<td align="center">Personality</td>
<td align="left">35 <code>*</code> 0.2857 <code>=</code> 9.9995 (5 × 10<sup>-4</sup>)</td>
</tr>
<tr>
<td align="center">Money</td>
<td align="center">Intelligence</td>
<td align="left">38 <code>*</code> 0.5 <code>=</code> 19 (0)</td>
</tr>
<tr>
<td align="center">Time</td>
<td align="center">Intelligence</td>
<td align="left">33 <code>*</code> 0.303 <code>=</code> 9.999 (10 × 10<sup>-4</sup>)</td>
</tr>
</tbody></table>
<p><em>Experiment 3</em> </p>
<table><thead>
<tr>
<th align="center">Prime</th>
<th align="center">Assessment</th>
<th align="left">Ncond * %Cheat = Ncheat (deviation)</th>
</tr>
</thead><tbody>
<tr>
<td align="center">Money</td>
<td align="center">Mirror</td>
<td align="left">31 <code>*</code> 0.387 <code>=</code> 11.997 (0.003)</td>
</tr>
<tr>
<td align="center">Time</td>
<td align="center">Mirror</td>
<td align="left">28 <code>*</code> 0.321 <code>=</code> 8.988 (0.012)</td>
</tr>
<tr>
<td align="center">Money</td>
<td align="center">No Mirror</td>
<td align="left">30 <code>*</code> 0.667 <code>=</code> 20.01 (0.01)</td>
</tr>
<tr>
<td align="center">Time</td>
<td align="center">No Mirror</td>
<td align="left">31 <code>*</code> 0.355 <code>=</code> 11.005 (0.005)</td>
</tr>
</tbody></table>
</blockquote>
<br>
<h3>1. log-linear analysis of observed cell frequencies</h3>
<p>Log-linear analysis, or poisson regression using the generalised linear model, can be used to test whether relationships exist among the variables in a multi-way contingency table. Here I analyse the number of participants in each cell of the design: The observed frequencies take the role of the dependent variable and the levels of the design factors such as <code>Mediator</code>, <code>Prime</code> and <code>Cheating</code> are considered the levels of independent variables (another option would have been a logistic / probit regression with <code>Cheating</code> as the dependent binary / proportion variable). </p>
<p>Two types of result given for each experiment: </p>
<p><em>First</em>, a table listing deviance tests for the full (saturated) model. The analysis starts with the NULL model (all frequencies are equal) in the first row. Each subsequent row lists what happens to the deviance (of the model in the previous row) when a factor is added. A significant drop in deviance means adding the factor to the model contributes to predicting the difference between expected and observed frequencies. For hints of corroboration of the hypotheses reported in the paper, significant interactions between a design factor and <code>Cheating</code> are necessary. </p>
<p><em>Second</em>, a mosaic plot is displayed, this is a graphical representation of the conditional cell frequencies. The mosaic plot also indicates which residual frequencies (observed - expected) are significantly below (red) or above (blue) the expected frequencies (residuals are interpretable as a Z-score). The coloured cells contribute most to a high and possibly significant \( \chi^2 \) value. </p>
<blockquote>
<p><strong>Note:</strong>
The significance of the change in deviance can depend on the order in which factors are added to the model and is not the same as a significant beta weight in a regression model.</p>
</blockquote>
<pre><code>> [1] "Experiment 1"
</code></pre>
<pre><code>> Analysis of Deviance Table
>
> Model: poisson, link: log
>
> Response: Count
>
> Terms added sequentially (first to last)
>
>
> Df Deviance Resid. Df Resid. Dev Pr(>Chi)
> NULL 5 24.8
> Cheating 1 9.33 4 15.4 0.00225 **
> Prime 2 0.02 2 15.4 0.98981
> Cheating:Prime 2 15.41 0 0.0 0.00045 ***
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
</code></pre>
<p><img 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xYtUgMGDHgjCnkhDxiohQF/N+DAEI+n5zevRRqR2JUKPhLFQCYwUHoGXMHfeOON/jobgYE4GdhGmR0jBohviy3S09aaxiqo4GNVXGQWAxjAAAYa2MDLSt+dQ7cS09Jcp2nweWDNxiOo4ONRTuQyDwPdu3f/oGPHjnnsyS4YwAAGshrwp5lPF/40sz8RHMuggo9lsZHpTAYYB5/JSvHWMQ6+eO45ckEMtFMqe4pDC5JaERKhgi+CdA6JgSQYYBx8Ekq5pM/xJJ2dn7/3j+tZUsHHteTINwYwgAEMNJQBf6dlP3GaGNZQB2nodKngG9ow6TeaAcbBN5pqDoSBUjdwiE7wGbFMrBLdROyCCj52RUaGazLAOPiazBRnPePgi+OdoxbEwGSlcnM6pfM0dae72AUVfOyKjAxjIB4GGAcfj3IilxkN+K69Mr1ljaZrM+4V8ZVU8BEvILKHAQxgAAMYqIsBKvi6WOM3kTTAOPhIFguZwgAGimSgWZGOy2ExUHADHgevRC8reMIkWCcDHgc/atQovgdfJ3tJ/lGlmsavURP5q6sbxsJTLdX67mb3kg8q+JIvYk4QA8UxwDj44niP/1HnXZ5KzdPrYp9qyFOZ1JCJRyVtKviolAT5wAAGMIABG1gpHkNF/Q3wDL7+DkkhIgYYBx+RgiAbGMBAJAxQwUeiGMhEIQwwDr4QFguXBuPgC+eSlDBQFwM00dfFGr/BAAZyGgjGwY8ePTrnvuyAgcBAm9bbnrB2XcWNrVtvtSRYV8hpZeXq5mvXrTlz9eqFjxYy3SimRQUfxVIhTxjAAAaSaqBp092+d9A97bfrPqh9QyiYOeeR1HOTT9tDaZd8BU8TfUP8DSLNohhgHHxRtHNQDGAgogao4CNaMGSr9gb4HnztnTXkL/gefEPaJW0M5DZABZ/bEXtgAAN1MMA4+DpI4ydRNFAWxUzlkyeewedjiX0wgAEMYCApBsp1ojuETvaXmr9CvCoqQusjP8sdfOSLiAzma4Bx8PmaYj8MYCCLgW20bYwYIL4ttkhPW2saq6CCj1VxkdlsBhgHn81O429jHHzjO+eIBTGg1+Sm+oqtxLQ012n6iYhVUMHHqrjILAbiYyAYBx+fHJNTDGwysEhzp4vtxJab1sZshgo+ZgVGdjGAAQxgoFEMnKaj3CwOFYeLbiJWQQUfq+Iis9kMMA4+mx22YQADtTTwVmj/mZpfHlqOxSwVfCyKiUzmY4Bx8PlYarx9GAffeK45UsENfEcp9hTN0yn7rXqbpedjM6GCj01RkVEMxMsA4+DjVV7kdpOBPpo7XrQS44V7z+8n3Ls+VsE4+FgVF5nFAAYwgIEGNrC70r9FvC6mirHpqSbxCu7g41Ve5DaLAcbBZ5HDJgxgIF8D/gjNKOEX3rwoXMG7R33sggo+dkVGhmsywDj4mswUZz3j4IvjnaPW28ACpfBDMS+d0iRN9xIz0suxmVDBx6aoyCgG4mWAcfDxKi9y+xUD67VUGVrjcfH0og8JYRYDGMAABjCAgSIZoJNdkcRz2MIbYBx84Z2SIgYa3UBVZct3pv+lsqJidlVDHHv23MeaVqWqYvuFuNo4oYKvjS32jbQBj4NXBi+LdCYTlDmPgx81apR7JBMYyNvAFyvnXTxz9oOvirx/U4cdPfyt5IMKvuSLmBPEQHEMMA6+ON5L4KgrdA53lcB5FP0U6GRX9CIgAxjAAAYwgIHCG6CCL7xTUiySAcbBF0k8h8UABiJpgCb6SBYLmaqLAY+DHzhwYKpfv351+Tm/KbCBYBz8jBmxGz5cYBMkVxsD3bp1O7yqqurP7du3X1Kb3+W777p165ovX7787IULFz6T72/iuh8VfFxLjnxjIOIGgnHwo0ePjnhOyV6UDDRr1qz/DTfcUL7vvvv6TXIFj2eeeSZ11lln+WMyVPAFt0uCGMAABjCAgSwGVMmnOnTokGWPum9y2kkJnsEnpaQTcJ6Mg09AIXOKGMBA3gao4PNWxY5RN8D34KNVQnwPPlrlQW6SZ4AKPnllzhljoFEMMA6+UTRzkMIb8LOB+8SL4rB08hdo6g/OxCqS8zAiVsVCZjGAAQxgoEgG/CW534spYpxYK5qk0SQ+wR18fMqKnOYwwDj4HILYjAEM5GNgrnYaKHwDPEwMFf1F7IIKPnZFRoZrMsD34GsyU5z1wTj44hydo2Kgzgae1S/98oZOwnfvruTfFRUiVkEFH6viIrMYiI+BYBx8fHJMTjGwwYC/BX+/CJ6/r9L8PwTfg5cEAgMYwAAGMBB3A2+FTmCm5mNXwdPJLlSCzMbbAOPg411+5B4DETKwi/LSRxwiPhKPis9FrIIm+lgVF5nNZoBx8NnsNP42xsE3vnOOWBAD/pjFJcJ38BPFQnG7aCdiFdzBx6q4yCwG4mOAcfDxKSty+hUDu2vpT+Kl0NodNe93408NrYv8LBV85IuIDGIAAxjAQCMaeFDHukGcItws7970S8UfRayCCj5WxUVmsxnwOPgRI0akOnbsmG03tmEAAxjIZmCRNh4r3CTfWXwiYvf8XXlO8QzeFoiSMMA4+GgVI+Pgo1Ue5KbWBlypvy9iWbn7bKngbYHAAAYKboBx8AVXSoIYqJUBmuhrpYudMYABDGCggQ2UzZo1K3Xvvfc2yGEWL17sdBNxc0sF3yB/hUi0GAYYB18M6xwTA4U1MHfu3CsvvPDCBWVlZVWFTXljapWVlU0rKirGNETaUUuTCj5qJUJ+6mzA4+D148vqnAA/LKgBj4MfNWqUhxwRGKiNgc9UAV9bmx+wb2YDiWimyHzqrMUABhrSAOPgG9IuaWMgtwEq+NyO2AMDGMAABjAQOwM00ceuyMhwTQYYB1+TGdZjID4Gtt122+8qt3/YfPPNl26zzTbz9ejtoSD36njXVf/OBwfLddk+cuTIY5ctW3b+woUL/xWkU6pTKvhSLdkEnpfHwQ8cODDVr59fJU0U20AwDn7GDH9am8BAfgbat2+/169+9as9+vbtm2rVqpV/dHzwyyOPPDLl9UHUZXuTJk1S55xzziClQQUfiGSKAQxgoDYGgnHwo0ePrs3P2DfhBqZOnTpeCq7JpMF/p7bbbrtMmzasy2d7+qKgQXro15ixIm3gGXyRxHNYDGAAAxhofAO9e/dO9e/ff1KOI++i7eeKkWKY2ErELqjgY1dkZLgmA4yDr8kM6zGAgcBAt27dUuPHj58ZLGeYlsznYpNcwXdIF+w+mhoi5gb4Hny0CpDvwUerPOKSm8svv3wrdYIrZnb97gZ/LvYZ8W/xmPA7NspFrCKpnezuUik9KeaJs8RC0V08KAgMYKAABhgHXwCJCUzi5ptvPmrvvfdODRrkfnBFCdcDNwg+F1sU/fU7qD//t0KME38VPxXzxc2CCl4SCAxgAAOlamDKlCmpoUOHHqTzu7uGc+RzsTWIicPqNcrkZsLjL74hZoke4lNBxNiAx8EvWbIkxmdA1jGAgYY2MG/evNSnn366Yx7H8fC84HOxh2u+Wx6/idQuSXwGv1Ql8K5wL8orxTeFPzzgJhkixgb4Hny0Ci8YBx+tXJGbqBto167dgg4dgi5SRc3tW6Gju1Pe8tByLGaTWMG7YC4T+4lHhQtuf+ErNQIDGCiQgWAcfIGSI5mEGHj77bcfDr/Mpkin/R0dt6donj5+e03d8huriFoFf5Hsuck8jCvgqeIBsY2ob7RVAuekE/mRpm6y31z8Jr2OCQYwgAEMlKiBPMbB99Gpu3nej3HHi9bCN4SFqH+UTONF1Cr453Tqq4Sbzn+Ynq7V9Frxupgg6hu+IuuVTiR4p7E9uPMdEWMDjIOPceGRdQw0koE8xsHvrqzcIm4SV4uxIpYjzqJWwQ+VyEuFe7S/mZ5erukewtN1oosgMPA1A4yD/5qSoq5gHHxR9cf24BEYB+9Ht6NEuXhRuII/XcQuolbBuyf7jtUs7qxlN6M7fPe9csNc/f7ws5WThHvRe3qCIDCAgQIaYBx8AWUmKCmPg3/55ZeLecYLdHC3IM8TncRisZeYIWIVUavgx8neMPGMcNPIZHGKcFOJm+lni89FfeIL/fhWsV6457yn7h05ThAYwAAGMFDCBjwOftdddz0oxym6XqgU24kjhMfGu56IVUTtuYKvkPoKPxvfQfxOTBJ+Lu9nIu+I+kYLJeCCcucJooQM8D34EipMTgUDDWSgFuPgnYMKMdszcYyo3cHboV8bO1qcL/wsxJW7oxCVu9NxE39v4ecqHvpAlIgBxsFHqyAZBx+t8ohLbiI0Dt7K3hO3xcVd9XxG7Q7ebze4UewmfKcdxETNnBss1HPqZpdfCD9T8TvpXxBe5059TwoCAxgogIFgHDzfgy+AzAQlccstt0z87LPPrnviiSc2nHXr1q1T+++//yYDq1atSj3//POblmu7vaqqyr+N4s3tpnMq1EzUKvgLdGK+qz5bhJ93LCnUCafTKdM0eAbjt9q5d/7H6W1MMIABDGCgSAaGDx++qKKiwn2wNkTLli0rpk+f/tdgWRV/23PPPffEYLm22+fMmdNJ6X8Y/L6Up1Gr4P0iAd/BP9uA0v3ygseFn8EfJtyZgigBA61atVqkHrgrdHXvFplU165dq0466aTgEU9qwYIFTcaNG+eXVmwItjeKnxWBb6YYyMeA3mT3mfa7MLxvkyZNwouer8/2WdUTK9Xlr1kr8on+QMc/XvgO/pMGyktLpbut2EG4t77v3u1hiLhN1BhlZWXDxo4de8uQId6VKJYBvYlqpnrCuvyqh1t/Dgit9H8UL4SW2V5cP5uKolevXpMnTpy4X3l5+aZ1zBTPgO6QU4MHD35s2rRpvukhSsRA1O7gu8nroeIYMVNsuBPT1HfchXoGv1ppuYY+TrgDRZXw85iHBRFvA8uU/UeynALbi+snS9GwCQMYKLSBqFXwE3SCr2Y4yUI/g79Ex3ATvXvmBxcRUWvNyKCBVRjAAAZK20Cn5s17t23Z8opWzZpVdGm12bynjvjhQ8EZ3zFjeter33zt6GC5Ltuvev21Hy1ateLiz9asmRqkU6rTqFTw/5Jgd7DrL07OINu96M/JsL4+q/ws1mPs24i24hZxrSDia2AzZd0jMIjoGJitrHjoK4GBvAy0b9l60Dl77HnIvlt3S7Vp1jzVpGnZiOCHR++4c2qPzl2CxTptb96sWepnL01+XYlQwW8y2bAzpyr52eIDkWmo2mdaX+g4Vgn6ef8vhV+o43kixgbatu1xTbs25cdtsUUvOnZFohyrmsyc/XDFylWf9IxEdshELAxM/dGP72jevOlvMmV2s+bNU7tv2SnTpg3r8tneRmnoqawfzWaL72jjTsK999cK33z6sbFfYxubiModvMegOyzdnevWeCEd7vW+dbBQwOl8peW7Pd/1ueNdV0HE2EBZWcuW/fpc1WHrLv38PgWiyAbWrF2emjf/+U9VwRc5JxweA18a2L1jp9TuHTq+8NSCGu8D+mhv3/D5Dt+Pcn8k9hOrRawq+KgM9vdLbVyRXyYOSs972fxQ/FkUOm5Ugu3EDWKkuFUQGMAABjBQwgZ6tGuXeuiQw97Ncoq7a5sf2d4krhZjRVRuhpWV/CMqFfwwZXmlOFv8PT3vZXOzeEwUOnz55meDLswxYrogMIABDGCgiAbu+c+MdivXefRy0YLPxRZYva+U/GDkZ6Jfet7LvmryXXZD3F0fonTdWuBnLW7SPU8QGMAABjBQRAOXTHn5hOc/nlfEHGxohnfLsTPhB/6LhV9tPkPEKqJyB29pvmS7XZwvpog3xNvCHRvcXFLo6K0E3Trg5yrPiC0FgQEMYAADJWzg7U8Xp/Z99H7f2GWL9dpYKbYTR4hYfi42ShW8HKZ+Ktzp7S/iI+Fn45+LK0Sh4w4l6Ofw+4t7xN2CwAAGMICBEjYwq+Lz1PxVK3fN8xQrtN/sPPeN3G5R6ziwgwxdIzwu/jTxgPhAuOk+0/h4ra5z+OMyvpD4pvhUuEMfgQEMYAADRTSgF9wsa9M8ElWTX372XhrP+64+VhG1O3g/8ygXy4V71rvZfInwukLHuUrwu+JD4Q534aF5WiQwgAEMYKCxDbx59Il377f1No192EzHGxta+XPN+2YwVhGJy6SQMT9rf1m8L/xO8QnCFf39otDhpn+n6wqewAAGMICBBBjIYxx8YCFoNT5GKzxcLnYRtQr+XRnsKdy5wRX96WKpuE8UKvxa2rtESzFSvCWqxOPCz+QJDGAAAxgoUQPBOPjN7/DgrYxxtNa67nHLrmNP8WPhPmJ+ZBybiFoFb3EL0va6auoSKPSASDf/u1ckgQEMYAADETPgcfBH77xTqrXeGV+k8MdtdhRrxR/EbcIVfOwias/gnZ+Lhe+qJ4mDxMNiK1HI2E2JnZJOcH9NfUw/CiAwgAEMYKCIBiIwDt4tyFeJF8UY0VHEMqJWwZ8qiwPE4LTNZzR1xzuvL2RcqcSCt+NN1rwfAwwp5AFICwMYwAAGomcgz3HwzrhHc40Qt3ghjhG1Cn4/Sfy9+Dgt000k1wlX+oUKN/1PF8GjAKfrqzS/QY/AAAYwgIESNlDLcfDbS4VfdhPLiFoF7x7truTD8QMtzA+vqOe8K/ZeokMonaM0/0ZomVkMYAADGCiCgQiNg/fZ+9GtX5ceyyhaL4YabLlDw7/FIWJr4Z70PcTBopDxCyU2XqwSrcVU4ZfpEBjAAAYwUEQDdx8w8IGVTVNX/nPhxkbWVmVlqT06fdkNa01lZeq1xX5z7Mao7faq9X5fTZVfXJNPzNBOi/PZMYr7RK2C99fdfHft7++Wi+fTuNNDIcPfn3cHvjbCQyFi94Yi5ZnAAAYwUHIGTn328aUVTcrGBSfWunlZxb8H/+iOYPlf8z9uc9rk54YGy7XdvuiLFV1WrVrlijuf8KgrE8uIWgVviZbZWJ0avohlqZFpDGAAAyVqYEpFhe+YTw6fXou/fO0VJc/Vc3v45yU7H7UKfnOZvl7sJXx3HcTfNXN2sMAUAxjAAAYwgIHsBqJWwV+g7HYXPxfh5x6fZT8NtmIAAxjAAAYwEDYQtQrewxHc0e7JcCaZxwAGMICBZBjopNeVb5ZK/ULd11dsoRFUL6ZaPBSc+fWpNV3/98v3pKTqsl2N/ceo+97lK1OpuUG6pTqNWgX/oES7g91EUVWq0jkvDGAAAxjIbECV+9AhqbKhB6aaptprl6apJpcGew7TqLW9Qn2i67J9S1Ut/5Nat0AV/CVBuqU6jUoF7+FwuhjbEDvqT49L/0gEvdsf1/y5gsAABjCAgRI2oO7t/9ss1ewXmU6xXapJan9RU+SzPf0ClHU1pVFK66NSwZ8pqdXz4pfwuCQ9RG6JIDCAAQxgAAP1MrCzqpXtU6lXP6lXKvH4sSvRKITfItdX/ED4RTemTHi4XFvxgSAwgAEMYAAD9TLQU43++pLZlFok4roolhGVCt5fcxsuHg5ZfEXzfxL3i96h9cxiAAMYwECJGng3lWq+btPT2aKcpF+ydmAIPyL2sodxxyqiUsH7mftx4tWQPT8juUlcJ44OrWcWAxjAAAZK1MCR+pT308Wt4LeRWn+AbID4tnD/ME/9WvNYRRQqeD973168V4O557R+nxq2sRoDGMAABjCQt4EZ6kU/cOPL1Gr6jTt9+5GxX4A/LY1vNGP32D4KFbzv1NUqk+ojMoXXu7mewAAGMIABDNTLwHtqHVCnrlw3jYt0kNOF382yZb0OWMQfR6GC9+nfKtyhzs0gQThvxwg/n380WMkUAxjAAAZK14D+41/dPDqn57t59cmLZ1Qfmlass9DLiTY83/Cd+qfCV087iQrxQ/EPQWAAAxjAQIkbUJv4rc1STaPyEhq9UI/vwRfir5yfcYwW/tCMm0XeEnrnwYZx8JoQGMAABjCAgfoZyHMcfGcdxV82dR3kx8juQe8bzlhFVJroA2mrNeMmkXuEn8tXCgIDGMAABjBQEAN5joPfQQf7iXAlP0JE6KmBcpNnRK2CzzPb7IYBDGAAA6VoIALj4K3VN5p+/0pXsUrE8m2qVPAqOQIDGMAABqJhIALj4AMREzXzV+E+YrEMKvhYFhuZxgAGMICBuhjIYxx8kOzdmnEP+reDFXGbUsHHrcTILwYwgAEM1NlAnuPgnb6b5q+q84Ei8EMq+AgUAlnAAAYwgIGNBlQpRWkcfKyLJSrj4GMtkcxjAAMYwEBhDDyZSt2uW+dLpumVso7m+rzrjqFvwPtDNP8Jvau+ttvXb8xmzR+V37i9JP6kgi+JYuQkMIABDJSGgVNSqZWLU2sfC86mTSr1+YuplncEy6+mKtuemqocGizXdvuC1PptVcn/O/h9KU+p4Eu5dDk3DGAAAzEz8EIqNV9ZPiyc7VYpvyLlK3F/eKkO28M/L9l5nsGXbNFyYhjAAAYwkGQDVPBJLn3OHQMYwAAGStYAFXzJFi0nhgEMYAADSTZABZ/k0ufcMYABDGCgZA3Qya5ki5YTw0B0Daxdu7b5008/nWrVqlV0M5mgnK1YsSJVVVUVyw+qJKiYan2qVPC1VsYPMICB+hqYP3/+KWefffaB9U2H3xfOgC66Hi9caqQUBQNU8FEoBfKAgYQZWL58uT8HbQgMYKCBDPAMvoHEkiwGMIABDGCgmAa4gy+mfY6NAQwEBrpopmWwoOkCsSa0zPaG97NMvv2BFaJEDFDBl0hBchoYiJOB66+//ujhw4cf27p16w0vHD/jjDN6zZw5s21wDldeeeU7e+211xfBMtsb1s+RRx659+uvv/6vOXPmnBg4Zxp/A4l44X6hiqmsrGzY2LFjbxkyZEihkiSdOhjo3bv3zClTpuxQ/aft2/cce/ABt568dZd+1TexXAQDa9YuT933UO+pS5fN2K364Xv16jV54sSJ+5WXl1ffxHIRDEyfPj01ePDgx6ZNm/aVV8QWISscsoAGuIMvoEySwgAGMICBehvorRRuzpHKBdr+VI59Er+ZTnaJ/yuAAAxgIOkGunfvnhoxYsQjEfHwH+XjnDRvarpYXCdGiAfFZmK6IHIYoILPIYjNGMBA4Q1069ZtPi+5KbzXuqbYpk2blPo5fFjX3xf4d+7sp4/KbcDvSjhO3C5eEpeJieJgQeQwQAWfQxCbMYCBwhuYNGnSQ507dy58wqRYagYqdUJdq52U+3T4rp7IYYBn8DkEsRkDGMAABopm4Eod+XnxovhYHCLmCp6/S0Ku4A4+lyG2YwADGChxAx999FFq0KBBh0fwNMcoT98Tbp530/0vxSDBeH1JyBVU8LkMsR0DGCi4gfHjx3dZsyb8HpuCH4IEa2FArw5OzZ07N6pjFl/TqbiT3bXiYbFOEHkYoILPQxK7YAADhTUwcuTIoxYs8MvqCAxkNdBDW30X7+b5M4Qr+v8RRB4GqODzkMQuGMAABjBQFANjddSZYmT66FdreoLYOb3MJIsBKvgsctiEAQxgIAkGIjYOPlDuN61+S/xOBM/c52r+XjFAEDkMUMHnEMRmDGCg8AYYB194p/VJMWLj4INTWa+Z5SL8qmPXWX6d7nxB5DDAMLkcgtiMAQwU3oDHwSvVYwufMimWmAG/knaymCXWitPEu2KCIHIYoILPIYjNGMAABjBQNAPjdeSp4vuiufCrav0qWyIPAzTR5yGJXTCAAQyUsoEIj4P3uPfPhIfI/Va4ch8hDhBEDgNU8DkEsRkDGCi8AcbBF95pfVKM8Dj4U3VeLwt3tgvCPeh5z3FgI8uUCj6LHDZhAAMNY4Bx8A3jtQRTXalzOk9MEgNrcX7f1L7frra/Lwz2qLaupBd5Bl/SxcvJYQADGIi1gTLl/m/CvebdMfNSkc+b7DxWfivhl+ME8SPNbCeGBytKfcodfKmXMOeHAQxgIIeBiI6DD+f6FS3sK84VR4U31DDvF+OEK3fv9huRmMrdJ0wFbwsEBjDQqAYYB9+ounMeLKLj4J3vm0OZ91C5/uKfIp/PxW6j/dzzfnNxjjhSJCpook9UcXOyGIiGAcbBR6McYpCL31fLo3vU5/P+BF8I+Ll9T+G7+d6ihegobhGJCO7gE1HMnCQGMICB2Bjoo5y+kc6tvwX/Xgb8ydhscaI2ujl+ofAFwRDhdUeLxAR38Ikpak4UAxjAQGYDHgc/fPjww7V1YuY9GnWt31R3cvqIHvPuZvbqMbP6imrL7bW8SOwnPhF+WY4vHD4XiQkq+MQUNSeKgegY8Dj4ww8/PNWihVtNiWIbiNg4eDerh5+9Z9JzgVY+lWlDet1jmvrTsu6FP070EreJy0Viggo+MUXNiWIgOgY8Dn6fffZJlZeXRydT5CQqBvy2OneKcwwT7ix3p/hAHCjc1D5dZIu7tNEd8TqIB8T2wr3qnxWJCSr4xBQ1J4oBDGAgFgaWKZcvpHN6h6Z7iSXp5Zc03VIcLMaJ6jFZK1yph+Pi0MKTmj8/tFzSs1TwJV28nBwGMICB3AaCcfBnnnlm7p0bd49KHa6rCCp4H3038bRnMoQr7+YZ1ger8hleF+wb+ykVfOyLkBPAQPwMMA4+WmUWjIOPYAV/pUy5J/2L4mNxiJgranr+7hfiZIvW2TaW2jYq+FIrUc4HAzEwwDj4GBRSNLL4V2XjNTFAeAz7L8XDYp3IFp208Saxk3BHu6ailfBLck4QiQgq+EQUMyeJAQxgIJYG3JnOd+3VX3iT62R+qh02E38Rh4kxws/irxCJCV/VEBjAAAYwkGADEf4e/EcqFj9zr23soB9cI8YJ98J3T3qPrf+ZSExQwSemqDlRDETHAN+Dj05ZOCcRGwcfluM30bly/kS8E+J7ms8W87TRYzCXC79swT3v3VEvUeMyaaJXiRMYwEDjGmAcfOP6jvHRLlPer86Q//cyrAuvukULL4v3xSNignBFf79ITFDBJ6aoOVEMYAADsTPgDnaZwh3msoVfd+s34nmYnSv608VScZ9ITNBEn5ii5kQxgAEMZDYQjIPPvLWoaw/S0SeLt8VUMU242f77Ilcs0A5+H7173P9Z3CnWiMQEFXxiipoTxUB0DDAOPjpl4ZwE4+CjlasNuRmtP58UruDvEl6eLR4S2cKd6d7IQKbm/mzpxHobTfSxLj4yj4F4GmAcfDzLrZFz3UTH81fhLhdDxDfE9cJvthsk/i5qCl8AvJLe6HS6Cb/fPttv0ruXzoQKvnTKkjPBAAYwUEoG1utkvhCu5N8UPxaOfHrDz9R+JhxePl88F15ZyvNU8LUo3crKynVjxoypWrVqVVUtfsauBTbw/vvvdylwkiSHgUQbiNj34MNl4bfRvSW2Fz3EvWKA6CtqG24B8MVCYoIKvnZFffsLL7xQKWr3K/YutAG/3YqIsQG+Bx+twovwOHg/M39cuKOcO9z5Lv4P4gORLXynPiS0g99B310cH1pX8rNJruA3U+nuKdwj02Ml842/5rsj+2EAA5kNMA4+sxfWZjTgO3jHHHHZhrncf4zXLh4eF4QvENxE7171iYmkVvB+N/GF4iWxqygTx4jlgsAABjCAgeIZ6K1D35zj8Bdo+1MZ9nlG66p/Dz682yQt+P/+REQSK3j3qDxV+DnO2nQpu3I/S/w2vZxr4tcehp8D+6owfGXI9ob149aXFbkKie0YwEB+BoJx8BH5XOx/lGv3eHcME36X/J3CzfIHihNFTY/pLtI212s7iYvFjeIfopc4U7wuEhNJrOBd+bqpJqjcXdgeZ+nnOrmiydrTRvz97vend3pk1qzOwc6DynssGtpzl00VPNsb1s+e99+15dQln/5X4J9p/AwwDj5aZRaMg49IBb9MdoKOTndofi/hnvMOt7r6//CDxThRPYJmeT9rv0T49w5X8u8KXwDcIxIRvptNYozVSf9C+Pm74+fCf4Fu8UJNMUrfFL749BGVNW1nfeMY6DP+nplTFi3aofrR2rfvOfbgA249eesu/apvYrkIBtasXZ6676HeU5cum/G1r4GtX7/+OGXp7iJki0PWbODQJk2aTKx5c1G2+K79cOHKOQg3zV8nJgQrMkzdGutHrr8JbXPL7XfE0NC6kp5N4h28C9R/Oa4VvgvvKp4Q/rRg1hil/U9duSLVubVbiAkMYAADGGhgA1cq/efFi+JjcYiYKzI9f9fqTeGbOPe+9wtxXhF7i+2F+18lJpom5ky/PFG3WnwofiKGiO+Ky0UrkSsOfWLunFz7sB0DGMBArAxE+HvwYyTye8JN8266/6Vwpb1KZIsZ2thHjBN+HHun8GM9v742MZHECt633zek8V37n8VcMVIQMTBwdd99/TyNiLEBvgcfrcKL8Dj4fWXK/0+fIo4Svhl7R7iSzxWfaIe/iAuEe+W7BSBRkcQm+i9UwiekS3l7TV3BXyz8jmMiBgYO6LbNZzHIJlnMYoBx8FnksClsYJwW3DfqObFOBPF+MFNt+i8tu0LvL06uts2L7mMQ9NDPsLm0ViWxgncJupnew+L8PMZN9TX9ZdGmr8SCLpu1+coKFjCAAQxgoEEM+P/pduIq4ffS5xOnaqfZwp3znhTVI1E3B0lsom+hEn9GHCPGiN3FYOG32mWNUeq0MbB7edZ92IgBDGAgbgaCcfARy7crdXeAHib8/3Y+8aZ28rP6j8QC4bfgvSf2Ff7Pe6ZITCSxgvdV4V3CnS62EB5Tabg1l4Q4xHGTHmccXBwKKkseGQefRU4RNgXj4Itw6FyH3Fw7+EbsUzE9RK5n8G6id8XuF5L5Gb6HxnnInJ/lJyaS2ES/WqXrjhcDhe/k1wlX+kPEi4KIuIFZFcs6RjyLZC+HAb4Hn0MQmwMDl2jGHeuqx/vVV1RbPlHLw4XfdXKs2Fe4vvOwOz/TT0QksYJ3wf5aHCd8hVcl3JLxsMgaoxgHn9UPGzGAAQwU2ICb3DNFq0wrQ+vaa95vF91PuDf9VOFhc5+LxEQSm+hduL4qPF6MEfeI+8VHIlcwDj6XIbZjAAOxMxDhcfAHSeZk8bZwJT1N+K78+yJbPKaNfqHZDWKc6CVuExNEYiKpFbwLeIDYR7hpvrfYShAxMMA4+BgUUo4sMg4+h6BG3hzhcfCjpeJJ4Qrefae8PFs8JLKF9z1P+GbuKrFGnCHuEImJJFfwXVXKLvjZ4myxpyBiYIBx8DEopBxZ9Dj4BQsW5NiLzQk34L5Rbmr3M3j3pm8hrhdPi0EiV/jC4D7RWcwWz4pERZIreF8B+uruY+GrunkiVzAOPpchtmMAAxgojIH1SuYL4Urez+LdUc6xRJRvmKv5D9dtF4u3xCThpn73s0pUS22SK/iXVNgXCt/FjxTXiqwxinHwWf2wEQMYiKeBiI6Dt8ybhCtpP3/vIe4V/n/blXa2OFUbB4jB6Z2e0dQ3cV6fmEhyBe8rOQ+Xu1MME/sIIgYGGAcfg0LKkUXGwecQ1MibIzwO3kPa/MbRdcJ34X4W735TS0W2cO/53wu30DrWCne6c6WfmEhyBX+SSvk3wj0yPXWveiIGBhgHH4NCypFFj4Pv3NmPRgkMZDTQSmvNc8LDmT3v/6vd0jpUHC6yxYfa6Eo+HD/QwvzwilKfT+o4eJfrDDFItBRHp5c1qTlGMQ6+ZjlswQAGMFA4A48pqeBue2UoWT+Xd1O7H6tmiz9o47/FIWJr8bLoIQ4WiYkk38F7POQy0UK0ER4jmSsYB5/LENsxgIHYGYjgOHhXxM3F3emp541vSrsL39VnC9/te+y7h9XdIC4S2wq3BCQmklzBu5D9HOdZsVrsIYgYGGAcfAwKKUcWGQefQ1Ajb47gOHjfqVcKP2/383cPmXOTe67/pzfTPn72/rjwY9ex4hLhZ/HPCX9KNjGR5Ar+XJXyd8WKNH4RAhEDA4yDj0Eh5cgi4+BzCGLzMVLgStnvJ3E99YhwK6uHN98sago3zQ8Q7mV/vnAT/bHiNTFPnC0SE0l+Bv+5StmvqHVnjHyDcfD5mmI/DGAAA3Uz0EU/GyNOEVOEh7rtLr4pfDfvIXMe9eRn7NWjn1acJvzM/R3xO+HenP8tfHGQqEjiHXxblbCvBn2F+JLws3gvnymyxijGwWf1w0YMYCCeBiI2Dn5vWXxDPCCqxP8TvhmrEO5w97ToLTKFO9S9nt7wgaZ+Du+7+MRV7naQxAp+uc77CHGiKBfDxFBxoyBiYIBx8DEopBxZZBx8DkGNvDli4+Bb6/T9/3QQB2nmiWBB0w7CLbCZws/qfVHg8N3+XDHVC0mMJFbwLufrxJHpAu+oqa8U3QRExMAA4+BjUEg5ssg4+ByCkr3ZzfL9xa7iKOEK3Xftju2Ee9gHd+leVz06aYXx/+2OYNnTzTesScgfSXwG305l66FxQUeN6Zo/WZwtzhc1xqhUav2LEx6eFezgS8WJ3//BP4NlTwc99re+wTLbG8bP7IqKssBxeLpu3epPnn7+5MWbt+nupjyiyAYqq1aXrVmzbFWmbJx33nlt3n777U3/lvbZZ5/FV1xxxfvBvhMmTNjyj3/8407BMtsbxY87tUUhZisTo0Twf+tJmncn6OOEW1o99O1dUVO4M104FoUW3NR/bGi5pGddByUt2uiErxfuwBFEZ81cKH4WrMgybV9tm8fSh4PtYRsb3zUQXlMIP6uVYKaKwy1SibpCD4uN6Lyfmfo/5+rRXCs8pCkIv0p0RbCgKduL6ydUFEWb9Q3oelGZzoGfrzvmb5xk/LNlxrVfrnTzvf+uESVs4Fqd20WijzhMTBS9BYEBDGAAAxgoCQNJvIN3wfm8BwtX8H7j0cPCPS4JDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAaSbaCtTv/zNF9oWhVanqj5usS2+tGIuvyQ32AAAxjAAAYwUHgDfZXkJwVI9nil8WgB0iEJDGAAAxjAAAYKYKCmCv4Apf2mWCoeFJ2EY2fxT1EhXhP9RDfxsfC6OwWBAQxgAAMYwECRDWSq4LdSntyEf6LYWtwqrhGOB8QvRWvhJnlX8k3FcPG4cPM/gQEMFNmA/1ESGMAABqobGKwV74hHhJ/RXy4OFY51Ym/RU9wg+gg/w/d+a8VyQWAAA0U2QAVf5ALg8BiIqAF3mNtNzEjzgqYdxDbiPNFcvCKmiWMFgQEMRMwAFXzECoTsYCAiBlx5/0O4eT7Ad+1+zu47+KNEV3GTuF1sKQgMYAADGMAABiJkINMzeD9fXyb2TOfTz+LfEmXi7+IM4egsVgh3wDtCTBYEBjCAAQxgAAMRMJCpgne2zhJ+nu5m+jdEf+HwnbyX3Tz/qfi5cPQQi4W3ERjAAAYwgAEMRNiA79hran7fQtuaVcu7H/v57p/AAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAYwgAEMYAADGMAABjCAAQxgAAMYwAAGMIABDGAAAxjAAAayGvg/QJkYf8xzaHgAAAAASUVORK5CYII=" alt="plot of chunk loglin"/> </p>
<pre><code>> [1] "Experiment 2"
</code></pre>
<pre><code>> Analysis of Deviance Table
>
> Model: poisson, link: log
>
> Response: Count
>
> Terms added sequentially (first to last)
>
>
> Df Deviance Resid. Df Resid. Dev Pr(>Chi)
> NULL 7 19.64
> Cheating 1 13.86 6 5.78 0.0002 ***
> Prime 1 0.25 5 5.52 0.6146
> Test 1 0.00 4 5.52 1.0000
> Cheating:Prime 1 1.51 3 4.02 0.2198
> Cheating:Test 1 2.53 2 1.48 0.1114
> Prime:Test 1 0.03 1 1.45 0.8609
> Cheating:Prime:Test 1 1.45 0 0.00 0.2284
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
</code></pre>
<p><img 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mMIxImAr3rKYf6BLWR2VMEJnpffk75TaIcIlu2uOv0xT71+r7yrJF8RbiaNlXznwuay7jWpDf+MD616Pz+z9xW77UzpUMnMfKxNpBGSP8MOKzi2komy4PtRbKMa+u75eMH33Oz6Sj6ZiLP9UpX/hvQraZ9sQ4Lvz1NaP0riu5cFwwICENh4BPbWR10qJclRBQ6kkhQ3xmdUsv5ROLZPtm6SNo1CZSpUB58Y+hFYOY3vXjlpciwIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQgIAHf2CNJLB+/Xrz+l9vvmTJklaHHXbYkcGubdu2XfPcc889HqynvVwcLmvRosXcgEewnDJlykmDBg1yGF+NdezY8ZPx48d7lHGNzZ07t83RRx99eLCetvIxY8a8dsIJJ9wWtD9Y9u7d2xPCfH/zzTcPwqeCIpYFCKxcubJK36kfLVq06G85m3W7+OKLL5LVhbANGDDgK7Nnz+4abHfddddN3G+//ZYE60kunzZtWvsjjzxyxfTp088I2tt8y200H8fqKzOZbQtNRtWE6q1ShMzCX2hSSocGYxCoJVBVVbXXK6+8Ij+PFSJgRnvttdfF+b432u/PhfZNe1mfPn08Kcp/mfJHv/rqq2nHU3T7dcK0frPNNvv5fwHNZPoMHDhwVtEHTOgOOvFer+/YI3k4NUNWr8v0mgOdeK2vkB7UcXvUXWQ0QwM32kcmKSyq4tDWrVu32+uvv17xz4n7B1RXV2cWL168RdzbQf0hAAEIxJkADj7OvRfDui9YsMATyGAQgAAEIFBhAjj4CgPm8BsSOOaYYw7eMIe1MIGePXsuCq+TrhiBBUcddVQwjXHFPiQuB956660zQ4YMqRtDFJd6U8/CBHDwhflsUNqqVauX99xzzw3yWPlvAv369cucc845o/+7hJyGCDz11FP8yDYEqTzlCwcPHoyDz7Ls1KmT/2enlQdt4o5S7lkINxogHHwRqD/55JM3+vb1tNlYIQIa+Z455ZRTFhbahjIIQAACESXQS/U6JCS/HdDrnaRYGQ4+Vt0V/8rK+a+OfytoAQQgkGACW6ttt0qHSl+S/J4EL9tLsTIcfKy6K/6VffLJJ5+Nfysq14IXXnihS+WOzpFDBLrddddd3ULrqU5+8MEHmf79+9fNP5FqGLWvLt5XDPwipClZXaPlfClWhoMvorvatGnTT7HIReyRzk3NaO+99z4+na1vWquHDh3qV45ilSfQ/aGHHiKUM8t56dKlmbfffnvHymOPzSd4fMaPpO2kzWNT65yK4uBzgBRaJQ6+EJ3PyoiD/4wFKQhAILYEOqvme0jfjGsLcPBx7bmY1ps4+Jh2HNWGQPoInKQm+/n7/nFtelod/EHqsO9Ift75gOSQLp7HCUKljTj4woSJgy/Mp4ylxMGHYBIHH4JRm/R7Rw6UhktDa7Pi9zeNDt4zqf1GGi+dKY2Sfiv9UipoxMEXxFNXSBx8HYqiE8TBF42s1B2Igw+RIw4+BKM26QGHz0h+2dAqqacUO0ujg99KvfSa9KF0mOS3TE2XekgFjTj4gnjqComDr0NBAgIQiCeBCar2zdmqn6VlLCdFah1P9k2q9fvaexfJnTYjeyS9OjAzMZtmUUECxMFXEC6HhgAEykXAV+2BxXbujjRewa9Tr50k+Qr+dKm3NE8KztaUxCpFgDj4wmSJgy/Mp4ylxMGHYBIHH4KRoGQaHXxH9d9x0t3St6S3pL9Il0gFjTj4gnjqComDr0NRdII4+KKRlboDcfAhcsTBh2AkKJnGW/QeZLdztg8HaHmv5BOdBie9COLg99jDoZFYfQSIg6+PDPkQgEDDBNauzWSu0i3ylz9peNtStniqbSazNra33YtpcRodfDF82LbMBBwH3727Z4DEIAABCOQjMOfSTGbOpEzmqXyF5cp7slwHivJx0urgd1KnnCR9LrvsoCW2EQg4Dn7SJP3vYnkJOA5+yhRPf41VmEAQB+83h6Xegjj4c889NwosVqoSj0ahInGvQxqfwS9Xp90urZduyC6XaTlSKmjEwRfEU1dIHHwdiqITxMEXjazUHYiDD5EjDj4EI0HJNF7Bt1H/2aHfX2w/Og6+2H3SuH0QB//DH/4wjc2nzRCAAAQiQSCNDt63f/aSPIL+Z5JnKsI2EgHi4DcSaD4GAjEl0KH9Nt/7dE31je3bd/+4Ek1Yu/aTqk/XrD7tk0/mPVyJ40fpmGl08BqhmTlP6ic5VO4fkvM8u90TElZBAtk4eI+BwPIQIA4+D5TKZNXEwQ8aNKgyR4/ZUR0Hf9pppx2uao9r9qq3bLnrEYf9pct22x7pd4WU3d6d9VBm/IThu+vAiXfwaXwG7y9MK+kwJ2RvSv+RPvBKISMOvhCdz8qIg/+MRbEp4uCLJVby9sTBh9ARBx+CkaBkGh18O/Xf05KnIjxKekjyfPQNPl8P4uC1LVaAAHHwBeBQBAEIQGAjEUijg/fo+WGSZ7DzlbzNrwYcUpPiT0UJ8D74iuLl4BCAQPkJBH6i/Eeu8BHT6OA9O9Jg6TpprPSg5Kv4NI5HULM3rvE++MK8eR98YT5lLA3i4Mt4yPgeKoiDj28Lylpzz41wSEiPZdOdyvopG+FgaXTwxnqRNFDqLx0rHS2NkAoacfAF8dQVEgdfh6LoBHHwRSMrdQfi4EPkiIMPwchkttbardKh0pekTbPL9lrGytLq4N1J7ixPV/ii5EF2P5MKGu+DL4inrjCIg6/LIAEBCEAgPgQ81ea+UnfJ00pa10jzpVhZmh38Ceqp70ovSEdIsX3OorrHxoiDj01XUVEIpJnAAjX+R9J20uZxBZFmBz9XnbartInUVuohYRUmwPvgCwMmDr4wnzKW8j74EEzeBx+C8VlyuJI3S9+U/Bi3pxQrS7ODv1E91Vm6QbpQul0qaMTBF8RTV0gcfB2KohPEwReNrNQdiIMPkSMOPgTjs+S/P0tm3lXaU5zHytLs4Feop+ZJu0keUDFVKmjEwRfEU1dIHHwdChIQgEA8CXxF1d5JqspW37Pq+W5vrCzNDt7TMh4muSO7SmdJWIUJEAdfYcAcHgIQaCqBfXQAj8/ypGj3Sx6QfaDk0fWxsjQ7eL9wxs9XHBf/jBTbgRSqe2yMOPjCXUUcfGE+ZSwlDj4Ekzj4EIzau7q3KedP0u8lh1DHcp6UNDv4Ueo0P4c/SPqLdI9U0IiDL4inrpA4+DoURSeIgy8aWak7EAcfIkccfAhG7UtoLlZOL2miZAfvEfWxs1ielZSJsl8uc4v0RekjybdjChrvgy+Ip64wiIPnffB1SEhAAALxIfChqupJ0IIL4CeV7ietlGJlaXbwZ6qntpReyvbY6lj1XEwrSxx8TDuOakMgXQTWq7l+jXhgjouPnQVnKLGreBkqvFTHuF66L6tXynBMDtEAAeLgCwMiDr4wnzKWEgcfgkkcfAhGgpJpvILvqP67W/LkNhdKjnVcJz0m+Zl8veY4+BdffDGzxx571LsNBZmM4+BPPfXU48XiDXgUR4A4+OJ4NWHrmjj4QYMGNeEQydk1UnHw69a2/c/UW9ZWV8/073LZbebsR1uuy6xLxcylaXTwy/SNOaaUb00QB4+DL0yPOPjCfCiFAATqJ7B85Zzz3535wMtS/Rs1vcThb4m3NDp4d+qu0pclh0IclJXDIXgOLwiVNMfBd+/udzhgEIAABPIS8CRkvsuKNZFAWp/BXyZuj2bZTdBysTQ4u86iggSIgy8Mlzj4wnzKWEocfAgmcfAhGAlKptHB+6UynpbWoRCBeara/YKV+pbEwddHZsN84uA35FHMGnHwxdBq0rbEwYfwEQcfgpGgZBpv0dux7yx5elpfuduOk/5Vkyrwhzj4AnBCRcTBh2CQhAAEiiKgu1hHa7zT9V26dPm4qB0bufGaNWuqli1b9tN58+Y908hdYrtZGh28O+s8yYMsVkmeZ9ijvX8uYRUmQBx8hQFzeAjEnEDr1q33v+GGG3odcMABvSrRlGeeeSZz+umn+x0kOPhKAI7AMV9THfyimQ6SB3R4UgNsIxDIxsH7LU1YHgLEweeBUpmsmjh4wuRq4ToO/rTTTvMLuMZVBndxR5WTz3Tt6pus5TcfOy2Wxmfw4b5drpVGO3feBx9GV3+a98HXz6ahEuLgGyJUtnLeBx9CGak4+FC9SDaNQNodfFH0gjj4onZK4cbEwaew02kyBCAQOQI4+Mh1SbIrxPvgk92/tA4CCSDgZwOjpYnSUdn2/FJLv3AmVoaDj1V3xb+yxMEX7kPi4AvzKWMpcfAhmMTBh2DUvknuSuV8VTpROkJqkZUW8TEcfBF9RRx842ARB984Tvm2Ig4+H5WK5BEHH8JKHHwIRiYzW2tflzwab6g0RNpfip3h4IvoMsfB9+3bt4g90rlpEAefztbTaghAIOYE/q76vyV1kz6V7OTflKqlWBkOPlbdFf/KEgcf/z6kBRBIOAFHVo2Rgufvni/leckvKouV4eBj1V3xryzvgy/ch8TBF+ZTxlLeBx+CyfvgQzA+S/pV4oG9qwQOPqCRxCVx8I3rVeLgG8cp31bEweejUpE84uBDWImDD8GoTfbRYh/pQsm36OdLS6VYWXqm9ClDtzgOXoOgMm+84ZltNayyRYtM7kxYo0aNqvuktJYvXrw4s2jRop51IEhAAAIQiA8Bv3jsDOlmyc/dt5DulDyiPlZO3kP/sSIIaCT9sNDm6/TiAnd8nWkaxJPqVjKZ1JavXbv2AXFYFGJRk5w/f/4IvQ/+5Nx81msJ7LzzzjOmTJmyfS6PPn36jL777rsH7r777rlFrBcgcN9992WGDx9+9scff3xVzmZ9Bg4c+Njo0aMrMt95zmdFfnXq1KmZAQMGPKrvXvDcudnq3KtXr8v+/Oc/n3vkkUdWpA4PPfSQvxMXfvjhh5fU8wHDle+ruOdC5Xb4T2fzQ9nRTnIFX2T/yHHdFt7FV+k5RnkOkPCqHNgR++2336wgT7ekZ/fv37/urVFXXnnlNhMmTOiW1vLVq1dvE7SdZUUJBHHwOHhhDuLgzz333IpCj8nBfXFyg+SLOV+x+/dosfS/EgYBCBQg0F1lXwipc862aS/fJIdHzaqv4DW2YT1WHIExY8as32yzzfK+KVJHGlfc0RK/9Tfyffc2dp6v4MeNq1zXPPjgg+t79OhxQSPa5d8m/1bl/kY1YtdobMIVfPH98Pnid0nlHu+p1avztHyB8qz6LO3lfrshBgEIND8BX73H6pl7LjIcfC6RwuuHbrXVVg/utddeCwtvlu7SFStWtJ4+ffqImTNnXpRuErQ+ygTef//9Kg2craviFltskWnXrl3dut6bkFm5cmXdetLLNX6oqq6xJBJBAAdfRDdqgF3vyy+/vOPgwYM7FrFb6jbVM/TMsGHDfKs9n3mE6hfzFZBXQ+Df+jsZFpUn8J3vfGeqoj3qnNpZZ531sL63vvNUYxof8q133313u2A96eXbb799VK5WW82YMSNz7733BujLuly4sOb6rGVZDxrRg/3XCLGI1jMS1ZKDHzpixIjb5OAjUZ+oViLr4G+aNm3aabl13HXXXd87++yzGUiWCya7ftppp61Yvnx5h9xiRtHnEmnceoFR9I07AFs1B4FNNTf+yfq9/ez2ShlroYHSLfVK61t1yKic0JSxdRseiiv4DXmwVmECHTp0+JQTpPoh6w7RPIUq1b8BJRBIPoFFcsBXJ7+ZlW9hKm5TVB4jnwABCEAAAhCIFgGu4KPVH9QGAhDYOAQ6b7PNNn/t3Llzm43zcdH+FA02bKk5GO7TmAOunKPdVUXVDgdfFC42hgAEEkJga0XDfOH666/fNiHtaVIzPPBMM/t5WtZmd/A68TpY9fijnsMv1gQ8c/WCqrFB4zTwrsdvfvObAcF6KeUXXnjhCUuWLDl73rx5LwbHSeoSB5/UnqVdEIBAQQJVVVXrPYMbpgnXq6szCpPzu8+b3bp06dLvggsu2H3fffcNwha/G1Tq29/+dsb5gWXDGosq9+yjZ5xxhufBxcEHIFlCAAIQgAAEKk1AL/O6X5+R++6Amo/VGz0z221XF7n4X1VpTHn2pKAiI/T/q0LNnMEgu2buAD4eAhCAAAQ2HgE9msnsv//+TzbwiX1UfqZ0oTRUqm9eDxVF13Dw0e0bagYBCEAAAmUm0LNnz8z999//boHDejIuz8LpSafGSfOkO6XYzUmPg1evYRCAAAQgEA0Cl156aXcNgmvOyuymD79OekZ6SXpUekzqJcXKcPCx6i4qCwEIQCDZBG6++ebjJk2a1JyNfEAf7ve/j5Cuke6SdpD8jvhYGaPoY9VdVBYCEIAABJpCYPLkyZkhQ4YcpmPcU89x/EbLEyTfkt9Cmi/FclpbruDVcxgEIAABCKSDwJw5czIfffTRFxrRWoffTZPs3I+WekqxMhx8rLqLykIAAhBINgHNLvhh165do9BID7ILzIPylgUrcVni4OPSU9QTAhCAQAoIvP76638NT2bTTE3+ij53Jyl4nXAXpTdpprqU/LFRc/C/Vktm5MhnTh7ccJ+0tYRBAAIQgAAESiLQiDj4fXRg355vJ3nSnfbSgVLs/E/UHPx4QVwlXSb1zy4/1fJq6VXpEQmDAAQgAAEIlESgEXHwDpO7TfqT9HvJo+ljOSA9ag5+iED+VrpZei27vFTL3SUv10hbShgEIAABCCSQQATi4B8W1oulXtJEyQ7+R1LsLGoO/iMRzB3duKPyVmfJ+nnIymyaBQQgAAEIJIxABOLgPxRS30GeI3WTFkr9pLekWFnUHPxI0RsqPSP51sgEaZjkWyW+TT9TimU8ouqNQQACEIBAMxNwHPwuu+ziOPhCtl6Fa6XtpGMkx8Yzil4QmmI+Q9pXGiP55OMP0uckxyL6mYjPqjAIQAACEIBASQSKiIP38aulmU7E0aI4cMAT+9+UB+Z/8uSRBQEIQAACCSIQoTh4U307q1gSjpqD7yqKN0q7Sm1CRMcpfWZonSQEIAABCCSQwG233TZu0aJF1zz++OM1rWvfvn3moIMOqmvpqlWrMs8++2zderHl69at875Rezxd155yJqLm4H+pxnlCgZ9K4ecdH5ez0RwLAhCAAASiSeCUU05ZUF1d7TFYNda2bdvqqVOn/l+wLsff8cwzzxwUrBdbPmvWrG46/nvB/kleRs3BeyIBX8H/PcnQaRsEIAABCOQnoJnsFqnknHBpixYtwqtON6V8Ru7BkroetdsUDwj0DyS/wQeDAAQgAAEIQKBEAlFz8H5bzzeludI70tSsrtESgwAEIAABCECgkQSidoveU9G+nKfuPIPPA4UsCEAAAkkj0K2qaq+Obdv+T7vWrau3bLfJnKeO6T82aOOot6b2+P1rrxwfrJdSfvmrr5y4YNWK8xetXv1GcJykLqPi4F8UYA+w2186OQ9sj6I/I08+WRCAAAQgkCACXdq2P/KM3fc4/ICtemY6tK7KtGjZ6idB847/wo6Z3bfYMlgtqbyqdevMz5+b8KoOgoOvI1nZxKk6/ExpuvSElGsedIFBAAIQgEDCCbxx4g9GVVW1vCRfMzepqsrstnm3fEU1eY0p76BjKEquJlau3gNlMl9R2Q6SR+/7hWe++HxX8jS2sbGoPIP3i2WWZPW6lpND8gQ3njYQgwAEIAABCDSJwG6bdcvs1nWzfxQ4yD4q43WxBQAVW+RJbfzu3d9JniPY6UCenvZ6CYMABCAAAQg0iUDvzp0zYw8/6s0CB9lNZZ4andfFFoBUTNFQbbxS+qn0t2za69bN0qMSBgEIQAACCSfwl3fe6rxyjd8M3mzG62LLjN5nSn4w8nNpv2za6x4E2Fm6XcIgAAEIQCDhBC6aPOl7z34wpzlb6efsvnPsSviBP6+LFYSmmk/Z7pTOlvwM/l+Sn8d7YINvl2AQgAAEIACBJhF4/aOFmQMeHuNBdIXM477WSttJx0i8LlYQmmo/0wE2kW6R3pculPz+9/+RMAhAAAIQgECTCMyoXpqZu2rlLo08SLW2m9nIbSO3WVRG0QdgPq/EVdJIaWvpPslx8b51j0EAAhCAQMIJaIKbJR2qIjFFiyfAf1u6Q3I6dhY1B+9nHr2kZZJH1m8ufSw5D4MABCAAgYQTeO34QfccuJWv75rdRoRq8Aulvxhaj0UyEqdJIVJ+1j5JmiY9JD0i2dGPkTAIQAACEIBAkwgEcfBPfbiioeP47rFtoFT3+tqanJj8iZqDd2ziTpIHN9jR/0haLI2WMAhAAAIQgECTCARx8J1GOXgrrx2vXPue4AxgD6V/IHmM2HQpNhY1B29wDlGw9ZDcA80aEOmKYBBobgIrVqyouuKKK5b36dPn0+auS5w+/7HHHmu3fv36VXGqc9rr6jj443fcIdNec8Y3k/nlNl+Q/L/2R8nP4O3gY2fNRrAeUh4T8CvpBMmDGhwy92Pph5LDFDAIpJLArFmzhkmeQhMrnsATxe/CHs1FwHHw3TtukjmylyPUmsV8B/lyyf9vt0qbSbG0qDn4U0XxUGmA5LOoZyTHIDr/UgmDQFoJeLDpuLQ2nnZDoFwEHAd/0pNPfEXHs48pZH7L6RvSEYU2inJZ1EbRHyhYV0ofZKH5Fsk1kp0+BgEIQAACEGgSgSLj4LfXhzXbrYQmNVQ7R+0K/j3VyU5+vBTYsUrMDVZYQiDlBDwRVL8Qg2VKe9bHwCgvzCfgxDKiBCIUB29CbaTOEUXVYLWi5uA9oOEl6XBpK8kj6XtLX5MwCKSWgAaK7anG3zpnzpzWGmzXLQCx1VZbrTnvvPM8V3aNUZ6XzxdbtGixPGDEMtoE7vnq1+9b2TJz2Qvzasdbt2vVKrN7t+51lV69dm3mlYWfDckqtnzdes9Cu85jvBpjb2mjuv+vxuwQpW0a28iNWeeO+rATJU9u82xWHvTQ7NaqVauhI0aMuG3w4MHNXpcoV2DChAmZYcOG3TRt2rTTcuu57777vjtp0qTP5eazXktg5513njFlyhTfFtzA5OC/qozxG2Sy0lgCXeTgl+Zs3GfgwIGPjR492r8zqbepU6dmBgwY8Ki+e0c1N4w9O3XqVt2i1R+CerSvalX90oATRwXrz839oMPwCeOHBOvFlt825c0tz5k4/i19Id4JjpHUZdSu4M3ZtxxvSypw2gWBUgjsvffeg++4446MTgBK2Z19IBAbApOrq33FfHK4wm1uuTG86vT4cEYJ5eHdE5uOmoPvJNLXSn7G2CFE/W9K/zS0ThICqSKwfPnyTqtXr05Vm8vR2LW6nas7Rq3KcSyOAYG4EYiag/+lAG4red7f8HOPRXEDS30hAIHmJzB27NjM8OHDh6omfokVBoFUEYiag3c4ggfaMTFFqr6GNBYCEIBALQGNIN1JoSDnafj6ik0VQTUx02ZswObazOoef66dJ6Umq5Ry3ewfqOF7l67MZGYHx03qMmoO/gGB9gA7T+ixLqnQaRcEiiUwcuTIO/r27euXXmAQSDQBOfchgzOthhySaZnpopa2zLT4bdDgoYpa65fxKPhaK6V8c7mWczNrPpSDvyg4TlKXUXHwDofTyViNfUF/j5Pel4KefEzpMyUMAqkk8OUvf9mDT7EiCbTWfOYaQe8Js7CYEFBc2p9bZ1qfl6+6nTWD+UEFXs3emPKutQdOxTtOouLgHU6VW5eWynMYn0PkPE0nBgEIQKAoAv37989II+Xki9qPjZNLYEe5le0zmZfnJ7eJdS2zE42CeSaufaVjJU90Y3nkq8PlHBc/XcIgAAEIQAACTSKwk276P5nJTC7iILGNwoiKgz9fsE+R/hqC/k+lr5PGSHuF8klCIHUEHAf/5ptvpq7dNDh9BPQtr1pT93S2WdrvyY8OCcmPiL3uMO5YWVQcvJ+5f0d6OUTPz0j+JF0jHR/KJwmB1BEgDr60Lncc/MSJE2N7BVZaq+O917czmWFPN6+D31oE/ZrYQ6UvSR4f5mV7KVYWBQfvZ+/bS2/XQ2688veup4xsCEAAAvUScBz8scceO7TeDShIHYG3NIr+6xu+kCiXgQd9+5GxJ8CfkpUvNGP32D4KDt5X6r73uI+Uz5zv2/Xltk10wK9IXyj3gTkeBCAAAQhEk8DbujugQV0NXTQuUO1/JHluls2j2ZKGaxUFB+9a3i55QJ1vgwTmujnu18/nHw4yy7Q8Ssd5XDpa8pnZOMmD+TAIRJJANg4+knWjUhAoJwH98H9SVc4DNu1YvprXmLx4Wm5oWnO1QpMT1Tzf8JX6R5LPnnaQqqX+0vNSuczxMqdKh0qfZg/qE4nTpSuy6ywgECkCxMGX1h3EwZfGrTn30j3x21tnWkZlEhpNqBff98FH5Qre3ydfSXeWPODuMskj53tKz0rlNN9ueVcKnLuP7alxd3ICgwAEkkPAcfALFy4cmZwW0ZKmEgji4Bs4zhYq92NczbtTE90VuxH0bl+UHLzr84nkWyJ/kfxcfq1UbluoA/pEYsvQgX1F/1xonSQEIAABCCSQQCPj4D+vpv8/yTNI/kSK0FMD1aaRFjUH38hqN3kz3y24WvKzldclO3uPA8AgEEkCxMFHsluoVAUIRCAO3q3yhabvIveQVkmxnE01Ks/gxW+jmZ/Bvyf57KydtFryi22cXiFhEIgcAeLgS+sS3gdfGrfm3Mtx8DdqpPsRBeac30j18+Dr/5PO3EifV/aPSeMVvJ+r3JDVVVpeL82WLpQwCEAgQQSIg09QZ5apKY2Igw8+6R4lgru8QV6slmm8gl+uHvpetpe219IO3qF412bzWEAAAhCAQEIJhOLgH2ugib41f3kD20S6OI0O3h3i2/QOiztK8q36aRIGgcgS4H3wke0aKlZmArqtHKU4+DK3buMeLo0O3nGNnuTGjyeuk3bLaoaWr0oYBCJHgDj40rqEOPjSuDXnXopZvlOXzhdNqRka5eHrLTTdqK/Jas0vonknNFd9seXraw/z2QGDAydwmUYH7469O9uXfolAYPODBEsIQCAZBHgffPz6cVgms3Jh5tNHg5p3yGSWTsy0HRWsv5xZ2/HUzNohwXqx5R9m1m8jJ/9SsH+Sl2l08I61v0X6uvSM5Lnw7fQHSxMlDAIQgAAEmonAPzKZufpoPz6ts3Y1U6TUrToxJrxWQnl498SmfZs6jfYbNdq358dKD0oPSWk82VGzsTgQIA4+Dr1EHSEQLQJpdWoXqRvs2LeVHDbnWYrmSRgEIkmAOPjSuoU4+NK4sVcyCKT1Ct69d6i0t+Rb856xqLuEQQACCSJAHHyCOpOmFE0gzQ6+h2g5xnGm9FNpDwmDAAQgAAEIJIJAmh28n7/7Vv0HkkdozpEwCESSAO+Dj2S3UCkIRJpAWp/Bu1Oek/xCgVbSNtIMCYNAJAkQB19atxAHXxo39koGgTRfwfuZu8Pl7pKGSn4ej0EAAgkiwPvgE9SZNKVoAml28CeJ1iWSR897+V0JgwAEIAABCCSCQJod/FvqwSOlttLxktcxCESSAHHwkewWKgWBSBNIs4N/RD2zRPLc9JrtMHOHhEEgkgSIgy+tWxwHP3HiRI+zwSCQOgJpdvDu7MXS3yVPX7u7hEEAAgkiQBx8gjqTphRNIM2j6M8UrS2l4KUDq4umxw4QgAAEIFBuAp547OYGDvpLlT/VwDapL06zg1+q3vcLC95L/bcAAJEnwPvgI99FVLB8BN7Roc7IHm6olltLjnaaLh0iDZKmSlgDBNLo4DuKiV8X68F1F0r/ltZJj0k3ShgEIkeAOPjSuoQ4+NK4NfNeHhv1j2wdRmnZT/o4u+75SzaXviaNlLACBNLo4JeJxzGS4+AXSFtIn0qLJAwCEEgQAd4HH/vOXKsWeFrxwMG7QbtKTzuBFSaQ1kF21wjLt7NoNtPyPmm37DoLCEAAAhCIBoHLVI1nJU8tfoP0tmTj+Xsth4J/0+jgO4uIQ+OCQRx+lnOy5LfKYRCIJAHi4CPZLVSq8gRu1UccIfnWvG/d/0ry/CWrJKwBAmm8Re9bPn7+HjZ/WVqEM0hDIEoEiIMvrTd4H3xp3CK21yuqj8dKdZUWS2skrBEE0ngFv1xcfCb4a2kf6SjpDukeCYMABBJEgDj42Hdmb7XAV/F+6+ePJT9ePVfCGkEgjQ7eWH4u+db8cdJO0v+TXpYwCEAAAhCIDoERqsq70oXZKv1ey+9JO2bXWRQgkMZb9MaxXro/K69jEIg0AeLgI909VK4yBPzYtK/kZ/Dfz37EbC3vlQ6VggF32SIWuQTSegWfy4F1CESagOPgq6qqIl3HKFaOOPgo9kqj6+QLMYc1OywuMPssP1adG2SwrJ8ADr5+NpRAAAIxJ8D74GPegZmMp6SdIPmxqkObZ0vVkl8WhjVAIK236BvAQjEEIAABCESAgB+lviF9S/ItrAekdySsEQS4gm8EJDaBQHMTIA6+uXuAz28mAo579yyjV0tXSHbuP5G+KmENEMDBNwCIYghEgQBx8KX1Au+DL41bhPY6VXWZJHmwXWAeQe8pxrEGCODgGwBEMQQgEF8CxMHHt++yNV+p5VnSk9LXs3mNWXxRG30pZ0OfGOyek5foVZ7BJ7p7aRwEIACBWBNopdo/KHnUvOej/63UmJnsHCvvF4p5cpzATlRiO+mUICPpSxx80nuY9iWCAHHwiehGGlE6gX9q1wOkv0kdpOelQhZMjBPe5pLwShrS3KJPQy/TxtgTIA6+tC4kDr40bhHa6+ZQXWYovb/0grQwlF9fcmsVeOR9J+kMyWF2qTIcfKq6m8ZCIF0EiIOPfX9fmdMCj6g/Qfp7Tn7uqk8E3pa2lK6Shki+gh8mpcZw8KnpahoKAQhAIBYE/BKwf2Vr+qyWdtS58itjC9kgFfpZ+zzJJwSDJecdL6XGeAafmq6moXEm4Dj4O+64I7PzzjvHuRnUHQKNIfCmNjo5u6Fj3vPN0ewX0BSyLipcIB0ozZc8WY5PHJZKqTEcfGq6mobGmQBx8KX1Hu+DL41bM++1kz7/5gbq8EuVP1Vgm0dV5lfLehT+SMlnxn4t+KVSagwHn5qupqEQSB8Bx8EPHz58qFru57BYPAh4tjoPirO57zxY7i5punSI5Fvtft13IbtbhR6I11W6T9pecshcQ8/utUlyDAefnL6kJRCAAASSQGCJGvGPbENGadlP+ji7/pyWm0tfk0ZKuTZBGXbqYTs/tPKE0meH1hOdxMEnuntpXFIIEAeflJ6kHUUSWKvte0iBg/fuu0pPO5HH7Lyr8uQHWY0Jrwu2jf0SBx/7LqQBaSDgOPg0tLPcbSQOvtxEN/rxLtMneiT9ROkD6XBptlTf83dPiFPI2hcqTFpZy6Q1iPZAAAIQCAgQBx+QiO3y/1TzI6TnJN+6/5XkELlVUiHrpkI/e39NekPyyHyPvL9NSo1xBZ+arqahEIAABGJHwIPpfNV+ZZE1/5m230S6RTpKulXys/j/kVJjXMGnpqtpaJwJ8D74OPcedW8Cgfe1r5+5F2uf1w6OnBgpeRS+r+YdW/9zKTWGg09NV9PQOBMgDr603uN98KVxi9BenonOztmT1fwnJN+2L2RzVNhL8tiVNpJH3nugnvNSY9yiT01X01AIpI8AcfCx7/PfqQW/z9MKT11byPysfZI0TXpIekSyox8jpcZw8KnpahoKAQhAIHYEXqmnxu3qyQ+yPahuJ8lhdnb0P5IWS6Ol1Bi36FPT1TQ0zgSycfBxbgJ1h0ApBA7TThOk1yWPhp8i+bb9t6SG7ENt4Pno10jXS3dJq6XUGFfwqelqGhpnAsTBl9Z7xMGXxi1Ce92kutwp7SzZwS+Tvi+NlQqZB9P9IM8GnsnO89inwnDwqehmGgmBdBJwHLw0skWLFukEEO9Wu9P8VrhLpcHS56RrJc9s51j4v0n1mU8A/pkt9HF6Sp7fvtA+2c2Ts8DBJ6cvaQkEIACBJBFYr8Ysl+zkPWFNcEXemNHwntTGCpvXz5bGhzOTnOYZfJJ7l7YlhgBx8InpShpSHIE/afN/S74931u6VzpHelIq1nwHwCcLqTGu4FPT1TQ0zgSIgy+t93gffGncIrSXQ+QekzxQzgPufBX/R2m6VMh8pe7b+oF5Dvptpe8GGWlY4uDT0Mu0EQIpJUAcfCI63lfwtlnS72pSDf+5X5s4PC4wnyD4Fr1H1afGcPCp6WoaCgEIQCAWBPZSLW9uoKYeCf9Unm2eUV7u++DDm/nWvm/xp8Jw8KnoZhoZdwK8Dz7uPUj9iyDwjrb1iHfbUGlr6S7Jt+UPkQZJfglNPvu1Mu3XdpDOl26UnpccZnea9KqUGsPBp6araWicCRAHX1rvEQdfGrdm3suvhf1Htg6jtOwneeS87TnJ88p/TRop5VpwW97P2i+SvL/NTv5NyScAf5FSYYyiT0U300gIpJMA74OPfb+vVQsc9x42v11uYTgjT7paeb1z8nfRekP75ewS71Wu4OPdf9QeAhCAQJIJXKbGPStNlD6QDpdmS/mevyu7zkYo5dH3nhDHE97sKW0vHSWlxriCT01X09A4EyAOPs69R92bQOBW7XuE5FvzvnX/K8lOe5VUyN5S4T7SSOlT6S7py9K/pNQYV/Cp6WoaGmcCxMGX1nvEwZfGLUJ7HaC6XCL5Nr0vSI+TLpU8CM9X6IVsvgpvKbRB0stw8EnvYdoHgRQTIA4+9p0/Ui24TRovrZECmxYkcpYvat0hdPtLJ+eUeXWcFIzQz1OcrCwcfLL6k9ZAAAIQSAqBFmpIZ+lyyfPSN8ZO1UYzJYfUPSHl2qLcjCSv8ww+yb1L2xJDgPfBJ6YraUjjCdipPy4Nldo0cje/lMbP6t+XPpQ8C97bkm/195I8m11qDAefmq6moXEm4Dj4qqqqODehWepOHHyzYC/nh3bSwW6VPpKmhuSBdoXMt+jt2LeUrpKGSH6WP0xKjXGLPjVdTUMhkD4CvA8+9n3uyWo8qC7X6nsGH2w3SIlTpHnSCZKv4O3vHHbnZ/qpMBx8KrqZRkIAAhCIJQHfcs9n7fJlhvK6KO0XyxwoeTS9XzfrsLmlUmqMW/Sp6WoaGmcCxMHHufeoexMIHKZ9J0ivS3bSUyRflX9LKmSPqvAa6QZppLSzdIf0iJQa4wo+NV1NQ+NMgDj40nqPOPjSuEVor5tUlzslO2g7+GXS96WxUiG7W4WeltZvlrtP2l76sfR3KTXGFXxqupqGQiB9BBwHf+yxxw5NX8sT0WKHyflWu5/BezS9R9JfKz0tNTTITpvUhMmN1nILaaaUKueu9tbMDOQlBgEIQAACEIgSgfWqzHLJTt7P4j1QzuY3yznkrZD54vV8yWFyT0q+1f9XqbuUGuMKPjVdTUPjTIA4+Dj3HnVvAoE/aV87ad+e7y3dK50j2WkXMk94c6g0ILvRM1rOkZyfGsPBp6araWicCRAHX1rvEQdfGrcI7eWQNr8Bbo3kq3APthssLZYKmUfPXyl9kN3oUy096M5OPzWGg09NV9NQCKSPAO+Dj22fOwzOGi95whqnPXr+ammIdLRUyN5ToZ182I7VytxwRtLTjKJPeg/TPghAAALxI+Awt+Bqe2Wo+n4u71vtF4by8iX/qMyXpMOlraRJUm/pa1JqjCv41HQ1DY0zAeLg49x71L0EAnbEVdI92aXTli9Kt5V8VV/IfLXv0DqH2TkW/tfSNpLvBKTGuIJPTVfT0DgTIA6+tN4jDr40bhHYy1fqayU/b/fzdzv3AyS/SOYVqT7bRAW/lXaR7pdulXysL0rjJT+TP1FKhXEFn4puppEQSCcB4uBj2+8DVXM74z0k+6mHJM9EN1a6WarPfGv+UMmj7M+WDpc8F71PCuZIP5VSY1zBp6araSgEIACBWBDYUrX0lfcwabLkULfdJF+F+2reIXN7S37Gnmv7KWO45Gfu/5H+IHmim+9LPjlIlXEFn6ruprFxJUAcfFx7jnqXQGBP7fMvyVPMrpO+IY2RqiUPuHta2kvKZx5Q92q2YLqWfg7vq/jUOXczwMGbAgaBiBMgDr60DiIOvjRuzbxXe33+slAdHP/uqWoD66rE0mAlZ+npbX1SYPPV/mzJV/ypNBx8KrudRkMgHQSIg49lP/u2/P6SB8odJ9mh+6rdtp3kEfbBVbrzcq2bMqzNsgXBupedsnmpWPAMvohu1ojc6WefffayUaNGLShit9RtOmvWrM6rV6/+MF/Dp02b1vrwww+fka+MPL3AekH+r9a55567yeTJk+u47bDDDktvvPHGNwNmL7zwQqcLLrigb7BO+QZ8PItZri0ZP358W76LtVjmz5/ffsWKFYtyITXT+kx97sXSC5LtJGm19B3pRsmhb3XffaVzzYPpwhb+pxqjghPChUlO+3YGVhyBLsVtntqt/bwsuFUWhtBOK23DGaQ3ILBKa59skFO70kqLjqF8335cHlqnvDCfEKq6ZAeluMipw1FzW9yhaVEx941D3II6+fm6bW7tIu/fhn5b/JuU74Qv78HIhAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIgwgY6q29Kslmu5LrQ+TulSbBvt9JNSdmQfCEAAAhCAAATKT2BfHXJ+GQ77XR3j4TIch0NAAAIQgAAEIFAGAvU5+K/q2K9Ji6UHpG6SbUfpBalaekXaT+opfSA57y4JgwAEIAABCECgmQnkc/DdVSffwh8kbSXdLl0l2e6TfiW1l3xL3k6+pXSK9Jjk2/8YBCDQzAT8T4lBAAIQyCUwQBn/kR6S/Iz+Uumbkm2NtKe0k3SDtI/kZ/je7lNpmYRBAALNTAAH38wdwMdDIKIEPGBuV+mtrP6hZVdpa+ksqUr6pzRFOkHCIACBiBHAwUesQ6gOBCJCwM77ecm35wP5qt3P2X0Ff5zUQ/qTdKe0uYRBAAIQgAAEIBAhAvmewfv5+hJpj2w9/Sz+31Ir6W/SjyXbFtIKyQPwjpEmSBgEIAABCEAAAhEgkM/Bu1qnS36e7tv0/5L2l2y+kve6b89/JP1CsvWWFkouwyAAAQhAAAIQiDABX7HXd/t9U5W1zqm7H/v56h+DAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQKEvj/SM3B7x4FVRoAAAAASUVORK5CYII=" alt="plot of chunk loglin"/> </p>
<pre><code>> [1] "Experiment 3"
</code></pre>
<pre><code>> Analysis of Deviance Table
>
> Model: poisson, link: log
>
> Response: Count
>
> Terms added sequentially (first to last)
>
>
> Df Deviance Resid. Df Resid. Dev Pr(>Chi)
> NULL 7 11.50
> Cheating 1 2.14 6 9.36 0.144
> Prime 1 0.03 5 9.32 0.855
> Test 1 0.03 4 9.29 0.855
> Cheating:Prime 1 4.24 3 5.05 0.040 *
> Cheating:Test 1 2.85 2 2.21 0.092 .
> Prime:Test 1 0.50 1 1.71 0.481
> Cheating:Prime:Test 1 1.71 0 0.00 0.191
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
</code></pre>
<p><img 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" alt="plot of chunk loglin"/> </p>
<pre><code>> [1] "Experiment 4"
</code></pre>
<pre><code>> Analysis of Deviance Table
>
> Model: poisson, link: log
>
> Response: Count
>
> Terms added sequentially (first to last)
>
>
> Df Deviance Resid. Df Resid. Dev Pr(>Chi)
> NULL 5 21.3
> Cheating 1 4.22 4 17.1 0.03996 *
> Prime 2 0.29 2 16.8 0.86607
> Cheating:Prime 2 16.76 0 0.0 0.00023 ***
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
</code></pre>
<p><img 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" alt="plot of chunk loglin"/> </p>
<p><strong>Conclusion log-linear analysis:</strong><br/>
This alternative, and in my opinion more appropriate analysis is in agreement with the results after correction for multiple comparisons and continuity: </p>
<ul>
<li>The mosaic plots show that there may be some unexpected factors driving the “effects” reported in the paper:
<ul>
<li>In experiment 1 & 4 it is not so much the observed frequency of people that <em>did</em> cheat, but the number of participants that <em>did not</em> cheat that deviate from the expected frequencies based on table margins.<br/></li>
<li>The <code>Money</code> prime caused <em>less</em> people to <strong>NOT</strong> cheat, whereas the <code>Time</code> prime caused <em>more</em> people to <strong>NOT</strong> cheat</li>
</ul></li>
<li>If there is a difference in amount of <code>Cheating</code> between samples, it is likely a “main effect” between the <code>Time</code> and <code>Money</code> prime (<code>Cheating:Prime</code> interaction), it is found to cause a significant drop in deviance in Experiments 1, 3 and 4. </li>
<li>Experiment 2 stands out, because observed differences in <code>Cheating</code> are unlikely due to chance, but none of the other factors contribute to explain differences between expected and observed frequencies.</li>
</ul>
<p>The point about the mosaic plots is not just semantics or methodologists' nit-picking. What it tells us is that, e.g. in the mosaic plot Table.1.1, among the observed frequencies of <code>CheatYES</code>, the cell <code>Money</code> does not stand out much from <code>Time</code> and <code>Control</code> from what may be expected by chance, for <code>CheatNO</code> on the other hand, the cell <code>Money</code> does stand out as different. </p>
<h3>2. Exact odds ratios of 2x2 subtables to test hypotheses using Effect Size CIs</h3>
<p><strong>Effect Size Confidence Intervals:</strong><br/>
To get a clearer idea about the significance between cell differences I calculate confidence intervals around the effect size associated with contingency tables. The CIs in Figure 1 below are based on the exact Odds Ratio (using the non-central hypergeomteric distribution) for a 2x2 sub-table of the full design obtained from <code>Fisher's Exact Test</code>, testing against \( H_0: OR = 1 \). </p>
<pre><code>> [1] "Figure 1. Exact log Odds Ratio's of 2x2 tables comparing frequency of Cheating between independent samples in each experiment."
</code></pre>
<p><img 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" alt="plot of chunk ChiCIs"/> </p>
<blockquote>
<p><strong>Note:</strong><br/>
Here, the Confidence Levels have been adjusted to account for the fact that 3 (EXP1&4) and 4 (EXP2&3) subtables of the full design were compared (<code>1-(0.05 / #tests)</code>). The exact p-value from Fisher's exact test reported in the Figure was multiplied by the number of comparisons in each experiment.</p>
</blockquote>
<h3>Conclusion Proportion data</h3>
<ul>
<li>If there is an effect, it exists as a “main-effect” difference between the <code>Money</code> and <code>Time</code> primed samples in Experiment 1 and 4.<br/></li>
<li>Experiment 3 <code>No Mirror: Money - Time</code> is a marginal case. </li>
<li>Experiment 2 did not yield any substantial effects.<br/></li>
<li>4-5 out of 7 statistical inferences in the paper that are made based on proportion data should be considered invalid.<br/></li>
</ul>
<hr/>
<h2>II. Analysis of extent of cheating</h2>
<p>The extent of <code>Cheating</code> concerns the difference between actual accuracy (which is not provided as a result) and reported accuracy by a participant.<br/>
Experiment 1-3 report analyses of extent of <code>Cheating</code> including means and SD's. Sample size assumptions for Experiments 2 and 3 are the same as above. </p>
<h4>Compare Cohen's d CIs</h4>
<p>I created CIs around the effect sizes based on the means and SD reported for Experiment 1-3 using the <code>R</code> package <code>MBESS</code>. </p>
<pre><code>> [1] "Figure 2. Cohen's d with exact CIs comparing extent of Cheating between independent samples in experiment 1-3."
</code></pre>
<p><img 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" alt="plot of chunk ExtCIs"/> </p>
<h3>Conclusion Extent of Cheating</h3>
<p>The pattern is the same as the previous analyses:</p>
<ul>
<li>Experiment 1 shows a clear effect between <code>Money</code> and <code>Time</code> samples<br/></li>
<li>Experiment 3 <code>No Mirror: Money - Time</code> is again a close call<br/></li>
</ul>
<hr/>
<h2>III. HAPPE-ing (Hypothesising After Post-Publication Evaluation) </h2>
<p>Should reviewers have noticed these issues with data analysis? </p>
<p><strong>Yes, they should have!</strong> </p>
<p>Even without re-analysing the published data as I have done here, the conclusions by the authors can be questioned based on a comparison of very elementary results: </p>
<blockquote>
<p>Across four experiments, using different primes and a variety of measures and tasks, we consistently<br/>
found that shifting people’s attention to time <strong>decreases dishonesty</strong>. Priming time makes people reflect<br/>
on who they are, and this self-reflection <strong>reduces their likelihood of behaving dishonestly</strong>. </p>
</blockquote>
<p>The clue is to compare the results across the 4 experiments and evaluate whether it is valid to infer that the core postulates have been corroborated. The designs and materials are slightly different each time, but if variation in outcomes (e.g., proportion cheating behaviour) varies systematically with one or more of the experimental differences, there may be another variable at work here. </p>
<p>One result that begs explanation is the drop in proportion <code>Cheating</code> in all the samples of Experiment 2 when compared to the other experiments. What is special about the procedure and methods? Regrettably more than 1 potential intervening factor changes with respect to Experiment 1. </p>
<p>A second odd omission in the interpretation of the results is the level of accuracy achieved by participants. In Experiments 1-3, the urge to cheat must have been <em>less</em> when a participant had achieved 90% accuracy. Experiment 4 is somewhat different in that the cheating opportunity concerns one “bottleneck” problem that is difficult to solve, but has to be correct in order to make other more easily solvable problems count in adding to the final reward. Here, accuracy could have an opposite effect in which less accurate participants cheat less. If 0 or only 1 extra item past the “bottleneck” item were solved, a participant might be less inclined to cheat than a participant who solved every problem except for the “bottleneck” item. </p>
<h4>What is mediating what?</h4>
<p>The figure below shows the interaction between the maximal financial incentive that could be awarded and the proportion cheating for each prime and experimental condition (indicating whether a mediator variable was manipulated in addition to being exposed to a prime). Note that the <code>Intelligence</code> and the <code>No Mirror</code> condition of Experiments 2 and 3 respectively are considered similar to Experiment 1 and 4, that is, they reflect a condition in which <code>Self-reflection</code> was not induced by any other means than priming: </p>
<p><img src="data:image/png;base64,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" title="plot of chunk Exp2_Reward" alt="plot of chunk Exp2_Reward" style="display: block; margin: auto auto auto 0;" /></p>
<p>This relationship can be tested in a generalised linear model, of course being fully aware that this is <em>exploratory HAPPE-ing</em>. I assume the samples from each experiment are independent and use the number of cheaters vs. no cheaters as the dependent binomial variable. The model contains only those effects for which data are available (e.g., no interactions with both <code>Prime</code> and <code>Mediator</code>) </p>
<blockquote>
<p><strong>Note:</strong><br/>
A generalised linear mixed model (GLMM) with sample ID as a random effect gives similar results. </p>
</blockquote>
<pre><code>>
> Call:
> glm(formula = cbind(CheatYES, CheatNO) ~ Reward + Prime + Mediator +
> Reward * Prime + Reward * Mediator, family = binomial, data = reward)
>
> Deviance Residuals:
> Min 1Q Median 3Q Max
> -1.153 -0.695 -0.122 0.251 1.956
>
> Coefficients:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -0.4495 0.2195 -2.05 0.0405 *
> Reward 0.0111 0.0219 0.51 0.6125
> PrimeNone 0.5868 0.3973 1.48 0.1397
> PrimeMoney 0.6040 0.2824 2.14 0.0325 *
> MediatorSelf-reflection -0.8128 0.3147 -2.58 0.0098 **
> Reward:PrimeNone 0.0167 0.0359 0.47 0.6416
> Reward:PrimeMoney 0.0698 0.0327 2.13 0.0329 *
> Reward:MediatorSelf-reflection -0.0189 0.0434 -0.44 0.6626
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> (Dispersion parameter for binomial family taken to be 1)
>
> Null deviance: 76.292 on 13 degrees of freedom
> Residual deviance: 11.035 on 6 degrees of freedom
> AIC: 82.48
>
> Number of Fisher Scoring iterations: 4
</code></pre>
<pre><code>> [1] "Null-model deviance test: p < 1.33525644154704e-11"
</code></pre>
<p>In the table above the model <code>Intercept</code> corresponds to the odds of <code>Cheating</code> compared to the Null-model when the predictors have the values: <code>Prime</code> = <code>Time</code>, <code>Mediator</code> = <code>None</code> and <code>Reward</code> = 0. Compared to the overall probability of observing <code>Cheating</code> behaviour, it thus seems that when the <code>Time</code> prime is presented without an induction of <code>Self-reflection</code> and a financial reward incentive, the odds of <code>Cheating</code> drop. </p>
<p>This appears to be a corroboration of the second postulate, but note that in this analysis (just as in the previous analyses), there is no real difference between the <code>Time</code> prime and prime = <code>None</code>. The standard errors around these parameters are quite high. A clearer picture emerges when the Intercept is defined as <code>Prime</code> = <code>None</code>, <code>Mediator</code> = <code>None</code> and <code>Reward</code> = 0 and the Odds Ratios are compared (exponentiation of the parameter estimates): </p>
<pre><code>> [1] "Odds Ratios compared to Prime = None, with profile likelihood CI.95"
</code></pre>
<pre><code>> OR 2.5 % 97.5 %
> (Intercept) 1.15 0.60 2.21
> Reward 1.03 0.97 1.09
> PrimeTime 0.56 0.25 1.21
> PrimeMoney 1.02 0.47 2.21
> MediatorSelf-reflection 0.44 0.24 0.81
> Reward:PrimeTime 0.98 0.92 1.05
> Reward:PrimeMoney 1.05 0.98 1.14
> Reward:MediatorSelf-reflection 0.98 0.90 1.07
</code></pre>
<p>The odds ratios in the table above are multiplicative changes to the <code>Probability of Cheating</code> = 1 when the predictor increases by 1 unit. So an OR < 1 will decrease the odds of observing <code>Cheating</code> behaviour and an OR > 1 will increase it. The 95% CIs are based on the profile likelihood and show that in most cases the effect covers a range below and above 1. The range for the effect of <code>Self-Reflection</code> is always below 1. </p>
<p>One can interpret the modelled relationship between these variables as follows: </p>
<ul>
<li>There is a weak positive association between the <code>Maximal Financial Reward</code> and the <code>Probability of Cheating</code><br/></li>
<li>The association changes with the value of <code>Prime</code>, becoming stronger when <code>Money</code> is primed, weaker when <code>Time</code> is primed<br/></li>
<li>The induction of <code>Self-reflection</code> does not cause the association to change, it changes the intercept, the base-line <code>Probability of Cheating</code> at <code>Reward</code> = 0<br/></li>
</ul>
<p>A graphical representation of the model predictions more clearly reveals this relationship: </p>
<p><img src="data:image/png;base64,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" title="plot of chunk Exp2_GLM3" alt="plot of chunk Exp2_GLM3" style="display: block; margin: auto auto auto 0;" /></p>
<hr/>
<h3>Conclusions, Discussion and further HAPPE-ing</h3>
<ol>
<li>The significant results between <code>Time</code> and <code>Money</code> in Experiments 1 and 4 probably arise due to the increase in <code>Probability of Cheating</code> when there is a financial reward and <code>Money</code> is primed.<br/>
<ul>
<li>It is unlikely there are any other “real” differences in these data except for the induction of <code>Self-reflection</code>: Model predictions show it decreases the <code>Probability of Cheating</code> by the same amount for different primes</li>
<li>Note that there were no actual data points for <code>None</code> + <code>Self-reflection</code><br/></li>
</ul></li>
<li>The missing predictors in the <code>Probability of Cheating</code> analysis are the actual and reported <em>accuracy</em> of the performance (amount of correctly solved problems and money received respectively). These values cannot be inferred from the extent of cheating analyses. It seems reasonable to assume in most experiments there was less incentive to engage in <code>Cheating</code> by participants who were more accurate.
<ul>
<li>This brings up the question of whether the effects are driven by some sort of Speed-Accuracy instruction: Naturally, <code>Time = Money</code>, but taking the time to solve the problems may lead to higher accuracy and less incentive to cheat, likewise a focus on getting as many answers as possible may introduce errors and promote cheating. </li>
</ul></li>
</ol>
<p>In science there is a moral obligation to do the best one can to be as accurate as possible and usually this means it is wise to be as modest as possible about ones' scientific claims. I am not an expert in this field, but the sheer amount of questions that can be raised about the validity of the inferences made in this paper makes one wonder who the peers were that achieved consensus about the credibility of this research and what their area of expertise was. </p>
<p>I am not saying this is irrelevant, or poor research; the two effects that survive the scrutiny of <strong>3PR</strong> are certainly interesting. I am just a little worried this paper says more about the morality of contemporary scientific publishing than the scientific study of moral behaviour. </p>
<hr/>
<p>Some notes about this file:</p>
<ul>
<li>This file was created using <a href="https://support.rstudio.com/hc/en-us/articles/200552086-Using-R-Markdown">Markdown</a> in <a href="http://www.rstudio.com">RStudio</a>: Unless otherwise indicated in the code blocks (e.g., by <strong>require</strong>), the basic R packages are used.</li>
<li>All the analyses are based on results reported in the <a href="http://pss.sagepub.com/lookup/doi/10.1177/0956797613506438">publication</a>. </li>
<li>The one true gospel on statistical inference does not exist and more than one approach to analyse these data may be defensible.</li>
<li>Therefore: Please be aware these comments and suggestions reflect my own preferences and standards in these matters. If you feel I should change some of my preferences and/or standards please let me know, because I review and adjust them on a regular basis.<br/></li>
</ul>
</body>
</html>
Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-917938371711042322013-11-08T00:04:00.001+01:002013-11-08T12:19:46.772+01:00The Explorer Delusion - We need to conquer theory evaluation and strong inference, not new continents<span style="font-family: inherit;"><br /></span>
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<span style="font-family: inherit;">[This is an edited version of a previous post]</span></div>
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<b><span style="font-family: inherit; font-size: large;">"It is the theory that decides what may be observed"</span></b></div>
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<b><span style="font-family: inherit; font-size: large;">- Einstein (quoted by Heisenberg)</span></b></div>
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<span style="font-family: inherit;">Yes!</span></div>
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<span style="font-family: inherit;">We are discussing theory... finally! </span></div>
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<span style="font-family: inherit;">The context in which theory is discussed is mostly its potential relevance for finding more replicable effects (see Twitter, Blog posts <a class="g-profile" href="http://plus.google.com/110098308161411677670" target="_blank">+Rolf Zwaan</a>, <a class="g-profile" href="http://plus.google.com/100316636865880303074" target="_blank">+Michael Kraus</a>, <a class="g-profile" href="http://plus.google.com/100841335754826929747" target="_blank">+Andrew Wilson</a> and the commentaries on those posts). I've seen statements like <i>methods are more important than theory,</i> <i>theory can be useful sometimes</i>, <i>theory and method inform each other</i> and even <i>theory is essential</i>... </span></div>
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<span style="font-family: inherit;">The thing is... </span></div>
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<span style="font-family: inherit;">It takes a long time, but try staring at a method for a while, examine it and ask "why?" like a 5-year old, over and over... at some point you will realise the truth: <b>There is no method! </b><i>Only theory.</i></span></div>
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<a href="http://www.dharmaflix.com/" target="_blank"><img border="0" height="308" src="http://www.dharmaflix.com/w/images/f/fd/Matrix_spoon.jpg" width="640" /></a></div>
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<span style="font-family: inherit;">Any effect discovered by a method is in fact a prediction by a theory. It's probably more precise to say that a measurement context is predicted by the formalism within which a theory is defined. The effect is something that may be expected to emerge as a pattern in the data measured in a specific context, dictated by a formalism or theoretical framework.</span></div>
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<span style="font-family: inherit;">Most of mainstream psychological science uses sample based statistics and probability theory as tools of inference (e.g., <a href="http://fredhasselman.com/main/wp-content/papercite-data/pdf/bosman2013.pdf" target="_blank">Bosman, Cox, Hasselman & Wijnants, 2013)</a>. These tools were developed to study <a href="http://en.wikipedia.org/wiki/Ergodic_theory" target="_blank">ergodic systems</a> and assume <a href="http://en.wikipedia.org/wiki/Newtonian_mechanics#Limits_of_validity" target="_blank">classical physical measurements</a> apply to living systems (e.g., property attribution by measurement outcome). </span></div>
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<span style="font-family: inherit;">That's theory/formalism.</span></div>
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<span style="font-family: inherit;">Next time you run an analysis of variance on a sample of subjects randomly assigned to conditions of a factor, ask yourself why it is you are looking for sources of unique variance in your data that are attributable to efficient causes (the levels of the factor you manipulated)? </span></div>
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<span style="font-family: inherit;">You do this because you <i>assume</i> <i>ergodic theory applies to human beings</i> and therefore you can use <i>probability theory on the sample based statistics</i> of which you <i>assume they are an aggregate of properties of the individuals that make up the sample</i>, because you <i>assume you are performing a classical physical measurement.</i></span></div>
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<span style="font-family: inherit;">All the decisions you made to design the measurement context in which you collected your data were guided by theory. Those assumptions usually escape falsification however (but that's another story)</span></div>
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The Explorer Delusion</span></h2>
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<span style="font-family: inherit;">If you believe it is possible a true effect can somehow be discovered, out there in reality, like a land mass across the ocean where everyone said there would be dragons, or a new species of silicon based life forms at the other end of the worm-hole, then you show one of the symptoms of participating in a failing system of theory evaluation and revision that I dubbed <i>the explorer delusion</i>. </span><br />
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<span style="font-family: inherit;">This refers to the belief expressed by many experimental psychological scientists that the purpose of scientific inquiry is to go where no man has gone before and observe the phenomena that are “out there” in reality waiting to be uncovered by clever experimental manipulation and perhaps some more arbitrary poking about as well. </span><br />
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<span style="font-family: inherit;">A laboratory experiment is however not a field study or an excursion beyond the neutral zone. Even if it were, I would argue that wherever you go as a scientist, boldly, or otherwise, you will be guided and quite possible even be blinded, by a theory or a mathematical formalism about reality that is in most cases implicitly present in your theorising.</span><br />
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<span style="font-family: inherit;">Let's analyse this delusion by scrutinising a recent paper by Greenwald (2012) entitled: “<i>There is nothing so theoretical as a good method</i>”, which is a reference to the famous quote by a giant of psychological science, Kurt Lewin (1951). This also allows me to comment on what it actually is that Platt meant to say by the term "strong inference" in his 1964 paper.</span><br />
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<span style="font-family: inherit;">Greenwald is explicit about his position towards theory; he is not anti-theoretic, as he acknowledges that theories achieve parsimonious understanding and guide useful applications (but he does not specify… of what?). The author is however also skeptical of theory, because he noticed the ability of theory to restrict open-mindedness. This is indeed a proper description of a theory: It is a specific tunnel-vision, but from the perspective of the Structural Realist (forgive me, I will explain this position more precisely in the near future), this tunnel-vision is is only temporary. </span><span style="font-family: inherit;">It will be no surprise I disagree with the following: </span></div>
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<span style="font-family: inherit;">“When alternative theories <i>contest the interpretation of an interesting finding</i>, researchers are drawn like moths to flame. J. R. Platt (1964) gave the approving label “strong inference” to experiments that were designed as crucial <i>empirical</i> <i>confrontations between theories that competed to explain a compellingly interesting empirical result</i>.” (Greenwald, 2012, pp. 99–100, emphasis added)</span></blockquote>
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<span style="font-family: inherit;">That is not at all what Platt meant by strong inference, but incidentally we find another symptom of a failing system of theory evaluation, the <i>interpretation fallacy</i>:<i> </i>Theories do not compete for their ability to provide an understandable description or explanation of empirical phenomena. They compete for the ability to predict measurement contexts in which phenomena may be observed and they compete for the accuracy with which measurement outcomes were predicted. And J.R. Platt agrees with this perspective as he describes very clearly:</span></div>
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<span style="font-family: inherit;">“Strong inference consists of applying the following steps to every problem in science, <i>formally</i> and <i>explicitly</i> and <i>regularly</i>: </span></blockquote>
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<span style="font-family: inherit;">1) Devising alternative hypotheses;</span></blockquote>
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<span style="font-family: inherit;">2) Devising a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly as possible, exclude one or more of the hypotheses; </span></blockquote>
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<span style="font-family: inherit;">3) Carrying out the experiment so as to get a clean result; </span></blockquote>
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<span style="font-family: inherit;">1') <i>Recycling</i> the procedure, making subhypotheses or sequential hypotheses to refine the possibilities that remain; and so on.” </span></blockquote>
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<span style="font-family: inherit;">(Platt, 1964, p. 347, emphasis added)</span></blockquote>
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<span style="font-family: inherit;">Strong inference starts with devising alternative hypotheses to a problem in science and not with an interesting finding. Platt comments that step 1 and 2 require <i>intellectual invention</i>, which I take the liberty to translate as ‘theorizing about reality’. That is what you do when you device a method.</span></div>
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<span style="font-family: inherit;">Science does not test whether posited ontology is true</span></h2>
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<span style="font-family: inherit;">One source of evidence for his argument concern 13 papers, listed in a table that have started controversies on average 44 years ago in psychological science, but which still have no resolution. The author claims that in order to resolve the controversies, the method of strong inference was applied, which obviously failed. Also, it is claimed that philosophy of science provides no answers to resolve the controversies, because it discusses (apparently endlessly) whether such issues can be resolved empirically in principle. It is clear that Greenwald is referring to the resolution of these controversies as a resolution about the ‘reality’ of the ontology of a theory. This is again a matter of interpretation and is not what formal theory evaluation is about. The constituents of reality posited to exist by a theory are irrelevant in theory evaluation. As long as everything behaves according to the predictions by the theory, we should just accept those constituents as temporary vehicles for understanding. I believe these controversy theories were not properly evaluated for their predictive power and empirical accuracy. I don't know if they can be evaluated in that way, if they <i>cannot</i>, the conclusion must be the theories are trivial.</span><br />
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<span style="font-family: inherit;">This impression that ontology evaluation seems to be the problem here is indeed supported by the descriptions provided for the 13 controversies: It is primarily a list of clashes of ontology, e.g., Spreading activation vs. Compound cueing. Further support comes from the examples provided to argue that even if philosophy had an answer, this would not refrain scientists to continue the debate. The fact that scientists do not do this implies to the author there must be another way than strong inference to resolve controversies in science. This is illustrated by examples in which a scientific community was able to achieve consensus about a problem in their discipline (the classification of Pluto as a dwarf planet, HIV as the cause of AIDS and the influence of human activity on global warming). The author suggests that controversies in psychology could be resolved if only a reasonable consensus could be achieved.</span><br />
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<span style="font-family: inherit;">I cannot disagree with the author on his wish for a science that worked towards reaching consensus about the phenomena in its empirical record, instead of wasting energy on definite existence proofs for the ontologies of competing theories. Recall the history of the quantum formalism, two very different theoretical descriptions of reality (waves vs. particle ontologies) were found to be the same for all intents and purposes. I am certain that scientists in cosmology, virology and climatology used strong inference to work towards those consensus resolutions, but I did not check it. Strong inference and consensus formalism science go hand in hand.</span><br />
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<span style="font-family: inherit;">What I <i>can</i> say is that Platt’s recycling procedure (step 1’) suggests replication attempts should be carried out and apparently there is somewhat of a problem with replication of phenomena in psychological science. So this makes it again very unlikely any strong inference has been applied to resolve theoretical disputes in psychological science. Indeed, one of the authors listed to have caused a controversy that was unresolved by strong inference, recently challenged the discipline to start replicating the ‘interesting findings’ in its empirical record (e.g. Yong, 2012).</span></div>
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<i>(There must be some proverb about dismissing something before its merits have been properly examined...)</i></div>
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A second source of evidence to support his suspicions about the benefits of theorising, Greenwald examines the description of Nobel Prizes for their being rewarded due to theoretical or methodological contributions. <i>The explorer delusion</i> is obvious here; Greenwald highly values the appearance of the word ‘discovery’: </div>
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“Most “discovery” citations were for methods that permitted previously impossible observations, but in a minority of these, “discovery” indicated a theoretical contribution.” </blockquote>
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He concludes that theory was important for the development of methods, and that novel methods produced inconceivable results, that prompted new theory.<br />
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I am quite certain that the referred inconceivable results were predicted by a theory or considered as an alternative hypothesis. They concern measurement contexts one just does not accidentally stumble upon. If outcomes were surprising given the predicted context, an anomaly to the theory was found, and in that case, naturally, a new theory would have to be created. It was however due to an <i>anomaly</i> <i>to a theoretical prediction,</i> not due to a ‘discovery’ of a phenomenon by a method! The Large Hadron Collider (or any other billion-dollar instrument of modern physics) was not built as a method, a vehicle to seek out previously unknown phenomena like the starship U.S.S Enterprise. Theory, <i>very strongly predicted a measurement context</i> in which a boson should be observable that completed the standard model of particle physics. The methods scientists use for obtaining knowledge about the structure of reality is the result of testing predictions by theories, without exception. Satellites are not sent into space equipped with multi-million dollar X-ray detectors just to see what they will find when they get there. </div>
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Æther-dragging vs. Social Priming</h2>
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I conclude by commenting on the way the author describes why <a href="http://en.wikipedia.org/wiki/Albert_Michelson" style="color: #6e89a2; text-decoration: none;" target="_blank">Michelson</a> won the Nobel Prize for Physics in 1907. This involves a recurring theme in a paper I am about to submit: The luminiferous Æther. Experimental physicists like Michelson and Morley spent most of their academic careers (and most of their money) on experiments that tested the empirical accuracy of theories that predicted a very specific observable phenomenon called Æther-dragging. Their most famous experiment reported in<a href="http://www.aip.org/history/gap/PDF/michelson.pdf" style="color: #6e89a2; text-decoration: none;" target="_blank"> “On the Relative Motion of the Earth and the Luminiferous Ether”</a> (Michelson & Morley, 1887), showed very accurately and consistently that there was no such thing as an Æther, or at least, that its influence on light and matter was not as large as the Æther-dragging hypothesis predicted it would be. This of course harmed the precision and accuracy of Æther-based theories of the cosmos, but to hint, as Greenwald seems to do, that the method ‘caused’ Einstein to create special relativity theory is farfetched.<br />
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Michelson won the Nobel Prize for Physics in 1907 for the very consistent null-result (yes psychological science, such things can be important) and for the development of the interferometer instruments that meticulously failed to measure any trace of the Æther (cf.<a href="http://www.aip.de/pr/Michelson.e.html" style="color: #6e89a2; text-decoration: none;" target="_blank">Michelson, 1881)</a>. Their commitment to the Æther was adamant though. To be absolutely certain that the minute interferences that were occasionally measured were indeed due to measurement error, instruments of increasing accuracy and sensitivity were built. The largest were many meters wide and placed on high altitude on heavy slabs of marble floating on quicksilver in order to avoid vibrations interfering with the measurement process. Now that is a display of ontological commitment! It was however as much motivated by theoretical prediction as the construction of the Large Hadron Collider. Not a theory-less discovery by some clever poking about.<br />
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<b>Is there an analogous example of the severe tests of the Æther dragging effect for the tests that Social Priming effects have been put through?</b></div>
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Greenwald admits that the word theory is often used in Michelson and Morley’s 1885 article, so theory must have played an important role in the design of the instruments. The role was not just 'important', without the theory there would have been no method at all. In fact, if a theory of special relativity had been published 20 years before 1905 (physicists knew something like relativity was necessary), there would have been no instruments constructed at all because:</div>
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"Whether the ether exists or not matters little - let us leave that to the metaphysicians; what is essential for us is, that everything happens as if it existed, and that this hypothesis is found to be suitable for the explanation of phenomena. After all, have we any other reason for believing in the existence of material objects? That, too, is only a convenient hypothesis; only, it will never cease to be so, while some day, no doubt, the ether will be thrown aside as useless." (Poincaré, 1889/1905, p. 211). </blockquote>
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<span style="font-family: inherit;">And indeed, the Æther was thrown aside as useless, because a method devised to test a prediction by a theory yielded null results. Strong inference means this repeated null-result has consequences for the credibility of the theory that predicted the phenomenon. Apparently, in psychological science, this id a difficult condition to achieve.</span><br />
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<span style="font-family: inherit;">The Structural Realist's take home message is: </span></div>
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<li style="margin: 0px 0px 0.25em; padding: 0px; text-align: justify;"><span style="font-family: inherit;">We should believe what scientific theories tell us about the structure of the unobservable world, but</span></li>
<li style="margin: 0px 0px 0.25em; padding: 0px; text-align: justify;"><span style="font-family: inherit;">We should be skeptical about what they tell us about the posited ontology of the unobservable world. </span></li>
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<span style="font-family: inherit; text-align: justify;">In this quote by Poincaré may lie the answer to Greenwald's interpretation of current practice of psychological science (which is in fact a very accurate description of the problems we have with theory evaluation, I just do not agree with the interpretation): Why does Poincaré reserve a special place for the hypothesis about material objects, which will never cease to to be so? </span><br />
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<span style="font-family: inherit;">Still believe it is possible to use a method that was not predicted to yield measurement outcomes by a theory about reality? </span><br />
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<span style="font-family: inherit;">Ok.</span><br />
<span style="font-family: inherit;">I'll think of some more examples (again).</span></div>
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<span style="font-family: inherit;">天</span></div>
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<span style="font-family: inherit;"><b>References</b><br />Greenwald, A. G. (2012). There Is Nothing So Theoretical as a Good Method. <i>Perspectives</i><i>on Psychological Science, 7(2)</i>, 99–108. <a href="doi:10.1177/1745691611434210" style="color: #6e89a2; text-decoration: none;" target="_blank">doi:10.1177/1745691611434210</a><br />Michelson, A. . (1881). The Relative Motion of the Earth and the Luminiferous Ether. <i>American Journal of Science, 22(128)</i>, 120–129. Retrieved from <a href="http://www.archive.org/details/americanjournal62unkngoog" style="color: #6e89a2; text-decoration: none;">http://www.archive.org/details/americanjournal62unkngoog</a><br />Michelson, A. ., & Morley, E. W. (1887). On the Relative Motion of the Earth and the Luminiferous Ether. <i>American Journal of Science, 34(203)</i>, 333–345. Retrieved from <a href="http://www.aip.org/history/gap/PDF/michelson.pdf" style="color: #6e89a2; text-decoration: none;">http://www.aip.org/history/gap/PDF/michelson.pdf</a><br />Platt, J. (1964). Strong Inference. <i>Science, 146(3642)</i>, 347–353. Retrieved from <a href="http://clustertwo.org/articles/Strong%20Inference%20(Platt).pdf" style="color: #6e89a2; text-decoration: none;" target="_blank">http://clustertwo.org/articles/Strong Inference (Platt).pdf</a><br />Poincaré, H. (1905).<i> Science and Hypothesis.</i> New York: The Walter Scott Publishing Co., LTD. Retrieved from <a href="http://www.archive.org/details/scienceandhypoth00poinuoft" style="color: #6e89a2; text-decoration: none;">http://www.archive.org/details/scienceandhypoth00poinuoft</a><br />Yong, E. (2012). Nobel laureate challenges psychologists to clean up their act. <i>Nature. </i>Retrieved from <a href="http://www.nature.com/doifinder/10.1038/nature.2012.11535" style="color: #6e89a2; text-decoration: none;">http://www.nature.com/doifinder/10.1038/nature.2012.11535</a></span><br />
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Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-33087484857144519752013-10-06T01:36:00.006+02:002013-10-06T01:59:56.258+02:00Respect your elders: Lykken's (1968) correlated ambient noise: Do fractal scaling and violations of the ergodic condition evidence the crud factor?<div style="text-align: justify;">
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Lykken (1968) estimated that the “unrelated” molar variables involved in most studies in psychology share 4-5% common variance, meaning, with 0 measurement error a correlation of about .20 can be expected between any one of them. This really depends on the field of inquiry, but it has been suggested that estimates between .15 and .35 are by no means an exaggeration.<br />
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The origins of such correlations are debated (and of course disputed), but I consider them as an example of the violation of the ergodic theorems for studying human behaviour and development (Molenaar & Campbell, 2009; Molenaar, 2008). The ergodic condition applies to systems whose current state in a state/phase space (that describes all the theoretically possible states a system could be in), is very weakly, or not at all influenced by its history, or its initial conditions. Hidden Markov models are an example of such systems. These systems have no "memory" for their initial state and formally this means their time averaged trajectories through phase space are about equal to their space averaged trajectories. Given enough time, they will visit all the regions of the phase space (formally there's a difference between phase and state space, which I will ignore here).<br />
<br />
For Psychological Science the ergodic assumptions related to probability theory are important: In an ergodic system it does not matter if you measure a property of the system 100 times as a repeated measurement (time average), or, you measure the property of 100 ergodic systems at the same point in time (space average). The latter is of course the sample of participants from which inferences are drawn social science. The former would be repeated measurements within a single subject. In ergodic system, the averages of these different types of measurement would be the same. It does not matter for the expected averaged result whether you roll 1 die 100 times in a row, or 100 dice in 1 throw.</div>
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<h2>
Trick or treat?</h2>
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Now, the <i>trick</i> question is, do you think such is the case for psychological variables? Would I get the same developmental trajectory if I measured IQ in a single human being each year from 1 to 80 (assuming I have a 0-error, unbiased, IQ measuring instrument and a very long lifespan) as when I would draw a sample of 80 participants aged 1 through 80 and measured their IQ on one occasion. Very few scientists would predict I would obtain the same results, in both situations, but in social science we do act as if such would be the case. To me, any evidence of a system's future state being influenced by a state at a previous point in time (memory), is a violation of the ergodic condition and basically should indicate to a scientist to stop using central tendency measures and sampling theory to infer knowledge about the properties of this system. If you do not want to go that far, but still feel uncomfortable about my IQ example, you should probably accept that there may be some truth to Lykken's suggestion about a default ambient correlation between variables in social science. Simply put, if you walk like a duck, there is a small base expectancy that you will also talk like a duck. </div>
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Another line of evidence revealing that everything is correlated (over time), or "has memory", is of course ubiquitous fractal scaling in repeated measurements of human physiology and performance (e.g., Kello et al., 2010). If measurements are interdependent rather than independent it does not necessarily point to a violation of the ergodic condition, but combined, the two frameworks do predict very different measurement outcomes in certain contexts (e.g., Diniz, et al., 2011). <a href="http://www.frontiersin.org/Fractal_Physiology/10.3389/fphys.2013.00075/full" target="_blank">My money is still on the "long memory" interpretation</a>. </div>
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Based on the lower estimates of Lykken's correlation, the expected difference between any sample-based averages would be about 0.5 standard deviations. The test against a null hypothesis of “no association” is often a test against a “straw man” null hypothesis because it can be known in advance that an assumption of no association at all is false. Therefore, a researcher can maximize his chances to corroborate any weak prediction of association between variables, by making sure a large enough number of data points are collected. You know, those statistical power recommendations you have been hearing about for a while now. A genuine “crud factor” (cf. Meehl, 1990) implies a researcher has a chance of 1 in 4 to evidence an association using a sample size of 100 data points, without even needing a truth-like theory to predict an association or its sign.</div>
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<i>Figure 1.</i> A simulation of the effect of sampling from different regions of a population distribution (N<span style="font-size: xx-small;">pop</span> = 500000) in the presence of a crud factor, a population-level correlation between any two random variables. Each dot represents the number of significant results (p < .05) observed in 100 t-tests for independent groups of the size represented on the x-axis (10 – 100). Two random variables were generated for each population correlation: .1, .2, .3 (columns). One random variable was used to sample data points in the 10th (top row) or 25th (bottom row), and between the 25th and 75th percentile (comparison group). The means concern the aggregated values of the second random variable for each sampled case. The directional hypothesis tested against the null was (M<span style="font-size: xx-small;">[.25,.75]</span> – M<span style="font-size: xx-small;">[0,.10]</span>) > 0 or (M<span style="font-size: xx-small;">[.25,.75]</span> – M<span style="font-size: xx-small;">[0,.25]</span>) > 0 .</div>
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<h2>
Psychologists need to change the way they theorise about reality</h2>
The crud factor, or the violation of the ergodic condition, are not statistical errors that one can resolve by changing the way psychologists analyse their data. It requires adopting a different formalism about measurement of properties of non-ergodic systems, it requires theories that make different kinds of predictions. No worries, such theories already exist and there are social scientists who use them. To encourage others, here are some examples of what can happen if one continues to assume the ergodic condition is valid and use the prediction of signs of associations between variables (or group differences) as the ultimate epistemic tool for inferring scientific knowledge.<br />
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<br />
<div class="BodyTextTHESIS" style="text-indent: 0cm;">
<span lang="EN-GB" style="font-family: inherit;">Suppose two variables x (e.g., a standardised reading ability test) and y (amount of music training received in childhood) were measured in samples drawn from a population that was cut into regions
in order to compare dyslexic readers (the 10<sup>th</sup> percentile and lower, and
the 25<sup>th </sup>and lower on variable x) and average readers (between the 25<sup>th</sup>
and 75<sup>th</sup> percentile on variable x) on variable y. The sample size for each group
was varied from 10 to 100 data points and 100 tests were performed for each group
size. For each test a new random group sample was drawn.</span></div>
<div class="BodyTextTHESIS" style="text-indent: 0cm;">
<br /></div>
Figure 1 represents the number of significant (<i>p</i> < .05) t tests found in the series of 100 tests conducted for each group size. If the crud factor were .1, then comparing to the samples from the 10th and 25th percentile would yield 25% significant results at group sizes of 44 and 58 data points respectively. The total study sample size would be 88 and 116. At this crud factor level the chances do not get much better than 1 in 4 corroborative events without there being any theory to pat on the back and grant some verisimilitude. When the correlation is .2, 25% significant tests can be expected at group sizes of 12 (10th) and 23 (25th) and at a correlation of .3 it’s 10 (10th) and 12 (25th) participants in each group to find 25% significant differences. The crud factor of .3 even implies that 100% of the conducted tests could give a significant result if the group size is larger than 87 and the dyslexic group is drawn from the 10th percentile of the population distribution of reading ability.<br />
<br />
So, what's the use of a priori sample size calculations again? To get a sample size that will allow you to evidence just about anything you can(not) think of, as long as you limit your predictions to signs of associations (Figure 2). A real <i>treat</i>.</div>
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<br /></div>
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<div style="text-align: justify;">
天<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjwqK2wRidpJO8LlEreD3Ed6rD4T5XP7l6RUuYJ0KR81N8SL0YCWh2UbxNZNPgmE9hxWLNfQX17UISK89wVTaNiV1Ar7guZSqB2QtNxepYxM9TMA806CpkW2GZhec8XMsJUN5MfMnPOtNc/s1600/crudtot.tiff" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjwqK2wRidpJO8LlEreD3Ed6rD4T5XP7l6RUuYJ0KR81N8SL0YCWh2UbxNZNPgmE9hxWLNfQX17UISK89wVTaNiV1Ar7guZSqB2QtNxepYxM9TMA806CpkW2GZhec8XMsJUN5MfMnPOtNc/s1600/crudtot.tiff" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Figure 2. </i>Same simulations as described in Figure 1, but for a range of crud factors between 0 and 0.4.</td></tr>
</tbody></table>
<br />
<br />
<br />
<b>References</b><br />
<b><br /></b>
<br />
<div style="margin-left: 24pt; text-indent: -24.0pt;">
Diniz, A., Wijnants, M.L., Torre, K., Barreiros, J., Crato, N., Bosman, A.M.T., Hasselman, F., Cox, R.F.A., Van Orden, G.C., & Delignières, D. (2011). Contemporary theories of 1/f noise in motor control. <i>Human movement science</i>, <i>30</i>(5), 889–905. doi:10.1016/j.humov.2010.07.006</div>
<br />
<div style="margin-left: 24pt; text-indent: -24.0pt;">
Kello, C. T., Brown, G. D. A., Ferrer-i-Cancho, R., Holden, J. G., Linkenkaer-Hansen, K., Rhodes, T., & Van Orden, G. C. (2010). Scaling laws in cognitive sciences. <i>Trends in Cognitive Sciences</i>, <i>14</i>(5), 223–232. </div>
<br />
<div style="margin-left: 24pt; text-indent: -24.0pt;">
Lykken, D. T. (1968). Statistical significance in psychological research. <i>Psychological bulletin</i>, <i>70</i>(3), 151–9.</div>
<div style="margin-left: 24pt; text-indent: -24.0pt;">
<span style="text-indent: -24pt;">Meehl, P. E. (1990). Why Summaries of Research on Psychological Theories Are Often Uninterpretable. </span><i style="text-indent: -24pt;">Psychological Reports</i><span style="text-indent: -24pt;">, </span><i style="text-indent: -24pt;">66</i><span style="text-indent: -24pt;">(1), 195. doi:10.2466/PR0.66.1.195-244</span></div>
<br />
<div style="margin-left: 24pt; text-indent: -24.0pt;">
Molenaar, P. C. M. (2008). On the Implications of the Classical Ergodic Theorems : Analysis of Developmental Processes has to Focus on Intra-Individual Variation. <i>Developmental Psychobiology</i>, <i>50</i>(1), 60–69. doi:10.1002/dev<br />
<br /></div>
<div style="margin-left: 24pt; text-indent: -24.0pt;">
Molenaar, P. C. M., & Campbell, C. G. (2009). The New Person-Specific Paradigm in Psychology. <i>Current Directions in Psychological Science</i>, <i>18</i>(2), 112–117. doi:10.1111/j.1467-8721.2009.01619.x</div>
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Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-67237109453908228492013-09-20T03:51:00.002+02:002013-09-20T20:05:55.583+02:00Defending Psychology in the Science Wars: Part 2 - Clone of the Attack<span style="font-family: inherit;"><br></span>
<br>
<div class="QUOTEBLOCK" style="text-align: center;">
<blockquote class="tr_bq" style="text-align: center;">
<span style="font-family: Helvetica Neue, Arial, Helvetica, sans-serif;">“<i>If a science is to have fair play, it is well for it if it
does not become popular. […] A popular science, one that is "made
easy," is not likely to have much vitality in it. The intension of a
science is in the inverse ratio to the extension.</i>”</span></blockquote>
</div>
<div class="QUOTEBLOCK" style="margin-left: 46.35pt;">
</div>
<div style="text-align: right;">
<div style="text-align: right;">
<span style="font-family: inherit;">– J.H. Balfour Browne, Esq. (1870)</span></div>
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<span style="font-family: inherit;">After my previous post (which immediately became the most "popular" post on my blog), some people noticed this is not a regular science blog and I am not a regular science writer. I had to clarify to some I do not aspire to be one. That is, I have no intention to contribute to popularising science via this blog, because that often means science "made easy" and I like my science vital, with just a little <i><a href="http://en.wikipedia.org/wiki/Intension" target="_blank">intension</a></i> to produce maximal extension like Browne (1870).</span></div>
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<span style="font-family: inherit;"><span style="font-family: inherit; text-align: justify;">I said somewhere I should have preregistered my expectations about the responses I would get about my exposé of the </span><i style="font-family: inherit; text-align: justify;">Phantom Arguments</i><span style="font-family: inherit; text-align: justify;">, this was not really necessary, because it was a </span><i style="font-family: inherit; text-align: justify;">Clone of the Attack</i><span style="font-family: inherit; text-align: justify;"> as I described in the little fictional piece, I'll discuss some of it later on.</span></span></div>
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<i><span style="font-family: inherit;">(There were also compliments! Thank you, I was hoping you would appreciate at least some of my subtext and meta-sarcasm... or is it irony when sarcasm can be used meta?)</span></i></div>
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<span style="font-family: inherit;">There are other reasons to call this </span><i style="font-family: inherit;">Clone of the Attack</i><span style="font-family: inherit;">, for one I do not want young scientists to become the clones of their supervisors as can be evidenced from their </span>responses to the attacks. I will discuss the roots of the Hard/Soft schism that seems to really get the blood boiling for some. First though, the attack was cloned to another target, t<span style="font-family: inherit;">he Economists are facing the same allegations... not a science. Their response is not very different from the soft psychologists in terms of phantom arguments.</span></div>
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<span style="font-size: x-large; text-align: center;">Clone of the Attack: The Economists</span></h3>
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<span style="font-family: Times, Times New Roman, serif;"><span style="font-size: small; text-align: center;"><b>Tweet:</b></span> </span></div>
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<span style="font-family: Times, Times New Roman, serif;">This week's installment of "Academics Saying Dumb Shit in the Times": <a href="http://t.co/e6yaTHGNhe">http://t.co/e6yaTHGNhe</a> #andIhateeconomics
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<span style="font-family: inherit;">The link is to an article about the lack of corroboration by prediction in sciences studying the economy. Ok, I have to mention the tone of tweet is not representative, subsequent responses to a question I asked were more thoughtful and academic :) However, the arguments used in defence of Economy are mostly the same phantom arguments as psychologists use. I received a link to this paper:</span><a href="http://ejt.sagepub.com/content/6/1/43" style="font-family: inherit;"> God Gave Physics the Easy Problems: Adapting Social Science to an Unpredictable World </a><span style="font-family: inherit;"> from the Journal of International Relations (more on the possibilities for a "hard" science of international relations and conflict later).</span></div>
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<span style="font-family: inherit;">This is just the <a href="http://anti-ism-ism.blogspot.nl/2013/08/defending-psychology-in-science-wars.html#.UiJ8DRa9YwI" target="_blank">"physics is easy"</a> argument. The authors say the paper is a plea for humility and they want to overcome physics envy. That's very sensible and all fine with me, but they suggest that the ideas about what makes a field of inquiry a genuine science are based on Newtonian physics and deductive logic and this is the wrong model to study social phenomena. Yes, but there is no physicist or philosopher of science that forced you to use Newtonian physics as a model. In fact, I believe they would advise against it. The fact that there is a belief in social science that Newtonian causality should be used to understand complex phenomena is "Newton's Curse". The very same I described in my previous post. It apparently also applies to the scientific study of International Relations and Economy.</span></div>
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<span style="font-family: inherit;">Moreover, the suggestion that there is a definition, or measure in philosophy of science, or meta-theory (the empirical study of scientific theorising) that is somehow based on a model of Newtonian causality is of course completely false and qualifies as <span style="text-align: left;">Academics Saying Dumb S.. stuff in scientific journals.</span> The quantum formalism: 1930s, General relativity: 1910s. The great modern philosophers who defined what modern science is, Popper, Kuhn, Lakatos based their ideas on <i>modern</i> physics (e.g., <a href="http://www.amphilsoc.org/guides/ahqp/" target="_blank">Kuhn's interviews with the founding fathers of QM</a> ). Here is Karl Popper reflecting on probability:</span></div>
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<span style="font-family: Times, Times New Roman, serif;">"I had always been convinced that the problem of the interpretation of the quantum theory was closely linked with the problem of the interpretation of probability theory in general, and that the Bohr-Heisenberg interpretation was the result of a subjectivist interpretation of probability. My early attempts to base the interpretation of quantum theory upon an objective interpretation of probability (it was the frequency interpretation) had led me to the following results.</span></div>
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<span style="font-family: Times, Times New Roman, serif;">(I) The so-called 'problem of the reduction of the wave packet' turns out to be a problem inherent in every probabilistic theory, and creates no special difficulty.</span></div>
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<span style="font-family: Times, Times New Roman, serif;">(2) Heisenberg's so-called indeterminacy relations must not be interpreted subjectively, as asserting something about our possible knowledge, or lack of knowledge, but objectively, as scatter-relations. (This removes an asymmetry between p and q which is inherent in Heisenberg's interpretation unless we link it with a phenomenalist or positivist philosophy; see my Logic of Scientific Discovery, p. 451.)</span></div>
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<span style="font-family: Times, Times New Roman, serif;">(3) The particles have paths, i.e. momentum and positions, although we cannot predict these, owing to the scatter relations.</span></div>
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<span style="font-family: Times, Times New Roman, serif;">(4) This was also the result of the imaginary experiment ('thought- experiment') of Einstein, Podolski, and Rosen.</span></div>
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<span style="font-family: Times, Times New Roman, serif;">(5) I also produced an explanation of the interference experiments ('two-slit-experiments'), but I later gave this up as unsatisfactory." Popper (1959, pp. 27-28) </span></div>
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<span style="font-family: inherit;">Very modern. If quantum logic can be a part of "hard" science why not the complex, context sensitive, nonlinear, nested and circular causality studied by the social sciences? My response to the Economists and the International relations scientists who want to use the "physics is easy" argument is the same as my response to the Psychologist, realise that you are saying:</span></div>
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<span style="font-family: inherit;">"We are using the wrong tools to study the phenomena we are interested in and we are not planning to work very hard to try and fix it any time soon". </span></div>
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<span style="font-family: inherit;">I'll repeat, if these disciplines are serious about being a more difficult endeavour than physics and serious about being a science, then why don't they teach the methods and tools of quantum physics and general relativity to their undergraduates? That is the kind of formal language and abstract reasoning to depart from when they want to study those complex phenomena. It would be a logical, scientific thing to do: Start learning everything about the scientific methods and tools that made that easy science about dead matter so extremely accurate and successful.</span></div>
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<span style="text-align: center;"><b><span style="font-family: inherit; font-size: x-large;">Some authors are more equal than others...</span></b></span></h3>
<span style="font-family: inherit;">Another thing happened that I expected to be cloned from my post. I complained about disciplines of science in which papers can be rejected because the maths and models are too difficult and expected to receive the very same complaint myself about my post. Several people told me this, some hinted at it by sending a link and one of those was a confession about the phenomenon in economy! Here's an excerpt from the link I was sent: </span><a href="http://krugman.blogs.nytimes.com/2013/08/18/communicating-economics/" style="font-family: inherit;">http://krugman.blogs.nytimes.com/2013/08/18/communicating-economics/</a></div>
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<span style="font-family: Times, Times New Roman, serif;">"What every economist, and for that matter every writer on any subject, needs to realize is that unless you are a powerful person and people are looking for clues about what you’ll do next, <i>nobody has to read what you write</i> — and lecturing them about what they’re missing doesn’t help. You have to provide the hook, the pitch, whatever you want to call it, that pulls them in. It’s part of the job." </span></div>
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<span style="font-family: inherit;">Yes the prophets were terrible authors and I haven't found any hooks or pitches in the Bible, but am deeply annoyed to be lectured all the time on what I am missing.</span></div>
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<span style="font-family: inherit;">I thought we were talking about science. Scientists have to read what other scientists write. Scientific papers are not novels and junior scientists should not be selected for tenure based on their communicative skills. In high school, most kids-now-scientist, spent their time practicing other abilities than social skills. This kind of reasoning taken seriously would have probably resulted in discarding each and every one of Einsteins 1905 Miracle Year articles because they weren't pitched right. (as groundbreaking Nobel worthy, legendary contributions to science).</span></div>
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<span style="font-family: inherit;">If authors are allowed to ignore parts of the scientific record that may be relevant to their claims, it is no longer a discipline of a science that has as a goal to accumulate veridical knowledge about the structure of reality. It can't have that goal if you can dismiss any article by saying you didn't connect with its message after the first paragraph. Apparently Krugman is a powerful enough person to be allowed to hold this belief and I apparently have to resort to Sesame-street language in the future or be ignored forever. What's next? Social scientists confessing they've never read the old man and the sea (because there are no hooks to be found in that book) so they can ignore the Humanities are a science?</span></div>
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<span style="font-family: inherit;">Here is a beautiful description of one of the few cases (in mathematics) in which I could understand a commentary such as Krugman's:</span></div>
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<span style="font-family: inherit;"><i>The Paradox of the Proof</i> - By Caroline Chen</span></div>
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<a href="http://projectwordsworth.com/the-paradox-of-the-proof/"><span style="font-family: inherit;">http://projectwordsworth.com/the-paradox-of-the-proof/</span></a></div>
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<span style="font-family: inherit;">Then again <a href="http://en.wikipedia.org/wiki/Shinichi_Mochizuki" target="_blank">Shinichi Mochizuki</a> didn't send in the proof for review if I understand correctly, but mathematicians<i> read and check the things their colleagues' post as working or submitted papers</i>.</span></div>
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<span style="font-family: inherit; font-size: x-large;"><b style="text-align: center;">Clone of the Attack:</b><b style="text-align: center;"> Finally Some Evidence for Priming?</b></span></h3>
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<span style="font-family: inherit;">I'll be (relatively) short about this, the introduction of my previous post including the short story had a different function than the main part containing the critique of the phantom arguments. Some specific terminology had a function (e.g., "average psychologists" in combination with measurement criticism) as well as choice for a specific style.</span></div>
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<span style="font-family: inherit;"> I expected that responses ...</span></div>
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<li style="text-align: justify;"><span style="font-family: inherit;">... would <i>not</i> be calm and erudite <b>(occurred)</b>, even though I literally mentioned this is what often happens when you criticise soft psychology.</span></li>
<li style="text-align: justify;"><span style="font-family: inherit;">... would try to depict me as a member of an outgroup <b>(<b>occurred</b>),</b> even though I am a psychologist myself and even though I described that this is what happens to psychologists who express a non-average psychology opinion in the fictional story. I was even using my own name as the future outcast! </span></li>
<li style="text-align: justify;"><span style="font-family: inherit;">.... would attack the style (e.g., quality / intelligibility of communication) and ignore the content <b>(<b>occurred</b>)</b> even though I mentioned I consider such a thing unscientific. Or in Meehls words, "<i>intellectually dishonest".</i></span></li>
<li style="text-align: justify;"><span style="font-family: inherit;">... would reveal some of my arguments are not understood. <b>(I don't know yet, or does that count as: occurred?)</b></span></li>
<li style="text-align: justify;"><span style="font-family: inherit;">More commentaries would appear based on arguments (see below) I did not discuss yet in my first post <b>(<b>occurred</b>). </b>But his was of course not due to the previous post, It's just that the there were some phantom arguments left to be used.</span></li>
<li style="text-align: justify;"><span style="font-family: inherit;">... would accuse me of being rude, making ad hominem attacks <b>(<b>occurred</b>).</b> This was expected because English is not my native tongue and I am Dutch, which can be a dangerous mix. Besides, if I have really been rude, I certainly did not intend to be <i>more</i> rude than the rudeness towards the author of the newspaper article (mentioned <i>by name</i> in all commentaries I responded to) who was basically called a dummy who didn't know S... science. I never used authors' names in my post, instead I addressed "Psychology" as the focus of my critique in my commentaries. I do understand that it was unfair to literally quote just one blogpost and leave the others uncommented. To make up I comment on all of them as an appendix to this post, including new excuses that appeared, the deleted scenes of part 1.</span></li>
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<span style="font-family: inherit;">Generally speaking I wanted the younger scientists at the softer end of the spectrum to respond and maybe even to be pissed off. Why? Because we need them, psychological science needs them. However, we don't need them to be clones of their predecessors. They're bright and smart and they can communicate (jay!) and they have to be so much better than their advisors and current superiors ever had to be. We need them to become harder scientists, but in order to do so they really, really have to accept the fact that psychology is as soft as it gets when empirical science is concerned. You do not turn into a "hard" science when you use an MRI scanner or a gene-sequencing technique.</span></div>
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<span style="font-family: inherit; font-size: x-large; text-align: center;">A New Hope... (oh... wait a minute, yep It's in fact an old hope)</span></h3>
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<span style="font-family: inherit;"> <a href="http://blogs.scientificamerican.com/psysociety/2013/08/13/psychology-is-a-science/">http://blogs.scientificamerican.com/psysociety/2013/08/13/psychology-is-a-science/</a> </span></blockquote>
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<span style="font-family: Times, Times New Roman, serif;">"Selectively using these qualifications as an excuse to exclude psychology and other “soft sciences” (excuse me while I roll my eyes so hard that I risk sending them permanently into the back of my head) from the scientific discipline without questioning the fact that “hard sciences” routinely address topics that are both “unnatural” and “unobservable” is simply lazy." </span></blockquote>
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<span style="font-family: inherit;"><a href="http://psych-your-mind.blogspot.nl/2013/08/the-psychology-of-psychology-isnt.html">http://psych-your-mind.blogspot.nl/2013/08/the-psychology-of-psychology-isnt.html</a> </span></blockquote>
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<span style="font-family: Times, Times New Roman, serif;">"Psychologists like to weigh in on the psychology is a science perspective because we are engaging in upward social comparison--We want a seat at the table with the hard sciences, we want to be published in the most prestigious science journals, and we want a larger share of the grant funding from our government. In contrast, the harder sciences engage in downward social comparison with psychology--Hard sciences seek to maintain their elevated position in the science hierarchy, and sometimes they accomplish this by disparaging the softer sciences."</span></blockquote>
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<br>
<div class="BodyTextTHESIS" style="text-align: justify;">
<span lang="EN-GB" style="font-family: inherit;">Unnatural and unobservable (or laziness) have nothing to do with the Hard/Soft divide, nor the "desire" of the hard sciences to maintain an elevated position <i>(that's an odd accusation by the way). </i>When scientists are asked to
differentiate between different kinds of science, a consistent classification
of disciplines is found along three dimensions: Hard/Soft, Pure/Applied and
Life/Non-Life. The Hard/Soft dimension is often used to draw some line between
the natural sciences and the rest of science. A consensus description of the
divide is provided by <a href="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0010068" target="_blank">Fanelli (2010)</a> who showed 91.5% of psychology / psychiatry papers report positive findings.<o:p></o:p></span><br>
<span lang="EN-GB" style="font-family: inherit;"><br></span></div>
<blockquote class="tr_bq" style="text-align: justify;">
<span lang="EN-GB" style="font-family: Times, Times New Roman, serif;">“ […]
in some fields of research (which we will henceforth indicate as ‘‘harder’’)
data and theories speak more for themselves, whereas in other fields (the
‘‘softer’’) sociological and psychological factors –for example, scientists’
prestige within the community, their political beliefs, their aesthetic
preferences, and all other non-cognitive factors– play a greater role in all
decisions made in research, from which hypothesis should be tested to how data
should be collected, analyzed, interpreted and compared to previous studies.”
(Fanelli, 2010, p. e1068) </span></blockquote>
<span style="font-family: inherit;"></span><br>
<div style="text-align: justify;">
<span style="font-family: inherit;"><span style="font-family: inherit;">This does not imply anything about the veracity of scientific claims made in any of those fields. It does imply that for some reason the evaluations of the veracity of scientific claims in the soft sciences are influenced by other factors than just the theory and the data. (e.g., being a famous economist you can disregard literature you believe is badly written). The deceivingly simple and elegant statement “<b>data and theories speak more for themselves</b>”, has been expressed in less elegant varieties in order to classify the social sciences as belonging to the softer fields of scientific research (I finally found the source again, so here is the literal version):</span></span></div>
<span style="font-family: inherit;">
</span><br>
<blockquote class="tr_bq" style="text-align: justify;">
<span style="font-family: Times, Times New Roman, serif;"><span lang="EN-GB">“After reading Meehl [1967] and Lykken
[1968] one wonders whether the function of statistical techniques in the social
sciences is not primarily to provide a machinery for producing phoney
corroborations and thereby a semblance of ‘scientific progress' where in fact,
there is <i>nothing but an increase in
pseudo-intellectual garbage</i>. [...] Or, as Lykken put it: 'Statistical
significance [in psychology] is perhaps the least important attribute of a good
experiment; it is never a sufficient condition for claiming that a theory has
been usefully corroborated, that a meaningful empirical fact has been
established, or that an experimental report ought to be published.' [...] Thus
the methodology of research programmes might help us in devising laws for
stemming <i>this intellectual pollution
which may destroy our cultural environment</i> even earlier than industrial and
traffic pollution destroys our physical environment.” </span><!--[if supportFields]><span
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emphasis added)</span></span></blockquote>
<br>
<div class="BodyTextTHESIS" style="text-align: justify;">
<span style="font-family: inherit;"><span lang="EN-GB" style="text-indent: 0cm;">Lakatos’ strong rejection of the kind of theorizing in social science, which he classifies as one of the worst kinds of ad hockery leading to degenerative research programmes, (however: “we make no mockery of honest ad hockery” Good, 1965) is not new and can be appended to a long list of critiques tracing back to the earliest conceptions of some fields of scientific inquiry. Often some discontent is expressed about the way the softer disciplines pretend to act like a genuine
science (e.g., “semblance of scientific progress”). Some authors go further and
claim no real efforts are undertaken to become a natural science; as if the
soft fields are just dressing up to play “scientist”, putting on thick glasses,
smoking pipes and learning Klingon just to give their claims that extra hint of
profound intellectual scientific insight (“<a href="http://www.brainpickings.org/index.php/2012/06/08/richard-feynman-caltech-cargo-cult-science/" target="_blank">Cargo Cult Science” criticises the
same kind of conduct, </a></span><span lang="EN-GB" style="text-indent: 0cm;"><a href="http://www.brainpickings.org/index.php/2012/06/08/richard-feynman-caltech-cargo-cult-science/" target="_blank">Feynman, 1974</a>)</span><span lang="EN-GB" style="text-indent: 0cm;">. </span></span></div>
<div class="BodyTextTHESIS" style="text-align: justify; text-indent: 0cm;">
<span lang="EN-GB" style="font-family: inherit; text-indent: 0cm;"><br></span></div>
<div class="BodyTextTHESIS" style="text-align: justify; text-indent: 0cm;">
<span lang="EN-GB" style="font-family: inherit; text-indent: 0cm;">Statistical techniques are indeed often abused to cover up logical
weaknesses in theories or to feign exactitude. Moreover, the directional hypothesis test is the weakest prediction on can test. The true nature of this critique
is not statistical, but much more profound as it predates the invention of most inferential
statistics. For example, Ladd (1892), also quoted in the previous post, reviewing William James’ “The Principles
of Psychology” (1890, spanning two volumes) concludes:</span><br>
<span lang="EN-GB" style="font-family: inherit; text-indent: 0cm;"><br></span></div>
<blockquote class="tr_bq" style="text-align: justify;">
<span lang="EN-GB" style="font-family: Times, Times New Roman, serif;">“Of the conception of psychology, its
nature, problems, and method, which is proposed in these volumes, and of the
defence in detail of this conception, the following statements seem to me true:
The conception is such, and so narrow, that a consistent adherence to it
compels us to admit <b><i>the utter
impossibility of establishing psychology as a natural science</i>. </b>It excludes
almost all the really scientific data and conclusions; it includes only those
data and conjectures which are most remote from genuine science.” (Ladd, 1892, p.
28, emphasis added)</span></blockquote>
<br>
<div class="BodyTextTHESIS" style="text-align: justify; text-indent: 0cm;">
<span lang="EN-GB" style="font-family: inherit;">The “utter impossibility” is explicated when
Ladd comments on James’ suggestion that psychology is using the methods of the
natural sciences to test deep hypotheses about its object of study when in
fact:</span></div>
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<span lang="EN-GB" style="font-family: Times, Times New Roman, serif;">“[…] psychology as a science, <b>devoid of all postulating</b> <b>of
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thoughts and feelings as actually known, and <b>the possibility of ascertaining uniform</b> <b>relation<i>s</i></b> among them.”
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<div class="BodyTextTHESIS" style="text-indent: 0cm;">
<div style="text-align: justify;">
<span lang="EN-GB" style="font-family: inherit;">The emphasized
passages are relevant and appear in Figure 1.
They express key characteristics of scientific theorising still practiced in
the soft sciences today that, in my opinion, can no longer be ignored as a
major cause of the contemporary problems, soft science produces t<i>heories of construction, </i>hard science produces<i> theories of principles.</i> In
fact, to explain the previously
mentioned high number of positive results reported in the soft sciences,
Fanelli offered an explanation:</span></div>
</div>
<div class="BodyTextTHESIS" style="text-indent: 0cm;">
<span lang="EN-GB" style="font-family: inherit;"></span></div>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: Times, Times New Roman, serif;"><i>"</i><b>1B-Deepness of hypotheses tested</b></span></div>
<div style="text-align: justify;">
<span style="font-family: Times, Times New Roman, serif;">This has been suggested to reflect the level of “maturation” of a science <a href="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0010068#pone.0010068-Bunge1" style="color: #3c63af; text-decoration: none;">[56]</a>. Younger, less developed fields of research should tend to<i> </i><b>produce and test hypotheses about observable relationships between variables (“phenomenological” theories) </b><i>[Ladd 1892: <span style="text-indent: 0cm;">“assumes the phenomena [...] ascertaining uniform relations among them”]</span></i><span style="text-indent: 0cm;">. The more a field develops and “matures”, the more it tends to develop and test hypotheses about non-observable phenomena underlying the observed relationships (“mechanistic” theories) . These latter kinds of hypotheses reach <b>deeper levels of reality, are logically stronger, less likely to be true, and are more conclusively testable</b></span><span style="text-indent: 0cm;"> </span><a href="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0010068#pone.0010068-Bunge1" style="color: #3c63af; text-decoration: none; text-indent: 0cm;">[56]</a><span style="text-indent: 0cm;">. <i>[Ladd 1892: </i></span><span style="text-indent: 0cm;"><i>postulating of "deeper-lying entities,"]" </i>(<a href="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0010068#s3" target="_blank">Fanelli, 2010,</a> </span><span style="text-indent: 0cm;">p. e10068, </span><span style="text-indent: 0cm;">emphasis and quotes added)</span></span></div>
</blockquote>
<br>
<div class="BodyTextTHESIS" style="text-indent: 0cm;">
<div style="text-align: justify;">
<h2>
<span style="font-family: inherit; font-size: x-large;"><br></span><span style="font-family: inherit; font-size: x-large;">Schism</span></h2>
<span style="font-family: inherit;">I already claimed that Psychology is in fact not so very young and immature, at least, it should not be. It is at least 160 years old as an empirical science and so are many, many modern fields of science, including some of the harder ones. Here's Titchener who in 1893 calls the modern psychologist, the experimental psychologist:</span></div>
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<br>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: Times, Times New Roman, serif;">“Modern Psychology surely began, not
"three or four years ago," with the publication of the <i>Willenshandlung</i>, –but some forty years
ago, with Fechner's notion of the definite functional correlation of psychical
with physical processes. <b>The modern
psychologist <span lang="EN-GB">is the experimental psychologist</span><span lang="EN-GB">.</span></b>” <span lang="EN-GB">(Titchener, 1893, p. 456, emphasis
added)</span></span></div>
</blockquote>
<div class="BodyTextTHESIS">
<span style="font-family: inherit;"><span lang="EN-GB">Don't think that social psychology wasn't around at the time, listen to what Elwood (1899), has to say:</span></span><br>
<blockquote class="tr_bq">
<span style="font-family: Times, Times New Roman, serif;"></span><br>
<div style="text-align: justify;">
<span style="font-family: Times, Times New Roman, serif;"><br></span></div>
<span style="font-family: Times, Times New Roman, serif;">
<div style="text-align: justify;">
"Some sort of social psychology, it is true, has usually been assumed by social science; but the plea of this article is for a systematically worked out and carefully verified social psychology as a condition of complete social knowledge.<b> For, if it be assumed that the phenomena of society are chiefly psychical, a knowledge of the psychical processes which characterize group-life as such is manifestly a most important condition of complete social knowledge. </b>A few preliminary statements of position may, however, be helpful in rendering our plea more intelligible. Kulpe <a href="http://[See Kulpe's Outlines of Psychology, translated by Titchener, p. 7; cf. also the original. 656]" target="_blank">[See Kulpe's Outlines of Psychology, translated by Titchener [1895], p. 7; cf. also the original. 656] </a>speaks of social psychology as the science which "treats of the mental phenomena dependent upon a community of individuals." This we may accept as a rough, working definition of the science. </div>
</span></blockquote>
<blockquote class="tr_bq">
<span style="font-family: Times, Times New Roman, serif;"></span><br>
<div style="text-align: justify;">
<span style="font-family: Times, Times New Roman, serif;">Now, the assumption that there are "mental phenomena dependent upon a community of individuals" presupposes<b> psychical processes which are more than merely individual, which are inter-individual; </b>in last analysis it implies that through the action and reaction of individuals in a group upon one another <b>there arise psychical processes which cannot be explained by reference to any or all of the individuals</b> <b>as such</b>, but only by reference to the group-life considered itself as a unity. <b>Social psychology,</b> then, if somewhat more strictly defined, has as its task to examine and explain the form or mechanism of these group psychical processes.<b> </b>It <b>is an interpretation of the psychical processes manifested in the growth and functioning of a group as a unity.</b> " (Elwood, 1899, p. 656, emphasis added)</span></div>
<span style="font-family: Times, Times New Roman, serif;">
</span></blockquote>
<div style="text-align: justify;">
<span style="font-family: inherit;"><span lang="EN-GB">Elwood describes here, based on earlier work by Kulpe, that social psychology should dispel Newton's curse: Individuals interacting together in a group, act as a whole, a unity whose emergent properties and behaviour cannot be attributed to the behaviour of its individual components. Wholes are not just "causally impotent epiphenomena, i.e. merely aggregates of microphysical constituents.” (van Leeuwen, 2009, </span> p. 38).</span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">Elwood continues to say that the definition of a whole can be anything from a society to a family and I would say anything more than 1 individual. I believe that what is seeping through here, is one of the realisations that led to the great advances in physics: It's all about the relations between things (interactions) and not the things themselves:</span></div>
<blockquote class="tr_bq">
<span style="font-family: Times, Times New Roman, serif;">“The aim of science is not things themselves, as the dogmatists in their simplicity imagine, but <b>the relation between things</b>” (Poincaré, 1905, p. xxiv). </span></blockquote>
<div style="text-align: justify;">
<span lang="EN-GB" style="font-family: Times, Times New Roman, serif;">Psychological science keeps forgetting this fact, and those who point out its importance face banishment to a sub-discipline or -ism of psychology , J.J. Gibson's affordances (inspired by Kurt Lewin's valence), Hebb's reverberation (connectionism), etc. here's Ashby who argued as early as the 1940s to adopt the concept of self-organizing systems in psychology :</span></div>
<blockquote class="tr_bq" style="text-align: justify;">
<span style="font-family: Times, Times New Roman, serif;">“It follows that a substantial part of the theory of organization will be concerned with properties that are not intrinsic to the thing but are relational between observer and thing.” (Ashby 1962, emphasis in original)</span> </blockquote>
<div style="text-align: justify;">
<span lang="EN-GB"><span style="font-family: inherit;">It seems that time in the late 1800s, the true schism into softer and harder fields
of science had not not occurred yet and a young modern psychologist could truly claim he was contributing to an immature, but advancing science that studied phenomena of the mind. The schism happened in the first half of the 20</span><sup style="font-family: inherit;">th</sup><span style="font-family: inherit;"> century with the success of General Relativity and Quantum Mechanics. Around the time of the publication of James’
Principles and the defining of the field of Social Psychology there were actually discussions </span></span><!--[if supportFields]><span
lang=EN-GB style='mso-bidi-font-weight:bold'><span style='mso-element:field-begin;
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}<span style='mso-element:field-separator'></span></span><![endif]--><span lang="EN-GB" style="font-family: inherit;">(e.g.,
Mach, 1891, 1892)</span><!--[if supportFields]><span lang=EN-GB
style='mso-bidi-font-weight:bold'><span style='mso-element:field-end'></span></span><![endif]--><span lang="EN-GB" style="font-family: inherit;"> on how physics and psychology
could mutually inform each other:</span></div>
</div>
<div class="MsoBlockText">
</div>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: Times, Times New Roman, serif;">“[…] the time has now come when each
science should profit from the progress of the other. Physical science can
better eliminate errors of observation by learning what is known of their cause
and nature. <span lang="EN-GB">Psychology will gain greatly in clearness and accuracy by using
the methods of physics and mathematics.” </span><span lang="EN-GB">(Cattell, 1893, p. 285)</span><span lang="EN-GB">.</span></span></div>
</blockquote>
<div class="BodyTextTHESIS" style="text-align: justify; text-indent: 0cm;">
<span lang="EN-GB" style="font-family: inherit;">I am quite certain contemporary psychological
science doesn’t have a lot of scientific knowledge to provide on the cause and
nature of anything that would be remotely relevant to the daily scientific work of a modern physicist (save the handful of social scientists who occasionally publish in
physics journals, see below, I make a deep bow). About a century ago, people actually thought psychology and physics would go hand in hand... up up the ladder to the top of the hierarchy of sciences. Of
course psychology did just that and used the methods of physics and mathematics and as it slowly
matured, it gained greatly in clearness and accuracy just like Cattell predicted in 1893.</span></div>
<div class="BodyTextTHESIS" style="text-align: justify; text-indent: 0cm;">
<span lang="EN-GB" style="font-family: inherit;"><i><br></i></span></div>
<div class="BodyTextTHESIS" style="text-align: center; text-indent: 0cm;">
<span lang="EN-GB" style="font-family: inherit;"><i>(damn, wrong timeline again)</i><o:p></o:p></span></div>
<div class="BodyTextTHESIS" style="text-align: justify; text-indent: 0cm;">
<span lang="EN-GB" style="font-family: inherit;"><br></span></div>
<!--EndFragment--><span style="font-family: inherit;"><br></span>
<br>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjbIPfA7hfHb16EquZeZjCjn-2PxOKLlDUOAksh37a8PtpTkprz2T9lxNUxeFGDQO2i8MbAHPCjnquD7U8vo5GCyX94NBL2UN_NQxQJZ4_1ZXpgGqIsu35wh_20ucNSSRvv91tlO7j3pkQ/s1600/Pages+from+Figure1.7.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><span style="font-family: inherit;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjbIPfA7hfHb16EquZeZjCjn-2PxOKLlDUOAksh37a8PtpTkprz2T9lxNUxeFGDQO2i8MbAHPCjnquD7U8vo5GCyX94NBL2UN_NQxQJZ4_1ZXpgGqIsu35wh_20ucNSSRvv91tlO7j3pkQ/s1600/Pages+from+Figure1.7.png" height="480" width="640"></span></a></td></tr>
<tr><td class="tr-caption" style="font-size: 13px;"><span style="font-family: inherit; font-size: small;"><i>Figure 1. </i>Einstein's distinction between theories of construction and theories of principles (see van Dongen 2010, pp. 52-53). Quoted text is from Ladd's 1892 characterisation of William James' proposal for Psychology as an empirical science</span></td></tr>
</tbody></table>
<br>
<br>
<span style="font-family: inherit;"></span><br>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br></span></div>
<div style="text-align: justify;">
<h3 style="text-align: -webkit-auto;">
<span style="font-family: inherit;"><span style="font-family: inherit; font-size: x-large;">How "hard science" do you want your social psychology?</span></span></h3>
</div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><span style="font-family: inherit;">There's a lot more to say about this, and I will at some point, just finished a 50+ page chapter on the subject... :). In short relativity happened and modern cosmology, quantum mechanics happened producing the </span>most accurate predictions about phenomena in the universe ever measured and physics and the harder sciences moved towards consensus formalism science: <b>The theory and the data speak for themselves.</b> No disputes about what priming is, how it should be measured and if you have to take into account whether people ate carrots the night before: Formal language, formal predictions, formal evaluation of precision and accuracy in a joint effort to understand the unobservable structure of the universe.</span></div>
<div style="text-align: justify;">
<div style="text-align: center;">
</div>
</div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">Funny, isn't it? Despite all the fundamental knowledge about social processes, individual drives and group dynamics the social sciences cannot get their act together and finally start testing some theories based on precision and accuracy instead of cultural conventions, "<i>prestige within the community, their political beliefs, their aesthetic preferences, and all other non-cognitive factors</i>" (Fanelli, 2010). And the nerds can achieve consensus and they get to build spaceships and atom smashers by working together with 10.000 individuals towards the same common goal: A deeper understanding of reality.</span></div>
<br>
<div style="text-align: justify;">
<br></div>
<div style="text-align: justify;">
I leave you with some references of work by social psychologists who are very much working in the tradition Elwood lined out, but they resort to publishing in Physics and Technology journals, about intractable conflict, individual decisions in Economy, close relationships in social networks and what not. </div>
<div style="text-align: center;">
<br>
<br>
<div style="text-align: -webkit-auto;">
<span style="font-family: inherit;">(Social) psychology + hard science = not possible?</span></div>
<div style="text-align: -webkit-auto;">
<span style="font-family: inherit;">International conflict + hard science = not possible?</span></div>
<div style="text-align: -webkit-auto;">
<span style="font-family: inherit; text-align: center;">Economy + hard science = not possible?</span></div>
<div>
<br></div>
<br>
<div>
<span style="font-family: inherit;"> <i>(Not what I am saying, this is what people keep telling me)</i></span></div>
<br>
<br></div>
<span style="font-family: inherit;">Ok, read a selection of some recent work by <b>social psychologists</b> <a href="http://psy2.fau.edu/~nowak/Publications_Andrzej_Nowak.html" target="_blank">Nowak</a>, <a href="http://psy2.fau.edu/~vallacher/research_DSP.html" target="_blank">Vallacher</a> and colleagues and then we'll discuss whether it is possible <i>–in principle–</i> for Social Psychology to be a hard science or not:</span><br>
<span style="font-family: inherit;"><br></span>
<br>
<div style="margin-left: 24pt; text-indent: -24pt;">
<span style="font-family: inherit;"><span style="text-indent: -24pt;">Liebovitch, L., Naudot, V., Vallacher, R., Nowak, A, Buiwrzosinska, L., & Coleman, P. (2008). Dynamics of two-actor cooperation–competition conflict models. </span><i style="text-indent: -24pt;">Physica A: Statistical Mechanics and its Applications</i><span style="text-indent: -24pt;">, </span><i style="text-indent: -24pt;">387</i><span style="text-indent: -24pt;">(25), <a href="tel:6360%E2%80%936378" x-apple-data-detectors="true" x-apple-data-detectors-type="telephone" x-apple-data-detectors-result="5">6360–6378</a>. doi:10.1016/j.physa.2008.07.020</span></span></div>
<div style="margin-left: 24pt; text-indent: -24pt;">
<span style="font-family: inherit; text-indent: -24pt;"><br></span></div>
<div style="margin-left: 24pt; text-indent: -24pt;">
<span style="font-family: inherit;"><span style="text-indent: -24pt;">Nowak, A., Kuś, M., Urbaniak, J., & Zarycki, T. (2000). Simulating the coordination of individual economic decisions. </span><i style="text-indent: -24pt;">Physica A: Statistical Mechanics and its Applications</i><span style="text-indent: -24pt;">, </span><i style="text-indent: -24pt;">287</i><span style="text-indent: -24pt;">(3-4), 613–630. doi:10.1016/S0378-4371(00)00397-6</span></span></div>
<div>
<div style="margin-left: 24pt; text-indent: -24pt;">
</div>
<div style="margin-left: 24pt; text-indent: -24pt;">
<span style="font-family: inherit;"><br></span></div>
</div>
<div style="margin-left: 24pt; text-indent: -24pt;">
<span style="font-family: inherit;">Nowak, A., & Vallacher, R. R. (2003). Synchronization Dynamics in Close Relationships: Coupled Logistic Maps as a Model of Interpersonal Phenomena. In W. Klonowski (Ed.), <i>Frontiers on nonlinear dynamics: Vol. 2. From quanta to societies</i> (pp. 165–180). Berlin: Pabst Science Publishers.</span></div>
<div style="margin-left: 24pt; text-indent: -24pt;">
<span style="font-family: inherit;"><br></span></div>
<div style="margin-left: 24pt; text-indent: -24pt;">
<span style="font-family: inherit;">Staab, S., Domingos, P., Mike, P., Golbeck, J., Ding, L., Finin, T., Anupam, J., <span style="text-indent: -24pt;">Nowak, A., </span><span style="text-indent: -24pt;">Vallacher, R. R. (2005). Social networks applied. </span><i style="text-indent: -24pt;">Intelligent Systems, IEEE, 20(1)</i><span style="text-indent: -24pt;">, 80–93.</span></span></div>
<div style="margin-left: 24pt; text-indent: -24pt;">
<span style="font-family: inherit; text-indent: -24pt;"><br></span></div>
<div style="margin-left: 24pt; text-indent: -24pt;">
<span style="font-family: inherit;"><span style="text-indent: -24pt;">Vallacher, R. R., Coleman, P. T., Nowak, A., & Bui-Wrzosinska, L. (2010). Rethinking intractable conflict: the perspective of dynamical systems. </span><i style="text-indent: -24pt;">The American Psychologist</i><span style="text-indent: -24pt;">, </span><i style="text-indent: -24pt;">65</i><span style="text-indent: -24pt;">(4), 262–78. doi:10.1037/a0019290</span></span></div>
<div style="margin-left: 24pt; text-indent: -24pt;">
<span style="font-family: inherit; text-indent: -24pt;"><br></span></div>
<div style="margin-left: 24pt; text-indent: -24pt;">
<span style="font-family: inherit;"><br></span></div>
</div>
<div>
<div>
<div style="text-align: center;">
<i><span style="font-family: inherit;">(Yes, thats two papers in a physics journal and one IEEE, why not in JPSP I wonder?)</span></i></div>
<span style="font-family: inherit;"><br></span>
<br>
<div style="text-align: justify;">
<br></div>
<div style="text-align: justify;">
Next part in the saga, I will provide more examples to the attacking physicist to show the study of psychological phenomena is "hard" science scientific. A consequence may be that such studies will not be conducted by psychologists in the future, but at places such as the Google Campus or Boston Dynamics.</div>
<br>
<div style="text-align: justify;">
<br></div>
<span style="font-family: inherit;"><br></span><span style="font-family: inherit;">天</span><br>
<br>
<br>
<span style="font-family: inherit;"><br></span></div>
<div>
<i><span style="font-family: inherit;">-------------------------------------------------</span></i><i style="font-family: inherit; text-align: center;">-------------------------------------------------</i></div>
<div style="text-align: center;">
<br>
<h2 style="text-align: left;">
<span style="font-family: inherit;"><b>DELETED SCENES from PART 1</b></span></h2>
</div>
</div>
<blockquote class="tr_bq">
<span style="font-family: inherit;"> <a href="http://articles.latimes.com/2012/jul/12/opinion/la-oe-wilson-social-sciences-20120712">http://articles.latimes.com/2012/jul/12/opinion/la-oe-wilson-social-sciences-20120712</a></span></blockquote>
<blockquote class="tr_bq" style="text-align: justify;">
<span style="font-family: inherit;">"There has long been<i> snobbery in the sciences</i>, with the "hard" ones (physics, chemistry, biology) <i>considering themselves to be more legitimate</i> than the "soft" ones ( psychology, sociology)." <span style="background-color: #eeeeee;">(No, they can <i>prove</i> that the theories their community produces about the structure of reality are more <i>precise</i> and more <i>accurate</i> than the theories the soft sciences produce. This is not about "them" being more legitimate, their theories about reality are)</span></span></blockquote>
<blockquote class="tr_bq" style="text-align: justify;">
<span style="font-family: inherit;">"Many people benefit from the results, including those who, <i>in their ignorance</i>, believe that science is limited to the study of molecules."[proceeds to list examples of "successes of psychology] <span style="background-color: #eeeeee;">(None of the critiques are saying science is limited to the study of molecules (a lot happened after <a href="http://en.wikipedia.org/wiki/Jean_Baptiste_Perrin" target="_blank">Perrin, 1913</a>), they are saying psychological phenomena are not studied in a scientifically rigorous manner. Moreover, the examples proving psychology is a science are proofs of application, the technology based on knowledge provided by the science: Therapy and intervention. That's comparing the effectiveness of cognitive behavioural therapy to treat clinical depression, to the very existence of smartphones, airplanes and the internet. Moreover, the most severe cases of depression still benefit from electroshock therapy and we don't know why. Did psychology help to reduce the rate of teenage pregnancies? The average birth rate for every 1000 adolescents aged 15-19 since 1996 is 7.7 in the Netherlands. In the US the number is 55.5, the highest in the developed world. Does the Netherlands have better psychologists who are not sharing their findings with the US? Ah yes, the achievement gap. Why are there no interventions to close the caucasian-asian gap?) </span></span></blockquote>
<span style="font-family: inherit;"><br></span>
<br>
<blockquote class="tr_bq">
<span style="font-family: inherit;"> <a href="http://www.davenussbaum.com/psychology-is-science/">http://www.davenussbaum.com/psychology-is-science/</a> </span></blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">"I hope that most people who read Alex Berezow’s editorial in the Los Angeles Times denying that psychology is a science found it <i>misinformed and bordering on absurd</i>." <span style="background-color: #eeeeee;">(We are informed this conclusion be drawn based on the article I commented on above. That is odd, because Berezow's editorial was a critique on the article I commented on above, he didn't agree with it and neither do I)</span></span></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">"<i>Unfortunately</i>, there are <i>still</i> people<i> out there</i> who have <i>a distorted and caricatured idea</i> of what psychology is, a problem that Wilson was trying to combat. <i>Sadly</i>, the LA Times found one of <i>these people</i> and gave them editorial space to <i>perpetuate their ignorance</i>." <span style="background-color: #eeeeee;">(This is an elaborative way to say Berezow is an uneducated fool and the LA times should have known better to provide such people a stage for the nonsense that comes out of their keyboards. Still no real arguments to back these strong claims. If anyone made caricature of psychology is, it's Wilson's "combat" article)</span></span></div>
</blockquote>
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<span style="font-family: inherit;">"Berezow himself does a perfectly good job <i>refuting his own claims</i> when he tells us that <i>his own example is a terrible one</i>: "To be fair, not all psychology research is equally wishy-washy. Some research is far more scientifically rigorous. And the field often yields interesting and important insights." Well said. But let’s put Berezow’s <i>abject ignorance</i> of the empirical methods of psychological research aside for a moment" <span style="background-color: #eeeeee;">(Pssst... When he made the exception for some research, he wasn't talking about social psychological research and he did not refute his own claims and he most certainly did not tell any of us that his example was terrible... that's just what this blog author claims) </span> </span></blockquote>
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<span style="font-family: inherit;"><a href="http://psych-your-mind.blogspot.nl/2013/08/the-psychology-of-psychology-isnt.html">http://psych-your-mind.blogspot.nl/2013/08/the-psychology-of-psychology-isnt.html</a> </span></div>
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<span style="font-family: inherit;">"Some people (usually who know little about psychology) argue that psychologists don't define their terms clearly enough to be considered a science. In one example of this, a physicist named <a href="http://articles.latimes.com/2012/jul/13/news/la-ol-blowback-pscyhology-science-20120713">Alex Berezow</a> (using a bunch of sciency terms that my poor psychologist brain struggled to understand) argued that happiness research is a perfect example of a failure to define terms. He states that "the meaning of the word differs from person to person and especially between cultures."<span style="background-color: #eeeeee;">(I have the least problems with this blog, wasn't necessary to chip in on physicist bashing though. Now, usually, the people who know little about psychology and argue this, are scientists who are used to define terms using formal language like mathematics. Or who postulate statements that are logically coherent, you must have heard of <a href="http://en.wikipedia.org/wiki/Clark_L._Hull" target="_blank">Hull's principles of behaviour</a>? There have been and are enough psychologists who do so (e.g., mathematical psychology, ecological psychology). Simple example. Happiness could be something you want to describe as "takes on different meanings in different contexts (i.e., it can look differently in a different culture) but has some core universal content that is retained and we call this happiness". You could use a mathematical object that is transformation invariant, or symmetric with respect to the property or dimension of happiness. A position on the dimension itself would be someone's personal experience of happiness. Go from there to define operators and what not with which you can understand statistical regularities in happiness data. Don't say "can't" without trying first!)</span></span><br>
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<span style="font-family: inherit;">I should have continued with two arguments I wanted to address, because two posts appeared that more or less used them:</span><br>
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<b><span style="font-family: inherit;">5. "<a href="http://blogs.discovermagazine.com/neuroskeptic/2013/08/19/is-psychology-science-is-the-wrong-question/#.Uhfkhha9YwI" target="_blank">But science cannot be defined anyway, so what are you talking about?</a>" </span></b><br>
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<span style="font-family: inherit;">I really like almost everything the author of this post writes, but on this argument I disagree completely. Judging from the commentaries, I am not the only one. Here's one that sums it up for me:</span><br>
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<span style="font-family: inherit;">"Psychology continues to discredit itself. Perhaps better than even "is it true," would be to ask "can it manage to sustain a continued session of tests of its theories" or does it just continue to discredit itself."</span></blockquote>
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<i><span style="font-family: inherit;">(By the way, very glad to see many comments reflect some of the same problems I have with the defence strategy. Someone nicknamed <b>Seriously?</b> on that page is not me, but it could very well have been. Excellent comments!) </span></i></blockquote>
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<b><span style="font-family: inherit;">6. "<a href="http://mindhacks.com/2013/08/20/dont-panic-but-psychology-isnt-always-a-science/" target="_blank">But we need funding, so look at all the stuff we are about to do correctly in the future</a>")</span></b></div>
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<span style="font-family: inherit;">This is an honest post about the difference between quantitative and qualitative science and I link to it because it mentions the motivation some people may have to use the phantom arguments I dismissed as nonsense. It also explains why people are viciously trying to draw away attention from the actual content of the criticism of the <a href="http://blogs.scientificamerican.com/cross-check/2013/04/04/is-social-science-an-oxymoron-will-that-ever-change/" target="_blank">hard sciences</a> (e.g., <a href="http://blogs.scientificamerican.com/psysociety/2013/08/13/psychology-is-a-science/" target="_blank">discrediting</a> <a href="http://www.davenussbaum.com/psychology-is-science/" target="_blank">the</a> <a href="http://psych-your-mind.blogspot.nl/2013/08/the-psychology-of-psychology-isnt.html" target="_blank">messenger</a>, <a href="http://articles.latimes.com/2012/jul/12/opinion/la-oe-wilson-social-sciences-20120712" target="_blank">playing the role of victim of a bully</a>): <b>Money,</b> <b>Funding of research</b>. The author is correct to identify this motivation, I have seen the argument pop up in tweets posts, commentaries and what not. In fact I closed my previous post with Meehl's (1990) suggestion to save some taxpayer money:</span><br>
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<span style="font-family: inherit; line-height: 21px;">"I am prepared to argue that a tremendous amount of taxpayer money goes down the drain in research that pseudotests theories in soft psychology and that it would be a material social advance as well as a reduction in what Lakatos has called “intellectual pollution” (Lakatos, 1970, fn. 1 on p. 176) if we would quit engaging in this feckless enterprise. "</span></blockquote>
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<b><span style="font-family: inherit;">IT'S PUBLIC MONEY!</span></b></div>
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<b><span style="font-family: inherit;"><br></span></b></div>
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<span style="font-family: inherit;">YOU ARE FUNDED BY SOCIETY TO THINK <b>DEEP THOUGHTS</b></span></div>
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<span style="font-family: inherit;">TO DO THE BEST YOU POSSIBLY CAN</span></div>
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<b><span style="font-family: inherit;"><br></span></b></div>
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<span style="font-family: inherit;">SOCIETY WANTS TO BENEFIT (EVENTUALLY) </span></div>
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<span style="font-family: inherit;">FROM THE MIRACULOUS THINGS YOU WILL DISCOVER ABOUT (HUMAN) <b>NATURE</b></span></div>
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<span style="font-family: inherit;">NOW IT TURNS OUT THAT </span></div>
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<span style="font-family: inherit;">ONLY UNDER THREAT OF LOOSING FUNDING FOR YOUR <b>FECKLESS ENTERPRISE </b>YOU BEGIN TO REFLECT ON WHAT IT IS YOU'VE BEEN DOING ALL THESE DECADES AND WHY?</span></div>
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<b><span style="font-family: inherit;">HOW DARE YOU?</span></b></div>
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<i><span style="font-family: inherit;">(Sorry, it just came out like this, capitalised and all. </span></i></div>
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<i><span style="font-family: inherit;">You = Soft Psychology, I suppose)</span></i></div>
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<span style="font-family: inherit;">So there you have it, instead of agreeing and publishing the criticism like Meehl did and devoting energy and resources to turn Psychology into a into a natural science, one that studies <i>human</i> <i>nature </i>it is suggested we play a game of politics and power. At this point I think it would be a blessing if Psychology was forced to prove to society it is worth funding by producing knowledge that results in reliable technology (accurate diagnosis of mental illness, efficient psychotherapy, resolution of international conflicts by diplomacy, eradicate bullying, fundamental knowledge about perception and action so we can finally have robots to take carte of us, etc.).</span></div>
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<i><span style="font-family: inherit;">(And no, none of those examples in parentheses have already been achieved.</span></i></div>
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<i><span style="font-family: inherit;">Don't tempt me.)</span></i></div>
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<b><span style="font-family: inherit; font-size: small;">References:</span></b></div>
<span style="font-weight: normal;"><span style="font-size: small;"><br><br>Cattell, J. (1893). On Errors of Observation. The American Journal of Psychology, 5(3), 285–293. Retrieved from http://www.jstor.org/stable/10.2307/1410994</span></span><br>
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<span style="font-weight: normal;"><span style="font-family: inherit; font-size: small;">van Dongen, J. (2010). <i>Einstein’s unification</i>. Cambridge: Cambridge University Press.</span></span><br>
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<span style="font-size: small; font-weight: normal;">Ellwood, C. (1899). Prolegomena to Social Psychology . I . The Need of the Study of Social Psychology. <i>The American Journal of Sociology, 4(5)</i>, 656–665. Retrieved from http://www.jstor.org/stable/10.2307/2761588</span></div>
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<span style="font-weight: normal;"><span style="font-family: inherit; font-size: small;"><span style="text-indent: -24pt;">Good, I. J. (1965). </span><i style="text-indent: -24pt;">The estimation of probabilities: An essay on modern Bayesian methods</i><span style="text-indent: -24pt;"> (Vol. 30). MIT press Cambridge, MA.</span> </span></span></div>
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<span style="font-weight: normal;"><span style="font-family: inherit; font-size: small;">Ladd, G. (1892). Psychology as So-Called “ Natural Science .” <i>The Philosophical Review</i>, <i>1</i>(1), 24–53. Retrieved from http://www.jstor.org/stable/10.2307/2175528</span></span></div>
<span style="font-family: inherit; font-size: small;"><span style="font-weight: normal;"><br></span></span>
<span style="font-family: inherit; font-size: small;"><span style="font-weight: normal;"><span style="text-align: center;">Mach, E. (1891). SOME QUESTIONS OF PSYCHO-PHYSICS. SENSATIONS AND THE ELEMENTS OF REALITY. <i>The Monist, 1(3)</i>, 393–400. Retrieved from http://www.jstor.org/stable/10.2307/27896875</span></span></span><br>
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<span style="font-family: inherit; font-size: small;"><span style="font-weight: normal;">Mach, E. (1892). Facts and mental symbols. <i>The Monist, 2(2)</i>, 198–208. Retrieved from http://www.jstor.org/stable/10.2307/27896941</span></span></div>
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<span style="font-weight: normal;"><span style="font-family: inherit; font-size: small;"><span style="text-align: center; text-indent: -24pt;">Popper, K. R. (1959). The Propensity interpretation of probability. </span><i style="text-align: center; text-indent: -24pt;">The British journal for the philosophy of science</i><span style="text-align: center; text-indent: -24pt;">, </span><i style="text-align: center; text-indent: -24pt;">10</i><span style="text-align: center; text-indent: -24pt;">(37), 25–42. Retrieved from http://www.jstor.org/stable/685773</span></span></span></div>
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<span style="font-weight: normal;"><span style="font-family: inherit; font-size: small;">Titchener, E. (1893). Two Recent Criticisms of “Modern” Psychology. <i>The Philosophical Review</i>, <i>2</i>(4), 450–458. Retrieved from http://www.jstor.org/stable/10.2307/2175721</span></span></div>
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Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-89629417517976966282013-08-19T16:46:00.003+02:002013-08-21T14:49:19.335+02:00Defending Psychology in the Science Wars - Part 1: The Phantom Arguments<div>
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<span style="font-family: inherit;">“<i>If Psychology is ever to become anything more than a mere aggregation
of opinions, it can only be by the establishment of some datum universally
agreed to.</i>”<o:p></o:p></span></div>
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<span style="font-family: inherit;">– Herbert Spencer (1855, p. 8)<o:p></o:p></span></div>
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<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">Yes, I will defend that psychology is a genuine scientific endeavour and more than a mere aggregation of opinions against people who recently claimed otherwise (e.g. <a href="http://articles.latimes.com/2012/jul/13/news/la-ol-blowback-pscyhology-science-20120713" target="_blank">here</a> and <a href="http://www.realclearscience.com/articles/2012/05/30/what_separates_science_from_non-science_106278.html" target="_blank">here</a> ). I will do so mostly by agreeing with their critiques in part 2 or part 3 of this <i>Science Wars</i> saga (there may even be droids and furry animals involved at some point).</span><br />
<span style="font-family: inherit;"><br /></span>
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<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjTTbaguV-oS_6NwJYhplt9z5rj-EF5XNvCbeqHWrNV5hy3Ny0uI4Wtb65aP6QHa5wdduQZkDIOALjbSLHjQwYH50q4H41m-e-wBrnPKBERJHAlj9k4qqBgVR0zACj-T9o3CekTkEltLuw/s1600/sciencewars1.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><span style="font-family: inherit;"><img border="0" height="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjTTbaguV-oS_6NwJYhplt9z5rj-EF5XNvCbeqHWrNV5hy3Ny0uI4Wtb65aP6QHa5wdduQZkDIOALjbSLHjQwYH50q4H41m-e-wBrnPKBERJHAlj9k4qqBgVR0zACj-T9o3CekTkEltLuw/s1600/sciencewars1.png" width="400" /></span></a></div>
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">More important right now is retreat, regroup and damage control: The defence against the claim of the hard sciences has been so poor in my opinion, these responses have only strengthened the proclamation by the math-o-philes that<b> Psychology isn't science! </b></span><br />
<span style="font-family: inherit;"><b><br /></b>
For some reason the defence was led by a platoon of social psychologists (e.g. <a href="http://blogs.scientificamerican.com/psysociety/2013/08/13/psychology-is-a-science/" target="_blank">here</a> and <a href="http://articles.latimes.com/2012/jul/12/opinion/la-oe-wilson-social-sciences-20120712" target="_blank">here</a> and <a href="http://www.davenussbaum.com/psychology-is-science/" target="_blank">here</a> and <a href="http://psych-your-mind.blogspot.nl/2013/08/the-psychology-of-psychology-isnt.html" target="_blank">here</a>), a subdiscipline of psychology that is not know for its calm and erudite response to fundamental criticism of their scientific claims (e.g. <a href="http://blogs.discovermagazine.com/notrocketscience/2012/03/10/failed-replication-bargh-psychology-study-doyen/#.UhIIcha9YwI" target="_blank">here</a> and <a href="http://www.dub.uu.nl/artikel/opinie/belediging-voor-sociale-psychologie.html" target="_blank">here</a>).</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">I am ashamed, really ashamed to have to read the nonsense raised again and again as arguments in defence of psychological science. I will, in great detail, explain why those arguments are nonsense and why they are something to be ashamed about (Summary: do not use the <span style="text-align: justify;">“not presently testable” argument; Meehl, 1990)</span></span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">First, to illustrate the origins of my objections against this crippling defensive strategy, a short fiction about the future of the discipline of psychology...</span><br />
<span style="font-family: inherit;"><br /></span></div>
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<div style="text-align: center;">
</div>
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<b><span style="font-family: inherit;">--------------------------------</span></b></div>
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<b><span style="font-family: inherit;"><br /></span></b></div>
</div>
<blockquote class="tr_bq">
<b><span style="font-family: inherit;">Not so long from now, in a barred spiral galaxy some 120.000 light-years in diameter...</span></b></blockquote>
<div>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">Psychology lost the Science Wars in what is now known as "the great battle of versimilitude" in which the accumulated knowledge of all of science was at the disposal of competing disciplines to prove the truth-likeness of their communities' scientific claims in battle. Soft science's arch-enemy, the natural sciences turned out to have developed theories and methodologies that actually allowed them to exploit the lawful structure beneath the chaotic and complex mess that makes up most of the observable universe. They could build weapons, spaceships and synchronise with their GPS satellites using general relativity, you know, the stuff you may need in a war. None of the soft sciences were able take advantage of that kind of knowledge, because it was written in a language they had heard about in high school, but never understood what it was for, you know, like German.</span></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">The soft sciences, psychology in particular, were taken by surprise. They had not realised the natural sciences (aka, "the nerds") had accumulated so much knowledge about the world that turned out to be very plausible, or at least, extremely useful. After all, scientists in all disciplines had used the same scientific method and the same rigorous methods of expert peer review to assure only the best theories were allowed to be added to the record of scientific knowledge. These procedures had shown many of the psychologists' hypotheses to be true, mind you, all their scientific knowledge consisted of true significant differences! It was even a known fact that much more hypotheses of the soft sciences had been found to be true when compared to the natural sciences. Apparently such high numbers of corroboration of theories in publications doesn't really give you an advantage when it comes to a clash of such claims with reality.</span></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">The psychologists fought bravely and found allies in the sociologists after the economists had finally given up the rational consumer model and joined the nerds in their masochistic math fetish to create more realistic models of extremely complex phenomena. The soft science alliance had thrown every plausible theory they had at those foul smelling nerds; from performing an n-back task for a couple of minutes before making complex strategic decisions, to priming the enemy troops with words and images associated with old-age in order to make them walk more slowly. As a last resort and in front of many stupefied enemy guards, a representative sample of soft science POWs attempted a replication of the Stanford prison experiment... to no avail. </span></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">Many psychologists went into hiding after the war, taking on new low-profile identities such as <i>novelist</i>, <i>science-writer</i> or <i>tv-personality.</i> Soon, an underground movement of veterans of the soft science alliance started to meet in secret. Here's what transpired at a typical private meeting of the SSA (Soft Scientists Anonymous): </span></div>
</blockquote>
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<div style="text-align: justify;">
<span style="font-family: inherit;">"My name is Fred Hasselman and I am a psychologist" </span></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<i><span style="font-family: inherit;"><i> "Hi Fred!" Replied the room.</i> </span></i></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">"I have been using the scientific method to study psychological phenomena for more than 15 years now..." </span></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;"><i>- applause -</i> </span></div>
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<span style="font-family: inherit;">
</span></blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">"... and me and my co-authors believe it's all got to change, because we produced a whole bunch of nothing as a discipline! Pseudo-intellectual garbage! Empiarrhea, clogging up the empirical record with so-called facts that have no say whatsoever at the level of appraising and amending theories! As long as we cannot decide between the veracity of a plethora of theories put forward to explain the same psychological phenomenon, we are in big trouble as a discipline of science!" </span></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<i><span style="font-family: inherit;"><i> - silence -</i> </span></i></div>
<i><span style="font-family: inherit;">
</span></i></blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;"><i>"</i>You don't look like a psychologist to me!" shouted a prof. from Harvard. "Are you sure you didn't study physics or something? Spying for the nerds, eh?<i>"</i> </span></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;"><i>- Soon the entire room turned against the speaker and his co-authors -</i> </span></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;"><i>"Or worse, maybe he's a philosopher!" a European PhD student yelled. "You obviously don't know anything about the complexity of human emotions... what about love? Appraise that with your formal system!" </i> </span></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">And all the speaker and his colleagues could do was to start their own "<i>society for psychologists who think just like we do</i>", together with a journal of approximately the same name in which they published their rebellious and provocative papers that none of the other psychologists ever cited.... until they were discovered by the nerds...</span></div>
</blockquote>
<blockquote class="tr_bq" style="text-align: center;">
<b><span style="font-family: inherit;"> <span style="text-align: justify;">[to be continued]</span><span style="text-align: justify;"> </span></span></b></blockquote>
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<b><span style="font-family: inherit;">--------------------------------</span></b></div>
<div style="text-align: center;">
<span style="font-family: inherit;"><br /></span></div>
<span style="font-family: inherit;">What occurred at the fictional SSA meeting has occurred many times in the history of psychological science. It still occurs to right now, to scientists who submit manuscripts to scientific journals, but get told by reviewers and editors that their work is "too difficult" for the "average psychologist". This usually means that some maths, some formal language or some ontology unknown to the reviewers was used in the manuscript. This is the most unscientific rejection notice imaginable. If these scholars were submitting to a popular science journal, or were being interviewed for a newspaper article, sure, then you can be "too difficult", but in a genuine scientific journal? It should not be possible to be too difficult for science!</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">That's not even my main point, these same "average psychologists", when attacked by a physicist for not being scientific, will not hesitate to point out that psychology is waaaay more difficult than physics and they are just doing the best they can.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">It's not true, psychological science is not doing the best it can and it hasn't been doing so for a long time. I'll get back to that story at a later point in the saga ("A tale of three principles").</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">In this part, I'll discuss the phantom arguments used to ward off the phantom menace that apparently are the natural sciences and hope they shall never be used again when someone criticises scientific psychology.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;"><br /></span>
<b><span style="font-family: inherit;">1. Is psychology the hardest science? - Newton's Curse and the Donders Legacy</span></b><br />
<i><span style="font-family: inherit;">(e.g. <a href="http://hardsci.wordpress.com/2009/03/14/making-progress-in-the-hardest-science/">http://hardsci.wordpress.com/2009/03/14/making-progress-in-the-hardest-science/</a>)</span></i><br />
<span style="font-family: inherit;"><br />
There will not be a physicist who denies psychology is the hardest science.<br />Interestingly, ask a physicist "Why is psychology more difficult than physics?" and you'll get pretty much the same answer from each of them. Ask a psychologist and... well... most of the other arguments discussed here concern such answers.<br /><br />The only correct answer is: Because physics studies dead matter systems and psychology studies living matter systems. The problem is the persistence of mainstream psychology to study the brain-body-environment system as predominantly linear, ergodic, component-dominant and open to efficient cause. Behaviour and cognition are conceived of as linear arrangements of specialised (invisible) causal components whose powers of causation (effects) are additive in nature and mostly located in the brain… Mainstream neuroscience is still just F.C. Donders’ mental chronometry conducted with more expensive equipment, so are most experimental designs in psychology.<br /><br /> In other words: Psychology is studying human behaviour based on a crude conception of Newtonian physics, completely detached from contemporary developments in physics, mathematics, biology and philosophy. Van Leeuwen (2009) recently dubbed this Newton’s Curse, which refers to:</span></div>
<div>
<blockquote class="tr_bq">
<span style="font-family: inherit;"> “[…] conceptualising causal primacy in terms of a reduction of wholes to parts, where the wholes are causally impotent epiphenomena, i.e. merely aggregates of microphysical constituents.” (pp. 38).</span></blockquote>
<span style="font-family: inherit;">This is an untenable assumption for scientific inquiries into the behaviour of living systems capable of maintaining and increasing their internal complexity against the second law of thermodynamics. The disciplines of the hard sciences know it is an untenable position and when they question psychological science about it, we have to say, "you're right, so what do you think we should do?" You do not tell them to go away and shut up already.<br /><br />Paradoxically, as will become clear from the arguments that follow, psychologists actually claim to study phenomena that are inherently non-Newtonian, massively context sensitive, closed to efficient cause (e.g., must be defined as non-well founded sets), but completely ignore all the impressive scientific progress that has been made to describe and study such systems and phenomena! In fact, psychologists do not even fully exploit one of Newton's important contributions to science, which is the mathematical description of change processes (difference and differential equations). Psychology does claim to study behavioural change, but those who dare to suggest the mathematics of change should be used have met a very similar fate as described in the fictional story above.<br /><br /> In a way the arguments listed here are a confession of a scientific discipline. It is saying: "We are using the wrong tools to study the phenomena we are interested in". Or worse: "We will continue to do so even though we know there is a science of physical complexity applied to living systems, robotic systems, artificial agents, ecosystems, complex physiological networks, massively context sensitive behavioural phenomena such as chaos and turbulence.... that far supersede the complexity of human behavioural modes. Leave us alone."<br /><br />Yes, that's unscientific. I think that it is not unfair to say so, especially from the perspective of the complexity sciences where physics meets chemistry and mathematics meets biology and what not (well, not mainstream psychology obviously). It is unscientific to not question your assumptions about the object of study, to not question whether your measurement theory or rules of inference are adequate, or for that matter, to not question your philosophy of science, preferred ontology, epistemology.</span></div>
<div>
<span style="font-family: inherit;">Not impressed?<br /><br />Ok, let me convince you this argument is really silly by making a simple observation: If psychology is serious about being more difficult than physics and serious about being a science, then why doesn't it teach the methods and tools of quantum physics and general relativity to its undergraduates? That would be a logical, scientific thing to do, because what this argument implies is that if psychology wants to advance as a science that produces knowledge about the behaviour of living systems, it will have to do better than physics and needs to learn everything about the scientific methods and tools that made that easy science about dead matter so extremely accurate and successful.</span><br />
<span style="font-family: inherit;"><br /></span>
<i><span style="font-family: inherit;">Next please.</span></i><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;"><br /></span>
<b><span style="font-family: inherit;">2. "But there are also cases of fraud in the hard sciences"</span></b><br />
<span style="font-family: inherit;">(e.g.,<b> </b><a href="http://psych-your-mind.blogspot.nl/2013/08/the-psychology-of-psychology-isnt.html">http://psych-your-mind.blogspot.nl/2013/08/the-psychology-of-psychology-isnt.html</a>)</span><br />
<span style="font-family: inherit;"><b><br /></b>
Yes, that's true.</span><br />
<span style="font-family: inherit;"><br />
Please understand they are deeply offended by the nonsense Bem (2011) was allowed to publish as a product of state-of-the-art psychological science and subsequently deduced such a thing can only happen in a discipline that cannot distinguish between false claims even if they are based on pseudoscience or fictional data. There's also <a href="https://www.commissielevelt.nl/" target="_blank">a report, drafted by psychologists</a> that confirms this idea and I do not blame other disciplines to refuse to consider disciplines of science that allow sloppy science to continue, as a discipline of genuine science. <i>(that's a lot of science for one sentence)</i></span><br />
<span style="font-family: inherit;"><br />
<i>Next please.</i></span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;"><br /></span>
<b><span style="font-family: inherit;">3. Physicists also have problems measuring unobservable stuff: The interpretation fallacy</span></b><br />
<span style="font-family: inherit;">(This may be entirely about:<b> </b><a href="http://blogs.scientificamerican.com/psysociety/2013/08/13/psychology-is-a-science/">http://blogs.scientificamerican.com/psysociety/2013/08/13/psychology-is-a-science/</a>)</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">First, let's introduce this argument about the measurement of complex phenomena in psychology by quoting from the blog entry linked above (yes, this is literal):</span><br />
<blockquote class="tr_bq">
<span style="font-family: inherit;"><span style="background-color: white; color: #222222; font-family: Georgia, ApresTT, Prelude, Verdana, san-serif; font-size: 16px; line-height: 24px;">"During the very first week of my Intro to Social Psychology class, I send my students home with one simple assignment — come back next class with an answer to the question, “What is Love?” I play the </span><a href="https://www.youtube.com/watch?v=xhrBDcQq2DM" style="background-color: white; border: 0px; color: #19437c; font-family: Georgia, ApresTT, Prelude, Verdana, san-serif; font-size: 16px; line-height: 24px; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;" target="_blank">Haddaway song</a><span style="background-color: white; color: #222222; font-family: Georgia, ApresTT, Prelude, Verdana, san-serif; font-size: 16px; line-height: 24px;"> as they’re walking out the door and tell them to come back in 2 days with a way that they would define and measure “love” if they were creating their own experiments."</span></span></blockquote>
<span style="font-family: inherit;">We learn that the students return with answers such as: <i>rate</i><i> love for someone on a scale;</i> <i>write a story about how much someone loves another and independently judge the story for amount of love felt by the author;</i> <i>use a physiological measure such as heart rate change when a loved one enters the room to index amount of love;</i> and some surprising answers that were apparently not very memorable except for <i>willingness to sacrifice for a loved one</i>. The lesson learned by the students is that there is no single obvious answer to the assignment.</span><br />
<span style="font-family: inherit;"><br />
Now, aside from the ethics of polluting fresh young minds with 90s Eurodance (<i>baby don't hurt me </i>indeed!), a scientist from the "hard" disciplines and probably most philosophers of science, would point out that this assignment and/or the answers are incorrect for several reasons:</span><br />
<ol>
<li><span style="font-family: inherit;">The phenomenon to evidence in the experiment is love, but all the "definitions" are in terms of measurement outcomes that are attributed to the object of measurement (the participant) as a property. Property attribution by means of measurement outcomes is only unproblematic for classical physical measurements (<a href="http://anti-ism-ism.blogspot.nl/2013/07/representative-samples-generalisations.html#.UhKLoBa9YwI" target="_blank">as I mentioned earlier</a>): Is measuring love assumed to be a classical physical measurement? There will not be many physicists who will agree with this assumption. Moreover, apparently love is not a relational concept to psychologists? None of the students provided a formal relational definition of love.</span></li>
<li><span style="font-family: inherit;">An experiment is a measurement context in which a phenomenon may be observed that was predicted by a theory about the phenomenon (+ auxiliary theories), <a href="http://anti-ism-ism.blogspot.nl/2011/09/the-thin-line-between-science-fact-and.html#.UhH-6Ba9YwI" target="_blank">an intricate part of the scientific method that psychology just does not care enough about</a>. The formal scientific definition and theory of love should dictate the measurement context in which it may be observed, not the other way around. Hey, there even may be competing theories of love! For instance, using heart rate needs a postulate linking increased heart rate to love (and much more). It is reasonable to consider in advance that increased heart-rate may be a <i>necessary condition</i> to evidence love, but definitely not a <i>sufficient condition</i> as there are many things that are associated with increased heart rate. So why should this be a reasonable scientific suggestion for an experiment to evidence amount of love? Physicists seek to conduct the most risky tests of their claims about the workings of the universe they can think of. Measuring increased heart-rate can be considered as uninformative a priori, as I mentioned earlier: <a href="http://anti-ism-ism.blogspot.com/2013/06/truths-glorified-truths-and-statistics_14.html" target="_blank">Strong inference is how you evidence your scientific claims</a> (or see <a href="http://www.frontiersin.org/Fractal_Physiology/10.3389/fphys.2013.00075/full#" target="_blank">Hasselman, 2013</a>).</span></li>
<li><span style="font-family: inherit;">Not all the suggested measurement contexts concern a true experiment.</span></li>
</ol>
<span style="font-family: inherit;"><br />
Ok, maybe you think that is all nitty gritty nerdy science stuff and this is just simplified for the sake of a blog post and in reality the psychologists know better. Well, this text follows the paragraphs about the answers of the students (<a href="http://articles.latimes.com/2012/jul/13/news/la-ol-blowback-pscyhology-science-20120713" target="_blank">Dr. Berezow is the evil physicist who dared to attack psychology</a>):</span><br />
<blockquote class="tr_bq">
<span style="font-family: inherit;"><span style="background-color: white; color: #222222; font-family: Georgia, ApresTT, Prelude, Verdana, san-serif; font-size: 16px; line-height: 24px;">"Each and every semester, my class of 100 sophomore-aged undergraduates immediately comes to understand what Dr. Berezow has apparently yet to learn:</span><strong style="background-color: white; border: 0px; color: #222222; font-family: Georgia, ApresTT, Prelude, Verdana, san-serif; font-size: 16px; line-height: 24px; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;"><em style="background-color: transparent; border: 0px; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">Measurement is complicated. No matter what you study. Period</em></strong><span style="background-color: white; color: #222222; font-family: Georgia, ApresTT, Prelude, Verdana, san-serif; font-size: 16px; line-height: 24px;">. It’s complicated if you’re trying to explain </span><a href="http://www.smbc-comics.com/index.php?db=comics&id=2524#comic" style="background-color: white; border: 0px; color: #19437c; font-family: Georgia, ApresTT, Prelude, Verdana, san-serif; font-size: 16px; line-height: 24px; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;" target="_blank">what exactly Schrodinger was trying to say with his infamous cat problem</a><span style="background-color: white; color: #222222; font-family: Georgia, ApresTT, Prelude, Verdana, san-serif; font-size: 16px; line-height: 24px;">. It’s complicated if you’re trying to figure out why there are </span><a href="https://en.wikipedia.org/wiki/Conversion_of_units_of_temperature#Comparison_of_temperature_scales" style="background-color: white; border: 0px; color: #19437c; font-family: Georgia, ApresTT, Prelude, Verdana, san-serif; font-size: 16px; line-height: 24px; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;" target="_blank">EIGHT DIFFERENT SCALES</a><span style="background-color: white; color: #222222; font-family: Georgia, ApresTT, Prelude, Verdana, san-serif; font-size: 16px; line-height: 24px;"> that measure the exact same physical concept. It’s complicated if you’re trying to create the technology that might let you detect a </span><a href="http://en.wikipedia.org/wiki/Higgs_boson" style="background-color: white; border: 0px; color: #19437c; font-family: Georgia, ApresTT, Prelude, Verdana, san-serif; font-size: 16px; line-height: 24px; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;" target="_blank">hypothetical particle</a><span style="background-color: white; color: #222222; font-family: Georgia, ApresTT, Prelude, Verdana, san-serif; font-size: 16px; line-height: 24px;"> that, if found, would validate an entire model of particle physics. So why exactly are we supposed to be surprised that it’s </span><em style="background-color: white; border: 0px; color: #222222; font-family: Georgia, ApresTT, Prelude, Verdana, san-serif; font-size: 16px; line-height: 24px; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">still </em><span style="background-color: white; color: #222222; font-family: Georgia, ApresTT, Prelude, Verdana, san-serif; font-size: 16px; line-height: 24px;">complicated when you’re trying to measure something abstract? Like feeeeeeelings?"</span></span></blockquote>
<span style="font-family: inherit;"><br />
As a well trained psychologist I'll start with providing positive feedback: Good to learn "feeeeeeelings" are considered abstract concepts, that means there is hope for a formal description of feelings.</span><br />
<span style="font-family: inherit;"><br />
But... Really?</span><br />
<span style="font-family: inherit;"><br />
Is psychology really comparing the difficulty it has to decide whether to measure love on a 5 point rating scale or by judging a love letter for its love-content, to evidencing the Higgs Boson by building the LHC?</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">Oh boy.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">The damage caused here may be beyond repair. I'll just leave the discussion of the differences between the construction of the most complex scientific instrument, "<a href="http://www.computerworlduk.com/news/infrastructure/10948/collider-test-called-a-great-milestone-of-mankind/" target="_blank">one of the great engineering milestones of mankind</a>" that constitutes a collaborative effort involving over 10,000 scientists from 100 different countries and a Likert rating scale to rest for now. Except for this question: <i>Do you think psychology would be able to organise the same amount of resources, effort, devotion and collaboration from the community of psychological scientists and cousin disciplines, funding agencies and policymakers from so many different nations to evidence a theoretical construct proposed by a theory of psychology?</i></span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">I'm open for suggestions on what that might be.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">In the meantime, the other comments are not helping either, because they do not evidence measurement problems, but are solutions or corroborations of theories with high verisimilitude. For instance, how does the existence of different scales used to measure <i>any</i> quantity counter the critiques of measurement in psychology? There are more than 13 fundamentally different ways to calculate <a href="http://en.wikipedia.org/wiki/Avogadro_constant" target="_blank">Avogadro's number</a> and that fact alone was seen as evidence that atoms existed (e.g., J.B. Perrin's argument, 1909). Temperature, like pressure and entropy, are thermodynamic quantities defined as collective variables observable at the level of an ensemble of particles (a single particle does not possess the property temperature or pressure). Incidentally, the thermodynamic laws and quantities that operate at the level of ensembles are linked to theories of particle mechanics through statistical mechanics. So it naturally follows from formal definitions and theoretical connections and unifications that there are many different ways to express the temperature of an ensemble of particles, just by using different kinds of matter that have different properties (e.g. phase transitions). These different scales can all be transformed into one another, they all measure the same thing! This corroborates the (3) scientific theories involved, strengthens their veracity, it doesn't contradict them!</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">Let's transform heartrate change into the Likert scale and back. That's difficult because love is not formally defined by a theory that is linked to a proper measurement theory that is able to deal with interaction between the object of measurement and the the measurement instrument. Interaction such as between a "true attitude" and asking a student to rate the attitude on a 5 point scale in a cubicle in a psychology laboratory. <a href="http://fredhasselman.com/main/wp-content/papercite-data/pdf/cox2011.pdf" target="_blank">Physics created a theory of preparation and measurement that is able to deal with such things, why doesn't psychology do the same?</a></span><br />
<div>
<span style="font-family: inherit;"><br /></span></div>
<span style="font-family: inherit;">The thought experiment about Schrödingers cat is about the interpretation of measurement outcomes. Whether you interpret the outcomes in terms of descriptive explanations of sensory experiences, that is, your everyday experience of reality, is irrelevant for the precision and accuracy of a scientific theory. It does not matter whether Schrödingers cat "really" is in a state between life and death or not, as long as the universe behaves according to the theory that needs such a concept to make accurate predictions that can be verified empirically. To believe otherwise is to fall prey to the interpretation fallacy in theory evaluation and many psychologists do. <a href="http://en.wikipedia.org/wiki/Interpretations_of_quantum_mechanics#Comparison_of_interpretations" target="_blank">There are over 15 different interpretations of quantum physics</a>, but they are all interpretations of the most precise and accurate scientific theory about the structure of reality ever produced by human minds.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">Heisenberg's uncertainty principle, entanglement, superposition, duality, they are all precisely, formally described theoretical concepts that were conceived by scientists in order to build better theories about the phenomena observed in the quantum world. The isomorphism of the mathematical description of light and matter as either waves or particles was one of the profound insights that gave rise to quantum physics: Waves and particles are two sides of the same structural coin and many more such dualities exist.</span><br />
<span style="font-family: inherit;"><br />
Still not convinced? </span><br />
<span style="font-family: inherit;"><br />
Here is the most accurate confirmation of a prediction of a measurement outcome (!) by a scientific theory: The existence of
an anomalous electron magnetic dipole moment (which is anomalous to the Dirac
equation) by Quantum Electrodynamics (QED). According to the most accurate
calculations the anomaly, when measured in a metal trap with a cylindrical
cavity whose resonance structure is known, should have a value of: a<sub>µ</sub><sup>QED</sup>
= 11659180.4(5.1) x 10<sup>-10</sup> (Aoyama,
Hayakawa, Kinoshita, & Nio, 2008; Hagiwara, Martin, Nomura, & Teubner,
2007). The most accurate empirical measurement of the predicted value of the
anomaly in the described measurement context is: a<sub>µ</sub><sup>exp</sup> =
11659208.0(6.3) x 10<sup>-10 </sup>(Bennett et al., 2006). </span><br />
<span style="font-family: inherit;"><br /></span>
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<!--StartFragment--><span style="font-family: inherit;"><span style="font-size: 12pt;">Such
examples of empirical accuracy do not exist in any other discipline of science, moreover
note that the theory predicts the entire measurement context in which the
phenomenon may be observed to yield the predicted measurement outcomes. Don't you think it is worthwhile to study how the theories of physics got so accurate? How they deal with things that on closer examination may be just as complex and fuzzy as </span>"feeeeeeelings"?<span style="font-size: 12pt;"> That would be the scientific thing to do. If it turns out you can't use anything they came up with (which I doubt, </span><a href="http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=8918806" style="font-size: 12pt;" target="_blank">see applications of quantum probability theory to psychological science</a> and <a href="http://www.apa.org/science/resources/ati/nonlinear.aspx" target="_blank">APA's advanced training institute in nonlinear methods</a>)<span style="font-size: 12pt;">, that's fine, but you cannot dismiss these successes just like that. Moreover, I am certain it would be of the utmost scientific importance to find out why the tools of physics would </span><i style="font-size: 12pt;">not</i><span style="font-size: 12pt;"> work for psychological science. It would be win-win situation for science, no matter what the outcome of such an inquiry might be.</span></span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">Theorising in a science should not depend on the ability of its practitioners to understand what the theory implies in terms of the way humans experience reality. The universe was not built to accommodate humans, we hitched a ride. If the theory is precise and accurate according to severe testing by repeated application of the scientific method, you have to accept it is like the theory says it is. Don't worry, history of science shows that eventually the theory will be discarded again...</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;"><i>That</i> is science baby. Take it or leave it.</span><br />
<span style="font-family: inherit;"><br /></span>
<i><span style="font-family: inherit;">Next please.</span></i><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;"><br /></span>
<b><span style="font-family: inherit;">4. "But psychology is a young science, don't have such high expectations of us."</span></b><br />
<i><span style="font-family: inherit;">(e.g. <a href="http://psych-your-mind.blogspot.nl/2013/08/the-psychology-of-psychology-isnt.html">http://psych-your-mind.blogspot.nl/2013/08/the-psychology-of-psychology-isnt.html</a>)</span></i><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">This seems like a sensible argument.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">It does assume two things:</span><br />
<ol>
<li><span style="font-family: inherit;">It is true that psychology is younger than physics as a modern empirical science.</span></li>
<li><span style="font-family: inherit;">Psychological science has been frantically and rigorously attempting to improve the empirical accuracy of its theories in order to grow up to become a genuine empirical science.</span></li>
</ol>
<span style="font-family: inherit;">I believe the veracity of these two premises can be questioned.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">As for the first point, of course, Newton and Leibniz were esteemed scholars, but were they atrophysicists? They were much more than that and what to think of Decartes, was he a cognitive psychologist who studied consciousness?</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">I am not a historian, but I think it is not very controversial to suggest the contours of modern science, with its separate disciplines and use of the scientific method started to take form in the 1800s. We're talking about the century of Maxwell's unification of electricity and magnetism, the Lorenz transformation, the mathematical advances of Hilbert and Poincaré, about Darwin's theory of evolution, the recognition of the cell as building block of life and its role in health and pathology (Virchow / Remak, 1855) and about the psychophysics of Wechner and Feber. There was a theory of "unconscious cerebration" that was fiercely debated in the Bristish Jounral of Psychiatry (see e.g. Browne, 1870; Davies, 1873; Ireland, 1875; Laycock, 1876). Also, there were two scholars who had produced a: "Principles of Psychology" (one by Herbert Spencer in 1855 and one by William James in 1890).</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">Scripture (1891) provides an insightful taxonomy of scientific disciplines and analysed what kind of science psychology should be, distinguishing it from psychophysics. He concludes:</span><br />
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">"In the foregoing attempt to define the problem of Psychology- an establishment of the following statements was striven for:—</span></div>
<ol>
<li style="text-align: justify;"><span style="font-family: inherit;">Psychology is the science of mental processes, not mental products. </span></li>
<li style="text-align: justify;"><span style="font-family: inherit;">It is a mental science, not a physiology of the brain. </span></li>
<li style="text-align: justify;"><span style="font-family: inherit;">It is one of the special sciences, not a part of philosophy. </span></li>
<li style="text-align: justify;"><span style="font-family: inherit;">It is a descriptive and explanatory, not a critical science. </span></li>
<li style="text-align: justify;"><span style="font-family: inherit;">It is an indispensable auxiliary to the physical, other mental, philosophical and didactic sciences."</span></li>
</ol>
</blockquote>
<span style="font-family: inherit;">The quote mentions "the problem of Psychology" and this problem bears undeniable resemblances to what we are discussing today. Scripture provides conditions a young "would-be science" (as it was perceived in 1891!) had to meet. Here's the second condition (the first is about point 1 in the previous quote):</span><br />
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">"The second condition which our science is to fulfill is that it must be a modern science. The subjects of science are facts and hypotheses. The distinguishing characteristics of modern science are the establishment of accurately determined facts and the founding of hypotheses upon such facts alone. <b>A would-be science that neglects any possible means of developing these characteristics finds itself at once in disrepute.</b></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">The science of mental processes dare not begin with metaphysical hypotheses and twist the facts to suit them; it dare not rest contented with <b>loose and insufficient methods of ascertaining facts</b>." Scripture (1891, p. 316-317, emphasis added)</span></div>
</blockquote>
<div>
<span style="font-family: inherit;">Still wondering why we find ourself in disrepute? This is a criticism, or rather a warning from the era before statistics which provided a whole new dimension to the meaning of resting contented with "<span style="text-align: justify;">loose and insufficient methods of ascertaining facts<b style="font-style: italic;">".</b></span></span></div>
<div>
<span style="font-family: inherit; text-align: justify;"><b style="font-style: italic;"><br /></b></span></div>
<div>
<span style="font-family: inherit;">We cannot seriously continue to claim to be a young "would-be science", because we have apparently been doing so for at least 120 years. Is it really that hard to imagine other disciplines are starting to loose patience? </span></div>
<div>
<span style="font-family: inherit; text-align: justify;"><i><b><br /></b></i></span></div>
<div>
<span style="font-family: inherit;"><span style="text-align: justify;">About point 2, the parental advisory for the young science wasn't the only criticism of that era, there were more "directed attacks" that had to do with </span><i>Psychology as So-Called “Natural Science” </i>(Ladd, 1892)<i>. </i>This is about the claims of Psychology to use the scientific methods of the natural sciences to study psychological phenomena <span style="text-align: justify; text-indent: 35.45pt;">when in fact: </span></span></div>
<blockquote class="tr_bq">
<span style="font-family: inherit;"><span style="text-align: justify; text-indent: 35.45pt;">“</span><span style="text-align: justify; text-indent: 35.45pt;">[…]
psychology as a science, </span><span style="text-align: justify; text-indent: 35.45pt;"><b>devoid of all
postulating</b></span><span style="text-align: justify; text-indent: 35.45pt;"><b> of "deeper-lying entities,"</b> does nothing of the kind.
<b>It</b> </span><span style="text-align: justify; text-indent: 35.45pt;"><b>assumes only the phenomena</b></span><span style="text-align: justify; text-indent: 35.45pt;"> - the
thoughts and feelings as actually known, and </span><span style="text-align: justify; text-indent: 35.45pt;"><b>the possibility of ascertaining uniform relations</b></span><span style="text-align: justify; text-indent: 35.45pt;"><b> among them</b>.” </span><span style="text-align: justify; text-indent: 35.45pt;">Ladd (1892, pp. 29–30, reviewing William James’ “The
Principles of Psychology”, emphasis added)</span><span style="text-align: justify; text-indent: 35.45pt;">.</span></span></blockquote>
<div>
<span style="font-family: inherit;">These criticisms, that psychological science is "dressing up" as a science, have resurfaced again and again (see e.g., Spence, 1944; Meehl, 1967; Feynman, 1974). Add to that all the warnings of the great statisticians like Cohen (1962, yes, we knew back then already that power was very important!) and philosophers of science:</span></div>
<blockquote class="tr_bq">
<span style="font-family: inherit;">"After reading Meehl (1967) [and other psychologists] one wonders whether the function of statistical techniques in the social sciences is not primarily to provide a machinery for producing <b>phony corroborations and thereby a semblance of ‘scientific progress’ where, in fact, there is nothing but an increase in pseudo-intellectual garbage.</b>" -Lakatos (1978, pp. 88–89, emphasis added)</span></blockquote>
<span style="font-family: inherit;">You cannot seriously maintain that psychological science has been doing everything it possibly can to outgrow its "would-be science" status.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">I have used this quote by Meehl (a psychologist) on previous occasions, but it just sums up, in a very profound way, that this argument of a being the new kid on the block that needs to be treated as a young child whose mistakes should be overlooked, cannot be an argument against the recent (but in fact very ancient) critiques by </span>the hard sciences<span style="font-family: inherit;">:</span><br />
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">"<b>I am prepared to argue that a tremendous amount of taxpayer money goes down the drain in research that pseudotests theories in soft psychology and that it would be a material social advance as well as a reduction in what Lakatos has called “intellectual pollution” (Lakatos, 1970, fn. 1 on p. 176) if we would quit engaging in this feckless enterprise. </b></span></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">I think that if psychologists would face up to the full impact of the above criticisms, something worthwhile would have been achieved in convincing them of it. Besides, before one can motivate many competent people to improve an unsatisfactory cognitive situation by some judicious mixture of more powerful testing strategies and criteria for setting aside complex substantive theory as “not presently testable,” it is necessary to face the fact that the present state of affairs is unsatisfactory. </span></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">My experience has been that most graduate students, and many professors, engage in a mix of defense mechanisms (most predominantly, denial), so that they can proceed as they have in the past with a good scientific conscience. The usual response is to say, in effect, “<i>Well, that Meehl is a clever fellow and he likes to philosophize, fine for him, it’s a free country. But since we are doing all right with the good old tried and true methods of Fisherian statistics and null hypothesis testing, and since journal editors do not seem to have panicked over such thoughts, I will stick to the accepted practices of my trade union and leave Meehl’s worries to the statisticians and philosophers.</i>” </span></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">I cannot strongly fault a 45-year-old professor for adopting this mode of defense, even though I believe it to be intellectually dishonest, because I think that for most faculty in soft psychology the full acceptance of my line of thought would involve a painful realization that one has achieved some notoriety, tenure, economic security and the like by engaging, to speak bluntly, in a bunch of nothing." </span></div>
</blockquote>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">-Meehl (1990, emphasis and markup added).</span></div>
</blockquote>
<br />
<i><span style="font-family: inherit;">Next please.</span></i><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">No, I'm done for now.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;"><br /></span>
<span style="background-color: #e9edec; font-family: inherit; font-size: 15px; line-height: 21px; text-align: justify;">天</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;"><br /></span>
<b><span style="font-family: inherit;">References:</span></b><br />
<span style="font-family: inherit;"><br />
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<!--StartFragment--><span style="font-size: 12pt;">Aoyama, T., Hayakawa, M., Kinoshita, T., & Nio, M.
(2008). Revised value of the eighth-order QED contribution to the anomalous
magnetic moment of the electron. <i>Physical Review D</i>, <i>77</i>(5), 1–24.
doi:10.1103/PhysRevD.77.053012</span></span><br />
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<span style="font-family: inherit; font-size: 12pt;">Bennett, G., Bousquet, B., Brown, H., Bunce, G., Carey, R.,
Cushman, P., Danby, G., et al. (2006). Final report of the E821 muon anomalous
magnetic moment measurement at BNL. <i>Physical Review D</i>, <i>73</i>(7),
1–41. doi:10.1103/PhysRevD.73.072003</span><br />
<span style="font-family: inherit; font-size: 12pt;"><br /></span>
<span style="font-family: inherit;">
Browne, J. H. B. (1870). On the Method of the Study of Mind. <i>The British Journal of Psychiatry, 16(74)</i>, 233–247. doi:10.1192/bjp.16.74.233</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit; font-size: 12pt;">Hagiwara, K., Martin, a, Nomura, D., & Teubner, T.
(2007). Improved predictions for g−2g−2 of the muon and αQED(MZ2). <i>Physics
Letters B</i>, <i>649</i>(2-3), 173–179. doi:10.1016/j.physletb.2007.04.012</span><br />
<span style="font-family: inherit; font-size: 12pt;"><br /></span>
<span style="font-family: inherit;">Davies, W. G. (1873). Consciousness and “Unconscious Cerebration.” <i>The British Journal of Psychiatry, 19(86)</i>, 202–217. doi:10.1192/bjp.19.86.202</span><br />
<span style="font-family: inherit;"><br />
Feynman, R. P. (1974). Cargo cult science. <i>Engineering and Science, 37(7)</i>, 10–13.</span><br />
<span style="font-family: inherit;"><span style="font-size: 12pt;"><br /></span>
</span><br />
<div>
<span style="font-family: inherit;">Ireland, W. W. (1875). Can Unconscious Cerebration be proved? <i>The British Journal of Psychiatry, 21(95)</i>, 366–387. doi:10.1192/bjp.21.95.366</span></div>
<div>
<span style="font-family: inherit;"><br /></span></div>
<div>
<span style="font-family: inherit;">Ladd, G. (1892). Psychology as So-Called “ Natural Science .” <i>The Philosophical Review, 1(1),</i> 24–53. Retrieved from http://www.jstor.org/stable/10.2307/2175528</span></div>
<div>
<span style="font-family: inherit;"><br /></span></div>
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<span style="font-family: inherit;">Laycock, T. (1876). Reflex, Automatic, and Unconscious Cerebration: A History and a Criticism. <i>The British Journal of Psychiatry, 21(96)</i>, 477–498. doi:10.1192/bjp.21.96.477</span></div>
<div>
<span style="font-family: inherit;"><br /></span></div>
<span style="font-family: inherit;">Meehl, P. E. (1967). Theory testing in psychology and physics: a methodological paradox. <i>Philosophy of science, 34</i>, 103–115.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
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<span style="font-family: inherit;">Meehl, P. E. (1990). Why Summaries of Research on Psychological Theories Are Often Uninterpretable. <i>Psychological Reports, 66(1),</i> 195. doi:10.2466/PR0.66.1.195-244</span><br />
<span style="font-family: inherit;"><br /></span>
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<span style="font-family: inherit;">Scripture, E. W. (1891). Psychology as philosophy. <i>Mind</i>, 63, 306-326.</span><br />
<span style="font-family: inherit;"><br /></span></div>
<div>
<span style="font-family: inherit;">Spence, K. W. (1944). The nature of theory construction in contemporary psychology. <i>Psychological Review, 51(1), 47–68</i>. doi:10.1037/h0060940</span><br />
<span style="font-family: inherit;"><br /></span></div>
<span style="font-family: inherit;">Spencer, H. (1855). <i>The principles of psychology</i>. London, UK: Longman, Brown, Green and Longmans. Retrieved from http://oll.libertyfund.org/title/1394</span><br />
<span style="font-family: inherit;"><br /></span>
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<div class="MsoBibliography">
<span style="font-family: inherit;"><span lang="EN-GB">van
Leeuwen, M. (2009). <i>Thinking Outside the
Box: A Theory of Embodied and </i></span><i><span lang="EN-GB" style="font-size: 12pt;">Embedded Concepts</span></i><span lang="EN-GB" style="font-size: 12pt;">. Universal
Press, Veenendaal, The Netherlands.</span></span></div>
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Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-25064067996359489542013-07-07T00:48:00.002+02:002013-07-07T01:16:28.360+02:00Respect your elders: First, you watch Meehl's videotaped philosophy of psychology lectures - then we'll discuss your "pseudo-intellectual bunch of nothing"<br />
<br />
<br />
<div style="text-align: justify;">
<span style="font-family: inherit;">I've never understood how it was possible a reviewer or editor of a scientific journal could write something like: This subject matter is too difficult and complex for our reader audience (to be interested in). I even heard a colleague exclaim once that a mathematical ps. journal found his mathematics too complex. </span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">That can only mean the audience does not want to get educated on things they do not know about yet, which is strange behaviour for scientists. An editor should invite an author to write a primer possibly as supplementary material, I've seen some examples of that recently, <a href="http://www.p-curve.com/" target="_blank">the p-curve article</a> to appear in JEP: General is one of them.</span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">More often than not, psychological theories and their predictions are evaluated for their descriptive value, which means: can the reviewer relate what it is about to his own preferred theories. This should not matter in science, theories should (as long as they do not claim a re-write of well-established theories based on some statistical oddities: Bem, 2011), be evaluated for the precision of the predictions they make, their empirical accuracy and their logical structure.</span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">Problem is, we do not get educated on these matters in psychology. Whether you do or not seems to trivially depend on whether there's a professor at your university who knows about theses things.</span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<i><span style="font-family: inherit;">(How lucky they were in Minnesota!)</span></i></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">It's plain and simple: If we really want psychology to be taken seriously as a scientific endeavour, we need to discuss it at the level of <i>metatheory</i>, how do we evaluate theories, what is their verisimilitude, their similarities so we can hope to unify them. </span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">We need to discuss it at the level <a href="http://www.tc.umn.edu/~pemeehl/" target="_blank">Paul Meehl</a> discussed it.</span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">Now, his list of publications is long, so are the publications themselves and my list of quotes I would like to paste here is endless and going by the popular journals, our generation of scientists is likely to doze off by anything longer than 5000 words anyway.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">How about some video then? </span></div>
<br />
<div style="text-align: justify;">
<span style="font-family: inherit;">Meehl's <a href="http://www.psych.umn.edu/research/meehl/1989%20Phil%20Psych%20notes.pdf" target="_blank">Philosophical Psychology Seminar is available online</a> as video!</span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">12 lectures of about 1.5 hours and you'll know all you need to know to have a proper discussion about the credibility of the theory you use to study the phenomena you are interested in.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
<br />
<div style="text-align: justify;">
<span style="font-family: inherit;"><i>(You do know <a href="http://www.ted.com/" target="_blank">TED</a> last only 20 minutes or so?)</i>,</span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">Ok, get through the first 7 at least (this will not be a difficult task, I even enjoyed to hear him speak about the practicalities of the course)</span></div>
<div style="text-align: justify;">
<b><span style="font-family: inherit;"><br /></span></b></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><b>The videos are here: </b><a href="http://www.psych.umn.edu/meehlvideos.php">http://www.psych.umn.edu/meehlvideos.php</a> </span></div>
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<div style="text-align: justify;">
<i><span style="font-family: inherit;"><br /></span></i></div>
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<i><span style="font-family: inherit;">Recommendations of Meehl's work by others:</span></i></div>
<blockquote class="tr_bq" style="text-align: justify;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit;">"After reading Meehl (1967) [and other psychologists] one wonders whether the function of statistical techniques in the social sciences is not primarily to provide a machinery for producing phony corroborations and thereby a semblance of ‘scientific progress’ where, in fact, there is nothing but an increase in <b>pseudo-intellectual garbage</b>." (Lakatos, 1978, pp. 88–9)</span></span></blockquote>
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<br /></div>
<div style="text-align: justify;">
<i><span style="font-family: inherit;">Just one quote sums it up for me</span></i></div>
<div style="text-align: justify;">
<i><span style="font-family: inherit;"><br /></span></i></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">Whenever I try to evaluate what someone is claiming about the world based on their data, or "theory" from the perspective of theory-evaluation, they look at me <a href="http://billhicks.com/The_Vision.php" target="_blank">like a dog who has just been shown a card trick</a>. It's so unreal that I cannot use a word like ontology or epistemology or ask about the measurement theory or rules of inference someone used to make a claim about the way the universe works that I considered leaving academia, but I guess leaving without trying to change the world is not how I <a href="http://anti-ism-ism.blogspot.nl/2010/09/day-3-where-is-fred-or-whats-in-name.html#.UdidpRa9YwI" target="_blank">was raised</a>, or<a href="http://en.wikipedia.org/wiki/Baron_Karl_Ludwig_von_der_Pfordten" target="_blank"> genetically </a>determined, The quote below summarises how I feel almost exactly:</span></div>
<blockquote class="tr_bq" style="text-align: justify;">
<span style="font-family: inherit;">"I am prepared to argue that <b>a tremendous amount of taxpayer money goes down the drain in research that pseudotests theories in soft psychology</b> and that it would be a material social advance as well as a reduction in what Lakatos has called “<b>intellectual pollution</b>” (Lakatos, 1970, fn. 1 on p. 176) if we would quit engaging in this feckless enterprise. </span></blockquote>
<blockquote class="tr_bq" style="text-align: justify;">
<span style="font-family: inherit;">I think that if psychologists would face up to the full impact of the above criticisms, something worthwhile would have been achieved in convincing them of it. Besides, before one can motivate many competent people to improve an unsatisfactory cognitive situation by some judicious mixture of more powerful testing strategies and criteria for setting aside complex substantive theory as “not presently testable,” it is necessary to face the fact that the present state of affairs is unsatisfactory. </span></blockquote>
<blockquote class="tr_bq" style="text-align: justify;">
<span style="font-family: inherit;">My experience has been that most graduate students, and many professors, engage in a mix of defense mechanisms (most predominantly, denial), so that they can proceed as they have in the past with a good scientific conscience. The usual response is to say, in effect, “<i>Well, that Meehl is a clever fellow and he likes to philosophize, fine for him, it’s a free country. But since we are doing all right with the good old tried and true methods of Fisherian statistics and null hypothesis testing, and since journal editors do not seem to have panicked over such thoughts, I will stick to the accepted practices of my trade union and leave Meehl’s worries to the statisticians and philosophers.</i>” </span></blockquote>
<blockquote class="tr_bq" style="text-align: justify;">
<span style="font-family: inherit;">I cannot strongly fault a 45-year-old professor for adopting this mode of defense, even though I believe it to be <b>intellectually dishonest</b>, because I think that for most faculty in soft psychology the full acceptance of my line of thought would involve a painful realization that one has achieved some notoriety, tenure, economic security and the like by engaging, to speak bluntly, <b>in a bunch of nothing</b>." (Meehl, 1990, emphasis and markup added)</span></blockquote>
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<b><span style="font-family: inherit;">References</span></b></div>
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<div style="margin-left: 24pt; text-align: justify; text-indent: -24pt;">
<span style="font-family: inherit;">Meehl, P. E. (1990). Why Summaries of Research on Psychological Theories Are Often Uninterpretable. <i>Psychological Reports</i>, <i>66</i>(1), 195. doi:10.2466/PR0.66.1.195-244</span></div>
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<span style="font-family: inherit;">天</span></div>
Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-68933113932076400872013-07-05T01:23:00.001+02:002013-07-05T02:53:13.391+02:00Representative samples, Generalisations, Populations and Property attribution<b><span style="font-family: inherit;">This is a reply to a reply to my reply to<a href="http://blog.dansimons.com/2013/07/six-simple-steps-scientists-can-take-to.html" target="_blank"> a post</a> by </span><a class="g-profile" href="https://plus.google.com/107191542129310486499" style="font-family: inherit;" target="_blank">+Daniel Simons</a><span style="color: #427fed; font-family: inherit;"> </span>
</b><br />
<div style="color: #323333; min-height: 16px;">
<span style="font-family: inherit;"><span style="letter-spacing: 0.0px;"></span><br /></span></div>
<span style="color: #323333; letter-spacing: 0px;"><span style="font-family: inherit;">What is described as examples of generalisation problems, are problems that occur when a sample is not representative of the population in which you expected to observe a phenomenon, or variable, or trait. </span></span><span style="color: #323333; font-family: inherit;"><span style="letter-spacing: 0px;">In general, it is the phenomenon predicted by a theory or hypothesis that determines what the population is. That's because the population you sample from, is a theoretical construct, a mathematical model whose parameters you are going to estimate </span></span><span style="color: #323333; font-family: inherit;"> (e.g., </span><span style="color: #323333;">Fiedler & Juslin, 2006).</span><span style="color: #323333;"><span style="font-family: inherit;"><span style="letter-spacing: 0px;"> The parameters define a probability distribution of values around the true value of the predicted population trait. Therefore, the population is always defined as the ensemble in which the true value of the trait can be </span></span>observed <span style="font-family: inherit;"><span style="letter-spacing: 0px;">as the outcomes of a </span></span></span><span style="color: #323333;">measurement procedure.</span><br />
<span style="color: #323333;"><br /></span>
<span style="color: #323333;"><i>(you <a href="http://anti-ism-ism.blogspot.nl/2013/06/truths-glorified-truths-and-statistics_14.html#.UdYDyha9YwI" target="_blank">remember</a> I don't believe in true scores right?)</i></span><br />
<i>
</i>
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<div style="color: #323333; min-height: 16px;">
<span style="font-family: inherit;"><span style="letter-spacing: 0.0px;"></span><br /></span></div>
<div style="color: #323333;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit;">If the phenomenon of “racial bias” is operationalised in a specific culture, it is likely the racial bias theory used will tell a researcher to select stimuli capable of measuring bias in that culture. Still, one would generalise the results to a population in which <i>racial bias is an observable phenomenon in principle</i>. Unless of course this theory can only predict a very specific bias, in a very specific culture. I am not an expert, but cannot believe every culture needs its own personal racial bias theory.</span></span></div>
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<span style="font-family: inherit;"><span style="letter-spacing: 0.0px;"></span><br /></span></div>
<div>
<span style="letter-spacing: 0px;"><span style="font-family: inherit;"><span style="color: #323333;">The same way, if a researcher is only interested in discrimination between the colours </span><span style="color: red;">red</span><span style="color: #323333;"> and </span><span style="background-color: #f3f3f3; color: lime;">green</span><span style="color: #323333;">, then results do not generalise to a population that includes people with </span><a href="http://www.color-blindness.com/2007/04/17/deuteranopia-red-green-color-blindness/" style="color: #323333;" target="_blank">Deuteranopia</a><span style="color: #323333;">, but that is also specified by the sentence: "<i>capable of performing the task</i>", which is the same as "<i>a population in which the phenomenon may be observed.</i>" </span></span></span></div>
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<span style="font-family: inherit;"><span style="letter-spacing: 0.0px;"></span><br /></span></div>
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<span style="letter-spacing: 0px;"><span style="font-family: inherit;"><span style="color: #323333;">If you use the </span><span style="color: red;">red</span><span style="color: #323333;">-</span><span style="color: lime;">green</span><span style="color: #323333;"> discrimination experiment as a measurement procedure in a sample that is </span><b style="color: #323333;">not</b><span style="color: #323333;"> representative of a population in which the phenomenon may be observed (e.g., many participants with Deuteranopia), it would be wrong to conclude humans in general cannot discriminate between the two colours, but the cause is <b>not</b> the measurement procedure, the predictions, or the theory that prompted the predictions and measurement procedure. It’s a problem of representativity of the sample.</span></span></span></div>
<div style="color: #323333; min-height: 16px;">
<span style="font-family: inherit;"><span style="letter-spacing: 0.0px;"></span><br /></span></div>
<div style="color: #323333;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit;">If one is truly concerned about representation, the proper thing to do in my opinion is a replication of the results in a representative sample, which is not as convenient as convenience sampling, but still much more convenient than building a Large Hadron Collider or sending satellites out into space in order to observe phenomena. Therefore, a smart journalist who would read a generalisation warning on a paper, about a very general human quality such as emotion, attention, stress, anxiety or happiness influencing some decision, judgement, performance or overt behaviour, should ask: "<i>But why didn’t you bother to measure your variables in a representative sample?</i>"</span></span></div>
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<br /></div>
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<span style="letter-spacing: 0.0px;"><span style="font-family: inherit;"><i>(The point I actually wanted to make was)</i></span></span><br />
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit;"><i><br /></i></span></span>
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit;">If you operationalise the phenomenon <i>colour discrimination</i> as an object of measurement that is observable as a summary statistic at the level of the sample (a latent trait or variable), you actually <i>predicted</i> a population in which colour discrimination may be observed, from which you sample. You hope to have sampled enough of the population property to indeed observe it at the level of the sample. Our models do not allow (an easy way) back from predicted/inferred trait of the population, to the specific characteristics of an individual</span></span> in that population<span style="font-family: inherit; letter-spacing: 0px;">, or: </span><i style="font-family: inherit; letter-spacing: 0px;">"anyone with normal colour perception" </i><span style="font-family: inherit; letter-spacing: 0px;">(e.g., Borsboom et al., (2003); Ellis et al, 1993).</span></div>
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<span style="letter-spacing: 0px;"><span style="font-family: inherit;"><br /></span></span></div>
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<span style="color: #323333; font-family: inherit;"><span style="letter-spacing: 0px;">That's when all the assumptions of our measurement theory and rules of inference apply, when we introduce emergence, violation of the ergodic principle, etc., p</span></span><span style="color: #323333;">roperty</span><span style="color: #323333; font-family: inherit;"><span style="letter-spacing: 0px;"> attribution based on measurement outcomes becomes even more problematic: </span></span><span style="color: #323333; font-family: inherit;"><span style="letter-spacing: 0px;">Temperature exists as a property of ensembles of particles, but a single individual particle cannot be attributed the property "temperature". There is a theory though that links particle mechanics to ensemble dynamics (thermodynamics) which is called statistical mechanics. It also </span></span><span style="color: #323333;">contains</span><span style="color: #323333;"><span style="font-family: inherit;"><span style="letter-spacing: 0px;"> the word statistical, but the analogy should </span></span>probably<span style="font-family: inherit;"><span style="letter-spacing: 0px;"> end there: The techniques for inductive inference we use in psychology, can only generalise to population parameters and they do so in a very straightforward way (theory of </span></span></span><span style="color: #323333;">measurement error).</span><br />
<span style="color: #323333;"><br /></span><span style="color: #323333;"><i>(Should I mention our model of <a href="http://www.frontiersin.org/Journal/Abstract.aspx?ART_DOI=10.3389/fphys.2012.00116&name=fractal_physiology" target="_blank">nested scales of constraint</a> and my position on inferring <a href="http://www.frontiersin.org/Fractal_Physiology/10.3389/fphys.2013.00075/full" target="_blank">scaling phenomena</a>? ...nah... some other time)</i></span></div>
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<div style="color: black; margin-bottom: 12px; margin-left: 32px; text-indent: -32px;">
<span style="letter-spacing: 0px;"><span style="font-family: inherit;">Borsboom, D., Mellenbergh, G. J., & Van Heerden, J. (2003). The theoretical status of latent variables. <i>Psychological Review</i>, <i>110</i>(2), 203–219. doi:10.1037/0033-295X.110.2.203</span></span></div>
<div style="color: black; margin-bottom: 12px; margin-left: 32px; text-indent: -32px;">
<span style="letter-spacing: 0px;"><span style="font-family: inherit;">Ellis, J. L., & Wollenberg, A. L. (1993). Local homogeneity in latent trait models. A characterization of the homogeneous monotone irt model. <i>Psychometrika</i>, <i>58</i>(3), 417–429. doi:10.1007/BF02294649</span></span></div>
<div style="margin-bottom: 12px; margin-left: 32px; text-indent: -32px;">
Fiedler, K., & Juslin, P. (2006). Information Sampling and Adaptive Cognition.</div>
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<span style="font-family: inherit;">天</span>Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-61511417542710015162013-07-03T01:24:00.000+02:002013-07-03T01:35:33.059+02:00Respect your elders: Fads, fashions, and folderol in psychology - Dunnette (1966)<br />
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<b>Some reflections on novelty in psychological science</b></div>
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In the discussion on open data I commented on <a href="http://anti-ism-ism.blogspot.nl/2013/06/truths-glorified-truths-and-statistics_28.html#.UdNeYBa9YwI" target="_blank">recently</a> results were reported on data sharing:</div>
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Because the authors were writing in APA journals and PLoS One, respectively, they had agreed at the time of submitting that they would share their data according to the journals' policies. But only 26 % and 10 %, respectively, did. (I got the references from a paper by Peter Götzsche, there may be others of which I am unaware.</div>
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Yes, there are other studies, interestingly, in the historical record: <i><a href="http://www.lyricsfreak.com/r/rush/circumstances_20119994.html" target="_blank">plus ça change, plus c'est la même chose.</a></i></div>
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To stress the importance of efforts to change these statistics, an excerpt from Dunnette (1966) who reports a 1962 study found 13.5% authors complied to data requests. Reasons for being unable to comply with a request sound familiar, this is not an issue of "modern" science it seems. (I can recommend the entire article)</div>
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THE SECRETS WE KEEP </div>
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We might better label this game "Dear God, Please Don't Tell Anyone." As the name implies, it incorporates all the things we do to accomplish the aim of looking better in public than we really are. The most common variant is, of course, the tendency to bury negative results. </div>
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I only recently became aware of the massive size of this great graveyard for dead studies when a colleague ex- pressed gratification that only a third of his studies "turned out"—as he put it. </div>
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Recently, a second variant of this secrecy game was discovered, quite inadvertently, by Wolins (1962) when he wrote to 37 authors to ask. for the raw data on which they had based recent journal articles. </div>
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Wolins found that of 32 who replied, 21 reported their data to be either misplaced, lost, or inadvertently destroyed. Finally, after some negotiation, Wolins was able to complete seven re-analyses on the data supplied from 5 authors. </div>
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Of the seven, he found gross errors in three—errors so great as to clearly change the outcome of the results already reported. Thus, if we are to accept these results from Wolins' sampling, we might expect that as many as one-third of the studies in our journals contain gross miscalculations."</div>
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30% gross miscalculations might have been a high estimate, but as a 50 year prospective prediction it's not bad: Bakker & Wicherts (2011) found "number of articles with gross errors" across 3 high and 3 low impact journals ranging from 9% to 27.6% </div>
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In the light of these (and other) historical facts & figures, maybe its time for a historical study, lots of recommendations in those publications. </div>
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Again Dunnette (1966):</div>
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THE CAUSES<br />
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When viewed against the backdrop of publication pressures prevailing in academia, the lure of large-scale support from Federal agencies, and the presumed necessity to become "visible" among one's colleagues, the insecurities of undertaking research on important questions in possibly untapped and unfamiliar areas become even more apparent. </blockquote>
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THE REMEDY </blockquote>
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[…]<br />
1. Give up constraining commitments to theories, methods, and apparatus!<br />
2. Adopt methods of multiple working hypotheses!<br />
3. Put more eclecticism into graduate education!<br />
4. Press for new values and less pre-tense in the academic environments of our universities!<br />
5. Get to the editors of our psychological journals! </blockquote>
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THE OUTCOME: UTOPIA </blockquote>
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How do I envision the eventual outcome if all these recommendations were to come to pass? What would the psychologizing of the future look like and what would psychologists be up to? Chief among the outcomes, I expect, would be a marked lessening of tensions and disputes among the Great Men of our field.<br />
I would hope that we might once again witness the emergence of an honest community of scholars all engaged in the zestful enterprise of trying to describe, understand, predict, and control human behavior.</blockquote>
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<a href="http://www.amazon.co.uk/Stewart-Francis-Live-Outstanding-Field/dp/B007RE8C98/ref=sr_1_2?ie=UTF8&qid=1345650421&sr=8-2" target="_blank">Did I already do my déjà vu joke?</a></div>
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<b>References</b></div>
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Bakker, M., & Wicherts, J. M. (2011). The (mis)reporting of statistical results in psychology journals. <i>Behavior research methods</i>, <i>43</i>(3), 666–78. doi:10.3758/s13428-011-0089-5</div>
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Dunnette, M. D. (1966). Fads, fashions, and folderol in psychology. <i>The American psychologist</i>, <i>21</i>(4), 343–52. Retrieved from <a href="http://www.ncbi.nlm.nih.gov/pubmed/5910065">http://www.ncbi.nlm.nih.gov/pubmed/5910065</a></div>
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Wolins, L. (1962). Responsibility for raw data. <i>The American Psychologist</i>,<i> 17</i>, 657-658. doi: 10.1037/h0038819</div>
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天Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-78284304851298784192013-06-28T16:45:00.000+02:002013-06-28T23:22:29.015+02:00Truths, Glorified Truths, and Statistics (III): The power-credibility paradox of empirical phenomena observed in psychological experiments<br />
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<b><span style="font-family: inherit;">The power-credibility paradox of empirical phenomena observed in psychological experiments</span></b></div>
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<span style="font-family: inherit;">I started this post in response to an e-mail exchange at openscienceframework@googlegroups.com on the necessity of reporting multiple experiments in one session, which I take to mean one participant.</span></div>
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<span style="font-family: inherit;"><a href="https://groups.google.com/d/msg/openscienceframework/ckyzVZXgqA4/Nb6ukwUje_EJ" target="_blank">Joshua Hartshorne wrote</a>:</span></div>
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<span style="font-family: Times, Times New Roman, serif;">My concern is that the conversation on this list is often focused on solving the problems of social psychology and then imposing the solutions on other fields,</span></blockquote>
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<span style="font-family: inherit;">In our lab, which for the most part is buzzing with social psychological experiments, as a researcher you can opt to run your experiment in a "chunk": One participant gets to press buttons in a cubicle for an hour or so, and is rewarded with a nice round number in course credit. The chunk usually consists of 3-4, 15-20 minute experiments. I often collect more than a thousand data points per participant, so my experiments are not "chunkable". The majority of studies are, and will be conducted this way. I hear other labs use the same system. In a publication I have seen people report something along the lines of: "The data were collected in a 1-hour session in which the participant performed tasks in addition, but unrelated to the present experiment."</span></div>
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<span style="font-family: Times, Times New Roman, serif;">Running many manipulations and only reporting the one that "works" is a problem and needs to be discouraged. But we wouldn't want the method of discouragement to result in discouraging the running of multiple EEG experiments in the same session, which is actually a very good idea!</span></blockquote>
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<span style="font-family: inherit;">The chunk seems comparable to the multi EEG and fMRI experiments in one session, <a href="https://groups.google.com/d/msg/openscienceframework/ckyzVZXgqA4/lTWcet6EG7sJ" target="_blank">except for one thing</a>… </span></div>
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<span style="font-family: Times, Times New Roman, serif;">I don't know about fMRI, but having several unrelated experiments in one EEG session is pretty common and certainly not something we want to discourage. Good EEG experiments require that your critical trials be only a small fraction of all trials. The rest can be random filler, but it's more efficient to simply run several studies, with each one using the others as fillers</span><span style="font-family: inherit;">.</span></blockquote>
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<span style="font-family: inherit;">… The question is whether unrelated experiments of other researchers are used as fillers in a single EEG / fMRI session, or whether all the experiments run are in fact experiments designed, and / or intended to publish about by the same research group, possibly the same phenomenon?</span></div>
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<span style="font-family: inherit;">My guess is the latter will be true in the majority of cases and I will argue that that is not a good idea and there is a lot of cause to impose the solutions of other fields, like the ones suggested for behavioural studies in social psychology, to solve the problems with this particular practice.</span></div>
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<span style="font-family: inherit;">First, I very much agree with this important remark by <a href="https://groups.google.com/d/msg/openscienceframework/ckyzVZXgqA4/3ZTmzKPIK-EJ" target="_blank">Gustav Nilsonne</a>:</span></div>
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<span style="font-family: Times, Times New Roman, serif;">Do you agree that the publications from an EEG experiment should ideally mention what the "filler" experiments were, and which other papers (if any) were based on data from the same experimental runs?</span></blockquote>
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<span style="font-family: inherit;">I would add that this should be the case for all experiments measured from the same participant, at the same location for the duration of chunked measurements.</span></div>
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<span style="font-family: inherit;">To pre-emptively tackle responses about the feasibility of such an accounting system, <b>Yes: hard, difficult, time, energy, money, ethical concerns, anonymity</b>, but reality is not affected by those matters and neither should your scientific claims about reality be if these issues can in principle be resolved.</span></div>
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<i><span style="font-family: inherit;">(there's much more possible if you let fantasies run wild: If a smart system of token generation and data storage is in place that guarantees anonymity, a participant could be linked to the raw datasets of all the studies they participated in. How many publications would they have contributed to? Are they always the outlier that makes the small effect a moderate one? This potentially provides an interesting window on assessing magnitude of individual variability in different measurement paradigms, perhaps even estimation of compliance and experiment experience / expectation effects)</span></i></div>
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<span style="font-family: inherit;">Back to reality, <a href="https://groups.google.com/d/msg/openscienceframework/ckyzVZXgqA4/dOgeLAXNULEJ" target="_blank">Gustav</a> wrote: </span></div>
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<span style="font-family: Times, Times New Roman, serif;">In my area this is a real issue. I see fMRI papers from time to time that report one experiment out of several that were performed in the same scanning session, with no mention of the other experiments. Of course there is no way to know in such a case whether there might have been carryover effects from an earlier experiment. This seems to be a fully accepted practice in the fMRI field.</span></blockquote>
<span style="font-family: inherit;"> Carryover effects may be a problem, but what about power and the credibility hurdle of phenomena?</span><br />
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<i><span style="font-family: inherit;">(note: assuming NHST as model for scientific inference)</span></i></div>
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<span style="font-family: inherit;">Suppose you publish a multi-experiment article that concerns close replications of an interesting phenomenon observed in experiment 1. </span><span style="font-family: inherit;">Why do you publish multiple experiments? One reason is to assert credibility for the phenomenon, say at 5 significant independent replications p < .05 you'd be quite convinced there is a structure you bumped into. Then the prob. of making a type I error effectively reduces to .05^5 = .000000312 (Schimmack, 2012). Apparently this is approximately the credibility level used for phenomena of particle physics. What about type II errors? </span></div>
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<span style="font-family: inherit;">This argument was also made recently in the Power Failure article (Button et al., 2013): To maintain an equal power for each sig. replication due to the decrease of effective alpha of the total, you need to increase sample size for each study that adds to the credibility of the phenomenon (in physics you would use / invent a more precise / accurate measurement procedure. Note that this is also a valid strategy for psychology to increase power, just one that is rarely used).</span></div>
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<span style="font-family: inherit;">Schimmack (2012) provides a table with the total power of the multiple-experiment study necessary to maintain 80% power/study for large, moderate and small effect sizes, here's an excerpt:</span></div>
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<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse: collapse; border: none; position: static; z-index: auto;"><tbody>
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<span style="font-family: inherit;">N experiments<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: solid solid solid none; border-top-color: windowtext; border-top-width: 1pt; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">total Power needed<o:p></o:p></span></div>
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<span style="font-family: inherit;">Large (d=.8)<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: solid solid solid none; border-top-color: windowtext; border-top-width: 1pt; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">Moderate (d=.5)<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: solid solid solid none; border-top-color: windowtext; border-top-width: 1pt; padding: 0cm 5.4pt; width: 85.2pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">Small (d=.2)<o:p></o:p></span></div>
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<tr><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-left-color: windowtext; border-left-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">1<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">80.0<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">52<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">128<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.2pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">788<o:p></o:p></span></div>
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<tr><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-left-color: windowtext; border-left-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">2<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">89.4<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">136<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">336<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.2pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">2068 <o:p></o:p></span></div>
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<tr><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-left-color: windowtext; border-left-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">5<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">95.6<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">440<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">1090<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.2pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">6750<o:p></o:p></span></div>
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<tr><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-left-color: windowtext; border-left-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal" style="text-align: justify;">
<span style="font-family: inherit;">10<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal" style="text-align: justify;">
<span style="font-family: inherit;">97.8<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal" style="text-align: justify;">
<span style="font-family: inherit;">1020<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal" style="text-align: justify;">
<span style="font-family: inherit;">2560<o:p></o:p></span></div>
</td><td style="border-bottom-color: windowtext; border-bottom-width: 1pt; border-right-color: windowtext; border-right-width: 1pt; border-style: none solid solid none; padding: 0cm 5.4pt; width: 85.2pt;" valign="top" width="85"><div class="MsoNormal" style="text-align: justify;">
<span style="font-family: inherit;">15820<o:p></o:p></span></div>
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<span style="font-family: inherit;">Now, suppose I want to publish an article about the credibility of observing a phenomenon in our lab by means of chunked experiments. </span><span style="font-family: inherit;">I'll use all the experiments in a randomly selected chunk that was run in our lab to investigate this question, so it's gonna be a multi-experiment article. </span></div>
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<span style="font-family: inherit;">Each experiment in the chunk can provide credibility to the phenomenon of observing chunked phenomena in our lab if whatever effect was examined in the experiment is observed at p < .05.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
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<span style="font-family: inherit;">We have an amazing lab, so all the experiments in the chunk worked out and </span>everyone used G*Power to <span style="font-family: inherit;">calculate N needed to detect their well know Effect Sizes. Of course, I am post-hoc evaluating, so I cannot adjust the sample size anymore. Here's what happens to the power of my study to detect whether my lab actually detects phenomena in a chunk if I report an increasing number of </span>successful <span style="font-family: inherit;">chunk experiments:</span></div>
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<w:LidThemeAsian>JA</w:LidThemeAsian>
<w:LidThemeComplexScript>X-NONE</w:LidThemeComplexScript>
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<w:BreakWrappedTables/>
<w:SnapToGridInCell/>
<w:WrapTextWithPunct/>
<w:UseAsianBreakRules/>
<w:DontGrowAutofit/>
<w:SplitPgBreakAndParaMark/>
<w:EnableOpenTypeKerning/>
<w:DontFlipMirrorIndents/>
<w:OverrideTableStyleHps/>
<w:UseFELayout/>
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<m:mathFont m:val="Cambria Math"/>
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<m:brkBinSub m:val="--"/>
<m:smallFrac m:val="off"/>
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<m:defJc m:val="centerGroup"/>
<m:wrapIndent m:val="1440"/>
<m:intLim m:val="subSup"/>
<m:naryLim m:val="undOvr"/>
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<style>
/* Style Definitions */
table.MsoNormalTable
{mso-style-name:"Table Normal";
mso-tstyle-rowband-size:0;
mso-tstyle-colband-size:0;
mso-style-noshow:yes;
mso-style-priority:99;
mso-style-parent:"";
mso-padding-alt:0cm 5.4pt 0cm 5.4pt;
mso-para-margin:0cm;
mso-para-margin-bottom:.0001pt;
mso-pagination:widow-orphan;
font-size:12.0pt;
font-family:Cambria;
mso-ascii-font-family:Cambria;
mso-ascii-theme-font:minor-latin;
mso-hansi-font-family:Cambria;
mso-hansi-theme-font:minor-latin;}
table.MsoTableGrid
{mso-style-name:"Table Grid";
mso-tstyle-rowband-size:0;
mso-tstyle-colband-size:0;
mso-style-priority:59;
mso-style-unhide:no;
border:solid windowtext 1.0pt;
mso-border-alt:solid windowtext .5pt;
mso-padding-alt:0cm 5.4pt 0cm 5.4pt;
mso-border-insideh:.5pt solid windowtext;
mso-border-insidev:.5pt solid windowtext;
mso-para-margin:0cm;
mso-para-margin-bottom:.0001pt;
mso-pagination:widow-orphan;
font-size:12.0pt;
font-family:Cambria;
mso-ascii-font-family:Cambria;
mso-ascii-theme-font:minor-latin;
mso-hansi-font-family:Cambria;
mso-hansi-theme-font:minor-latin;}
</style>
<![endif]-->
<!--StartFragment-->
<br />
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse: collapse; border: none; mso-border-alt: solid windowtext .5pt; mso-padding-alt: 0cm 5.4pt 0cm 5.4pt; mso-yfti-tbllook: 1184;">
<tbody>
<tr>
<td style="border: solid windowtext 1.0pt; mso-border-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">N Sig. chunk <o:p></o:p></span></div>
</td>
<td style="border-left: none; border: solid windowtext 1.0pt; mso-border-alt: solid windowtext .5pt; mso-border-left-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">total Power<o:p></o:p></span></div>
</td>
<td style="border-left: none; border: solid windowtext 1.0pt; mso-border-alt: solid windowtext .5pt; mso-border-left-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">N for Large<o:p></o:p></span></div>
</td>
<td style="border-left: none; border: solid windowtext 1.0pt; mso-border-alt: solid windowtext .5pt; mso-border-left-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">N for Moderate<o:p></o:p></span></div>
</td>
<td style="border-left: none; border: solid windowtext 1.0pt; mso-border-alt: solid windowtext .5pt; mso-border-left-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; width: 85.2pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">N for Small <o:p></o:p></span></div>
</td>
</tr>
<tr>
<td style="border-top: none; border: solid windowtext 1.0pt; mso-border-alt: solid windowtext .5pt; mso-border-top-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">1<o:p></o:p></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; mso-border-alt: solid windowtext .5pt; mso-border-left-alt: solid windowtext .5pt; mso-border-top-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">80 – 81 <o:p></o:p></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; mso-border-alt: solid windowtext .5pt; mso-border-left-alt: solid windowtext .5pt; mso-border-top-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">52<o:p></o:p></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; mso-border-alt: solid windowtext .5pt; mso-border-left-alt: solid windowtext .5pt; mso-border-top-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">128<o:p></o:p></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; mso-border-alt: solid windowtext .5pt; mso-border-left-alt: solid windowtext .5pt; mso-border-top-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; width: 85.2pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">788<o:p></o:p></span></div>
</td>
</tr>
<tr>
<td style="border-top: none; border: solid windowtext 1.0pt; mso-border-alt: solid windowtext .5pt; mso-border-top-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">2<o:p></o:p></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; mso-border-alt: solid windowtext .5pt; mso-border-left-alt: solid windowtext .5pt; mso-border-top-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">71 – 72<o:p></o:p></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; mso-border-alt: solid windowtext .5pt; mso-border-left-alt: solid windowtext .5pt; mso-border-top-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; width: 85.15pt;" valign="top" width="85"><div class="MsoNormal">
<span style="font-family: inherit;">52<o:p></o:p></span></div>
</td>
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<span style="font-family: inherit;">128<o:p></o:p></span></div>
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<span style="font-family: inherit;">788<o:p></o:p></span></div>
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<span style="font-family: inherit;">5<o:p></o:p></span></div>
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<span style="font-family: inherit;">0.5 – 0.9 <o:p></o:p></span></div>
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<span style="font-family: inherit;">52<o:p></o:p></span></div>
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<span style="font-family: inherit;">128<o:p></o:p></span></div>
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<span style="font-family: inherit;">788<o:p></o:p></span></div>
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<span style="font-family: inherit;">10<o:p></o:p></span></div>
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<span style="font-family: inherit;">0<o:p></o:p></span></div>
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<span style="font-family: inherit;">52<o:p></o:p></span></div>
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<span style="font-family: inherit;">128<o:p></o:p></span></div>
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<span style="font-family: inherit;">788<o:p></o:p></span></div>
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<span style="font-family: inherit;"></span>
<br />
<div style="text-align: justify;">
<span style="font-family: inherit;"><b>Why does this happen?</b></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">Because it becomes increasingly unlikely to observe <i>n+1</i> significant effects out of <i>N</i> attempts to observe the phenomenon at the total power level. The probability of observing 5 significant results in 5 studies whose total power is 50% is 0</span>.0313. So in 3 out of 100 five-experiment studies of the same power, we would expect to see 5 significant results. </div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">This is the "hurdle" for the credibility of a phenomenon predicted by a theory that needs to be adjusted in order for the phenomenon to maintain its credibility</span> (see also Meehl, 1967).</div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><i><br /></i></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><i>(In physics this hurdle is even more difficult due to requirements of predicting actual measurement outcomes)</i></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><i><br /></i></span></div>
<div style="text-align: justify;">
Schimmack (2012) calculates an Incredibility Index (IC-index) as the binomial probability of observing a non-significant result given the observed power to detect an effect, which in this example would simply be 1-total power = 96.9%. That's how incredible my results would be if every effect turned out to be significant. </div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
<b>
Paradoxically</b>, <i>or whatever logic-defying concept applies here</i>, i<span style="font-family: inherit;">n this case it may not be that bad for science, it's just bad for the phenomenon I am interested in, which is just too incredible to be true. </span><span style="font-family: inherit;">The individual phenomena of the studies in the chunk are likely the result of independent tests of effects predicted by different theories (strictly they are not independent measurements of course). The individual observations could still end up in a very credible multi-study article that contains a lot of nonsignificant results.</span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">Back to the EEG / fMRI filler conditions... it seems much more likely in these cases that the conditions </span>cannot be regarded as independently studied phenomena, as is the case with the chunked experiments querying independent psychological phenomena within the same participant. </div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
More importantly, suppose the results of 3 conditions that measure different aspects of the same phenomenon measured in one session are published in 3 separate papers (effect of bigram, trigram and quadgram frequency on p123) shouldn't we be worried about increasing the credibility hurdle for each subsequent observation of the phenomenon?</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
My personal opinion is that we need a (new) measurement theory for psychological phenomena, <a href="http://fredhasselman.com/main/wp-content/papercite-data/pdf/cox2011.pdf" target="_blank">but that's another story</a>.</div>
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">天</span><br />
<span style="font-family: inherit;"><br /></span>
<br />
<span style="font-family: inherit;"><b>References</b></span><br />
<span style="font-family: inherit;"><b><br /></b></span>
<br />
<div style="margin-left: 24pt; text-indent: -24.0pt;">
Button, K. S., Ioannidis, J. P. a., Mokrysz, C., Nosek, B. a., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: why small sample size undermines the reliability of neuroscience. <i>Nature Reviews Neuroscience</i>, <i>14</i>(May). doi:10.1038/nrn3475</div>
<div style="margin-left: 24pt; text-indent: -24.0pt;">
<span style="text-indent: -24pt;"><br /></span></div>
<div style="margin-left: 24pt; text-indent: -24.0pt;">
<span style="text-indent: -24pt;">Meehl, P. E. (1967). Theory testing in psychology and physics: a methodological paradox. </span><i style="text-indent: -24pt;">Philosophy of science</i><span style="text-indent: -24pt;">, </span><i style="text-indent: -24pt;">34</i><span style="text-indent: -24pt;">, 103–115. Retrieved from http://www.jstor.org/stable/10.2307/186099</span></div>
<div style="margin-left: 24pt; text-indent: -24.0pt;">
<span style="text-indent: -24pt;"><br /></span></div>
<div style="margin-left: 24pt; text-indent: -24.0pt;">
<span style="text-indent: -24pt;">Schimmack, U. (2012). The ironic effect of significant results on the credibility of multiple-study articles. </span><i style="text-indent: -24pt;">Psychological methods</i><span style="text-indent: -24pt;">, </span><i style="text-indent: -24pt;">17</i><span style="text-indent: -24pt;">(4), 551–66. doi:10.1037/a0029487</span></div>
Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-49521678069909937892013-06-26T00:12:00.000+02:002013-06-26T01:39:19.035+02:00Codes, Information, Meaning and Intelligence (and some neuroscience as well)<div style="text-align: right;">
<h4>
Note: This has been "under construction" for too long, but it still is. Background is whether a formalism of necessary and sufficient conditions and rules of induction for psychological phenomena could be postulated in a similar way to the formalisms from which physical theories depart. This would include postulates about the type of system in which such phenomena can be observed and the relevant levels of analysis of such a system. I am posting anyway... </h4>
<div style="text-align: left;">
<br /></div>
<i>(Re-) Defining Ontology for Theories of Psychological Phenomena</i><br />
<br /></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><b><br /></b></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><b>The use of Codes, Information and Meaning in Theories of Psychological Science</b></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">I have been willing to write about this subject forever, and many have before me, so I make no promises to be telling you something you didn't already know. I can only promise a perspective that should send you off to reconsider some of your assumptions about common terminology used in psychological science: information, meaning and codes.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">It was especially an anecdote shared by <a href="http://archive.org/details/Brain_Network_Dynamics_2007-03_Walter_freeman" target="_blank">Walter Freeman</a> in Amsterdam at the 2009 inaugural conference of the</span><a href="http://perceptualdynamics.be/index.php?option=com_content&view=article&id=60&itemid=210" style="font-family: inherit;" target="_blank"> Society for Complex Systems in Cognitive Science</a><span style="font-family: inherit;"> (SCSCS) that triggered me to sort some things out for myself. He told us that <a href="https://en.wikipedia.org/wiki/Claude_Shannon" target="_blank">Shannon</a> had visited his lab to talk about the </span>exciting<span style="font-family: inherit;"> fields of information and communication science he had basically inspired into existence. Shannon had been very clear about one thing, the way physiologists and psychologists were using his concept of information and information-processing in </span>biological<span style="font-family: inherit;"> systems was wrong, or at </span>least, not properly examined to be right. Now, I do not like arguments of authority, but if the progenitor of a scientific field tells you you need to do some more serious thinking 'till you're certain you're right, I would be very worried. Less so, were the proponents of the computer metaphor of human behaviour and cognition (a contemporary version of mechanistic philosophy confused for experimental philosophy).</div>
<div style="text-align: justify;">
<br />
<i>What's wrong about information processing in biological systems?</i></div>
<div style="text-align: justify;">
I'm not sure it's in principle wrong to use the term information and processing in relation to the behaviour of living systems, as long as you know what you mean by the words. My point is, that this is not the case in psychological science.<br />
<br />
<a href="http://www.cs.princeton.edu/~rit/geo/Miller.pdf" target="_blank">Miller (2003)</a>, looking back at a very impressive career, acknowledges that Shannon's theory of information did not get him very far, so he adopted Chomsky's syntax theory:<br />
<blockquote class="tr_bq">
"I was therefore ready for Chomsky’s alternative to Markov processes. Once I understood that Shannon’s Markov processes could not converge on natural language, I began to accept syntactic theory as a better account of the cognitive processes responsible for the structural aspects of human language. The grammatical rules that govern phrases and sentences are not behavior. They are mentalistic hypotheses about the cognitive processes responsible for the verbal behaviors we observe."</blockquote>
What happens here, in my opinion, is that information gets mixed up with meaning, cognition and code. Of course did Shannon's Markov processes not converge on natural language, they weren't supposed to. Grammatical rules are codes that can give meaning to information, in a very embodied and embedded way, the rules of grammar of natural languages provide us information about the structure of the physical world, not just human language (but that's <a href="http://www.sciencedirect.com/science/article/pii/S0388000102000207" target="_blank">another story</a>).<br />
<br />
Here the grammatical rules are considered evidence of cognitive processes that can explain our verbal behaviour. This seems sympathetic, but our verbal behaviour cannot be neatly described by syntactic rules that knit together packages of information in order to produce meaningful language. The rules, the code, connect the world of auditory signals with the world of our conscious mind by their ability to decode or encode a message as verbal auditory information. In syntactic theory, both meaning, code and information are internalised as cognitive processes or representations.<br />
<br />
In <a href="http://fredhasselman.com/main/wp-content/supmat/btb/ch5/CHAPTER5_BEYOND_THE_BOUNDARY.pdf" target="_blank">chapter 5 (Beyond the static phoneme boundary</a>) of my dissertation I discuss some of these issues and quote Lisker and Abramson who point out the relevant question to ask is what the relation (code) is between the information signal (acoustic signal) and meaning (linguistic expression):<br />
<blockquote class="tr_bq">
Lisker and Abramson gave an excellent description of this biased view of reality some 40 years ago when discussing the Chomsky and Halle –linguist– theory of speech: “<i>Their concern is not how an articulatory sequence and its associated acoustic signal, both of them physically neither purely continuous nor purely digital in nature, are related to a linguistic expression, but rather to impose digitalization on the physical description in such a way that it will necessarily be a description of the segments in the linguist’s spelling of the expression.</i>” (Lisker & Abramson, 1971, p. 781). </blockquote>
So, first, I'll try to separately define the concepts of information, meaning and cognition (defined as intelligence) as is common in information theory and physics. Second, I'll try to make sense of the significance of the concept of a code with respect to information and meaning. Third, I'll give an example of the inappropriate use of information, defined as a meaningful representation / decoded information that I used in my dissertation.</div>
<div style="text-align: justify;">
<br />
<br /></div>
<div style="text-align: justify;">
<i>1. Intelligence: Making sense of information</i></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">Suppose you receive an e-mail filled with </span>Chinese<span style="font-family: inherit;"> characters and one picture of a pile of blue-ish powder. It's quite easy to measure the information contained in that e-mail, including the image, and express it in bits and bytes. That's what information is, a measurable quantity used in information science and physics. Information and physical systems are linked </span>through<span style="font-family: inherit;"> the concept of </span><i style="font-family: inherit;">entropy. </i><span style="font-family: inherit;">Shannon </span><span style="font-family: inherit;">Entropy may be loosely defined as the amount of bits needed to describe the unique features of an information structure. Analogously, entropy in physics could be described as the </span>amount of information that is needed to describe the<span style="font-family: inherit;"> unique modes of behaviour of a physical system. A completely random system has many such modes, is disordered, anything can happen</span><span style="-webkit-composition-fill-color: rgba(175, 192, 227, 0.230469); -webkit-composition-frame-color: rgba(77, 128, 180, 0.230469); -webkit-tap-highlight-color: rgba(26, 26, 26, 0.292969);">and and thus a high entropy</span><span style="font-family: inherit;">. A deterministic system has low entropy, is highly ordered, only a few things described by a deterministic rule can happen.</span><span style="font-family: inherit;"> The i</span>nformation<span style="font-family: inherit;"> of a </span>physical system are therefore its degrees of freedom and the mathematical description of the entropy of a physical system is equivalent for all intents and purposes to Shannon entropy.</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
Back to our e-mail. Here's what information is <b>not</b>: Your guess that this must be a spam e-mail about some Asian Viagra alternative. What you have done is decode the information based on expectation, in order to add <i>meaning</i> to it. This is not what information theory and information science is about: <b>There is no intrinsic meaning to information. </b>You used your <i>intelligence</i> (<i>inter-ligere</i>: linking things together) to make sense of the information (see <a href="http://books.google.ca/books/about/Classical_and_Quantum_Information_Theory.html?id=XVt3kxEvc9sC" target="_blank">Desurvire, 2009, pp.38</a>). This is the purpose of intelligence in all the different contexts the word can be used, from secret service agencies to scientists studying the behaviour of living systems: C<i>ontrol the flow of information to select what is relevant given current needs, expectations and priorities</i>.</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
So you ask your friend about the Chinese e-mail, his wife is Asian and she says it's not a spam e-mail but some kind of poetry or prose. Finally a professor of Chinese Literature you contacted kindly tells you it's a an old poem praising the benefits of digesting large quantities of grounded rhinoceros tusk for keeping a marriage exciting after 50 years. You just got the mail by mistake, apparently a lot of people have an e-mail address that starts with <u>B.Lee@</u> as well in China.</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
You weren't that far off using your intelligence! The devil is of course in the details, as always.</div>
<div style="text-align: justify;">
<br />
<br />
<i>1.2 Intelligence as a postulated condition for psychological phenomena</i><br />
<br />
<b>[This general point is made much better than I can in <a href="http://dx.doi.org/10.1080/10407413.2012.645757" target="_blank">"On Intelligence From First Principles: Guidelines for Inquiry Into the Hypothesis of Physical Intelligence (PI)"</a> Turvey & Carello (2012)]</b><br />
<br /></div>
<div style="text-align: justify;">
Sometimes we forget, but what Psychological Science studies always boils down to <i>intelligence</i> as defined above. Most of the differences between theories in Psychological Science concern differences in the proposed mechanisms or ontology for intelligent behaviour as the end product of our ability to control the flow of information to select what is relevant given current needs, expectations and priorities. </div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
<b>The strength of this definition</b> is that it does not make any ontological claims about how humans, and other living systems actually achieve intelligent behaviour. That is a strength, because it helps us define what a psychological phenomenon is and what not. In fact, perhaps better to use <i>agent-environment system</i>, as this includes nonliving, artificial systems that can behave intelligently in a dynamically changing environment. Other postulates for a formalism of psychological phenomena could restrict the domain of psychological phenomena, but I believe it is important to not interpret the words <i>control</i> and <i>select</i> as actions by an agent, but as descriptors of the phenomenon.<br />
<br />
Let's attempt a scientific description of the professor decoding the e-mail. Intelligence as defined here can used by a theory to:<br />
- Suggest a computational integration algorithm that collates relevant perceptual input in order to match it to an internal mental <i>representation </i>of an exemplar category of grounded rhinoceros tusk.<br />
- Propose the visual information will resonate with the concept of grounded rhinoceros tusk, that is an aggregate of <i>parallel distributed</i> networks<i> </i>of time-locked sensory experiences with tusks, powders and what not.<br />
- Argue that there is a match between the needs, expectations and priorities emerging from the biophysical properties of the organism and the opportunities for action allowed for by the physical structures in its sociocultural environment (<a href="http://en.wikipedia.org/wiki/Affordance" target="_blank"><i>affordance</i></a>). </div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
<b>The weakness of this definition</b> is that this leaves a lot of room to fanny about.</div>
<div style="text-align: justify;">
<br />
<br /></div>
<div style="text-align: justify;">
<i>2.1 Codes, Organic and otherwise.</i></div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
<b>[The main reference for most of what I post in this paragraph is: The Organic Codes (<a href="http://books.google.nl/books/about/The_Organic_Codes.html?id=cvskkoD7rx0C&redir_esc=y" target="_blank">Barbieri, 2003</a>)]</b></div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
The Chinese e-mail example shows that one source of information can be given different meanings, depending on the code that one uses. Barbieri defines the following about codes:</div>
<div style="text-align: justify;">
</div>
<ol>
<li>They are rules of correspondence that connect two independent worlds </li>
<li>They give meaning to information structures</li>
<li>They are collective, or community rules, conventions that do not depend on the individual features of their structures</li>
</ol>
<div style="text-align: justify;">
With meaning and information defined as independent entities, an example can be given of different and independent evolutions of information and meaning (Barbieri, 2003, pp. 97).<br />
<blockquote class="tr_bq">
Evolution of information without a change of meaning (informatic process) :<br />
<i><b>Pater</b> -> Padre, Père, Vater, Father</i><br />
<br />
Evolution of meaning without a change of information (semantic process):<br />
<i><b>Ape</b> -> In English: A tailless primate, in Italian: A honey-making insect</i></blockquote>
</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
In the first case the same meaning is described by different information structures, the second case the same information structure describes different meanings. In the case of writing systems and natural languages it is clear how these rules of correspondence can be seen as community rules or conventions. Barbieri argues that the organic codes (also see Barbieri, 2006), like the genetic code are not very different from our writing systems:<br />
<ol>
<li>Two independent worlds are connected: nucleic acids and proteins</li>
<li>Genetic and epigenetic processes (transcription, translation, splicing, etc.) form codified assemblies by means of specific (meaningful) sets of correspondence rules between the independent worlds.</li>
<li>It is a convention of nature to do so.</li>
</ol>
</div>
<div style="text-align: justify;">
So, a code is a relational concept, not a "thing" and therefore an excellent subject for science to study: "<i>The aim of science is not things themselves, as the dogmatists in their simplicity imagine, but the relation between things.</i>" (Poincaré, 1905, pp. <span style="font-family: inherit;">xxiv).</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">There is of course more to tell, for instance that codes need (more than one kind of) memory in order to be able to give meaning. I </span>believe a generalisation of the concept of <i>degeneracy</i> as suggested by <a href="http://www.pnas.org/content/98/24/13763.short" target="_blank">Edelman and Gally (2001)</a> provides more than enough theory to deal with storage and "representation hungry" problems of cognition.<br />
<br />
Oh! There it is, <i>representation</i>.<br />
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<i>2.2 Enter Psychological Science: Representation.</i><br />
<br />
My impression is that theories in psychological science that use the concept of the (mental) representation, do so when they want to refer to a mixture of <i>code</i>, <i>information</i> and <i>meaning. </i>As I wrote in a previous post, my favourite example is the scientific theory about reading aloud written text that involves converting graphemes into phonemes. What are graphemes? Well, they are abstract representations of letters. What are phonemes? They are abstract representations of speech sounds. So reading printed or written text out loud is in fact converting letters into sounds by applying Grapheme-to-Phoneme Conversion rules (GPC-rules)<br />
<br />
...but that is the same as describing what sounding-out letters is, just using words we made up: Apply the <i>bloblob</i> to <i>pleplep</i> conversion rules to convert letters (<i>bloblobs)</i> into sounds (<i>plepleps).</i><br />
<br />
Why would we need graphemes and phonemes to explain this phenomenon scientifically? The information structures are the printed words. The codes are the conventional rules of the writing system and applying the rules gives these information structures meaning in terms of pronunciation, or sequences of speech gestures. Note that the meaning of the words themselves, used as a tool of language and communication, is of another order and requirers many more, mainly cultural conventions and intelligence, like the codified assemblies of epigenesis. As shown above, one can easily invent pseudo-words and neologisms that have no meaning (yet), but can be read aloud by applying the code. As was shown by the Chinese e-mail example, the interpretation or meaning given to the information structure depends on the codes and intelligence used. (A poem in this context is a good example of an information structure whose meaning can evolve within a lifetime).<br />
<br />
The only thing that matters to the agent-environment system that wants to behave intelligently when confronted with a stream of information in the form of strings of printed characters is to learn the code, the GPC-rules (that may be rather fuzzy and will contain many exceptions and perhaps contextual contradictions) that connect the world of written language to the world of spoken language through codified assemblies. All the representing is achieved by the information structures allowed for by the writing system on the one hand, on the other hand by the speech signal that comprises the auditory information structures that can be be given meaning by the codes of the language in question. What matters are the relations between the things.<br />
<br />
<br />
<i>2.3 (A/U)n informed summary.</i><br />
<br />
In many cases, the additional internal storage of information structures seems unnecessary (this resembles <a href="http://en.wikipedia.org/wiki/Andy_Clark" target="_blank">Andy Clark</a>'s 007-principle: "Know only as much as you need to know to get the job done"). Where the codes relevant to psychological phenomena reside (if anywhere), how science can describe them formally and and how they can be acquired and applied by a living system, is what should be the topic of scientific debate.<br />
<br />
This is not a simple matter, as Walter Freeman has shown in his study of the olfactory bulb of Rabbits: After a molecule (an information structure representing the scent of a banana) touches the sensory organ, the specificity of the neural pattern (a possible anchor for meaning) is already lost after the first layers of neurons. Whatever information processing in biological systems might look like, it is most certainly not like any kind of traceable information structure passed around the nervous system as a neat package.<br />
<br />
A discussion of many of theses issues with representation and information and intelligent behaviour by agent-environment systems can be found in <a href="http://mitpress.mit.edu/books/radical-embodied-cognitive-science" target="_blank">Radical Embodied Cognitive Science</a> by <a href="https://edisk.fandm.edu/tony.chemero/papers/" target="_blank">Tony Chemero</a>. Tony was kind enough to jump in last-minute, on a symposium I had organised about issues with psychological theorising at the 33rd anual meeting of the cognitive science society, <a href="http://fredhasselman.com/main/wp-content/papercite-data/pdf/hasselman2011should.pdf" target="_blank">CogSci2011 in Boston</a> (I also organised one <a href="http://fredhasselman.com/main/wp-content/papercite-data/pdf/hasselman2011still.pdf" target="_blank">at ICPA, same year</a>). Be sure to check his papers on theory of affordances and hypersets.<br />
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<br /></div>
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<br /></div>
To conclude, it seems that at least three evident category mistakes are possible when the term representation is not properly defined with respect to information, meaning or code:<br />
<br />
<ol>
<li>One and the same information structure (i.e., <i>ape</i>) that is given N different meanings by N different codes, is taken for N different information structures that need to be internally represented (a mental lexicon / library / file-drawer / similarity neighbourhood).</li>
<li>When different information structures, by means of an intelligently selected codified assembly, can be given one and the same meaning (<i>father</i> written in different alphabetic languages), the (ad-hoc) assembly of codes is taken for an information structure that needs to be internally represented (a category, exemplar representation, stereotype, ).</li>
<li>A code, or an assembly of codes, will be mistaken for an independent cognitive process, module or component in the cognitive architecture, when it is in fact a representation of an acquired set of (conventional) regularities between different information structures in the environment of the agent.</li>
</ol>
<br />
<br />
<br />
Finally, the brain.<br />
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<i>3. Circular circularity: The speech mode in auditory perception</i></div>
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<br />
Most of this is from my dissertation, <a href="http://fredhasselman.com/main/wp-content/supmat/btb/ch5/CHAPTER5_BEYOND_THE_BOUNDARY.pdf" target="_blank">chapter 5: Beyond the static phoneme boundary.</a><br />
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As an example of mistake 1 above, the problems that arise when qualitatively different intelligent responses to the very same stream of information are interpreted in terms of an existence proof for separate discrete abstract representations of encoded information, consider the following studies on the <i>The speech mode in auditory perception </i>(Serniclaes, Sprenger-Charolles, Carre, & Demonet, 2001)<i>.</i></div>
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When the same set of sine wave stimuli (<i>same information structure</i>) are introduced as electronic whistling sounds that need to be discriminated (<i>use a specific codified assembly: whistles, pitch, envelope, etc.</i>) or as speech-like sounds they need to discriminate (<i>use another (overlapping?) set of codes: speech</i>), the second instruction causes participants to perceive a phoneme boundary <i>(result of intelligent control of the information stream: pick up only speech-relevant information)</i>. This would be expected with discrimination of actual speech sounds only. The perceptual boundary is not observed for the exact same stimuli when the first instruction is used <i>(result of intelligent </i><i>intelligent control of the information stream: pick-up only pitch relevant information</i>. This effect has been interpreted as a so-called ‘<i>speech mode</i>’ of auditory perception, in which a non-speech stimulus only resonates with the representation of a speech sound when the instruction is given to discriminate speech sounds.<br />
<br />
The speech mode has neural correlates (Dehaene-Lambertz et al., 2005). Several experiments are reported in the study that basically uses the same instruction variation as mentioned above, adapted for measurements of brain activity using EEG, MEG, and fMRI. The authors report three main conclusions, of which the first is a confirmation of the instruction effect found by Serniclaes et al. (2001). </div>
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Text taken from the paper by Dehaene-Lambertz et al., 2005:</div>
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“First, the same auditory stimuli are processed differentially depending on whether they are perceived as speech or as nonsense electronic whistles. Second, the posterior part of the superior temporal sulcus and the supramarginal gyrus are crucial areas for syllable processing but are not involved in the processing of the same physical dimension when the stimuli are not perceived as speech. Third, non-phonemic auditory representation and phonemic representation are computed in parallel, but the phonemic network is more efficient and its activation may have an inhibitory effect on the acoustical network.” (Dehaene-Lambertz et al., 2005, pp. 32).</blockquote>
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The first conclusion seems straightforward, but is already circular: The same stimuli are processed differently depending on how they are perceived. Ask yourself this, how does the brain of a speaker know whether a sound will be perceived as a tone or as speech-sound, before it is perceived? Of course, in the context of the experiment the participant is alerted to intelligently decode either sound or speech-relevant information by the instruction, but how does this generalize to real life situations in which there is no instruction? The same problem applies to the second conclusion: How is it possible the brain regions that supposedly "compute" the representations of syllables, are only active when the sounds are (going to be) perceived as syllables when computation has finished? </div>
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As a pure description of the experimental findings by Dehaene-Lambertz et al., (2005) these conclusions might be acceptable when interpreted as reporting a correlation: This is what we observe when a stimulus is perceived as speech rather than as a sound. The circularity emerges due to the suggestion that different brain regions are involved in the processing of the same physical dimension of an auditory signal dependant on the outcome of this process. The third conclusion seems to confirm the assumption that the authors are not just reporting a correlation, but interpreting a difference they observed as the causal power of the phonemic representation to suppress an auditory percept.<br />
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This study inflates the amount of representing being done by the brain. Apparently the same auditory stimulus has two different representations (representing what exactly?, the information is the same!). One is non-phonemic and is processed by the auditory network, whereas the other is phonemic and processed by the phonemic network. This means the authors suggest auditory stimuli with physical dimensions that may be perceived as speech sounds -but are not- have a speech-sound representation as well as an auditory representation?<br />
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It is almost impossible to break out of the circular reasoning.<br />
<br />
<i>(told you it was still under construction)</i></div>
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<span style="font-family: inherit;"><br /></span></div>
<span style="font-family: inherit;">天</span><br />
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<span style="font-family: inherit;"><b>References</b></span><br />
<span style="font-family: inherit;"><b><br /></b></span>
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<div style="margin-left: 24pt; text-indent: -24.0pt;">
Barbieri, M. (2006). Life and semiosis: The real nature of information and meaning. <i>Semiotica</i>, <i>2006</i>(158), 233–254. doi:10.1515/SEM.2006.007</div>
<div style="margin-left: 24pt; text-indent: -24.0pt;">
Dehaene-Lambertz, G., Pallier, C., Serniclaes, W., Sprenger-Charolles, L., Jobert, A., & Dehaene, S. (2005). Neural correlates of switching from auditory to speech perception. <i>NeuroImage</i>, <i>24</i>(1), 21–33. doi:10.1016/j.neuroimage.2004.09.039<br />
<span style="text-indent: -24pt;">Edelman, G. M., & Gally, J. a. (2001). Degeneracy and complexity in biological systems. </span><i style="text-indent: -24pt;">Proceedings of the National Academy of Sciences of the United States of America</i><span style="text-indent: -24pt;">, </span><i style="text-indent: -24pt;">98</i><span style="text-indent: -24pt;">(24), 13763–8. doi:10.1073/pnas.231499798</span></div>
<div style="margin-left: 24pt; text-indent: -24.0pt;">
<span style="text-indent: -24pt;">Miller, G. a. (2003). The cognitive revolution: a historical perspective. </span><i style="text-indent: -24pt;">Trends in Cognitive Sciences</i><span style="text-indent: -24pt;">, </span><i style="text-indent: -24pt;">7</i><span style="text-indent: -24pt;">(3), 141–144. doi:10.1016/S1364-6613(03)00029-9</span></div>
<div style="margin-left: 24pt; text-indent: -24.0pt;">
<span style="text-indent: -24pt;">Serniclaes, W. (2001). Perceptual Discrimination of Speech Sounds in Developmental Dyslexia. </span><i style="text-indent: -24pt;">Journal of Speech, Language, and Hearing Research</i><span style="text-indent: -24pt;">, </span><i style="text-indent: -24pt;">44</i><span style="text-indent: -24pt;">(2), 384–399. doi:10.1044/1092-4388(2001/032)</span><br />
<span style="text-indent: -24pt;">Turvey, M. T., & Carello, C. (2012). On Intelligence From First Principles: Guidelines for Inquiry Into the Hypothesis of Physical Intelligence (PI). </span><i style="text-indent: -24pt;">Ecological Psychology</i><span style="text-indent: -24pt;">, </span><i style="text-indent: -24pt;">24</i><span style="text-indent: -24pt;">(1), 3–32. doi:10.1080/10407413.2012.645757</span><br />
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Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-29704122200248964342013-06-24T21:55:00.000+02:002013-06-28T23:22:43.522+02:00Respect your elders: Where do we go from here? - Tukey on unsolved problems of experimental statistics and designing experiments (1954,1960)<br />
<b>Where do we go from here? </b><br />
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I have been reading quite a few (too many?) "recommendation" papers lately: Why we should use a larger sample size, pay more attention to power, large effect sizes, direct replication, the works. Even worse, I am producing such articles myself! I even add to the numerous blog entries discussing these matters. </div>
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<i>(In many cases the blogs may actually be more informative than are some of the papers, see <a class="g-profile" href="https://plus.google.com/110098308161411677670" target="_blank">+Rolf Zwaan</a> <a class="g-profile" href="https://plus.google.com/107191542129310486499" target="_blank">+Daniel Simons</a> <a class="g-profile" href="https://plus.google.com/113040210411045341720" target="_blank">+Alex Holcombe</a> <a href="http://psychsciencenotes.blogspot.co.uk/">http://psychsciencenotes.blogspot.co.uk</a> <a href="http://psych-your-mind.blogspot.nl/">http://psych-your-mind.blogspot.nl</a> <a href="http://blogs.discovermagazine.com/neuroskeptic/">http://blogs.discovermagazine.com/neuroskeptic/</a>)</i></div>
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That's all very nice, but you felt a rant was coming up... and you were right, partly. </div>
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Here's my problem. No surprises: I'm concerned about what it is psychological science is actually producing. I have used the term <i>empiarrhea</i> before, but that doesn't quite cover the phenomenon of these recommendation papers. What amazes me is that so very few authors make an effort to do a proper search in the literature about what our elders had to say about the problems we currently face. You know, the giants that came before us. Apparently they have shoulders you can stand on. I am quite convinced that we are not standing on any shoulders right now. In fact, if the giants were still around, they wouldn't even let us try if they see the mess we've made. </div>
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<i>(damn, now I've started to use "we" as well)</i></div>
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Here are my conjectures:</div>
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<ol>
<li>Every problem with psychological science that has been identified in the literature as of 2010, has already been identified as a problem in the literature before 1970 and <b>was described as a threat that needed to be resolved</b>.</li>
<li>Every solution, reform and recommendation for best practice that has been suggested as of 2010, has already been suggested before 1970 and <b>was described as an essential solution, reform or recommendation</b>. (One exception may be the initiatives to create an open science, which is why that one is so important) </li>
<li>The real task at hand for psychological science is not to re-invent poorer versions of solutions provided by our intellectual parents, but to find a way to finally climb on those shoulders and stay put until the next generation takes over. Otherwise we will be playing the same reform game every 20 years or so.</li>
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In blogs to come I will look at what the elders had to say on: NHST is logically flawed and therefore possibly unscientific; Confirmation is important; Statistical power is important; Use of confidence intervals is important; To be precise, modest, honest and aware of assumptions (i.e., know what it is that you are doing) and by all means: Theory, theory theory!</div>
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First up is <a href="http://en.wikipedia.org/wiki/John_Tukey" target="_blank">John W. Tukey</a> on unsolved problems of experimental statistics, but especially because of his insight in the apparent cyclic nature of the "diseases" a scientific endeavour will succumb to.</div>
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In 1960 Tukey published a an article entitled "<i>Where do we go from here?</i>" one of several classical papers in which he tried to analyse in which direction experimental statistics should/would develop. Here's a striking passage from the introduction:</div>
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One answer to "Where do we go from here?" is always: "To the psychiatrist!" To what brand and for what diseases? As a collective statistical mind, our diseases are strangely like those of an individual human mind. <span style="text-align: center;">[...] :</span></div>
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(1) undue dependence on our intellectual parents -as expressed by a reluctance to rethink our problems -a reluctance to work carefully again and if need be again and again, from the actual problem to a mathematical formulation -to a formulation which, each time, is quite likely to differ from the previous. </blockquote>
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(2) retreat from the real world toward the world of infancy -as expressed by a desire to solve all our problems in the framework of our childhood -the childhood of experimental statistics, a childhood spent in the school of agronomy applying the analysis of variance to categories (not even to scales). </blockquote>
<blockquote class="tr_bq" style="text-align: justify;">
No one is immune to such diseases. Every one of us will sooner or later fall ill. All of us should resolve to try to be tolerant of the next healthy generation when it passes us in our sickness</blockquote>
<blockquote class="tr_bq">
Tukey (1960, pp. 80-81) </blockquote>
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I know of no prediction of the very troubling state of affairs that our beloved scientific discipline currently finds itself in, that is so accurately and gently expressed. The sickness concerns an attempt to solve all our problems in the <i>framework of our childhood statistics</i> and a reluctance to <i>rethink</i> our problems and to <i>work carefully again</i>. </div>
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That about sums it up for me: I see a reluctance confront and rethink our problems, the majority is attempting to find solutions that work under NHST and just do not question its flaws. They depend on our intellectual (grand)parents, without questioning whether we should continue to generalise to populations, or change our measurement theory</div>
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<i>... what's that? You didn't know you had one? My point exactly. Property attribution is what we should be talking about.</i></div>
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The last sentence could be interpreted sympathetically as a call to give room to a younger generation, but I am biased enough to interpret it to read: "surpasses us in our sickness". I sincerely believe that has happened, as younger generations have less and less formal knowledge about measurement, inference of knowledge by deduction, induction and abduction, and theory construction, evaluation, revision and unification.</div>
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<i>Don't believe me? </i></div>
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If psychological science has actually advanced as a science and accumulated increasingly accurate fundamental knowledge about human behaviour. If its scientific methods and tools of inference are contemporary, modern and up to date with the progress achieved by other scientific disciplines, then certainly,</div>
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<i>every psychological scientist can answer some 60 year old practical questions about statistical techniques. Right? </i></div>
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Or, <i>should</i> you be able to do so, if we assume psychological science and its cousin disciplines actually advanced as a scientific endeavour? </div>
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I believe you and I <i>should</i>, even so, I can't answer all of them. I have a pretty good idea what most of those questions are about (#5, #8 and #50 are probably my favourites), but I cannot answer them all, or have an idea whether they have been answered in the meantime. I believe it should not be like that, is what I am trying to say.</div>
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If you <i>do</i> know an answer (and you're a psyhco-, neuro-, behavio-, life- scientist), leave a comment, provide the number of the question together with your answer. Prove to me we have advanced. Please! </div>
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Tukey's Provocative Questions for Experimental Statistics posed in 1954:</div>
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<span style="font-family: inherit; letter-spacing: 0px;">(1) What are we trying to do with goodness of fit tests? (Surely not to test whether the model fits exactly, since we know that no model fits exactly!) What then? Does it make sense to lump the effects of systematic deviations and over-binomial variation? How should we express the answers of such a test? </span></div>
<span style="letter-spacing: 0.0px;"></span><br />
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<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;">(2) Why isn't someone writing a book on one and two-sample techniques? (After all, there is a book being written on the straight line!) Why does everyone write another general book? (Even 800 pages is now insufficient for a complete coverage of standard techniques.) How many other areas need independent monograph or book treatment? </span></span></div>
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</span><span style="letter-spacing: 0.0px;"></span>
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<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;">(3) Does anyone know when the correlation coefficient is useful, as opposed to when it is used? If so, why not tell us? What substitutes are better for which purposes? </span></span></div>
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</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(4) Why do we test normality? What do we learn? What should we learn? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(5) How soon are we going to develop a well-informed and consistent body of opinion on the multiple comparison problem? Can we start soon with the immediate end of adding to knowledge? And even agree on the place of short cuts? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(6) How soon are we going to separate regression situations from comparison situations in the analysis of variance? When will we clearly distinguish between temperatures and brands, for example, as classifications? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(7) What about regression problems? Do we help our clients to use regression techniques blindly or wisely? What are the natural areas in regression? What techniques are appropriate in each? How many have considered the "analyses of variance" corresponding to taking out the regression coefficients in all possible orders? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(8) What about significance vs. confidence? How many experimental statisticians are feeding their clients significance procedures when available confidence procedures would be more useful? How many are doing the reverse? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(9) Who has clarified, or can clarify, the problem of nonorthogonal (disproportionate) analysts of variance? What should we be trying to do in such a situation? What do the available techniques do? Have we allowed the superstition that the individual sums of squares should add up to the total sum of squares to mislead us? Do we need to find new techniques, or to use old ones better? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(10) What of the analysis of covariance? (There are a few -at least one [10]- discussions which have been thought about.) How many experimental statisticians know more than one technique of interpretation? How many of these know when to use each? What are all the reasonable immediate aims of using a covariable or covariables? What techniques correspond to each? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(11) What of the analysis of variance for vectors? Should we use overt multivariate procedures, or the simpler ones, ones that more closely resemble single variable techniques, which depend on the largest determinantal root? Who has a clear idea of the strength or scope of such methods? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(12) What of the counting problems of nuclear physics? (For some of these the physicists have sound asymptotic theory, for others repairs are needed-cf. Link [21].) What happens less asymptotically? What about the use of transformations? What sort of nuisance parameter is appropriate to allow for non-Poisson fluctuations? What about the more complex problems? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(13) What about the use of transformations? Have the pros and cons been assembled? Will the swing from significance to confidence increase the use of transformations? How accurate does a transformation need to be? Accurate in doing what? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(14) Who has consolidated our knowledge about truncated and censored (cf. [18], p. 149) normal distributions so that it is available? Why not a monograph here that really tells the story? Presumably the techniques and insight here are relatively useful, but how and for what? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(15) What about range-based methods for more complex situations? (We have methods for the analysis of single and double classifications based on ranges.) What about methods for more complex designs like balanced incomplete blocks, higher and fractional factorials, lattices, etc.? In which areas would they be quicker and easier? In which areas would they lead to deeper insight? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(16) Do the recent active discussions about bioassay indicate the solution or impending solution of any problems? What about logits vs. probits? MIinimum chi-square vs. maximum likelihood? Less sophisticated methods vs. all these? Which methods are safe in the hands of an expert? Which in the hands of a novice? Does a prescribed routine with a precise "correct answer" have any value as such? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(17) What about life testing? What models should be considered between the exponential distribution and the arbitrary distribution? What about accelerated testing? (Clearly we must use it for longlived items.) To what extent must we rely on actual service use to teach us about life performance? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(18) How widely should we use angular randomization [4]? What are its psychological handicaps and advantages? Dare we use it in exploratory experimentation? What will be its repercussions on the selection of spacings? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(19) How should we seek specified sorts of inhomogeneity of variance about a regression? What about simple procedures? Can we merely regress the squared deviations from the fitted line on a suitable function? (Let us not depend on normality of distribution in any case!) What other approaches are helpful? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(20) How soon can we begin to integrate selection theory? How does the classical theory for an infinite population (as reviewed by Cochran [8]) fit together with the second immediate aim of multiple comparisons (Bechhofer et al. [1, 2, 14]) and with the a priori views of Berkson [3] and Brown [6]? What are the essential parameters for the characterization of a specific selection problem? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(21) What are appropriate logical formulations for item analysis (as used in the construction of psychological tests)? (Surely simple significance tests are inappropriate!) Should we use the method introduced by Eddington [32, pp. 101-4] to estimate the true distribution of selectivity? Should we then calculate the optimum cut off point for this estimated true distribution? Or what?</span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(22) What should we do when the items are large and correlated? (If, for example, we start with 150 measures of personality, and seek to find the few most thoroughly related to a given response or attitude.) What kind of sequential procedure? How much can we rely on routine item analysis techniques? How does experiment for insight differ from experiment for prediction?</span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(23) How many experimental statisticians are aware of the problems of astronomy? What is there in Trumpler and Weaver's book [32] that is new to most experimental statisticians? What in other observational problems like the distribution of nebulae (e.g. [23, 26])?</span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(24) How many experimental statisticians are aware of the problems of geology? What is there in the papers on statistics in geology in the Journal of Geology for November 1953 and January 1954 that is new to most experimental statisticians? What untreated problems are suggested there?</span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(25) How many experimental statisticians are aware of the problems of meteorology? What is there in the books of Conrad and Pollak [9] and of Carruthers and Brooks [7] that is new to most experimental statisticians? What untreated problems are suggested there?</span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(26) How many experimental statisticians are aware of the problems of particle size distributions? What is there in Herdan's book [21] on small particle statistics that is new to most experimental statisticians? What untreated problems are suggested there?</span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(27) What is the real situation concerning the efficiency of designs with self-adjustable analyses-lattices, self-weighted means, etc. -as compared with their apparent efficiency? Meier [25] has attacked this problem for some of standard cases, but what are the repercussions? What will happen in other cases? Is there any generally applicable rule of thumb which will make approximate allowance for the biases of unsophisticated procedures?</span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(28) How can we bring the common principles of design of experiments into psychometric work? How can we make allowance for order, practice, transfer of training, and the like through specific designs? Are environmental variations large enough so that factorial studies should always be done simultaneously in a number of geographically separated locations? Don't we really want to factor variance components? If so, why not design psychometric experiments to measure variance components?</span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="letter-spacing: 0px;"><span style="font-family: inherit;">(29) How soon will we appreciate that the columns (or rows) of a contingency table usually have an order? When there is an order, shouldn't we take this in account in our analyses? How can they be efficient otherwise? Should we test only against ordered alternatives? If not, what is a good rule of thumb for allocating error rates? Yates [40] has proposed one technique. What of some others and a comparison of their effectivenesses? </span></span> </div>
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<span style="font-family: inherit; letter-spacing: 0px;">We come now to a set of questions which belong in the list, but which we shall treat only briefly since substantial work is known to be in progress:</span></div>
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<span style="letter-spacing: 0.0px;"></span><br />
<div style="text-align: justify;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;">(30) What usefully can be done with mXn contingency tables?</span></span></div>
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</span><span style="letter-spacing: 0.0px;"></span>
<div style="text-align: justify;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;">(31) What of a very general treatment of variance components?</span></span></div>
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</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(32) What should we really do with complex analyses of variance?</span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(33) How can we modify means and variances to provide good efficiency for underlying distributions which may or may not be normal?</span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(34) What about statistical techniques for data about queues, telephone traffic, and other similar stochastic processes?</span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(35) What are the possibilities of very simple methods of spectral analysis of time series? (36) What are the variances of cospectral and quadrature spectral estimates in the Gaussian case?</span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="letter-spacing: 0px;"><span style="font-family: inherit;">(37) What are useful general representations for higher moments of stationary time series? </span></span> </div>
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<span style="letter-spacing: 0.0px;"><span style="font-family: inherit;">Next we revert to open questions:</span></span></blockquote>
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<div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(38) How should we measure and analyze data where several coordinates replace the time? What determines the efficiency of a design? Should we use numerical filtering followed by conventional analysis? How much can we do inside the crater? </span></div>
<span style="letter-spacing: 0.0px;"></span><br />
<div style="text-align: justify;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;">(39) What of an iterative approach to discrimination? Can Penrose's technique [28] be usefully applied in a multistage or iterative way or both? Does selecting two composites from each of several subgroups and then selecting supercomposities from all these composites pay? If we remove regression on the first two composites from all variables, can we usefully select two new composites from among the residuals? </span></span></div>
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</span><span style="letter-spacing: 0.0px;"></span>
<div style="text-align: justify;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;">(40) Can the Penrose idea be applied usefully to other multiple regression situations? Can we use either the simple Penrose or the special methods suggested above? </span></span></div>
<span style="letter-spacing: 0.0px;">
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(41) Is there any sense in seeking a method of "internal discriminant analysis"? Such a method would resemble factor analysis in resting on no external criterion, but might use discriminant-function-like techniques. </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(42) Why is there not a clearer discussion of higher fractionation? Fractionation (by which we include both fractional factorials and confounding) is reasonably well expounded for the 2m case. But who can make 3m, 4m, 5m etc. relatively intelligible? </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(43) How many useful fractional factorial designs escape the present group theoretical techniques? After all, Latin Squares are kths of a k3, and most transformation sets do not correspond to simple group theory. </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(44) In many applications of higher fractionals, the factors are scaled why don't we know more about the confounding of the various orthogonal polynomials and their interactions (products)? Even a little inquiry shows that some particular fractionals are much better than others of the same type. </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="letter-spacing: 0px;"><span style="font-family: inherit;">(45) What about redundant fractions of mixed factorials? We know perfectly well that there is no useful simple (nonredundant) fraction of a 223341, but there may be a redundant one, where we omit some observations in estimating each effect. What would it be like? </span></span> </div>
</span></blockquote>
<blockquote style="text-align: justify;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit;">A number of further provocative questions have been suggested by others as a result of the distribution of advance copies of this paper and its oral presentation. I indicate some of them in my own words and attitude:</span></span> </blockquote>
<blockquote>
<div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(46) To what extent should we emphasize the practical power of a test? Here the practical power is defined as the product of the probability of reaching a definite decision given that a certain technique is used by the probability of using the technique. (C. Eisenhart) </span></div>
<span style="letter-spacing: 0.0px;"></span><br />
<div style="text-align: justify;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;">(47) What of regression with error in x.? Are the existing techniques satisfactory in the linear case? What of the nonlinear case? (K. A Brownlee) </span></span></div>
<span style="letter-spacing: 0.0px;">
</span><span style="letter-spacing: 0.0px;"></span>
<div style="text-align: justify;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;">(48) What of regression when the errors suffer from unknown autocorrelations? What techniques can be used? How often is it wise to use them? (K. A. Brownlee) </span></span></div>
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</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(49) How can we make it easier for the statistician to "psychoanalyze" his client? What are his needs? How can the statistician uncover them? What sort of a book or seminar would help him? (W. H. Kruskal) </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(50) How can statisticians be successful without fooling their clients to some degree? Isn't their professional-to-client relation like that of a medical man? Must they not follow some of the principles? Do statisticians need a paraphrase of the Hippocratic Oath? (W. H. Kruskal) </span></div>
</span><span style="letter-spacing: 0.0px;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">(51) How far dare a consultant go when invited? Once a consultant is trusted in statistical analysis and design, then his opinion is asked on a wider and wider variety of questions. Should he express his opinion on the general direction that a project should follow? Where should he draw the line? (R. L. Anderson)</span></div>
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<div style="margin-left: 24pt; text-indent: -24.0pt;">
Tukey, J. (1954). Unsolved Problems of Experimental Statistics. <i>Journal of the American Statistical Association</i>, <i>49</i>(268), 706–731. Retrieved from http://amstat.tandfonline.com/doi/abs/10.1080/01621459.1954.10501230</div>
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<div style="margin-left: 24pt; text-indent: -24.0pt;">
Tukey, J. (1960). Where Do We Go From Here? <i>Journal of the American Statistical Association</i>, <i>55</i>(289), 80–93.</div>
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<span style="font-family: inherit;">天</span>Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-47988832189589569212013-06-18T12:32:00.001+02:002013-06-18T12:32:20.842+02:00Chaos Iguana - Music from Maths (the logistic map in the chaotic regime)<br />
<br />
This is what the logistic map sounds like in the chaotic regime.<br />
Well, after you map the continuous outcomes to the chromatic scale.<br />
<br />
A little ditty I dreamed up while I was <a href="https://myspace.com/mrbotak" target="_blank">Mr. Botak </a> (Guess I still am, Botak means bald in <a href="https://en.wikipedia.org/wiki/Indonesian_language" target="_blank">Bahasa Indonesia</a>) for the occasion of the inauguration of <a href="http://annabosman.eu/" target="_blank">a dynamical professor</a>.<br />
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Also a tribute to a great monologue by <a href="http://speedlevitchonline.com/" target="_blank">Speed Levitch</a> in <a href="http://www.imdb.com/title/tt0243017/" target="_blank">Waking Life</a> and a great <a href="http://www.eliteskills.com/analysis_poetry/City_That_Does_Not_Sleep_by_Federico_Garc_a_Lorca_analysis.php" target="_blank">poem by Lorca</a><br />
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"It looks random, but it is not"<br />
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(<a href="http://en.wikipedia.org/wiki/Edward_Norton_Lorenz" target="_blank">Edward Lorenz</a> in the <a href="http://books.google.nl/books/about/The_Essence_of_Chaos.html?id=j5Ub6sMCoOsC&redir_esc=y" target="_blank">Essence of Chao</a>s described deterministic chaos and similar phenomena as "Things that look random, but are not")<br />
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天Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-10633075382116225372013-06-15T16:26:00.002+02:002013-06-17T12:09:32.926+02:00Why did you teach me to corrupt the scientific method? An open letter to my professors of 1993 and beyond.<span style="font-family: inherit;">Nijmegen, 15-06-2013</span><br />
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<span style="font-family: inherit;"><br /></span></div>
<blockquote class="tr_bq">
<span style="font-family: inherit;"><i>"There is no such thing as philosophy-free science; there is only science whose philosophical baggage is taken on board without examination.</i><i>"</i></span></blockquote>
<blockquote class="tr_bq">
<span style="font-family: inherit;">- Daniell Dennett </span></blockquote>
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<span style="font-family: inherit;">Esteemed prof. / dr. / PhD. / drs. / (emeritus),</span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">almost exactly 20 years ago, in the summer of 1993, I was preparing for an exciting new chapter in my life as I was about to start my education as a student of Psychological Science at the same University at which I am currently employed as a docent and researcher. You probably do not remember me, I now know from personal experience what a lecture hall filled with 300+ students looks like from the professor's perspective. I followed your lectures, with great interest. </span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">During this first year you may have heard me ask some questions that were the topic of the books I was reading at the time: <i>Gödel, Escher, Bach </i>by Douglas Hofstadter and <i>Consciousness Explained</i>, by Daniel Dennett. Of course you were right to point out that, although interesting, such questions of philosophy were not the things empirical scientists like yourself cared much about. I know that now. Also, it was pointed out to me there was a philosophy course in the second year that pretty much covered all those things. </span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<i><span style="font-family: inherit;">(That course was indeed about philosophy, but not so much about the philosophy of psychological science. So I decided in my second year <a href="http://fredhasselman.com/main/wp-content/papercite-data/pdf/hasselman1996meesters.pdf" target="_blank">to write an essay</a> about a story that appeared in a book edited by Hofstadter and Dennett. This you may remember, I won me the first prize in the 1995 university essay contest. Included was a short meet and greet with Daniel Dennett who was visiting in 1996 to promote his new book: Darwin's Dangerous Idea, from which I took the quote above this letter. By the way, do we still have an essay contest at our university?)</span></i></div>
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<span style="font-family: inherit;"><br /></span></div>
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<span style="font-family: inherit;">My question is about the following: </span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">In that same summer of 1993 an article appeared in the <i>Journal of Experimental Education</i> by Ronald Carver entitled: <b>The Case Against Statistical Significance Testing, Revisited</b></span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><b>Revisited</b>, refers to the fact that Carver has to conclude that all the issues with statistical inference and the lack of replication of phenomena he identified in an article published in 1978 in the <i>the Harvard Educational Review</i> by the same title (save <b>Revisited</b> of course), were still around in 1993.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">In fact, if you did not know the date of the publication, the excerpt from the article I copied below could very well have been published in the summer of 2013. The information in the abstract alone, contains all the answers one would need in order to change the corruption of the scientific method.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">My question is: Why didn't you change? Your scientific work, the content of your lectures?</span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">Why didn't we set up a study to quantify how bad this corruption really was? </span></div>
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<span style="font-family: inherit;">That would have been an excellent topic for a Master's thesis.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
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</div>
<span style="font-family: inherit;">Why didn't we at least discuss these issues during my formative years as a scientist?</span><br />
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<span style="font-family: inherit;"><br /></span></div>
<span style="font-family: inherit;"><br /></span>
<br />
<div style="text-align: justify;">
<span style="font-family: inherit;">Why?</span></div>
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<span style="font-family: inherit;"><br /></span></div>
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<span style="font-family: inherit;">Sincerely, </span></div>
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<span style="font-family: inherit;">Your former student,</span></div>
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<span style="font-family: inherit;"><br /></span></div>
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<span style="font-family: inherit;">Fred Hasselman</span></div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgO7HT0vaUiN0vXlUBzV9HuuHAD2d9yeYkVqmtf5eqaoVHXvOQ_ie1SUWRwd3LopmOVKVVGY5-UENvhKbNrMp1iqaTFxWatIrrrjOGfodKveYdaazgTmAqkoKZao6o6yMGQEQBYT1VvcDg/s1600/Untitled.001.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><span style="font-family: inherit;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgO7HT0vaUiN0vXlUBzV9HuuHAD2d9yeYkVqmtf5eqaoVHXvOQ_ie1SUWRwd3LopmOVKVVGY5-UENvhKbNrMp1iqaTFxWatIrrrjOGfodKveYdaazgTmAqkoKZao6o6yMGQEQBYT1VvcDg/s1600/Untitled.001.png" height="480" width="640" /></span></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><div style="color: #042eee;">
<span style="font-family: inherit; text-decoration: underline;"><a href="http://www.jstor.org/stable/10.2307/20152382" target="_blank">Carver, R. (1993). The Case Against Statistical Significance Testing, Revisited. The Journal of Experimental Educationl, 61(4), 287–292. Retrieved from http://www.jstor.org/stable/10.2307/20152382 </a></span></div>
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<div style="text-align: justify;">
<span style="font-family: inherit; font-size: x-small;"><b>ps.</b> Carver is one of many scholars who have been warning us about these problems. If you missed his work, here's a selection of just a few of the publications that appeared just before and during the time I was a student that could, maybe should have caught your attention:</span></div>
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<span style="font-family: inherit; font-size: x-small;"><br /></span></div>
<span style="font-family: inherit; font-size: x-small;">Cohen, J. (1990). Things I Have Learned (So Far). <i>American Psychologist, 45(12)</i>, 1304–1312.<br />Cohen, J. (1994). The earth is round (p<. 05). <i>American Psychologist, 49(12)</i>, 997–1003.<br />Freeman, W. J. (1997). Three centuries of category errors in studies of the neural basis of consciousness in intentionality. <i>Neural Networks, 10(7)</i>, 1175–1183.<br />Kugler, P. N., Shaw, R. E., Vincente, K. J., & Kinsella-Shaw, J. (1990). Inquiry into intentional systems I: Issues in ecological physics. <i>Psychological Research, 52(2),</i> 98–121.<br />Meehl, P. E. (1990). Why Summaries of Research on Psychological Theories Are Often Uninterpretable. <i>Psychological Reports, 66(1)</i>, 195.<br />Michell, J. (1997). Bertrand Russell’s 1897 Critique of the Traditional Theory of Measurement. <i>Synthese, 110(2)</i>, 257–276.<br />Van Orden, G. C., & Paap, K. R. (1997). Functional neuroimages fail to discover pieces of mind in the parts of the brain. <i>Philosophy of Science, 64(S1)</i>, 85–94.</span><br />
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<span style="font-family: inherit; font-size: x-small;"><br /></span></div>
<span style="font-family: inherit; font-size: x-small;"><b>pps.</b> I have to exclude the advisors on my Master's Thesis who taught me the important lesson to publish data only when you are more than 100% sure they are reliable. Some of the pronunciation errors I had categorised could not be labelled unambiguously, or in other words, another rater would arrive at a different conclusion. So we did not publish.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">天
</span>Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-85361369255181436442013-06-14T14:59:00.000+02:002013-06-16T02:06:07.746+02:00Truths, Glorified Truths and Statistics (II)<div style="text-align: center;">
<div style="text-align: center;">
<b><span style="font-family: inherit;">(part 2: "To boldly, go...")</span></b></div>
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<span style="font-family: inherit;"><br />
</span></div>
<blockquote class="tr_bq">
<blockquote class="tr_bq" style="text-align: justify;">
<span style="font-family: inherit;"><i>First a disclaimer</i>: I love the work on the p-curve and the estimation of effect sizes. I support the disclosure initiatives (<a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2268189" target="_blank">4 questions</a>, <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2160588" target="_blank">21 words</a>) and the call for more <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2205186" target="_blank">quality and less quantity</a> (however, also see <a href="http://anti-ism-ism.blogspot.nl/2013/06/truths-glorified-truths-and-statistics.html" target="_blank">part 1</a> in which I remind the reader there are many scientists for which there has been no life <i>before</i> p-hacking and a claim of ignorance on these matters is at the very least disrespectful to these scholars)</span></blockquote>
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<span style="font-family: inherit;"><br /></span>
<br />
<div style="text-align: justify;">
<span style="font-family: inherit;">Let me be the one to spoil all the fun: <b>There is no true effect!</b></span><br />
<span style="font-family: inherit;"><b><br /></b>
It does not exist as an entity in reality, it is not one of the constituents of the universe, it should be a measurement outcome observed in a measurement context that was predicted by by a theory about a specific domain in reality.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">As was pointed out by Klaus Fiedler at the<a href="http://beta.in-mind.org/psychology/moving-forward-solid-psychological-science-symposium" target="_blank"> Solid Science Symposium</a>: <i>"What does it mean there is an effect?"</i> (I am quoting from memory, this may be incorrect)</span></div>
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<span style="font-family: inherit;">According to the live tweet feed earlier that day:</span></div>
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhMVjdOIPcKOUhRQkj89EAg0PrGzSl850bJZB_Hj1N7sK5wcQ9I786symcggAljMD3EugN5HYOQl838eA4A6FBFk3k7HRm_geX2C7Cc1WgC2MjqcGVdPOK9aK1Kk9hzyo9mkkeeTFkq4rI/s1600/solidpsy.tiff" imageanchor="1" style="margin-left: auto; margin-right: auto;"><span style="font-family: inherit;"><img alt="" border="0" height="213" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhMVjdOIPcKOUhRQkj89EAg0PrGzSl850bJZB_Hj1N7sK5wcQ9I786symcggAljMD3EugN5HYOQl838eA4A6FBFk3k7HRm_geX2C7Cc1WgC2MjqcGVdPOK9aK1Kk9hzyo9mkkeeTFkq4rI/s400/solidpsy.tiff" title="" width="400" /></span></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-family: inherit; font-size: small;">Solid Science Symposium Tweet Feed - Excellent!</span></td></tr>
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<span style="font-family: inherit;"><br />
</span></div>
<div>
<div style="text-align: justify;">
<span style="font-family: inherit;">If you believe this is possible, that a true effect can somehow be discovered, out there in reality, like a land mass across the ocean where everyone said there would be dragons, or a new species of silicon based life forms at the other end of the worm-hole, then you show one of the symptoms of participating in a failing system of theory evaluation and revision that I dubbed <i>the [intergalactic] explorer delusion</i>. </span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">This refers the to the belief expressed by many experimental psychological scientists that the purpose of scientific inquiry is to go where no man has gone before and observe the phenomena that are “out there” in reality waiting to be uncovered by clever experimental manipulation and perhaps some more arbitrary poking about as well. </span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">A laboratory experiment is however not a field study or an excursion beyond the neutral zone. Even if it were, I would argue that wherever you go as a scientist, boldly, or otherwise, you will be guided and quite possible even be blinded, by a theory or a mathematical formalism about reality that is in most cases implicitly present in your theorising.</span></div>
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</div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
</div>
<div>
<div style="text-align: justify;">
<span style="font-family: inherit;">Let's analyse this delusion by scrutinising a recent paper by Greenwald (2012) entitled: “<i>There is nothing so theoretical as a good method</i>”, which is a reference to the famous quote by a giant of psychological science, Kurt Lewin (1951). This also allows me to comment on what it actually is that Platt meant to say by the term "strong inference" in his 1964 paper.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">Greenwald is explicit about his position towards theory; he is not anti-theoretic, as he acknowledges that theories achieve parsimonious understanding and guide useful applications (but he does not specify… of what?). The author is however also skeptical of theory, because he noticed the ability of theory to restrict open-mindedness. This is indeed a proper description of a theory: It is a specific tunnel-vision, but from the perspective of the Structural Realist (forgive me, I will explain this position more precisely in the near future), this tunnel-vision is is only temporary.</span></div>
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<div style="text-align: justify;">
<span style="font-family: inherit;">It will be no surprise I disagree with the following: </span></div>
</div>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">“When alternative theories <i>contest the interpretation of an interesting finding</i>, researchers are drawn like moths to flame. J. R. Platt (1964) gave the approving label “strong inference” to experiments that were designed as crucial <i>empirical</i> <i>confrontations between theories that competed to explain a compellingly interesting empirical result</i>.” (Greenwald, 2012, pp. 99–100, emphasis added)</span></div>
</blockquote>
<div style="text-align: justify;">
<span style="font-family: inherit;">That is not at all what Platt meant by strong inference, but incidentally we find another symptom of a failing system of theory evaluation, the<i> interpretation fallacy</i> I mentioned in <a href="http://anti-ism-ism.blogspot.com/2013/06/truths-glorified-truths-and-statistics.html" target="_blank">part 1</a>: Theories do not compete for their ability to provide an understandable description or explanation of empirical phenomena. They compete for the ability to predict measurement contexts in which phenomena may be observed and they compete for the accuracy with which measurement outcomes were predicted. And J.R. Platt agrees with this perspective as he describes very clearly:</span></div>
<blockquote class="tr_bq">
<span style="font-family: inherit;"><br /></span>
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<div style="text-align: justify;">
<span style="font-family: inherit;">“Strong inference consists of applying the following steps to every problem in science, <i>formally</i> and <i>explicitly</i> and <i>regularly</i>:</span></div>
<span style="font-family: inherit;"><br /></span>
<br />
<div style="text-align: justify;">
<span style="font-family: inherit;">1) Devising alternative hypotheses;</span></div>
<br />
<div style="text-align: justify;">
<span style="font-family: inherit;">2) Devising a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly as possible, exclude one or more of the hypotheses;</span></div>
<br />
<div style="text-align: justify;">
<span style="font-family: inherit;">3) Carrying out the experiment so as to get a clean result;</span></div>
<br />
<div style="text-align: justify;">
<span style="font-family: inherit;">1') <i>Recycling</i> the procedure, making subhypotheses or sequential hypotheses to refine the possibilities that remain; and so on.”</span></div>
<br />
<div style="text-align: justify;">
<span style="font-family: inherit;">(Platt, 1964, p. 347, emphasis added)</span></div>
</blockquote>
<div style="text-align: justify;">
<span style="font-family: inherit;">Strong inference starts with devising alternative hypotheses to a problem in science and not with an interesting finding. Platt comments that step 1 and 2 require <i>intellectual invention</i>, which I take the liberty to translate as ‘theorizing about reality’. That is what you do when you device a method.</span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">One source of evidence for his argument concern 13 papers, listed in a table that have started controversies on average 44 years ago in psychological science, but which still have no resolution. The author claims that in order to resolve the controversies, the method of strong inference was applied, which obviously failed. Also, it is claimed that philosophy of science provides no answers to resolve the controversies, because it discusses (apparently endlessly) whether such issues can be resolved empirically in principle. It is clear that Greenwald is referring to the resolution of these controversies as a resolution about the ‘reality’ of the ontology of a theory. This is again a matter of interpretation and is not what formal theory evaluation is about. The constituents of reality posited to exist by a theory are irrelevant in theory evaluation. As long as everything behaves according to the predictions by the theory, we should just accept those constituents as temporary vehicles for understanding. I believe these controversy theories were not properly evaluated for their predictive power and empirical accuracy. I don't know if they can be evaluated in that way, if they <i>cannot</i>, the conclusion must be the theories are trivial.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">This impression that ontology evaluation seems to be the problem here is indeed supported by the descriptions provided for the 13 controversies: It is primarily a list of clashes of ontology, e.g., Spreading activation vs. Compound cueing. Further support comes from the examples provided to argue that even if philosophy had an answer, this would not refrain scientists to continue the debate. The fact that scientists do not do this implies to the author there must be another way than strong inference to resolve controversies in science. This is illustrated by examples in which a scientific community was able to achieve consensus about a problem in their discipline (the classification of Pluto as a dwarf planet, HIV as the cause of AIDS and the influence of human activity on global warming). The author suggests that controversies in psychology could be resolved if only a reasonable consensus could be achieved.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">I cannot disagree with the author on his wish for a science that worked towards reaching consensus about the phenomena in its empirical record, instead of wasting energy on definite existence proofs for the ontologies of competing theories. Recall the history of the quantum formalism, two very different theoretical descriptions of reality (waves vs. particle ontologies) were found to be the same for all intents and purposes. I am certain that scientists in cosmology, virology and climatology used strong inference to work towards those consensus resolutions, but I did not check it. Strong inference and consensus formalism science go hand in hand.</span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">What I <i>can</i> say is that Platt’s recycling procedure (step 1’) suggests replication attempts should be carried out and apparently there is somewhat of a problem with replication of phenomena in psychological science. So this makes it again very unlikely any strong inference has been applied to resolve theoretical disputes in psychological science. Indeed, one of the authors listed to have caused a controversy that was unresolved by strong inference, recently challenged the discipline to start replicating the ‘interesting findings’ in its empirical record (e.g. Yong, 2012).</span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br />
<i>(There must be some proverb about dismissing something before its merits have been properly examined...)</i></span><br />
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">A second source of evidence to support his suspicions about the benefits of theorising, Greenwald examines the description of Nobel Prizes for their being rewarded due to theoretical or methodological contributions. The [intergalactic] explorer delusion is obvious here; Greenwald highly values the appearance of the word ‘discovery’: </span></div>
<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit;">“Most “discovery” citations were for methods that permitted previously impossible observations, but in a minority of these, “discovery” indicated a theoretical contribution.” </span></div>
</blockquote>
<div style="text-align: justify;">
<span style="font-family: inherit;">He concludes that theory was important for the development of methods, and that novel methods produced inconceivable results, that prompted new theory.</span></div>
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<span style="font-family: inherit;"><br />
</span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">I am quite certain that the referred inconceivable results were predicted by a theory or considered as an alternative hypothesis. They concern measurement contexts one just does not accidentally stumble upon. If outcomes were surprising given the predicted context, an anomaly to the theory was found, and in that case, naturally, a new theory would have to be created. It was however due to an <i>anomaly</i> <i>to a theoretical prediction,</i> not due to a ‘discovery’ of a phenomenon by a method! The Large Hadron Collider (or any other billion-dollar instrument of modern physics) was not built as a method, a vehicle to seek out previously unknown phenomena like the starship U.S.S Enterprise. Theory, <i>very strongly predicted a measurement context</i> in which a boson should be observable that completed the standard model of particle physics. The methods scientists use for obtaining knowledge about the structure of reality is the result of testing predictions by theories, without exception. Satellites are not sent into space equipped with multi-million dollar X-ray detectors just to see what they will find when they get there. </span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">I conclude by commenting on the way the author describes why <a href="http://en.wikipedia.org/wiki/Albert_Michelson" target="_blank">Michelson</a> won the Nobel Prize for Physics in 1907. This involves a recurring theme in a paper I am about to submit: the luminiferous Æther. Experimental physicists like Michelson and Morley spent most of their academic careers (and most of their money) on experiments that tested the empirical accuracy of theories that predicted a very specific observable phenomenon called Æther-dragging. Their most famous experiment reported in<a href="http://www.aip.org/history/gap/PDF/michelson.pdf" target="_blank"> “On the Relative Motion of the Earth and the Luminiferous Ether”</a> (Michelson & Morley, 1887), showed very accurately and consistently that there was no such thing as an Æther, or at least, that its influence on light and matter was not as large as the Æther-dragging hypothesis predicted it would be. This of course harmed the precision and accuracy of Æther-based theories of the cosmos, but to hint, as Greenwald seems to do, that the method ‘caused’ Einstein to create special relativity theory is farfetched. </span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">Michelson won the Nobel Prize for Physics in 1907 for the very consistent null-result (yes psychological science, such things can be important) and for the development of the interferometer instruments that meticulously failed to measure any trace of the Æther (cf. <a href="http://www.aip.de/pr/Michelson.e.html" target="_blank">Michelson, 1881)</a>. Their commitment to the Æther was adamant though. To be absolutely certain that the minute interferences that were occasionally measured were indeed due to measurement error, instruments of increasing accuracy and sensitivity were built. The largest were many meters wide and placed on high altitude on heavy slabs of marble floating on quicksilver in order to avoid vibrations interfering with the measurement process. Now that is a display of ontological commitment! It was however as much motivated by theoretical prediction as the construction of the Large Hadron Collider. Not a theory-less discovery by some clever poking about.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;">Greenwald admits that the word theory is often used in Michelson and Morley’s 1885 article, so theory must have played an important role in the design of the instruments. The role was not just 'important', without the theory there would have been no method at all. In fact, if a theory of special relativity had been published 20 years before 1905 (physicists knew something like relativity was necessary), there would have been no instruments constructed at all because:</span></div>
<blockquote class="tr_bq" style="text-align: justify;">
<span style="font-family: inherit;">"Whether the ether exists or not matters little - let us leave that to the metaphysicians; what is essential for us is, that everything happens as if it existed, and that this hypothesis is found to be suitable for the explanation of phenomena. After all, have we any other reason for believing in the existence of material objects? That, too, is only a convenient hypothesis; only, it will never cease to be so, while some day, no doubt, the ether will be thrown aside as useless." (Poincaré, 1889/1905, p. 211). </span></blockquote>
<div style="text-align: justify;">
<span style="font-family: inherit;">And indeed, the Æther was thrown aside as useless, because a method devised to test a prediction by a theory yielded null results. Strong inference means this repeated null-result has consequences for the credibility of the theory that predicted the phenomenon. Apparently, in psychological science, this id a difficult condition to achieve. </span><br />
<span style="font-family: inherit;"><br />
The Structural Realist's take home message is: </span><br />
<ol>
<li><span style="font-family: inherit;">We should believe what scientific theories tell us about the structure of the unobservable world, but</span></li>
<li><span style="font-family: inherit;">We should be skeptical about what they tell us about the posited ontology of the unobservable world. </span></li>
</ol>
<span style="font-family: inherit;">In this quote by Poincaré may lie the answer to Greenwald's interpretation of current practice of psychological science (which is in fact a very accurate description of the problems we have with theory evaluation, I just do not agree with the interpretation): Why does Poincaré reserve a special place for the hypothesis about material objects, which will never cease to to be so? </span></div>
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</span><br />
<div style="text-align: justify;">
<span style="font-family: inherit;">Still believe it is possible to use a method that was not predicted to yield measurement outcomes by a theory about reality? </span><br />
<span style="font-family: inherit;"><br />
Ok.</span><br />
<span style="font-family: inherit;">I'll think of some more examples.</span><br />
<span style="font-family: inherit;"><br />
</span></div>
<span style="font-family: inherit;">天</span></div>
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<b><span style="font-family: inherit;">References</span></b></div>
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<span style="font-family: inherit;"><br /></span></div>
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<span style="font-family: inherit;">Greenwald, A. G. (2012). There Is Nothing So Theoretical as a Good Method. <i>Perspectives</i></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit;"><i>on Psychological Science, 7(2)</i>, 99–108. <a href="doi:10.1177/1745691611434210" target="_blank">doi:10.1177/1745691611434210</a></span></div>
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<span style="font-family: inherit;">Michelson, A. . (1881). The Relative Motion of the Earth and the Luminiferous Ether. <i>American Journal of Science, 22(128)</i>, 120–129. Retrieved from <a href="http://www.archive.org/details/americanjournal62unkngoog">http://www.archive.org/details/americanjournal62unkngoog</a></span></div>
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<span style="font-family: inherit;"><br /></span></div>
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<span style="font-family: inherit;">Michelson, A. ., & Morley, E. W. (1887). On the Relative Motion of the Earth and the Luminiferous Ether. <i>American Journal of Science, 34(203)</i>, 333–345. Retrieved from </span><a href="http://www.aip.org/history/gap/PDF/michelson.pdf">http://www.aip.org/history/gap/PDF/michelson.pdf</a></div>
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<span style="font-family: inherit;"><br /></span></div>
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<span style="font-family: inherit;">Platt, J. (1964). Strong Inference. <i>Science, 146(3642)</i>, 347–353. Retrieved from <a href="http://clustertwo.org/articles/Strong%20Inference%20(Platt).pdf" target="_blank">http://clustertwo.org/articles/Strong Inference (Platt).pdf</a></span></div>
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<span style="font-family: inherit;">Poincaré, H. (1905).<i> Science and Hypothesis.</i> New York: The Walter Scott Publishing Co., LTD. Retrieved from <a href="http://www.archive.org/details/scienceandhypoth00poinuoft">http://www.archive.org/details/scienceandhypoth00poinuoft</a></span></div>
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<span style="font-family: inherit;">Yong, E. (2012). Nobel laureate challenges psychologists to clean up their act. <i>Nature. </i>Retrieved from <a href="http://www.nature.com/doifinder/10.1038/nature.2012.11535">http://www.nature.com/doifinder/10.1038/nature.2012.11535</a></span></div>
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Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-53568132497727954522013-06-14T09:53:00.002+02:002013-06-15T23:01:31.624+02:00How to measure a schism?<span style="font-family: inherit;">Measuring a distance is easy.<br /><br />Euclidean, city-block, you know the drill.<br /><br />Ok, ok, I know, that’s assuming boundary conditions for Newtonian physics apply, but even beyond that, we know how to deal with distance, right?<br /><br />Fractal geometry?<br />Yes.</span><br />
<div>
<span style="font-family: inherit;">Ok, but at least it’s a geometry.<br /><br /> <br /><br /><b>A schism is like a chasm but between ideas </b><br /><br /><i>(did you notice the Christian religion appears to suffer from a kind of schismism?)</i><br /><br /> <br />I see a great <a href="http://www.youtube.com/watch?v=UhjG47gtMCo" target="_blank">schism</a> between ideas about what science is, what it can tell us about the structure of reality, second principles, or the universal mother-in-law. </span><br />
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<span style="font-family: inherit;"><br /></span></div>
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<span style="font-family: inherit;">The schism is a vast ocean of useless empirical facts wide ("empiarrhea") and several schools of philosophy deep. Most importantly perhaps, one end seems to have drifted off into the <b>now</b>, detached from its own history and its friendly, but slightly nerdy neighbours.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
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<i><span style="font-family: inherit;">A double dissociation of sorts.</span></i><br />
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<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">天</span></div>
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Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-54347823001916387362013-06-14T03:39:00.002+02:002013-06-16T01:10:29.971+02:00Truths, Glorified Truths and Statistics (I)<div style="text-align: center;">
<span style="font-family: inherit;"><br />
<b style="letter-spacing: 0px;">(part 1: Just for the record)</b></span><br />
<span style="font-family: inherit;"><br />
</span><br />
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<span style="font-family: inherit; letter-spacing: 0px;"><i>The Appendix should probably be skipped by anyone who reads this</i></span></div>
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<span style="font-family: inherit; letter-spacing: 0px;"><b><br /></b></span></div>
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<span style="font-family: inherit;"><span style="letter-spacing: 0.0px;"></span><br /></span></div>
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<span style="font-family: inherit; letter-spacing: 0.0px;"><b>[Just for the record] {</b></span></div>
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<span style="font-family: inherit;"><span style="letter-spacing: 0.0px;"></span><br /></span></div>
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<span style="font-family: inherit; letter-spacing: 0.0px;"><i>I did not.</i> </span></div>
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<span style="font-family: inherit;"><span style="letter-spacing: 0.0px;"></span><br /></span></div>
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<span style="font-family: inherit; letter-spacing: 0.0px;">Engage in p-hacking, or any other exploitation of <a href="http://www.socio.mta.hu/dynamic/simmons_et_al_2011.pdf" target="_blank">researchers degrees of freedom</a>.</span></div>
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<blockquote class="tr_bq">
<div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0.0px;"><i>(ok, maybe once, but I did not inhale, or have any relations with the degrees, or the freedoms involved. None that are worth mentioning, or have been caught on tape anyway: See point <b>1 </b>of the Appendix below)</i></span></div>
</blockquote>
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<span style="font-family: inherit; letter-spacing: 0.0px;"></span></div>
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<span style="font-family: inherit;"><span style="letter-spacing: 0px;">Some would have us believe that we all studied Cohen, but did not act appropriately, just </span>ignored it all<span style="letter-spacing: 0px;"> in our daily practice of science (this was almost literally exclaimed at some point during this </span><a href="http://storify.com/jobvanwolferen/solid-psychological-science-symposium?utm_medium=sfy.co-twitter&utm_campaign=&utm_content=storify-pingback&awesm=sfy.co_eL9w&utm_source=t.co" style="letter-spacing: 0px;" target="_blank">very interesting symposium</a><span style="letter-spacing: 0px;">). </span></span></div>
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<div style="text-align: justify;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;"><br /></span></span></div>
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<div style="text-align: justify;">
<span style="font-family: inherit;"><span style="letter-spacing: 0px;">I do not understand how such a thing can happen to a scientist, it appears to me as a </span>post-meditated <a href="http://tap.sagepub.com/content/10/5/639.short" style="letter-spacing: 0px;" target="_blank">case of pathological science</a>, <span style="letter-spacing: 0px;">or was it just a little sloppy and careless? When you learn about something that should be implemented immediately, then why don't you? Or: Who else will? There is no scientist high council that will decide such things for you.</span></span></div>
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<div style="text-align: justify;">
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<span style="font-family: inherit;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;"><span style="letter-spacing: 0px;">On the other hand, maybe Cohen was studied very well, as evidenced by the conclusion of the paper entitled <a href="http://psych.colorado.edu/~willcutt/pdfs/Cohen_1990.pdf" target="_blank">What I have learned (so far)</a>: "</span></span><i style="font-family: inherit;">Finally, I have learned that there is no royal road to statistical induction, that the informed judgment of the investigator is the crucial element in the </i><i style="font-family: inherit;">interpretation of data, and that things take time.</i><span style="font-family: inherit;">"</span></div>
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<div style="text-align: justify;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;"><br /></span></span></div>
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<div style="text-align: justify;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;">Cohen makes a very serious error against formal theory evaluation, but he is in good company, as this is the most common flaw in theory evaluation as it is practiced by the social sciences. In a genuine science, the informed judgement of the investigator plays <b>NO</b> role whatsoever in the evaluation of the accuracy of the prediction by a theory. Quantum physical theories are the best scientific theories ever produced by human minds and there are <a href="http://en.wikipedia.org/wiki/Interpretations_of_quantum_mechanics" target="_blank">over 20 informed judgements</a> on how the theory should be interpreted, but that does not have any influence on the empirical accuracy of the theory: <i>highest ever!</i></span></span></div>
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<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;"><br /></span></span></div>
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<div style="text-align: justify;">
<span style="font-family: inherit;"><span style="letter-spacing: 0px;">Something that I'm picking up in how people are talking about </span>this worries me.<span style="letter-spacing: 0px;"> There seems to be a tendency to spin all the wrongdoing of the past as a necessary evil that was inescapable. As if to say: Forgive our ignorance, let's show some penance and go about our business as usual.</span></span></div>
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<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;"><br /></span></span></div>
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<div style="text-align: justify;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;">I'm not bringing this up because I feel it does not apply to me personally: <i>It is just not <b>true</b>. </i></span></span></div>
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<span style="font-family: inherit;">A scientist can never feign ignorance about his or her theorising about the way the universe works. It's either the best and most thorough and profound thinking you can possibly achieve, or it is not solid enough to share with other scientists.</span></div>
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<div style="text-align: justify;">
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<span style="font-family: inherit;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">Moreover, what about all those scholars who:</span></div>
</span><br />
<div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">- have spoken out against questionable research practices in the past. </span></div>
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<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;">- argued against the reluctance of scientists to abide by the rules of the scientific method</span></span></div>
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<div style="text-align: justify;">
<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;">- out of sheer frustration gave up because their colleagues would not accept falsification in the face of anomalies</span></span></div>
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<div style="text-align: justify;">
<span style="font-family: inherit;"><span style="letter-spacing: 0px;">- criticised our </span>preferred<span style="letter-spacing: 0px;"> model of inference, or pointed out those NHST rules are not obeyed at all.</span></span></div>
<div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">- complained about the logical inconsistencies in psychological theorising and the lack of a proper foundations debate.</span></div>
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<span style="font-family: inherit;"><div style="text-align: justify;">
<span style="font-family: inherit; letter-spacing: 0px;">To claim ignorance about these matters is at least disrespectful to those who dared to speak out, often at the risk of being marginalised and ridiculed for doing so. I believe it is more than disrespectful and find the idea there could be some kind of cleansing p-hack penance waiting to happen just outrageous.</span></div>
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<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;"><br /></span></span></div>
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<span style="font-family: inherit;"><span style="letter-spacing: 0px;"><span style="letter-spacing: 0px;">To whom this may concern: </span></span><b>You did not listen, and you should have!</b></span></div>
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<span style="letter-spacing: 0.0px;"><span style="font-family: inherit; letter-spacing: 0px;">That is what happened, you did not bother to spend time and energy to be educated on important matters of philosophy, mathematics, measurement theory, statistics or whichever discipline of science is somewhat relevant to help you answer your research questions.</span></span></div>
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<span style="font-family: inherit;"><span style="letter-spacing: 0px;">Science is not: "That with which you can get away with in peer review." It is about doing everything in your power to get it as right as inhumanly possible and we should not settle for anything less. The point is lucidly made </span><a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2205186" style="letter-spacing: 0px;" target="_blank">here</a><span style="letter-spacing: 0px;">, this will take time and should bring down the number of studies published. There is no excuse for not being on top of all the most relevant developments from all disciplines of science that could </span>potentially<span style="letter-spacing: 0px;"> help you get closer to </span>answering<span style="letter-spacing: 0px;"> the research questions you have.</span></span></div>
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<span style="font-family: inherit;">So let me be clear: <b>There will be no feigning of ignorance tolerated on my watch.</b></span></div>
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<span style="font-family: inherit; letter-spacing: 0.0px;">To summarise:</span></div>
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<span style="font-family: inherit;"><i>I did not </i><i>have a life before p-hacking.</i></span></div>
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<span style="font-family: inherit; letter-spacing: 0px;"><b>}</b></span></div>
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<span style="font-family: inherit;">-------------------------------</span></div>
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<b><span style="font-family: inherit;">[Appendix] {</span></b></div>
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<span style="font-family: inherit; letter-spacing: 0.0px;">Want proof?</span></div>
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<span style="font-family: inherit; letter-spacing: 0.0px;">Of course you do, you're the proud owner of scientific mind!</span></div>
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<span style="font-family: inherit;"><b style="font-size: 12px; letter-spacing: 0px;">1.</b><span style="font-size: 12px; letter-spacing: 0px;"> I have not published a single paper in a peer-reviewed journal as a first author before 2013. It just took me a long time to find out exactly <a href="http://anti-ism-ism.blogspot.com/2013/06/the-letters-name-effect.html" target="_blank">what it was I could contribute</a> </span></span></div>
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<span style="font-family: inherit;"><span style="font-size: 12px; letter-spacing: 0px;">(<i>note:</i> this usually has nothing to do with the </span><span style="font-size: 12px;">importance of those thoughts as </span><span style="font-size: 12px; letter-spacing: 0px;">perceived by others)</span></span></div>
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<span style="font-family: inherit; font-size: 12px; letter-spacing: 0px;"><b>2.</b> Before 2013, I submitted a paper as first author only twice, but they did concern the same study. First journal, they loved the theory, but not the experimental design, so it was rejected. Then I revised it and submitted it to another journal. They saw merit and wanted me to resubmit, again, they loved the theory, but asked me if I could lose 66% of the words I had used. That pretty much settled it. </span></div>
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<span style="font-family: inherit; font-size: 12px; letter-spacing: 0px;">(I will not relate here all the encouraging advice I received over the years to become less precise, engage more often in the practice of “huis tuin en keuken” science [probably translates to “middle of the road science”], or to “just send it in and see what reviewers say, because you never know in advance what they will say, they will be pissed off because you cite work that is over 2 years old anyway. Here’s a list of 10 journals, start at the top”)</span></div>
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<span style="font-family: inherit;"><b style="font-size: 12px;">3.</b><span style="letter-spacing: 0px;"> </span><span style="font-size: 12px; letter-spacing: 0px;">Even so, I have a decent number of publications to which I made substantial contributions either in study design or by performing the data analysis or even the theoretical part, imagine that! I disseminate the work that I do not publish and even teach about it and this is the best way to learn about all the things that I still need to be </span><span style="font-size: 12px;">educated on</span><span style="font-size: 12px; letter-spacing: 0px;">. Such a resume will not impress any research institute or funding agencies. Thank the </span><span style="font-size: 12px;">goddess</span><span style="font-size: 12px; letter-spacing: 0px;"> I have a permanent teaching job. </span></span></div>
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<span style="font-family: inherit; font-size: 12px; letter-spacing: 0px;"><i>("oh, one of those guys who can only teach and does not know how to write a proper scientific paper")</i></span></div>
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<span style="font-family: inherit; font-size: 12px; letter-spacing: 0px;"><b>4.</b> I did not defend my dissertation until I could 100% stand behind every word I wrote.</span></div>
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<span style="font-family: inherit; font-size: 12px; letter-spacing: 0px;"><i>(but that was already the case more than 5 years ago and still hasn't happened)</i></span></div>
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<span style="font-family: inherit;"><span style="font-size: 12px; letter-spacing: 0px;">Almost, just awaiting some </span><span style="font-size: 12px;">additional</span><span style="font-size: 12px; letter-spacing: 0px;"> results. </span></span></div>
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<span style="font-family: inherit; font-size: 12px; letter-spacing: 0px;">I did postpone, yes, mainly because I seriously considered leaving science, until about a year ago. Things have changed recently as you may have noticed. </span></div>
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<span style="font-family: inherit; font-size: 12px; letter-spacing: 0px;">Before the change, I wanted to leave because I </span><span style="font-family: inherit; font-size: 12px;">realised t</span><span style="font-family: inherit; font-size: 12px; letter-spacing: 0px;">hat a game was being played in which the winners were the ones who interpreted the "facts" of their scientific inquiries in such a way that it would maximally serve their own cause instead of the cause of science, which is to uncover the structure of reality. Decisions about funding, positions, courses in the curriculum, they are not based on quality, but on politics. Good luck with that strategy. </span></div>
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<span style="font-family: inherit; font-size: 12px; letter-spacing: 0px;">I have seen </span><span style="font-family: inherit; font-size: 12px;">too many gifted young students who understood this was the game they were supposed to be playing if they wanted to become a scientist and therefore, could not be saved for science.</span></div>
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<span style="font-family: inherit; font-size: 12px; letter-spacing: 0px;">If I had wanted to be engaged in an endeavour that interpreted facts any way the wind blows, I would have </span><span style="font-family: inherit; font-size: 12px;">chosen</span><span style="font-family: inherit; font-size: 12px; letter-spacing: 0px;"> a career in politics or finance and would have made a much better living out of it in the process. Science is for nerds who want to figure things out, not for bullies who take over the playing ground by loudly shouting out incoherent </span><span style="font-family: inherit; font-size: 12px;">authoritative</span><span style="font-family: inherit; font-size: 12px; letter-spacing: 0px;"> arguments to prove they are never wrong about anything.</span></div>
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<span style="font-family: inherit;">天</span></div>
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Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-19194896930191170222013-06-06T09:48:00.000+02:002013-06-15T23:01:18.787+02:00The [letters] name effect<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;"><br />I wrote about this effect more than a decade ago, but I didn’t publish it on any website / blog / social media (did such things even exist back then?) or in anything remotely like a journal or magazine. I’m just saying it wasn’t real until now, ok? <br /><br />I first noticed the <b>[letters] name effect</b> when I was a student of cognitive psychology.<br /><br />oh… those were the days…<br /><br /><i>(never understood the purpose of that false memory study I participated in)</i><br /><br />It is a curious phenomenon:</span><br />
<ol>
<li><span style="font-family: inherit;">When specific letters are in front of a speakers name, like PhD, dr. or prof. this is an excuse for others to stop thinking.</span></li>
<li><span style="font-family: inherit;">Even more worrisome, the effect generalises to people who also have letters in front of their name.</span></li>
<li><span style="font-family: inherit;">This is however moderated by the <b>name-letter effect</b> (not to be confused with the <b>[letters] name effect</b>): People with letters in front of their name only stop thinking when the letters of the speaker are the same as their own letters. If they are different, they grow deaf and do not listen at all.</span></li>
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<span style="font-family: inherit;"><br />It’s a kind of bi-directional multiplicative interaction thing, with information and brain areas involved. Encoding too. Lots of encoding.<br /><br />That is when I decided: Before I get those letters in front my name I have to be very very certain about what it is I am saying. And perhaps invest in a PA system.<br /><br />That takes about 10 years.</span><br />
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天</span>Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-11333699670125481632013-05-04T09:42:00.000+02:002013-06-16T01:24:27.217+02:00Once there was this Guy<span style="font-family: inherit;">I pause to catch my breath today and look back on things to come and can’t help thinking about Guy and what if, and why not, and what not.</span><br />
<span style="font-family: inherit;"><br />It is always cruelty and injustice that make reality loose its randomness and I catch myself conspiring with my breath and about to infer there really might be a…</span><br />
<span style="font-family: inherit;"><br />yeah… right… like that would ever happen.</span><br />
<span style="font-family: inherit;"><br />I just miss the feeling of being the deer caught in the headlights… this Guy… he always accomplished exactly that, sending me back to my drawing board, skipping and whistling nursery rhymes. There’s no feeling just quite like it.</span><br />
<span style="font-family: inherit;"><br />Those are the people you should listen to, the <b>un</b>-natural philosophers, the ones who make you feel <b>un</b>-comfortable about all the <b>un</b>-certainties you tricked your homunculus into believing they were <b>un</b>-real.</span><br />
<span style="font-family: inherit;"><br />Not the scientist salesmen, who believe science is an industry that produces facts about reality.</span><br />
<span style="font-family: inherit;"><br />Science is one of the arts that studies the structure of reality.<br />It should not produce anything other than <b>more questions</b>.</span><br />
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天</span>Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-10244311942996275252012-07-13T02:14:00.000+02:002013-06-16T02:16:14.731+02:00Robin<div class="separator" style="clear: both; text-align: center;">
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Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.comtag:blogger.com,1999:blog-3192184090298016307.post-32773952481706366262011-09-18T13:31:00.000+02:002013-08-20T01:59:21.436+02:00The thin line between science fact and science friction: Antisocial meateaters, Psi and other forms of empiarrhea<div class="separator" style="clear: both; text-align: center;">
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<span style="font-family: inherit;">There is a beautiful paragraph in the history of science <a href="http://mpseevinck.ruhosting.nl/" target="_blank">Michael Seevinck</a> introduced me to. I immediately incorporated it into talks and papers. It is a letter by Albert Einstein he wrote to his friend Maurice Solovine on the 7th of April 1954 in which he explains his philosophy of science. Einstein distinguishes between two types of theories: <em>Theories of construction</em> and <em>theories of principles</em>. Theories of construction are found in a science that is immature. Those theories basically just describe a phenomenon in the empirical record (the data collected in experiments) using fancy language. My favorite example is the scientific notion about reading aloud written text. This is involves converting <em>graphemes</em> into <em>phonemes </em>according to scientific theory. What are <em>graphemes</em>? Well, they represent letters. What are <em>phonemes?</em> They represent sounds. So reading aloud is actually converting letters into sounds... Duh! Of course it is! Why do you need graphemes and phonemes to explain it?</span><br />
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<span style="font-family: inherit;">Figure adapted from van Dongen (2010)</span><br />
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<span style="font-family: inherit;">A theory of principles is completely different. It embarks from a set of postulates and axioms (<strong>A</strong>). It derives laws from those claims (<strong>S</strong>) and seeks to test how accurate those laws are. Einstein understood that a theory of principles “<em>pays for its higher logical unity by having elementary concepts […], which are no longer directly connected with complexes of sense experiences.</em>”. To create a theory of principles means sacrificing explanatory power of a theory. We cannot explain the way the universe works in terms of sensory experience. This was not just a coincidental oddity for Einstein, he firmly believed the fundamental notions of physics cannot be induced from experience and they cannot be justified a priori on the basis of our faculty of knowledge. (This strongly opposes Kant’s notion of incomensurability between Biology and Physics ).</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">How do scientist get to those systems of axioms and postulates then? Einstein is very clear about this and claims there is no logical route to arrive at <strong>A </strong>directly from <strong>E </strong>(direct experience). A formalism is created due to an intuitive leap, a creative act in which a conjecture is raised to a postulate. The arrow that doesn’t quite touch <strong>E</strong> and jumps upwards to <strong>A</strong> represents this creative act. The only route that is deducible by logic and reason flows from the formalism in <strong>A</strong> to the derived laws <strong>S</strong> and from those laws back to <strong>E</strong>. Theories of construction on the other hand infer “laws” from complexes of immediate sensations (sensory phenomena) in <strong>E.</strong> These laws are crudely glued together to construct ad-hoc theories that, in a way, save those phenomena for the constructed theory so far.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">Well, what can I say... Sometimes it just doesn't feel entirely right to say "<i>I told you so</i>".</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">I am not saying that I expected anything like <a href="http://retractionwatch.wordpress.com/2011/09/07/dutch-university-investigating-psych-researcher-stapel-for-data-fraud/" target="_blank">the fraud committed by social psychologist prof. Diederik Stapel</a>, but the entourage, the context, the circumstances that allowed such fraud to go unnoticed and the responses of the universities and his colleagues... well let's just say that I am not entirely surprised it happened.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">The reason for me not being surprised is due to the fact that I have been trying to figure out what kind of philosophy of science social psychologists actually adhere to, by studying the argumentation in the papers they write and the way theories are evaluated by the social psychology community. I have for instance been analyzing the paper by <a href="http://bigthink.com/ideas/25271" target="_blank">Darryl Bem who claims to have found evidence for the existence of Psi.</a> More importantly perhaps, with the help of some bright students I have been trying to replicate some of the famous social psychology experiments. We failed to do so, but we also think we know why they did find something and we didn't, you know, what science is all about. No reason to be suspicious. I also didn't expect fraud because I know social psychologists are masters at inventing ingenious experiments. Really, they generate beautiful datasets by tweaking and fine tuning their stimuli and experimental procedures and they will continue to do so until they find the right conditions to support their hypotheses.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">:)</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">Yes, indeed, that's not exactly the way science should work. I thought that was the problem. They have no real theories, but just produce an ever increasing empirical record of interesting and sometimes very cute phenomena that have no say whatsoever on the level of the major psychological theories about cognition, behavior or development. I call this enormous structureless record of so called scientific facts: <strong>empiarrhea</strong> (see <a href="http://fredhasselman.com/pubs/MAN_CaugthintheUndertow.pdf" target="_blank">Caught in the undertow</a>).</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">Now when I say "them" or "they", to refer to social psychologists, of course I am generalizing: <strong>No I do not believe all social psychologists are pretesting designs until they work</strong>. What I do believe however, is that psychologists in general, but social psychologists in particular, should change the way they theorize about reality. "<em>It is the theory that decides what we may observe</em>" said Albert Einstein to Werner Heisenberg and he was right.</span><br />
<br />Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-75721310227258275432011-08-29T15:54:00.000+02:002013-06-14T23:32:40.519+02:00The connectome: Into the brain and beyondAs you may know, I am not a fan of contemporary neuroscience that attributes to the brain the central processor function that you may encounter in a computer.<br/><br/>Of course, it must be there for a reason and it is probably not just there to cool blood as <a href="http://www.philosophicalmisadventures.com/?p=13" target="_blank">Aristotle believed</a><br/><br/>As you may also know, I am currently studying the brain (again) using a <a href="http://fredhasselman.com/pubs/Hasselman%20-%202011%20-%20The%20promise%20of%20complex%20network%20analysis%20for%20Neuroscience%20Graphing%20the%20adaptive%20reconfiguration%20of%20structural%20and%20functional%20brain%20networks%20Abstract.pdf" target="_blank">complex network approach</a>. The video below is a very impressive talk I came across on <a href="http://www.ted.com" target="_blank">TED</a>. Although I would go much further and be more <a href="http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=11991" target="_blank">radical</a> about what it is that is connected, or rather <strong>nested</strong> this talk very elegantly gives an insight in the overwhelming complexity that is involved. Yes, much more complex than a machine that obeys the algorithmic laws of computation.<br/><br/>http://youtu.be/HA7GwKXfJB0<br/><br/>http://www.ted.com/talks/sebastian_seung.html<br/><br/>Just ignore the technology fallacy at the end (i.e. if we wait for bigger, better, fancier technology, we'll know everything...). Where are the robots?Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-48445909717911487082011-08-13T13:52:00.000+02:002013-06-14T23:50:32.598+02:00Music Clash: Lady Britannia vs. Lady Libertas[caption id="attachment_430" align="alignnone" width="245" caption="Lady Britannia"]<a href="http://dreamling.org/blog/wp-content/uploads/2011/08/dianacoin1MOS2601_468x547.jpg"><img alt="" class="size-medium wp-image-430" height="300" src="http://dreamling.org/blog/wp-content/uploads/2011/08/dianacoin1MOS2601_468x547-245x300.jpg" title="dianacoin1MOS2601_468x547" width="245" /></a>[/caption]<br />
<br />
[caption id="attachment_431" align="alignright" width="190" caption="Lady Liberty"]<a href="http://dreamling.org/blog/wp-content/uploads/2011/08/1ladyliberty003.jpg"><img alt="" class="size-medium wp-image-431 " height="300" src="http://dreamling.org/blog/wp-content/uploads/2011/08/1ladyliberty003-190x300.jpg" title="1ladyliberty003" width="190" /></a>[/caption]<br />
<br />
Ok...<br />
<br />
(I'm just pretending there isn't a huge gap in between my last post and this one.)<br />
<br />
...I came up with an interesting question while I was on the other side of the pond:<br />
<br />
"Name a band (not an individual artist) from the USA that equals in terms of<br />
<br />
(i) popularity,<br />
(ii) impact on music history,<br />
(iii) creative longevity (i.e. some core of the original group keeps performing for decades by reinventing itself, keeping up with/dictating trends in contemporary pop music),<br />
(iv) legend status<br />
<br />
with bands like Queen, Led Zeppelin, The Beatles, Pink Floyd, The Rolling Stones, Duran Duran, U2, Genesis."<br />
<br />
It's a tough one. In fact, you can probably name a whole range of individual artists from the US that do meet those requirements, but no other country has produced legendary groups like the UK did... and still does! I guess Radiohead will soon be in that list, maybe Depeche Mode and some of the triphoppers that are still around, some of the younger britpop bands. What about Autechre, Orbital, Underworld.<br />
<br />
Of course record sales do not tell you everything... but when I arrange <a href="http://en.wikipedia.org/wiki/List_of_best-selling_music_artists" target="_blank" title="List of best selling music artists">this list</a> so that only bands are in there you get something like this. It's a list of everything better than the Village People... (or UB40):<br />
<br /><table border="0" cellpadding="0" cellspacing="0"><tbody>
<tr><td valign="top" width="155"><br />
<div align="center">
<strong>RANK</strong></div>
<br /></td><td valign="top" width="155"><br />
<div align="center">
<strong>USA</strong></div>
<br /></td><td valign="top" width="155"><br />
<div align="center">
<strong>UK<br />(and some non-USA)</strong></div>
<br /></td></tr>
<tr><td colspan="3" valign="top" width="464"><br />
<div align="center">
<strong>300 million or more</strong></div>
<br /></td></tr>
<tr><td valign="top" width="155">1</td><td valign="top" width="155"></td><td valign="top" width="155">The Beatles</td></tr>
<tr><td valign="top" width="155">2</td><td valign="top" width="155"></td><td valign="top" width="155">ABBA</td></tr>
<tr><td valign="top" width="155">3</td><td valign="top" width="155"></td><td valign="top" width="155">Led Zeppelin</td></tr>
<tr><td valign="top" width="155">4</td><td valign="top" width="155"></td><td valign="top" width="155">Queen</td></tr>
<tr><td colspan="3" valign="top" width="464"><br />
<div align="center">
<strong>200 million to 299 million</strong></div>
<br /></td></tr>
<tr><td valign="top" width="155">5</td><td valign="top" width="155"></td><td valign="top" width="155">Pink Floyd</td></tr>
<tr><td valign="top" width="155">6</td><td valign="top" width="155"></td><td valign="top" width="155">AC/DC</td></tr>
<tr><td valign="top" width="155">7</td><td valign="top" width="155"></td><td valign="top" width="155">The Rolling Stones</td></tr>
<tr><td valign="top" width="155">8</td><td valign="top" width="155"></td><td valign="top" width="155">Bee Gees</td></tr>
<tr><td colspan="3" valign="top" width="464"><br />
<div align="center">
<strong>150 million to 199 million</strong></div>
<br /></td></tr>
<tr><td valign="top" width="155">9</td><td valign="top" width="155"></td><td valign="top" width="155">U2</td></tr>
<tr><td valign="top" width="155">10</td><td valign="top" width="155">Aerosmith</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">11</td><td valign="top" width="155"></td><td valign="top" width="155">Genesis</td></tr>
<tr><td colspan="3" valign="top" width="464"><br />
<div align="center">
<strong>120 million to 149 million</strong></div>
<br /></td></tr>
<tr><td valign="top" width="155">12</td><td valign="top" width="155">Backstreet Boys</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">13</td><td valign="top" width="155">Bon Jovi</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">14</td><td valign="top" width="155">Chicago</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">15</td><td valign="top" width="155">Eagles</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">16</td><td valign="top" width="155"></td><td valign="top" width="155">Dire Straits</td></tr>
<tr><td colspan="3" valign="top" width="464"><br />
<div align="center">
<strong>100 million to 119 million</strong></div>
<br /></td></tr>
<tr><td valign="top" width="155">17</td><td valign="top" width="155">The Carpenters</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">18</td><td valign="top" width="155"></td><td valign="top" width="155">Depeche Mode</td></tr>
<tr><td valign="top" width="155">19</td><td valign="top" width="155">Fleetwood Mac</td><td valign="top" width="155">Fleetwood Mac</td></tr>
<tr><td valign="top" width="155">20</td><td valign="top" width="155">Guns ‘n Roses</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">21</td><td valign="top" width="155">The Jackson 5</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">22</td><td valign="top" width="155">Kiss</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">23</td><td valign="top" width="155">Metallica</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">24</td><td valign="top" width="155"></td><td valign="top" width="155">Scorpions</td></tr>
<tr><td valign="top" width="155">25</td><td valign="top" width="155"></td><td valign="top" width="155">The Who</td></tr>
<tr><td colspan="3" valign="top" width="464"><br />
<div align="center">
<strong>75 million 99 million</strong></div>
<br /></td></tr>
<tr><td valign="top" width="155">26</td><td valign="top" width="155"></td><td valign="top" width="155">Boney M.</td></tr>
<tr><td valign="top" width="155">27</td><td valign="top" width="155"></td><td valign="top" width="155">B'z</td></tr>
<tr><td valign="top" width="155">28</td><td valign="top" width="155">The Doors</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">29</td><td valign="top" width="155"></td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">30</td><td valign="top" width="155"></td><td valign="top" width="155">Eurythmics</td></tr>
<tr><td valign="top" width="155">31</td><td valign="top" width="155"></td><td valign="top" width="155">Iron Maiden</td></tr>
<tr><td valign="top" width="155">32</td><td valign="top" width="155">Journey</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">33</td><td valign="top" width="155"></td><td valign="top" width="155"></td></tr>
<tr><td colspan="3" valign="top" width="464"><br />
<div align="center">
<strong>50 million to 74 million</strong></div>
<br /></td></tr>
<tr><td valign="top" width="155">34</td><td width="155"></td><td valign="top" width="155">Ace of Base</td></tr>
<tr><td valign="top" width="155">35</td><td width="155"></td><td valign="top" width="155">A-ha</td></tr>
<tr><td valign="top" width="155">36</td><td valign="top" width="155">The Black Eyed Peas</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">37</td><td valign="top" width="155"></td><td valign="top" width="155">Black Sabbath</td></tr>
<tr><td valign="top" width="155">38</td><td valign="top" width="155">Boyz II Men</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">39</td><td valign="top" width="155"></td><td valign="top" width="155">Coldplay</td></tr>
<tr><td valign="top" width="155">40</td><td valign="top" width="155">Def Leppard</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">41</td><td valign="top" width="155">Destiny's Child</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">42</td><td valign="top" width="155"></td><td valign="top" width="155">Dreams Come True</td></tr>
<tr><td valign="top" width="155">43</td><td valign="top" width="155"></td><td valign="top" width="155">Duran Duran</td></tr>
<tr><td valign="top" width="155">44</td><td valign="top" width="155"></td><td valign="top" width="155">Electric Light Orchestra</td></tr>
<tr><td valign="top" width="155">45</td><td valign="top" width="155">Green Day</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">46</td><td valign="top" width="155"></td><td valign="top" width="155">Jethro Tull</td></tr>
<tr><td valign="top" width="155">47</td><td valign="top" width="155">Linkin Park</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">48</td><td valign="top" width="155">Mötley Crüe</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">49</td><td valign="top" width="155">Mr.Children</td><td valign="top" width="155">Mr.Children</td></tr>
<tr><td valign="top" width="155">50</td><td valign="top" width="155">New Kids on the Block</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">51</td><td valign="top" width="155">Nirvana</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">52</td><td valign="top" width="155"></td><td valign="top" width="155">Oasis</td></tr>
<tr><td valign="top" width="155">53</td><td valign="top" width="155">Pearl Jam</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">54</td><td valign="top" width="155"></td><td valign="top" width="155">Pet Shop Boys</td></tr>
<tr><td valign="top" width="155">55</td><td valign="top" width="155"></td><td valign="top" width="155">The Police</td></tr>
<tr><td valign="top" width="155">56</td><td valign="top" width="155">Red Hot Chili Peppers</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">57</td><td valign="top" width="155">R.E.M.</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">58</td><td valign="top" width="155"></td><td valign="top" width="155">Roxette</td></tr>
<tr><td valign="top" width="155">59</td><td valign="top" width="155"></td><td valign="top" width="155">Sade</td></tr>
<tr><td valign="top" width="155">60</td><td valign="top" width="155">Tom Petty and the Heartbreakers</td><td valign="top" width="155"></td></tr>
<tr><td valign="top" width="155">61</td><td valign="top" width="155"></td><td valign="top" width="155">UB40</td></tr>
<tr><td valign="top" width="155">62</td><td valign="top" width="155">Village People</td><td valign="top" width="155"></td></tr>
</tbody></table>
<br />
So the US comes in at number 10 with Aerosmith...<br />
Music sociologists eat your heart out!<br />
<br />
<br />
<br />
天<br />
On FB or Hyves? Click through to original post<br />
<br />
Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-57331964476710423192010-10-26T02:19:00.000+02:002013-06-14T23:32:40.396+02:00The Club<a href="http://dreamling.org/blog/wp-content/uploads/2010/10/crash_sign_1.png"><img class="alignnone size-full wp-image-419" title="crash_sign_1" src="http://dreamling.org/blog/wp-content/uploads/2010/10/crash_sign_1.png" alt="" width="800" height="578" /></a><br/><br/>I was told on several occasions in the recent past that I could not do certain things because I was not a member of a certain club.<br/><br/>Well...<br/>You see...<br/>I have many ambitions in life, but becoming a member of a club has never been high on the list.<br/>It’s because you usually have to do stuff to become a member...<br/><br/>Pay money...<br/>Believe in holy scripture or some other form of fairy tale...<br/><br/>Usually both.<br/>My goddess <a href="http://www.theoi.com/Daimon/Aergia.html">Aergia</a> forbids me to do stuff anyway.<br/><br/>Now.<br/>There are also clubs that do not really require you to invest money or faith, but that require a sequence of certain rites to be performed, preferably ancient ones. Those clubs are the most difficult to become a member of. Not because those rites are impossible to perform, but because the members who have gone through the ordeal of becoming a member, want something in return...<br/><br/>Membership exclusivity and all the power of authority that comes along with that.<br/><br/>That’s all fine... roll through the mud if you must, learn those secret handshakes (don’t forget to wash). Just don’t tell me I cannot do certain things ignoring the quality of those things, just because I am not a member of your club. I have a complementary secret salute to your handshake for you to learn.<br/><br/>So I did, what most people apparently do in such a situation, I started a new club.<br/><br/>Anyone can join<br/>Anyone can start their own<br/><br/>Membership is automatic on attendance<br/><br/>The only requirement is that you have to be prepared put your ideas through some rigorous and ruthless testing. To see what they are worth.<br/><br/>We especially like big and wild ideas<br/><br/><a href="http://web.me.com/fred_hasselman/Crash_Test_Club/Welcome_to_Reality.html">Check it out!<br/></a><br/><br/> <br/><br/><a href="http://dreamling.org/blog/wp-content/uploads/2010/10/shapeimage_2.png"><img class="alignnone size-full wp-image-421" title="shapeimage_2" src="http://dreamling.org/blog/wp-content/uploads/2010/10/shapeimage_2.png" alt="" width="220" height="220" /></a><a href="http://web.me.com/fred_hasselman/Crash_Test_Club/Welcome_to_Reality.html"><br/></a><br/><br/> <br/><br/> <br/><br/> <br/><br/> <br/><br/> <br/><br/> <br/><br/> Anonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0tag:blogger.com,1999:blog-3192184090298016307.post-86509510933397404882010-09-28T03:50:00.000+02:002013-06-14T23:32:40.402+02:00Where’s my Focus? <br/><br/><a href="http://dreamling.org/blog/wp-content/uploads/2010/09/focus3.jpg"><img class="alignnone size-full wp-image-414" title="focus3" src="http://dreamling.org/blog/wp-content/uploads/2010/09/focus3.jpg" alt="" width="320" height="214" /></a><br/><br/> <br/><br/><a href="http://dreamling.org/blog/wp-content/uploads/2010/09/focus3.jpg">[gallery columns="2" orderby="rand"]<br/><br/></a><br/><br/> <br/><br/> <br/><br/>天<br/>On FB or Hyves? Click through to original postAnonymoushttp://www.blogger.com/profile/01414244802603249395noreply@blogger.com0