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<p class="MsoNormal"><img width="600" height="106" style="width:6.25in;height:1.1041in" id="Picture_x0020_1" src="cid:image001.png@01D9785B.9C4365C0" alt="Graphical user interface, text, application
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<p class="MsoNormal"><span style="font-family:YaleNew">FDS Seminar<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:YaleNew"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><i><span style="font-size:16.0pt;font-family:YaleNew">“Process models of complex mental computation”<o:p></o:p></span></i></b></p>
<p class="MsoNormal"><span style="font-family:YaleNew"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span style="font-family:YaleNew">Speaker: Wei Ji Ma</span></b><span style="font-family:YaleNew">
<br>
Center for Neural Science <br>
New York University <o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:YaleNew"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span style="font-family:YaleNew">Location: Mason Lab 211 (9 Hillhouse Avenue) or remotely via
<a href="https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=77784297-c06b-441e-b2d8-af93011fd6e8">
Panopto</a>.<o:p></o:p></span></b></p>
<p class="MsoNormal"><span style="font-family:YaleNew"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span style="font-family:YaleNew">Abstract:</span></b><span style="font-family:YaleNew"> Computational cognitive models commit to a sequence of steps in which an observer/agent mentally processes information leading up to a behavioral
response. Typically, both the model parameters and the model structure must be inferred solely from stimulus-response pairs. For more complex mental computations, these inferences tend to be more challenging, yet potentially yield greater insights. I will
illustrate this using two examples from disparate domains. In the first study, we test whether people perform unconscious Bayesian inference in visual search, specifically, whether they marginalize over nuisance variables. In the second study, we model human
planning in a two-player board game using a “humanized” variant of best-first search. I will describe the methodological challenges associated with unbiased estimation of log likelihoods and with parameter fitting, and our proposed solutions.
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:YaleNew"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span style="font-family:YaleNew">Speaker bio:</span></b><span style="font-family:YaleNew"> Wei Ji Ma is Professor of Neural Science and Psychology at NYU. His lab studies decision-making in planning, social cognition, working memory,
perception, and attention, using a combination of human behavioral experiments, computational modeling, and - through collaborations - electrophysiology and neuroimaging. Wei Ji grew up in the Netherlands and received his Ph.D. in Physics from the University
of Groningen. He continued as a postdoc in computational neuroscience, first with Christof Koch at Caltech and then with Alexandre Pouget at the University of Rochester. He was Assistant Professor of Neuroscience at Baylor College of Medicine from 2008 to
2013. He has been at NYU since 2013. He has affiliate appointments in the Neuroscience Institute, the Institute for the Study of Decision Making, the Center for Data Science, and the Center for Experimental Social Science, and is Collaborating Faculty of the
NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai. With Xiao-Jing Wang, Wei Ji is Program Director of the NIH-funded Training Program in Computational Neuroscience at NYU. Moreover, Wei Ji is active in mentorship, community-building, and outreach.
He is a founding member of the Scientist Action and Advocacy Network and of NeuWrite NYU. Wei Ji co-founded and leads the Growing up in Science seminar series, in which scientists tell their "unofficial stories". Read or listen to Wei Ji's own unofficial story.
Besides his academic work, Wei Ji is the co-founder of the Rural China Education Foundation.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:YaleNew"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-family:YaleNew">Hosted by John Lafferty<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:YaleNew"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span style="font-family:YaleNew">Upcoming:<o:p></o:p></span></b></p>
<p class="MsoNormal"><span style="font-family:YaleNew"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-family:YaleNew"><a href="https://campuspress.yale.edu/fds/calendar_event/fds-seminar-entry-wise-dissipation-for-singular-vector-perturbation-bounds/">April 27: FDS Seminar: Abhinav Bhardwaj (Yale Math), “Entry-wise dissipation
for singular vector perturbation bounds”</a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:YaleNew"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-family:YaleNew"><a href="https://campuspress.yale.edu/fds/calendar_event/data-science-for-healthcare/">May 1: Healthcare Data Science Workshop (registration required)</a><o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal"><span style="font-size:10.0pt;color:black">Emily E. H. Hau | Associate Director, Data Science @ Yale University</span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><b><i><span style="font-size:10.0pt;color:#4472C4">Yale Institute for Foundations of Data Science (FDS)</span></i></b><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><b><i><span style="font-size:10.0pt;color:#4472C4">Yale Institute for Network Science (YINS)</span></i></b><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;color:black">17 Hillhouse Avenue | Room 341 | New Haven, CT 06511</span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><u><span lang="PT-BR" style="font-size:10.0pt;color:#0563C1"><a href="mailto:emily.hau@yale.edu" title="mailto:emily.hau@yale.edu"><span style="color:#0563C1">emily.hau@yale.edu</span></a></span></u><span lang="PT-BR" style="font-size:10.0pt;color:#0563C1"> <u>|</u> </span><span lang="PT-BR" style="font-size:10.0pt;color:black">P:
203-436-4732 </span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;color:black">@yaledatascience <o:p>
</o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="color:black">-- <o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:black">FDS-announce mailing list<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:black"><a href="mailto:Fds-announce@mailman.yale.edu"><span style="color:#0563C1">Fds-announce@mailman.yale.edu</span></a>
<o:p></o:p></span></p>
<p class="MsoNormal">To subscribe or unsubscribe: <span style="color:black"><a href="https://mailman.yale.edu/mailman/listinfo/fds-announce"><span style="color:#0563C1">https://mailman.yale.edu/mailman/listinfo/fds-announce</span></a>
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