[WTI-trainee] FW: Computation and Cognition Joint Lab Meetings Starting Thursday, 9/26

[Guerrero-Medina, Giovanna]

Hi everyone,

See below for a new opportunity to discuss computation and cognition.

Best,

Giovanna

Giovanna Guerrero-Medina, PhD [A button for name playback in email signature]  <https://www.name-coach.com/giovanna-guerrero-medina>
She/Her/Ella
Director for Diversity Equity & Inclusion,
Wu Tsai Institute at Yale
giovanna.guerrero-medina at yale.edu<mailto:giovanna.guerrero-medina at yale.edu>
203.785.2915 (office), 616.643.7666 (cell)
wti.yale.edu<https://wti.yale.edu/>

From: Psych.others <psych.others-bounces at mailman.yale.edu> on behalf of Brooke-Wilson, Tyler <tyler.brooke.wilson at yale.edu>
Date: Friday, September 20, 2024 at 2:18 PM
To: psych.all at mailman.yale.edu <psych.all at mailman.yale.edu>
Subject: Computation and Cognition Joint Lab Meetings Starting Thursday, 9/26
Hi All,

I'm writing to announce a new joint lab meeting on the theme of Computation and Cognition. This will be a monthly meeting for anyone interested in computational approaches to the mind. Over the coming year, we'll be hearing from students, meeting to discuss papers, and presenting research directions on the broad theme of computational approaches to the mind.

Topics we'll cover include computational approaches to human language, vision, and social cognition. We're approaching these broad areas in tandem because many of the same themes come up across areas (themes like compositionality, concept learning, optimality, and inference) and because we believe that findings in each can illuminate issues in others.

Meetings are open to all and will draw on faculty and students across psychology, linguistics, cognitive science, and computer science. Our first meeting will take place this Thursday (9/26), 1-2:30 in 100 College St. RM 1167. Details about our first speakers can be found below.

Hope to see many of you there!

Best,
Tyler

Details:
Time: 1-2:30PM
Location: 100 College, RM 1167

Mailing List:
If you are interested in receiving updates and announcements, please add your email here – https://mailman.yale.edu/mailman/listinfo/compcog
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First Week Speakers –

Variability in the Speed, Accuracy, and Effort of Mental-State Inferences in Theory of Mind
Urvi Suwal

Abstract: Human social cognition is built on our capacity to think about other minds. Do people have a general capacity to infer the contents of other minds from observable behavior? Or is this capacity specialized for inferring certain mental states, while struggling with others? Using a novel paradigm, we tested people’s capacity to infer another person’s desires, beliefs, or visual experience across three tasks that were logically equivalent, and thus equally complex. Despite the tasks being logically identical, people were less accurate, slower, and reported expending more effort when inferring the content of others’ visual experience compared to inferring others' beliefs and desires (Experiments 1 and 2; N = 120 U.S. adults and N = 60 U.S. adults). However, additional training rapidly improved people’s capacity to infer others’ visual experience (Experiment 3; N = 60 U.S. adults). These results suggest that the quality and speed of Theory of Mind inferences might be specialized and shaped by everyday use—a property not adequately captured through content-general inferential algorithms.

Multi-area processing in primate inferotemporal cortex implements inverse graphics
Hakan Yilmaz

Abstract: Multi-area processing in the inferotemporal (IT) cortex is critical for transforming sensory features into useful representations of the world. What are these representations and how are they computed in IT networks? Here, we show that inferring scene-level causes may be an organizing objective of IT, via an algorithm analogous to the reverse of graphics-based generative models of how 3D scenes form and project to images. Using perception of bodies as a case study, we show that inverse graphics spontaneously emerges in inference networks trained to map images to their scene-level causes. Remarkably, this correspondence to the reverse of a graphics-based generative model also holds across the body processing network of the macaque IT cortex. Finally, inference networks recapitulate feedforward progression in IT, outperforming state-of-the-art computer vision models. This work suggests inverse graphics as a multi-area neural algorithm of IT, and points to ways for replicating primate vision capabilities in machines.
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