Pre-Conference Workshop

Overview

*Please visit the pre-conference website for pre-recorded videos and other workshop materials.

The pre-conference workshop will be held on April 10, 2024 from 1:30PM to 5:30PM at the Westin Harbour Castle, Toronto.

The workshop will be led by:

  1. Leor Hackel, University of Southern California
  2. Eshin Jolly, Dartmouth College
  3. Philip Kragel, Emory University

This highly experienced team has published multiple papers in the social computational space in both general science and cross-psychological area journals like Nature Neuroscience, Science Advances, Psychological Science, and Journal of Experimental Psychology: General. The topics covered in the workshop will reflect the work being done in their respective research programs, including social reinforcement learning, multivariate pattern classification as applied to complex phenomena like emotional experience, and using automated computer vision to analyze facial expressions.

Materials will be provided in advance for review, including pre-recorded talks with the relevant theory. On-site at the conference, you will put that theory into practice with this hands-on computational workshop where attendees will learn how to implement computational methods and evaluate results in small groups.

Please note: Familiarity with basic programming in Python/R is recommended, but not required for participation.

 

Leor Hackel

Leor Hackel

University of Southern California

Leor Hackel is an Assistant Professor of Psychology at USC. He completed his B.S. in Neuroscience & Behavior at Columbia University, working with Kevin Ochsner; his Ph.D. in Social Psychology at NYU, working with Jay Van Bavel and David Amodio; and post-doctoral training at Stanford University, working with Jamil Zaki. In 2019, he joined the Department of Psychology at USC, where he directs the USC Social Learning & Choice Lab.

Leor’s research asks how humans learn about their social worlds and make social decisions, including learning which individuals to interact with (partner choice), learning what others are like (impression formation), and choosing how to treat others (prosocial behavior). He investigates these questions by integrating methods from computational neuroscience and social cognition, with a focus on models of reinforcement learning and value-based choice, to inform how people assign value to social actions.

Eshin Jolly

Eshin Jolly

Dartmouth College

Eshin Jolly received his BA from the University of Rochester working with Jessica Cantlon and Brad Mahon, did a stint as a lab manager at Harvard University working with Jason Mitchell and Joe Moran, and completed his PhD at Dartmouth working with Luke Chang and Thalia Wheatley. He also spent a summer working at Microsoft Research with Duncan Watts and Sid Suri. He is currently a post-doctoral fellow at Dartmouth in the Center for Cognitive Neuroscience working with Emily Finn and the Consortium for Interacting Minds working with Luke Chang.

Eshin takes an interdisciplinary approach to understand how humans represent, remember, and share information about other people by combining approaches from cognitive neuroscience, social psychology, behavioral economics, computational social science, and artificial intelligence. In addition to his academic research Eshin develops and contributes to open-source scientific software to increase the accessibility of computational developments to social scientists.

Phil Kragel

Phil Kragel

Emory University

Dr. Philip A. Kragel received his Bachelor of Science and Engineering (2006) and a Masters in Engineering Management (2007) from the Pratt School of Engineering at Duke University. He went on to complete his Ph.D. in Psychology and Neuroscience (2015) at Duke University under the direction of Kevin LaBar. From 2015-2020, he was a postdoctoral associate with Tor Wager at the University of Colorado Boulder in the Institute of Cognitive Science.

Dr. Kragel joined the Psychology Department at Emory University as an Assistant Professor in 2020. Dr. Kragel’s research explores the neural and computational basis of cognitive and affective behavior in humans, with a particular focus on understanding the nature of emotions – where they come from and what makes them unique from other mental phenomena. His research program combines ideas from psychology, neuroscience, and machine learning to build multivariate models that are both sensitive and specific to the engagement of individual mental processes.

One line of research in Dr. Kragel’s lab is focused on developing quantitative models of brain activity that can be used to make predictions in independent studies and laboratories. This cumulative approach enables models to be shared and prospectively tested, making research more transparent and reproducible. It also facilitates strong inferences about the nature of emotion and may lead to new outcomes and targets for clinical interventions. A second, related area of research explains affective behavior using computational models (e.g., neural networks) and maps components of these models onto human brain activity, aiming to provide an objective account of the brain representations and transformations that underlie human emotion.

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