Computational Psychology

This preconference brings together researchers interested in computational methods to glean new insights into social behavior and cognition. In recent years, computational methods including text analysis, reinforcement learning, social network analysis, Bayesian modeling, dynamical systems modeling, deep learning, and agent-based modeling have opened new approaches to studying the social mind. These methods support both theory-driven and data-driven approaches to social-personality psychology. From a theory-driven perspective, these methods allow researchers to engage in rigorous psychological hypothesis testing via the development of quantitatively precise, mechanistic, and falsifiable cognitive models. From a data-driven perspective, these methods allow researchers to discover the structure hidden within large, complex, and naturalistic datasets and to model the emergence of population-level phenomena. This forum will unite the diverse community of researchers who already use computational methods, and inspire and educate new researchers to use these methods to answer substantive questions at the heart of social-personality psychology.

Preconference Organizers

Mark Thornton, [email protected], Dartmouth College
William Brady, [email protected], Northwestern University
Brent Hughes, [email protected], University of California, Riverside
Chujun Lin, [email protected], University of California, San Diego
Tessa Charlesworth, [email protected], Harvard University

Preconference submissions are now closed. If you have any questions about this preconference, please email the organizers (listed above).