Computational Modeling of Social Learning and Decision-Making
Damian Stanley, Ph.D.,
Adelphi University
The ability to learn and make accurate inferences about other people is critical for successful navigation of our social environment and the consequences of perturbation or failure can be severe (e.g., inequity, loss of employment, damage to relationships, etc.). Specialized neural systems may support these social-cognitive processes, and their impairment is characteristic of many mental health disorders. Research in my lab is aimed at developing a computational account of human social learning and decision-making, with a particular focus on how these processes can be influenced by situations (e.g., COVID-19) or social biases (e.g., implicit race bias), and perturbed in neurodiverse populations(e.g., individuals with autism). In this talk I will discuss one line of research aimed at characterizing neurocognitive computations that are unique to social learning and decision- making, and another that seeks to identify social computational phenotypes of mental disorder.
ORGANIZERS
Nesha Burghardt, Hunter College, CUNY
Asohan Amarasingham, City College of New York, CUNY