Neural computation of observational learning in nonhuman primate
Michael received a B.S in biomedical sciences from Handong University, South Korea. He pursued a Ph.D. at the University of Rochester with Benjamin Hayden and later completed his Ph.D. after moving to the University of Minnesota. He worked with nonhuman primates for his thesis to understand the neural basis of predictive behavior and economic decision-making. In his postdoc work with Mehrdad Jazayeri and Robert Desimone, he expanded his interest in computational principles and the neural basis of observational learning.
Observational learning refers to the capacity to understand the latent state of the environment based on others’ actions and the outcomes of those actions. This capacity has been highlighted across many behavioral repertoires in various animal models, and its neural signatures have been reported. However, a mechanistic understanding of how brain circuits represent and make use of others’ actions and experiences is lacking. We propose a computational hypothesis based on the notion of prediction error (PE), in which an observer compares an actor’s actions and outcomes to its own expectations to infer the latent states of the environment. We have developed a novel behavioral task that affords precise quantification PE for action (APE) and outcome (OPE) for self and others. We hypothesize that the neural population in the dorsal premotor cortex (PMd) encodes APE of self and other, and the anterior cingulate cortex (ACC) signals outcome expectations for self and other.
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Observational learning, Nonhuman Primates, Bayesian Predictive Coding, Multi-contact laminar electrophysiology