Bayesian Modeling of the Mind: Conceptual and Explanatory Foundations

FAIN: FA-232860-16

Michael Rescorla
UCLA; Regents of the University of California, Los Angeles (Santa Barbara, CA 93106-0001)

Four articles on cognitive science and Bayesian modeling of the mind.

Illuminating how the mind works has been a central concern of humanistic research stretching back to Plato. I seek to advance this enterprise by analyzing Bayesian cognitive science, a scientific research program that models the mind using probabilities. My analysis hinges upon the mind’s capacity to represent the world. I will argue that Bayesian cognitive science assigns a central explanatory role to mental representation. Bayesian modeling reveals that core mental activities such as perception, action, and decision-making all crucially involve representational aspects of mentality. My analysis should advance our understanding of the mind by establishing that mental representation is an indispensable theoretical notion. As an illustrative case study, I will discuss Bayesian modeling of autism. My discussion of this case study should clarify some important points of similarity and difference between typically developing individuals and individuals with autism.