title: Probabilistic models of cognition: exploring representations and inductive biases.
abstract: Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind faces involve induction. The probabilistic approach to modeling cognition begins by identifying ideal solutions to these inductive problems. Mental processes are then modeled using algorithms for approximating these solutions, and neural processes are viewed as mechanisms for implementing these algorithms, with the result being a top-down analysis of cognition starting with the function of cognitive processes. Typical connectionist models, by contrast, follow a bottom-up approach, beginning with a characterization of neural mechanisms and exploring what macro-level functional phenomena might emerge. We argue that the top-down approach yields greater flexibility for exploring the representations and inductive biases that underlie human cognition.
authors.affiliation: Department of Psychology, University of California, Berkeley, 3210 Tolman Hall MC 1650, Berkeley, CA 94720-1650, USA. tomgriffiths@berkeley.edu
authors.forename: Thomas L
authors.initials: TL
authors.lastname: Griffiths
authors.forename: Nick
authors.initials: N
authors.lastname: Chater
authors.forename: Charles
authors.initials: C
authors.lastname: Kemp
authors.forename: Amy
authors.initials: A
authors.lastname: Perfors
authors.forename: Joshua B
authors.initials: JB
authors.lastname: Tenenbaum
mesh_terms: D015982:Bias; D001921:Brain / Q000502:physiology; D003071:Cognition / Q000502:physiology*; D006801:Humans; D008960:Models, Psychological; D011237:Predictive Value of Tests; D011336:Probability