Probabilistic models of cognition: exploring representations and inductive biases.
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.
Griffiths
Thomas L
TL
Department of Psychology, University of California, Berkeley, 3210 Tolman Hall MC 1650, Berkeley, CA 94720-1650, USA. tomgriffiths@berkeley.edu
Chater
Nick
N
Kemp
Charles
C
Perfors
Amy
A
Tenenbaum
Joshua B
JB
D015982:Bias; D001921:Brain / Q000502:physiology; D003071:Cognition / Q000502:physiology*; D006801:Humans; D008960:Models, Psychological; D011237:Predictive Value of Tests; D011336:Probability