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Probabilistic models of cognition: exploring representations and inductive biases.
Thomas L Griffiths1, Nick Chater, Charles Kemp
1Department of Psychology, University of California, Berkeley, 3210 Tolman Hall MC 1650, Berkeley, CA 94720-1650, USA. tomgriffiths@berkeley.edu
Trends in Cognitive Sciences
|June 26, 2010
Summary
Cognitive science uses a top-down approach to understand the mind by modeling ideal solutions to inductive problems. This method offers greater flexibility in exploring cognitive representations and biases compared to bottom-up strategies.
Area of Science:
- Cognitive Science
- Computational Neuroscience
- Artificial Intelligence
Background:
- Cognitive science seeks to understand the mind, with induction posing significant engineering challenges.
- Probabilistic models of cognition define ideal inductive problem solutions.
- Neural processes are viewed as implementations of cognitive algorithms.
Purpose of the Study:
- To present a top-down approach to cognitive modeling.
- To contrast this with bottom-up connectionist models.
- To highlight the advantages of a functional, top-down perspective.
Main Methods:
- Modeling cognitive processes using algorithms that approximate ideal solutions to inductive problems.
- Analyzing neural processes as mechanisms for implementing these algorithms.
- Comparing top-down functional analysis with bottom-up connectionist approaches.
Main Results:
- The top-down approach provides a functional analysis of cognition.
- Connectionist models offer a bottom-up perspective starting from neural mechanisms.
- The top-down strategy allows for greater flexibility in exploring cognitive representations.
Conclusions:
- A top-down, probabilistic approach to cognitive science offers superior flexibility.
- This framework facilitates the investigation of representations and inductive biases.
- Understanding cognitive function is key to reverse-engineering the mind.
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