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ERIC Number: EJ785760
Record Type: Journal
Publication Date: 2008-Jan
Pages: 7
Abstractor: Author
Reference Count: 12
ISSN: ISSN-0364-0213
Is a Single-Bladed Knife Enough to Dissect Human Cognition? Commentary on Griffiths et al.
Fu, Wai-Tat
Cognitive Science, v32 n1 p155-161 Jan 2008
Griffiths, Christian, and Kalish (this issue) present an iterative-learning paradigm applying a Bayesian model to understand inductive biases in categorization. The authors argue that the paradigm is useful as an exploratory tool to understand inductive biases in situations where little is known about the task. It is argued that a theory developed "only" at the computational level is much like a single-bladed knife that is only useful in highly idealized situations. To be useful as a general tool that cuts through the complex fabric of cognition, we need at least two-bladed scissors that combine both computational and psychological constraints to characterize human behavior. To temper its sometimes expansive claims, it is time to show what a Bayesian model "cannot" explain. Insight as to how human reality may differ from the Bayesian predictions may shed more light on human cognition than the simpler focus on what the Bayesian approach "can" explain. There remains much to be done in terms of integrating Bayesian approaches and other approaches in modeling human cognition. (Contains 1 note.)
Lawrence Erlbaum. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site:
Publication Type: Journal Articles; Opinion Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A