Probabilistic models of cognition: exploring representations and inductive biases
Griffiths, Thomas L., Chater, Nick, Kemp, Charles, Perfors, Amy and Tenenbaum, Joshua B.. (2010) Probabilistic models of cognition: exploring representations and inductive biases. Trends in Cognitive Sciences, Vol.14 (No.8). pp. 357-364. ISSN 1364-6613Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.tics.2010.05.004
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.
|Item Type:||Journal Article|
|Subjects:||H Social Sciences > HV Social pathology. Social and public welfare|
|Divisions:||Faculty of Social Sciences > Warwick Business School > Behavioural Science
Faculty of Social Sciences > Warwick Business School
|Journal or Publication Title:||Trends in Cognitive Sciences|
|Official Date:||August 2010|
|Number of Pages:||8|
|Page Range:||pp. 357-364|
|Access rights to Published version:||Restricted or Subscription Access|
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