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Rational and mechanistic perspectives on reinforcement learning

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Chater, Nick (2009) Rational and mechanistic perspectives on reinforcement learning. Cognition, Vol.113 (No.3). pp. 350-364. doi:10.1016/j.cognition.2008.06.014

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Official URL: http://dx.doi.org/10.1016/j.cognition.2008.06.014

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Abstract

This special issue describes important recent developments in applying reinforcement learning models to capture neural and cognitive function. But reinforcement learning, as a theoretical framework, can apply at two very different levels of description: mechanistic and rational. Reinforcement learning is often viewed in mechanistic terms – as describing the operation of aspects of an agent’s cognitive and neural machinery. Yet it can also be viewed as a rational level of description, specifically, as describing a class of methods for learning from experience, using minimal background knowledge. This paper considers how rational and mechanistic perspectives differ, and what types of evidence distinguish between them. Reinforcement learning research in the cognitive and brain sciences is often implicitly committed to the mechanistic interpretation. Here the opposite view is put forward: that accounts of reinforcement learning should apply at the rational level, unless there is strong evidence for a mechanistic interpretation. Implications of this viewpoint for reinforcement-based theories in the cognitive and brain sciences are discussed.

Item Type: Journal Article
Subjects: H Social Sciences > H Social Sciences (General)
Divisions: Faculty of Social Sciences > Warwick Business School > Behavioural Science
Faculty of Social Sciences > Warwick Business School
Journal or Publication Title: Cognition
Publisher: Elsevier
ISSN: 0010-0277
Official Date: December 2009
Dates:
DateEvent
December 2009Published
Volume: Vol.113
Number: No.3
Number of Pages: 15
Page Range: pp. 350-364
DOI: 10.1016/j.cognition.2008.06.014
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

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