Origin of perseveration in the trade-off between reward and complexity.

Cognition
Samuel J Gershman

Abstract

When humans and other animals make repeated choices, they tend to repeat previously chosen actions independently of their reward history. This paper locates the origin of perseveration in a trade-off between two computational goals: maximizing rewards and minimizing the complexity of the action policy. We develop an information-theoretic formalization of policy complexity and show how optimizing the trade-off leads to perseveration. Analysis of two data sets reveals that people attain close to optimal trade-offs. Parameter estimation and model comparison supports the claim that perseveration quantitatively agrees with the theoretically predicted functional form (a softmax function with a frequency-dependent action bias).

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Citations

Feb 17, 2021·Wiley Interdisciplinary Reviews. Cognitive Science·Alexa RuelBen Eppinger
Jun 18, 2021·Frontiers in Psychology·Richard Futrell

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