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

TitleOrigin of perseveration in the trade-off between reward and complexity
Publication TypeJournal Article
Year of Publication2020
AuthorsGershman, SJ
Date Published11/2020
KeywordsDecision making, Information theory, reinforcement learning

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).

Short TitleCognition

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