%0 Journal Article %J Cognition %D 2020 %T Origin of perseveration in the trade-off between reward and complexity %A Samuel J Gershman %K Decision making %K Information theory %K reinforcement learning %X

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).

%B Cognition %V 204 %P 104394 %8 11/2020 %G eng %U https://linkinghub.elsevier.com/retrieve/pii/S0010027720302134 %! Cognition %R 10.1016/j.cognition.2020.104394