@article {4815,
	title = {Origin of perseveration in the trade-off between reward and complexity},
	journal = {Cognition},
	volume = {204},
	year = {2020},
	month = {11/2020},
	pages = {104394},
	abstract = {<p>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).</p>
},
	keywords = {Decision making, Information theory, reinforcement learning},
	issn = {00100277},
	doi = {10.1016/j.cognition.2020.104394},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0010027720302134},
	author = {Samuel J Gershman}
}
