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Beyond dichotomies in reinforcement learning

Collins, Anne G. E. and Cockburn, Jeffrey (2020) Beyond dichotomies in reinforcement learning. Nature Reviews. Neuroscience, 21 (10). pp. 576-586. ISSN 1471-003X.

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Reinforcement learning (RL) is a framework of particular importance to psychology, neuroscience and machine learning. Interactions between these fields, as promoted through the common hub of RL, has facilitated paradigm shifts that relate multiple levels of analysis in a singular framework (for example, relating dopamine function to a computationally defined RL signal). Recently, more sophisticated RL algorithms have been proposed to better account for human learning, and in particular its oft-documented reliance on two separable systems: a model-based (MB) system and a model-free (MF) system. However, along with many benefits, this dichotomous lens can distort questions, and may contribute to an unnecessarily narrow perspective on learning and decision-making. Here, we outline some of the consequences that come from overconfidently mapping algorithms, such as MB versus MF RL, with putative cognitive processes. We argue that the field is well positioned to move beyond simplistic dichotomies, and we propose a means of refocusing research questions towards the rich and complex components that comprise learning and decision-making.

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Collins, Anne G. E.0000-0003-3751-3662
Additional Information:© 2020 Nature Publishing Group. Accepted 20 July 2020; Published 01 September 2020; Issue Date October 2020. Author Contributions: The authors contributed equally to all aspects of the article. The authors declare no competing interests. Peer review information: Nature Reviews Neuroscience thanks the anonymous reviewer(s) for their contribution to the peer review of this work.
Subject Keywords:Cognitive neuroscience; Learning algorithms; Learning and memory; Psychology
Issue or Number:10
Record Number:CaltechAUTHORS:20200909-123052677
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Official Citation:Collins, A.G.E., Cockburn, J. Beyond dichotomies in reinforcement learning. Nat Rev Neurosci 21, 576–586 (2020).
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105295
Deposited By: Tony Diaz
Deposited On:09 Sep 2020 19:35
Last Modified:15 Sep 2020 17:49

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