Beyond dichotomies in reinforcement learning
- Creators
- Collins, Anne G. E.
- Cockburn, Jeffrey
Abstract
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.
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.Attached Files
Accepted Version - nihms-1656813.pdf
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Additional details
- Alternative title
- Beyond simple dichotomies in reinforcement learning
- PMCID
- PMC7800310
- Eprint ID
- 105295
- DOI
- 10.1038/s41583-020-0355-6
- Resolver ID
- CaltechAUTHORS:20200909-123052677
- Created
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2020-09-09Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field