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  Multi-task reinforcement learning in humans

Tomov, M., Schulz, E., & Gershman, S. (2021). Multi-task reinforcement learning in humans. Nature Human Behaviour, 5(6), 764-773. doi:10.1038/s41562-020-01035-y.

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Tomov, M, Author
Schulz, E1, 2, Author           
Gershman, SJ, Author
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1Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3189356              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: The ability to transfer knowledge across tasks and generalize to novel ones is an important hallmark of human intelligence. Yet not much is known about human multitask reinforcement learning. We study participants’ behaviour in a two-step decision-making task with multiple features and changing reward functions. We compare their behaviour with two algorithms for multitask reinforcement learning, one that maps previous policies and encountered features to new reward functions and one that approximates value functions across tasks, as well as to standard model-based and model-free algorithms. Across three exploratory experiments and a large preregistered confirmatory experiment, our results provide evidence that participants who are able to learn the task use a strategy that maps previously learned policies to novel scenarios. These results enrich our understanding of human reinforcement learning in complex environments with changing task demands.

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 Dates: 2021-012021-06
 Publication Status: Published in print
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 Identifiers: DOI: 10.1038/s41562-020-01035-y
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Title: Nature Human Behaviour
Source Genre: Journal
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Publ. Info: London : Nature Research
Pages: - Volume / Issue: 5 (6) Sequence Number: - Start / End Page: 764 - 773 Identifier: ISSN: 2397-3374
CoNE: https://pure.mpg.de/cone/journals/resource/2397-3374