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Experience replay supports non-local learning

View ORCID ProfileYunzhe Liu, View ORCID ProfileMarcelo G. Mattar, View ORCID ProfileTimothy E J Behrens, View ORCID ProfileNathaniel D. Daw, Raymond J Dolan
doi: https://doi.org/10.1101/2020.10.20.343061
Yunzhe Liu
1Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, UK
2Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
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  • ORCID record for Yunzhe Liu
  • For correspondence: yunzhe.liu.16@ucl.ac.uk
Marcelo G. Mattar
3Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Timothy E J Behrens
2Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
4Wellcome Centre for Integrative Neuroimaging, Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, Oxford OX3 9DU, UK
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Nathaniel D. Daw
3Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Raymond J Dolan
1Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, UK
2Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
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Abstract

To make effective decisions we need to consider the relationship between actions and outcomes. They are, however, often separated by time and space. The biological mechanism capable of spanning those gaps remains unknown. One promising, albeit hypothetical, mechanism involves neural replay of non-local experience. Using a novel task, that segregates direct from indirect learning, combined with magnetoencephalography (MEG), we tested the role of neural replay in non-local learning in humans. Following reward receipt, we found significant backward replay of non-local experience, with a 160 msec state-to-state time lag, and this replay facilitated learning of action values. This backward replay, combined with behavioural evidence of non-local learning, was more pronounced in experiences that were of greater benefit for future behavior, as predicted by theories of prioritization. These findings establish rationally targeted non-local replay as a neural mechanism for solving complex credit assignment problems during learning.

One Sentence Summary Reverse sequential replay is found, for the first time, to support non-local reinforcement learning in humans and is prioritized according to utility.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵6 Joint Senior author

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 21, 2020.
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Experience replay supports non-local learning
Yunzhe Liu, Marcelo G. Mattar, Timothy E J Behrens, Nathaniel D. Daw, Raymond J Dolan
bioRxiv 2020.10.20.343061; doi: https://doi.org/10.1101/2020.10.20.343061
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Experience replay supports non-local learning
Yunzhe Liu, Marcelo G. Mattar, Timothy E J Behrens, Nathaniel D. Daw, Raymond J Dolan
bioRxiv 2020.10.20.343061; doi: https://doi.org/10.1101/2020.10.20.343061

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