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The successor representation in human reinforcement learning

Author

Listed:
  • I. Momennejad

    (Princeton University)

  • E. M. Russek

    (New York University)

  • J. H. Cheong

    (Dartmouth College)

  • M. M. Botvinick

    (University College London)

  • N. D. Daw

    (Princeton University)

  • S. J. Gershman

    (Harvard University)

Abstract

Theories of reward learning in neuroscience have focused on two families of algorithms thought to capture deliberative versus habitual choice. ‘Model-based’ algorithms compute the value of candidate actions from scratch, whereas ‘model-free’ algorithms make choice more efficient but less flexible by storing pre-computed action values. We examine an intermediate algorithmic family, the successor representation, which balances flexibility and efficiency by storing partially computed action values: predictions about future events. These pre-computation strategies differ in how they update their choices following changes in a task. The successor representation’s reliance on stored predictions about future states predicts a unique signature of insensitivity to changes in the task’s sequence of events, but flexible adjustment following changes to rewards. We provide evidence for such differential sensitivity in two behavioural studies with humans. These results suggest that the successor representation is a computational substrate for semi-flexible choice in humans, introducing a subtler, more cognitive notion of habit.

Suggested Citation

  • I. Momennejad & E. M. Russek & J. H. Cheong & M. M. Botvinick & N. D. Daw & S. J. Gershman, 2017. "The successor representation in human reinforcement learning," Nature Human Behaviour, Nature, vol. 1(9), pages 680-692, September.
    • Handle: RePEc:nat:nathum:v:1:y:2017:i:9:d:10.1038_s41562-017-0180-8
    DOI: 10.1038/s41562-017-0180-8
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    Citations

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    Cited by:

    1. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
    2. Julie J Lee & Mehdi Keramati, 2017. "Flexibility to contingency changes distinguishes habitual and goal-directed strategies in humans," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-15, September.
    3. Ruohan Zhang & Shun Zhang & Matthew H Tong & Yuchen Cui & Constantin A Rothkopf & Dana H Ballard & Mary M Hayhoe, 2018. "Modeling sensory-motor decisions in natural behavior," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-22, October.
    4. Evan M Russek & Ida Momennejad & Matthew M Botvinick & Samuel J Gershman & Nathaniel D Daw, 2017. "Predictive representations can link model-based reinforcement learning to model-free mechanisms," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-35, September.
    5. Jaron T Colas & Wolfgang M Pauli & Tobias Larsen & J Michael Tyszka & John P O’Doherty, 2017. "Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-32, October.
    6. Lucas Lehnert & Michael L Littman & Michael J Frank, 2020. "Reward-predictive representations generalize across tasks in reinforcement learning," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-27, October.
    7. Momchil S Tomov & Samyukta Yagati & Agni Kumar & Wanqian Yang & Samuel J Gershman, 2020. "Discovery of hierarchical representations for efficient planning," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-42, April.
    8. Johann Lussange & Stefano Vrizzi & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2023. "Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1523-1544, April.
    9. Nicholas T Franklin & Michael J Frank, 2018. "Compositional clustering in task structure learning," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-25, April.
    10. Liu, Hui & Yu, Chengqing & Wu, Haiping & Duan, Zhu & Yan, Guangxi, 2020. "A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting," Energy, Elsevier, vol. 202(C).

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