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A Unifying Probabilistic View of Associative Learning

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  • Samuel J Gershman

Abstract

Two important ideas about associative learning have emerged in recent decades: (1) Animals are Bayesian learners, tracking their uncertainty about associations; and (2) animals acquire long-term reward predictions through reinforcement learning. Both of these ideas are normative, in the sense that they are derived from rational design principles. They are also descriptive, capturing a wide range of empirical phenomena that troubled earlier theories. This article describes a unifying framework encompassing Bayesian and reinforcement learning theories of associative learning. Each perspective captures a different aspect of associative learning, and their synthesis offers insight into phenomena that neither perspective can explain on its own.Author Summary: How do we learn about associations between events? The seminal Rescorla-Wagner model provided a simple yet powerful foundation for understanding associative learning. However, much subsequent research has uncovered fundamental limitations of the Rescorla-Wagner model. One response to these limitations has been to rethink associative learning from a normative statistical perspective: How would an ideal agent learn about associations? First, an agent should track its uncertainty using Bayesian principles. Second, an agent should learn about long-term (not just immediate) reward, using reinforcement learning principles. This article brings together these principles into a single framework and shows how they synergistically account for a number of complex learning phenomena.

Suggested Citation

  • Samuel J Gershman, 2015. "A Unifying Probabilistic View of Associative Learning," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-20, November.
  • Handle: RePEc:plo:pcbi00:1004567
    DOI: 10.1371/journal.pcbi.1004567
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    References listed on IDEAS

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    1. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
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    1. Athina Tzovara & Christoph W Korn & Dominik R Bach, 2018. "Human Pavlovian fear conditioning conforms to probabilistic learning," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-21, August.
    2. Payam Piray & Nathaniel D. Daw, 2024. "Computational processes of simultaneous learning of stochasticity and volatility in humans," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    3. Antonino Greco & Julia Moser & Hubert Preissl & Markus Siegel, 2024. "Predictive learning shapes the representational geometry of the human brain," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    4. Olschewski, Sebastian & Diao, Linan & Rieskamp, Jörg, 2021. "Reinforcement learning about asset variability and correlation in repeated portfolio decisions," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    5. Filip Melinscak & Dominik R Bach, 2020. "Computational optimization of associative learning experiments," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-23, January.
    6. Holger Mohr & Katharina Zwosta & Dimitrije Markovic & Sebastian Bitzer & Uta Wolfensteller & Hannes Ruge, 2018. "Deterministic response strategies in a trial-and-error learning task," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-19, November.
    7. Anna P. Giron & Simon Ciranka & Eric Schulz & Wouter Bos & Azzurra Ruggeri & Björn Meder & Charley M. Wu, 2023. "Developmental changes in exploration resemble stochastic optimization," Nature Human Behaviour, Nature, vol. 7(11), pages 1955-1967, November.

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