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Computational processes of simultaneous learning of stochasticity and volatility in humans

Author

Listed:
  • Payam Piray

    (University of Southern California)

  • Nathaniel D. Daw

    (Princeton University)

Abstract

Making adaptive decisions requires predicting outcomes, and this in turn requires adapting to uncertain environments. This study explores computational challenges in distinguishing two types of noise influencing predictions: volatility and stochasticity. Volatility refers to diffusion noise in latent causes, requiring a higher learning rate, while stochasticity introduces moment-to-moment observation noise and reduces learning rate. Dissociating these effects is challenging as both increase the variance of observations. Previous research examined these factors mostly separately, but it remains unclear whether and how humans dissociate them when they are played off against one another. In two large-scale experiments, through a behavioral prediction task and computational modeling, we report evidence of humans dissociating volatility and stochasticity solely based on their observations. We observed contrasting effects of volatility and stochasticity on learning rates, consistent with statistical principles. These results are consistent with a computational model that estimates volatility and stochasticity by balancing their dueling effects.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53459-z
    DOI: 10.1038/s41467-024-53459-z
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    References listed on IDEAS

    as
    1. Payam Piray & Nathaniel D Daw, 2020. "A simple model for learning in volatile environments," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-26, July.
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