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Arousal-related adjustments of perceptual biases optimize perception in dynamic environments

Author

Listed:
  • Kamesh Krishnamurthy

    (University of Pennsylvania)

  • Matthew R. Nassar

    (Linguistic, and Psychological Sciences, Brown University)

  • Shilpa Sarode

    (University of Pennsylvania)

  • Joshua I. Gold

    (University of Pennsylvania)

Abstract

Prior expectations can be used to improve perceptual judgments about ambiguous stimuli. However, little is known about whether and how these improvements are maintained in dynamic environments in which the quality of appropriate priors changes from one stimulus to the next. Here we use a sound-localization task to show that changes in stimulus predictability lead to arousal-mediated adjustments in the magnitude of prior-driven biases that optimize perceptual judgments about each stimulus. These adjustments depend on task-dependent changes in the relevance and reliability of prior expectations, which subjects update using both normative and idiosyncratic principles. The resulting variations in biases across task conditions and individuals are reflected in modulations of pupil diameter, such that larger stimulus-evoked pupil responses correspond to smaller biases. These results suggest a notable role for the arousal system in adjusting the strength of perceptual biases with respect to inferred environmental dynamics to optimize perceptual judgements.

Suggested Citation

  • Kamesh Krishnamurthy & Matthew R. Nassar & Shilpa Sarode & Joshua I. Gold, 2017. "Arousal-related adjustments of perceptual biases optimize perception in dynamic environments," Nature Human Behaviour, Nature, vol. 1(6), pages 1-11, June.
    • Handle: RePEc:nat:nathum:v:1:y:2017:i:6:d:10.1038_s41562-017-0107
    DOI: 10.1038/s41562-017-0107
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    Cited by:

    1. Peter Vincent & Thomas Parr & David Benrimoh & Karl J Friston, 2019. "With an eye on uncertainty: Modelling pupillary responses to environmental volatility," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-22, July.
    2. Florent Meyniel, 2020. "Brain dynamics for confidence-weighted learning," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-27, June.

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