Uncertainty in learning, choice, and visual fixation

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  • Hrvoje Stojić, University College London
  • ,
  • Jacob L. Orquin
  • Peter Dayan, Max Planck Institute for Biological Cybernetics
  • ,
  • Raymond J. Dolan, University College London
  • ,
  • Maarten Speekenbrink, University College London

Uncertainty plays a critical role in reinforcement learning and decision making. However, exactly how it influences behavior remains unclear. Multiarmed-bandit tasks offer an ideal test bed, since computational tools such as approximate Kalman filters can closely characterize the interplay between trial-by-trial values, uncertainty, learning, and choice. To gain additional insight into learning and choice processes, we obtained data from subjects’ overt allocation of gaze. The estimated value and estimation uncertainty of options influenced what subjects looked at before choosing; these same quantities also influenced choice, as additionally did fixation itself. A momentary measure of uncertainty in the form of absolute prediction errors determined how long participants looked at the obtained outcomes. These findings affirm the importance of uncertainty in multiple facets of behavior and help delineate its effects on decision making.

Original languageEnglish
JournalProceedings of the National Academy of Sciences of the United States of America
Pages (from-to)3291-3300
Number of pages10
Publication statusPublished - 2020

    Research areas

  • Decision making, Exploration–exploitation, Reinforcement learning, Uncertainty, Visual fixation

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