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Adaptive Sampling of Information in Perceptual Decision-Making

Author

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  • Thomas C Cassey
  • David R Evens
  • Rafal Bogacz
  • James A R Marshall
  • Casimir J H Ludwig

Abstract

In many perceptual and cognitive decision-making problems, humans sample multiple noisy information sources serially, and integrate the sampled information to make an overall decision. We derive the optimal decision procedure for two-alternative choice tasks in which the different options are sampled one at a time, sources vary in the quality of the information they provide, and the available time is fixed. To maximize accuracy, the optimal observer allocates time to sampling different information sources in proportion to their noise levels. We tested human observers in a corresponding perceptual decision-making task. Observers compared the direction of two random dot motion patterns that were triggered only when fixated. Observers allocated more time to the noisier pattern, in a manner that correlated with their sensory uncertainty about the direction of the patterns. There were several differences between the optimal observer predictions and human behaviour. These differences point to a number of other factors, beyond the quality of the currently available sources of information, that influences the sampling strategy.

Suggested Citation

  • Thomas C Cassey & David R Evens & Rafal Bogacz & James A R Marshall & Casimir J H Ludwig, 2013. "Adaptive Sampling of Information in Perceptual Decision-Making," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-13, November.
  • Handle: RePEc:plo:pone00:0078993
    DOI: 10.1371/journal.pone.0078993
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    Cited by:

    1. Frederick Callaway & Antonio Rangel & Thomas L Griffiths, 2021. "Fixation patterns in simple choice reflect optimal information sampling," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-29, March.
    2. Ian Krajbich & Todd Hare & Björn Bartling & Yosuke Morishima & Ernst Fehr, 2015. "A Common Mechanism Underlying Food Choice and Social Decisions," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-24, October.

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