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Probabilistic Computation in Human Perception under Variability in Encoding Precision

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  • Shaiyan Keshvari
  • Ronald van den Berg
  • Wei Ji Ma

Abstract

A key function of the brain is to interpret noisy sensory information. To do so optimally, observers must, in many tasks, take into account knowledge of the precision with which stimuli are encoded. In an orientation change detection task, we find that encoding precision does not only depend on an experimentally controlled reliability parameter (shape), but also exhibits additional variability. In spite of variability in precision, human subjects seem to take into account precision near-optimally on a trial-to-trial and item-to-item basis. Our results offer a new conceptualization of the encoding of sensory information and highlight the brain’s remarkable ability to incorporate knowledge of uncertainty during complex perceptual decision-making.

Suggested Citation

  • Shaiyan Keshvari & Ronald van den Berg & Wei Ji Ma, 2012. "Probabilistic Computation in Human Perception under Variability in Encoding Precision," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-9, June.
    • Handle: RePEc:plo:pone00:0040216
    DOI: 10.1371/journal.pone.0040216
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    References listed on IDEAS

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    1. Steven J. Luck & Edward K. Vogel, 1997. "The capacity of visual working memory for features and conjunctions," Nature, Nature, vol. 390(6657), pages 279-281, November.
    2. Marc O. Ernst & Martin S. Banks, 2002. "Humans integrate visual and haptic information in a statistically optimal fashion," Nature, Nature, vol. 415(6870), pages 429-433, January.
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    Cited by:

    1. Elina Stengård & Ronald van den Berg, 2019. "Imperfect Bayesian inference in visual perception," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-27, April.
    2. Shaiyan Keshvari & Ronald van den Berg & Wei Ji Ma, 2013. "No Evidence for an Item Limit in Change Detection," PLOS Computational Biology, Public Library of Science, vol. 9(2), pages 1-9, February.
    3. William T Adler & Wei Ji Ma, 2018. "Comparing Bayesian and non-Bayesian accounts of human confidence reports," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-34, November.
    4. Jannes Jegminat & Maya A Jastrzębowska & Matthew V Pachai & Michael H Herzog & Jean-Pascal Pfister, 2020. "Bayesian regression explains how human participants handle parameter uncertainty," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-23, May.

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