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A Bayesian Model of Sensory Adaptation

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  • Yoshiyuki Sato
  • Kazuyuki Aihara

Abstract

Recent studies reported two opposite types of adaptation in temporal perception. Here, we propose a Bayesian model of sensory adaptation that exhibits both types of adaptation. We regard adaptation as the adaptive updating of estimations of time-evolving variables, which determine the mean value of the likelihood function and that of the prior distribution in a Bayesian model of temporal perception. On the basis of certain assumptions, we can analytically determine the mean behavior in our model and identify the parameters that determine the type of adaptation that actually occurs. The results of our model suggest that we can control the type of adaptation by controlling the statistical properties of the stimuli presented.

Suggested Citation

  • Yoshiyuki Sato & Kazuyuki Aihara, 2011. "A Bayesian Model of Sensory Adaptation," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-7, April.
    • Handle: RePEc:plo:pone00:0019377
    DOI: 10.1371/journal.pone.0019377
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    References listed on IDEAS

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    1. Kurt A. Thoroughman & Reza Shadmehr, 2000. "Learning of action through adaptive combination of motor primitives," Nature, Nature, vol. 407(6805), pages 742-747, October.
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    Cited by:

    1. Jean-Rémy Martin & Anne Kösem & Virginie van Wassenhove, 2015. "Hysteresis in Audiovisual Synchrony Perception," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-13, March.
    2. Alex B Fine & T Florian Jaeger & Thomas A Farmer & Ting Qian, 2013. "Rapid Expectation Adaptation during Syntactic Comprehension," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-18, October.

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