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A unitary mechanism underlies adaptation to both local and global environmental statistics in time perception

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  • Tianhe Wang
  • Yingrui Luo
  • Richard B Ivry
  • Jonathan S Tsay
  • Ernst Pöppel
  • Yan Bao

Abstract

Our duration estimation flexibly adapts to the statistical properties of the temporal context. Humans and non-human species exhibit a perceptual bias towards the mean of durations previously observed as well as serial dependence, a perceptual bias towards the duration of recently processed events. Here we asked whether those two phenomena arise from a unitary mechanism or reflect the operation of two distinct systems that adapt separately to the global and local statistics of the environment. We employed a set of duration reproduction tasks in which the target duration was sampled from distributions with different variances and means. The central tendency and serial dependence biases were jointly modulated by the range and the variance of the prior, and these effects were well-captured by a unitary mechanism model in which temporal expectancies are updated after each trial based on perceptual observations. Alternative models that assume separate mechanisms for global and local contextual effects failed to capture the empirical results.Author summary: Our perceptual system can actively adapt to the statistical properties of the environment in multiple time scales. For example, the perceived duration of an event is biased by the mean duration of events observed in a relatively long period and also by the durations of recently processed events. Here we ask whether these two effects reflect the operation of separate mechanisms or a unitary mechanism. We develop a series of computational models of independent and unitary mechanisms, and use experimental manipulations that generate predictions which allow us to evaluate the models. We show that serial dependence, the signature of short-term adaptation, is modulated by the long-term context. The results are consistent with the predictions of a unitary mechanism model which assumes the global prior is updated in a trial-by-trial manner. The alternative models that assume separate mechanisms fail to capture the empirical results. These results provide a comprehensive picture of how the timing system jointly adapts to short- and long-term environmental statistics.

Suggested Citation

  • Tianhe Wang & Yingrui Luo & Richard B Ivry & Jonathan S Tsay & Ernst Pöppel & Yan Bao, 2023. "A unitary mechanism underlies adaptation to both local and global environmental statistics in time perception," PLOS Computational Biology, Public Library of Science, vol. 19(5), pages 1-23, May.
    • Handle: RePEc:plo:pcbi00:1011116
    DOI: 10.1371/journal.pcbi.1011116
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    1. Konrad P. Körding & Daniel M. Wolpert, 2004. "Bayesian integration in sensorimotor learning," Nature, Nature, vol. 427(6971), pages 244-247, January.
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