Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.

Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time in...

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Autores principales:	Luigi Acerbi, Daniel M Wolpert, Sethu Vijayakumar
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6eb9e030975045f1b6142e57c1824d3e
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spelling	oai:doaj.org-article:6eb9e030975045f1b6142e57c1824d3e2021-11-18T05:52:41ZInternal representations of temporal statistics and feedback calibrate motor-sensory interval timing.1553-734X1553-735810.1371/journal.pcbi.1002771https://doaj.org/article/6eb9e030975045f1b6142e57c1824d3e2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23209386/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior) and of the error (the loss function). The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality) seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed) distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory.Luigi AcerbiDaniel M WolpertSethu VijayakumarPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 11, p e1002771 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Luigi Acerbi Daniel M Wolpert Sethu Vijayakumar Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.
description	Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior) and of the error (the loss function). The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality) seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed) distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory.
format	article
author	Luigi Acerbi Daniel M Wolpert Sethu Vijayakumar
author_facet	Luigi Acerbi Daniel M Wolpert Sethu Vijayakumar
author_sort	Luigi Acerbi
title	Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.
title_short	Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.
title_full	Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.
title_fullStr	Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.
title_full_unstemmed	Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.
title_sort	internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/6eb9e030975045f1b6142e57c1824d3e
work_keys_str_mv	AT luigiacerbi internalrepresentationsoftemporalstatisticsandfeedbackcalibratemotorsensoryintervaltiming AT danielmwolpert internalrepresentationsoftemporalstatisticsandfeedbackcalibratemotorsensoryintervaltiming AT sethuvijayakumar internalrepresentationsoftemporalstatisticsandfeedbackcalibratemotorsensoryintervaltiming
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Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.

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