Internal representations of temporal statistics and feedback in sensorimotor interval timing

Luigi Acerbi, Daniel Wolpert, Sethu Vijayakumar

Research output: Contribution to conferencePosterpeer-review

Abstract / Description of output

Recent results have shown that bias and variance trade-offs in time perception can be accounted for by "optimal" probabilistic inference [1]. However, specific temporal statistics of the stimuli seem to induce sub-optimal behaviours; for instance adaptor distributions, in which one inter-stimulus duration appears overwhelmingly often, typically cause a temporal recalibration effect [2] that defies a simple account based on prior expectations.

The Bayesian ideal observer responses depend crucially on both the internal representation of the temporal context (subjective prior and likelihoods) and on the loss function; observed "sub-optimal" behaviours could be caused by a systematic mismatch between the objective statistics of the experiment and their subjective counterparts. When, how and the degree to which people can learn a correct internal representation of the temporal context can be revealing of the underlying mechanisms. In this work, we studied how internal representations of temporal statistics are affected by uniform and adaptor distributions of action-stimulus intervals in a time interval reproduction paradigm. By providing different shapes of performance feedback (i.e. loss functions) to the subjects (see Methods), we also investigated how the participants integrated external error signals with the temporal context.

Our results show that temporal context calibrates sensorimotor timing according to the "scalar property" of sensorimotor error on short/long intervals in the subsecond range. The subjects typically learnt smoothed approximations of the experimental distributions of stimuli, with a good estimate of their mean and variance but also took into account higher-order statistics. The responses were sensitive to the nature of the feedback provided, in general agreement with the behaviour predicted by the related loss function. Interestingly, the above results also held in the adaptor condition, implying that there are no significant limitations in learning complex temporal distributions of stimuli with the help of corrective feedback.

[1] Jazayeri, M. and Shadlen, M.N. "Temporal context calibrates interval timing"�. Nat. Neurosci 13, 8, 1020-1026 (2010).
[2] Stetson, C. et al. "Motor-sensory recalibration leads to an illusory reversal of action and sensation". Neuron 51, 5, 651-659 (2006).
Original languageEnglish
Publication statusPublished - 2012
Event9th Annual Annual Computational and Systems Neurscience Meeting (COSYNE 2012) - Salt Lake City, United States
Duration: 23 Feb 201226 Feb 2012


Conference9th Annual Annual Computational and Systems Neurscience Meeting (COSYNE 2012)
Country/TerritoryUnited States
CitySalt Lake City


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