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Observers Exploit Stochastic Models of Sensory Change to Help Judge the Passage of Time

Ahrens, MB; Sahani, M; (2011) Observers Exploit Stochastic Models of Sensory Change to Help Judge the Passage of Time. CURR BIOL , 21 (3) 200 - 206. 10.1016/j.cub.2010.12.043. Gold open access

Abstract

Sensory stimulation can systematically bias the perceived passage of time [1-5], but why and how this happens is mysterious. In this report, we provide evidence that such biases may ultimately derive from an innate and adaptive use of stochastically evolving dynamic stimuli to help refine estimates derived from internal timekeeping mechanisms [6-15]. A simplified statistical model based on probabilistic expectations of stimulus change derived from the second-order temporal statistics of the natural environment [16, 17] makes three predictions. First, random noise-like stimuli whose statistics violate natural expectations should induce timing bias. Second, a previously unexplored obverse of this effect is that similar noise stimuli with natural statistics should reduce the variability of timing estimates. Finally, this reduction in variability should scale with the interval being timed, so as to preserve the overall Weber law of interval timing. All three predictions are borne out experimentally. Thus, in the context of our novel theoretical framework, these results suggest that observers routinely rely on sensory input to augment their sense of the passage of time, through a process of Bayesian inference based on expectations of change in the natural environment.

Type: Article
Title: Observers Exploit Stochastic Models of Sensory Change to Help Judge the Passage of Time
Open access status: An open access publication
DOI: 10.1016/j.cub.2010.12.043
Publisher version: http://www.ncbi.nlm.nih.gov/pmc/ articles/PMC30947...
Keywords: NEURAL-NETWORK, INFORMATION, STATISTICS, MOTION, BRAIN
UCL classification: UCL > School of Life and Medical Sciences
UCL > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit
URI: http://discovery.ucl.ac.uk/id/eprint/741451
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