Temporal context calibrates interval timing

By Mehrdad Jazayeri, Michael N. Shadlen. Nature Neuroscience, 2010

751 citations recorded by the source corpus.

We use our sense of time to identify temporal relationships between events and to anticipate actions. The degree to which we can exploit temporal contingencies depends on the variability of our measurements of time. We asked humans to reproduce time intervals drawn from different underlying distributions. As expected, production times were more variable for longer intervals. However, production times exhibited a systematic regression toward the mean. Consequently, estimates for a sample interval differed depending on the distribution from which it was drawn. A performance-optimizing Bayesian model that takes the underlying distribution of samples into account provided an accurate description of subjects' performance, variability and bias. This finding suggests that the CNS incorporates knowledge about temporal uncertainty to adapt internal timing mechanisms to the temporal statistics of the environment.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/b5025bc7268dc99469aef9a20bb7f9f42f362a31

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Citations in the corpus

How the motor system both encodes and influences our sense of time (2016, background): “possible to capture both features when the temporal context changes, creating a testable framework regarding how the core timing network represents the prior distribution of intervals and measures time in a scalar fashion [5].”
Computational mechanisms of sensorimotor control. (2011, neutral): “Further evidence of Bayesian processing comes from work on force estimation (Kö rding et al., 2004) and interval timing (Jazayeri and Shadlen, 2010; Miyazaki et al., 2005).”
When the world becomes 'too real': a Bayesian explanation of autistic perception. (2012, background): “Interestingly, this theoretical approach (supported by clear data [41, 42]) suggests that priors do not need to be learned over a”
A model of interval timing by neural integration (2011, result): “These results are consistent with other studies of optimal temporal risk assessment (e.g., Hudson et al., 2008; Balci et al., 2009a; Jazayeri and Shadlen, 2010), but they extend the durations timed by an order of magnitude relative to these studies.”