Rapid calibration to dynamic temporal contexts

Rhodes, Darren, Bridgewater, Tyler, Ayache, Julia and Riemer, Martin 2024. Rapid calibration to dynamic temporal contexts. Quarterly Journal of Experimental Psychology 77 (9) , pp. 1923-1935. 10.1177/17470218231219507

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Abstract

The prediction of future events and the preparation of appropriate behavioural reactions rely on an accurate perception of temporal regularities. In dynamic environments, temporal regularities are subject to slow and sudden changes, and adaptation to these changes is an important requirement for efficient behaviour. Bayesian models have proven a useful tool to understand the processing of temporal regularities in humans; yet an open question pertains to the degree of flexibility of the prior that is required for optimal modelling of behaviour. Here we directly compare dynamic models (with continuously changing prior expectations) and static models (a stable prior for each experimental session) with their ability to describe regression effects in interval timing. Our results show that dynamic Bayesian models are superior when describing the responses to slow, continuous environmental changes, whereas static models are more suitable to describe responses to sudden changes. In time perception research, these results will be informative for the choice of adequate computational models and enhance our understanding of the neuronal computations underlying human timing behaviour.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology
Publisher:	SAGE Publications
ISSN:	1747-0218
Date of First Compliant Deposit:	23 January 2024
Date of Acceptance:	2 November 2023
Last Modified:	18 Nov 2024 14:30
URI:	https://orca.cardiff.ac.uk/id/eprint/165765

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