EXPRESS: Rapid calibration to dynamic temporal contexts

Rhodes, Darren; Bridgwater, Tyler; Ayache, Julia; Riemer, Martin

Abstract

TThe 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.

• Journal Article Type: Article
• Acceptance Date: Nov 28, 2023
• Online Publication Date: Nov 28, 2023
• Deposit Date: Dec 11, 2023
• Publicly Available Date: Dec 11, 2023
• Journal: Quarterly Journal of Experimental Psychology
• Print ISSN: 1747-0218
• Publisher: Routledge
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1177/17470218231219507
• Keywords: Physiology (medical), General Psychology, Experimental and Cognitive Psychology, General Medicine, Neuropsychology and Physiological Psychology, Physiology, Temporal Context, Rapid Recalibration, Timing, Time Perception, Bayesian models, Duration
• Publisher URL: https://journals.sagepub.com/doi/10.1177/17470218231219507