Skip to main content

Research Repository

Advanced Search

Generalization of prior information for rapid Bayesian time estimation

Roach, Neil W.; McGraw, Paul V.; Whitaker, David; Heron, James

Authors

NEIL ROACH NEIL.ROACH@NOTTINGHAM.AC.UK
Professor of Vision Science

PAUL MCGRAW paul.mcgraw@nottingham.ac.uk
Professor of Visual Neuroscience

David Whitaker

James Heron



Abstract

To enable effective interaction with the environment, the brain combines noisy sensory information with expectations based on prior experience. There is ample evidence showing that humans can learn statistical regularities in sensory input and exploit this knowledge to improve perceptual decisions and actions. However, fundamental questions remain regarding how priors are learned and how they generalize to different sensory and behavioral contexts. In principle, maintaining a large set of highly specific priors may be inefficient and restrict the speed at which expectations can be formed and updated in response to changes in the environment. However, priors formed by generalizing across varying contexts may not be accurate. Here, we exploit rapidly induced contextual biases in duration reproduction to reveal how these competing demands are resolved during the early stages of prior acquisition. We show that observers initially form a single prior by generalizing across duration distributions coupled with distinct sensory signals. In contrast, they form multiple priors if distributions are coupled with distinct motor outputs. Together, our findings suggest that rapid prior acquisition is facilitated by generalization across experiences of different sensory inputs but organized according to how that sensory information is acted on.

Citation

Roach, N. W., McGraw, P. V., Whitaker, D., & Heron, J. (2017). Generalization of prior information for rapid Bayesian time estimation. Proceedings of the National Academy of Sciences, 114(2), 412-417. https://doi.org/10.1073/pnas.1610706114

Journal Article Type Article
Acceptance Date Nov 28, 2016
Online Publication Date Dec 22, 2016
Publication Date Jan 10, 2017
Deposit Date Jan 4, 2017
Publicly Available Date Mar 29, 2024
Journal Proceedings of the National Academy of Sciences of the United States of America
Print ISSN 0027-8424
Electronic ISSN 1091-6490
Publisher National Academy of Sciences
Peer Reviewed Peer Reviewed
Volume 114
Issue 2
Pages 412-417
DOI https://doi.org/10.1073/pnas.1610706114
Keywords Bayesian inference, Time perception, Sensorimotor learning
Public URL https://nottingham-repository.worktribe.com/output/826857
Publisher URL http://www.pnas.org/content/114/2/412

Files





You might also like



Downloadable Citations