Generalization of prior information for rapid Bayesian time estimation

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

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: Jan 4, 2017
• 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
• Contract Date: Jan 4, 2017