Generalisation of prior information for rapid Bayesian time estimation
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 a...
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| Format: | Article |
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National Academy of Sciences
2016
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| Online Access: | https://eprints.nottingham.ac.uk/39536/ |
| _version_ | 1848795859103252480 |
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| author | Roach, Neil McGraw, Paul Whitaker, David Heron, James |
| author_facet | Roach, Neil McGraw, Paul Whitaker, David Heron, James |
| author_sort | Roach, Neil |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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 generalise to different sensory and behavioural 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. On the other hand, priors formed by generalising 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 generalising 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 generalisation across experiences of different sensory inputs, but organised according to how that sensory information is acted upon. |
| first_indexed | 2025-11-14T19:38:47Z |
| format | Article |
| id | nottingham-39536 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:38:47Z |
| publishDate | 2016 |
| publisher | National Academy of Sciences |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-395362020-05-04T18:19:51Z https://eprints.nottingham.ac.uk/39536/ Generalisation of prior information for rapid Bayesian time estimation Roach, Neil McGraw, Paul Whitaker, David Heron, James 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 generalise to different sensory and behavioural 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. On the other hand, priors formed by generalising 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 generalising 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 generalisation across experiences of different sensory inputs, but organised according to how that sensory information is acted upon. National Academy of Sciences 2016-11-28 Article PeerReviewed Roach, Neil, McGraw, Paul, Whitaker, David and Heron, James (2016) Generalisation of prior information for rapid Bayesian time estimation. Proceedings of the National Academy of Sciences, 114 (2). pp. 412-417. ISSN 1091-6490 Bayesian inference Time perception Sensorimotor learning http://www.pnas.org/content/114/2/412 doi:10.1073/pnas.1610706114 doi:10.1073/pnas.1610706114 |
| spellingShingle | Bayesian inference Time perception Sensorimotor learning Roach, Neil McGraw, Paul Whitaker, David Heron, James Generalisation of prior information for rapid Bayesian time estimation |
| title | Generalisation of prior information for rapid Bayesian time estimation |
| title_full | Generalisation of prior information for rapid Bayesian time estimation |
| title_fullStr | Generalisation of prior information for rapid Bayesian time estimation |
| title_full_unstemmed | Generalisation of prior information for rapid Bayesian time estimation |
| title_short | Generalisation of prior information for rapid Bayesian time estimation |
| title_sort | generalisation of prior information for rapid bayesian time estimation |
| topic | Bayesian inference Time perception Sensorimotor learning |
| url | https://eprints.nottingham.ac.uk/39536/ https://eprints.nottingham.ac.uk/39536/ https://eprints.nottingham.ac.uk/39536/ |