Perceptual classification in a rapidly changing environment.
Humans and monkeys can learn to classify perceptual information in a statistically optimal fashion if the functional groupings remain stable over many hundreds of trials, but little is known about categorization when the environment changes rapidly. Here, we used a combination of computational model...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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2011
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| _version_ | 1826279426486173696 |
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| author | Summerfield, C Behrens, T Koechlin, E |
| author_facet | Summerfield, C Behrens, T Koechlin, E |
| author_sort | Summerfield, C |
| collection | OXFORD |
| description | Humans and monkeys can learn to classify perceptual information in a statistically optimal fashion if the functional groupings remain stable over many hundreds of trials, but little is known about categorization when the environment changes rapidly. Here, we used a combination of computational modeling and functional neuroimaging to understand how humans classify visual stimuli drawn from categories whose mean and variance jumped unpredictably. Models based on optimal learning (Bayesian model) and a cognitive strategy (working memory model) both explained unique variance in choice, reaction time, and brain activity. However, the working memory model was the best predictor of performance in volatile environments, whereas statistically optimal performance emerged in periods of relative stability. Bayesian and working memory models predicted decision-related activity in distinct regions of the prefrontal cortex and midbrain. These findings suggest that perceptual category judgments, like value-guided choices, may be guided by multiple controllers. |
| first_indexed | 2024-03-06T23:58:32Z |
| format | Journal article |
| id | oxford-uuid:75184556-11e1-4c2b-8819-6bddbd6cea9e |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:58:32Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | oxford-uuid:75184556-11e1-4c2b-8819-6bddbd6cea9e2022-03-26T20:07:25ZPerceptual classification in a rapidly changing environment.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:75184556-11e1-4c2b-8819-6bddbd6cea9eEnglishSymplectic Elements at Oxford2011Summerfield, CBehrens, TKoechlin, EHumans and monkeys can learn to classify perceptual information in a statistically optimal fashion if the functional groupings remain stable over many hundreds of trials, but little is known about categorization when the environment changes rapidly. Here, we used a combination of computational modeling and functional neuroimaging to understand how humans classify visual stimuli drawn from categories whose mean and variance jumped unpredictably. Models based on optimal learning (Bayesian model) and a cognitive strategy (working memory model) both explained unique variance in choice, reaction time, and brain activity. However, the working memory model was the best predictor of performance in volatile environments, whereas statistically optimal performance emerged in periods of relative stability. Bayesian and working memory models predicted decision-related activity in distinct regions of the prefrontal cortex and midbrain. These findings suggest that perceptual category judgments, like value-guided choices, may be guided by multiple controllers. |
| spellingShingle | Summerfield, C Behrens, T Koechlin, E Perceptual classification in a rapidly changing environment. |
| title | Perceptual classification in a rapidly changing environment. |
| title_full | Perceptual classification in a rapidly changing environment. |
| title_fullStr | Perceptual classification in a rapidly changing environment. |
| title_full_unstemmed | Perceptual classification in a rapidly changing environment. |
| title_short | Perceptual classification in a rapidly changing environment. |
| title_sort | perceptual classification in a rapidly changing environment |
| work_keys_str_mv | AT summerfieldc perceptualclassificationinarapidlychangingenvironment AT behrenst perceptualclassificationinarapidlychangingenvironment AT koechline perceptualclassificationinarapidlychangingenvironment |