Hierarchical models of pain: inference, information-seeking, and adaptive control
Computational models of pain consider how the brain processes nociceptive information and allow mapping neural circuits and networks to cognition and behaviour. To date, they have generally have assumed two largely independent processes: perceptual inference, typically modelled as an approximate Bay...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
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Elsevier
2020
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| _version_ | 1797069623003185152 |
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| author | Seymour, B Mancini, F |
| author_facet | Seymour, B Mancini, F |
| author_sort | Seymour, B |
| collection | OXFORD |
| description | Computational models of pain consider how the brain processes nociceptive information and allow mapping neural circuits and networks to cognition and behaviour. To date, they have generally have assumed two largely independent processes: perceptual inference, typically modelled as an approximate Bayesian process, and action control, typically modelled as a reinforcement learning process. However, inference and control are intertwined in complex ways, challenging the clarity of this distinction. Here, we consider how they may comprise a parallel hierarchical architecture that combines inference, information-seeking, and adaptive value-based control. This sheds light on the complex neural architecture of the pain system, and takes us closer to understanding from where pain ’arises’ in the brain. |
| first_indexed | 2024-03-06T22:27:12Z |
| format | Journal article |
| id | oxford-uuid:570e3a6e-145d-4a74-a732-3a38d14c8590 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T22:27:12Z |
| publishDate | 2020 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:570e3a6e-145d-4a74-a732-3a38d14c85902022-03-26T16:54:18ZHierarchical models of pain: inference, information-seeking, and adaptive controlJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:570e3a6e-145d-4a74-a732-3a38d14c8590EnglishSymplectic ElementsElsevier2020Seymour, BMancini, FComputational models of pain consider how the brain processes nociceptive information and allow mapping neural circuits and networks to cognition and behaviour. To date, they have generally have assumed two largely independent processes: perceptual inference, typically modelled as an approximate Bayesian process, and action control, typically modelled as a reinforcement learning process. However, inference and control are intertwined in complex ways, challenging the clarity of this distinction. Here, we consider how they may comprise a parallel hierarchical architecture that combines inference, information-seeking, and adaptive value-based control. This sheds light on the complex neural architecture of the pain system, and takes us closer to understanding from where pain ’arises’ in the brain. |
| spellingShingle | Seymour, B Mancini, F Hierarchical models of pain: inference, information-seeking, and adaptive control |
| title | Hierarchical models of pain: inference, information-seeking, and adaptive control |
| title_full | Hierarchical models of pain: inference, information-seeking, and adaptive control |
| title_fullStr | Hierarchical models of pain: inference, information-seeking, and adaptive control |
| title_full_unstemmed | Hierarchical models of pain: inference, information-seeking, and adaptive control |
| title_short | Hierarchical models of pain: inference, information-seeking, and adaptive control |
| title_sort | hierarchical models of pain inference information seeking and adaptive control |
| work_keys_str_mv | AT seymourb hierarchicalmodelsofpaininferenceinformationseekingandadaptivecontrol AT mancinif hierarchicalmodelsofpaininferenceinformationseekingandadaptivecontrol |