Pain: A Statistical Account.
Perception is seen as a process that utilises partial and noisy information to construct a coherent understanding of the world. Here we argue that the experience of pain is no different; it is based on incomplete, multimodal information, which is used to estimate potential bodily threat. We outline...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC5230746?pdf=render |
| _version_ | 1811223136224411648 |
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| author | Abby Tabor Michael A Thacker G Lorimer Moseley Konrad P Körding |
| author_facet | Abby Tabor Michael A Thacker G Lorimer Moseley Konrad P Körding |
| author_sort | Abby Tabor |
| collection | DOAJ |
| description | Perception is seen as a process that utilises partial and noisy information to construct a coherent understanding of the world. Here we argue that the experience of pain is no different; it is based on incomplete, multimodal information, which is used to estimate potential bodily threat. We outline a Bayesian inference model, incorporating the key components of cue combination, causal inference, and temporal integration, which highlights the statistical problems in everyday perception. It is from this platform that we are able to review the pain literature, providing evidence from experimental, acute, and persistent phenomena to demonstrate the advantages of adopting a statistical account in pain. Our probabilistic conceptualisation suggests a principles-based view of pain, explaining a broad range of experimental and clinical findings and making testable predictions. |
| first_indexed | 2024-04-12T08:27:56Z |
| format | Article |
| id | doaj.art-baeb4433dc654ce09a9eeb90cb5d016f |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-12T08:27:56Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-baeb4433dc654ce09a9eeb90cb5d016f2022-12-22T03:40:19ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-01-01131e100514210.1371/journal.pcbi.1005142Pain: A Statistical Account.Abby TaborMichael A ThackerG Lorimer MoseleyKonrad P KördingPerception is seen as a process that utilises partial and noisy information to construct a coherent understanding of the world. Here we argue that the experience of pain is no different; it is based on incomplete, multimodal information, which is used to estimate potential bodily threat. We outline a Bayesian inference model, incorporating the key components of cue combination, causal inference, and temporal integration, which highlights the statistical problems in everyday perception. It is from this platform that we are able to review the pain literature, providing evidence from experimental, acute, and persistent phenomena to demonstrate the advantages of adopting a statistical account in pain. Our probabilistic conceptualisation suggests a principles-based view of pain, explaining a broad range of experimental and clinical findings and making testable predictions.http://europepmc.org/articles/PMC5230746?pdf=render |
| spellingShingle | Abby Tabor Michael A Thacker G Lorimer Moseley Konrad P Körding Pain: A Statistical Account. PLoS Computational Biology |
| title | Pain: A Statistical Account. |
| title_full | Pain: A Statistical Account. |
| title_fullStr | Pain: A Statistical Account. |
| title_full_unstemmed | Pain: A Statistical Account. |
| title_short | Pain: A Statistical Account. |
| title_sort | pain a statistical account |
| url | http://europepmc.org/articles/PMC5230746?pdf=render |
| work_keys_str_mv | AT abbytabor painastatisticalaccount AT michaelathacker painastatisticalaccount AT glorimermoseley painastatisticalaccount AT konradpkording painastatisticalaccount |