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...

Full description

Saved in:
Bibliographic Details
Published in:PLoS computational biology 2017-01, Vol.13 (1), p.e1005142-e1005142
Main Authors: Tabor, Abby, Thacker, Michael A, Moseley, G Lorimer, Körding, Konrad P
Format: Article
Language:English
Subjects:
Bayes Theorem
Biology and Life Sciences
Brain research
Colleges & universities
Funding
Humans
Medical research
Medicine and Health Sciences
Models, Neurological
Models, Statistical
Pain
Pain - physiopathology
Pain Perception - physiology
Perceptions
Physical Sciences
Psychological aspects
Review
Social Sciences
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c661t-6983ee5340f8ab4d6b65eb83e2c9d01f3a3b2ecdc011570a16ab3c33f40242183
cites cdi_FETCH-LOGICAL-c661t-6983ee5340f8ab4d6b65eb83e2c9d01f3a3b2ecdc011570a16ab3c33f40242183
container_end_page e1005142
container_issue 1
container_start_page e1005142
container_title PLoS computational biology
container_volume 13
creator Tabor, Abby
Thacker, Michael A
Moseley, G Lorimer
Körding, Konrad P
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.
doi_str_mv 10.1371/journal.pcbi.1005142
format article
fullrecord <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_1868997410</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A493714091</galeid><doaj_id>oai_doaj_org_article_baeb4433dc654ce09a9eeb90cb5d016f</doaj_id><sourcerecordid>A493714091</sourcerecordid><originalsourceid>FETCH-LOGICAL-c661t-6983ee5340f8ab4d6b65eb83e2c9d01f3a3b2ecdc011570a16ab3c33f40242183</originalsourceid><addsrcrecordid>eNqVkktv1DAQxyMEog_4BCBYiQscdvHEj8QckFYVj5UqQBTO1tiZLF5l422coPLt8XbTqkG9IB88Gv_mPw9Plj0HtgBewNtNGLoWm8XOWb8AxiSI_EF2DFLyecFl-fCOfZSdxLhhLJlaPc6O8pKVAFwcZ8--oW_fzZazix57H3vvsJktnQtD2z_JHtXYRHo63qfZz48ffpx9np9__bQ6W57PnVLQz5UuOZHkgtUlWlEpqyTZ5MudrhjUHLnNyVWOAciCISi03HFeC5aLHEp-mr086O6aEM3YVzRQqlLrQgBLxOpAVAE3Ztf5LXZ_TEBvrh2hWxvsUu0NGYtkheC8ckoKR0yjJrKaOStTMapOWu_HbIPdUuWo7TtsJqLTl9b_Muvw28ics0KoJPB6FOjC5UCxN1sfHTUNthSG67pBqDRfkdBX_6D3dzdSa0wN-LYOKa_bi5ql0OmvBdOQqMU9VDoVbb0LLdU--ScBbyYBienpql_jEKNZXXz_D_bLlBUH1nUhxo7q29kBM_vVvGnS7FfTjKuZwl7cnftt0M0u8r83f9wI</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1868997410</pqid></control><display><type>article</type><title>Pain: A Statistical Account</title><source>Publicly Available Content (ProQuest)</source><source>PubMed Central</source><creator>Tabor, Abby ; Thacker, Michael A ; Moseley, G Lorimer ; Körding, Konrad P</creator><contributor>Blohm, Gunnar</contributor><creatorcontrib>Tabor, Abby ; Thacker, Michael A ; Moseley, G Lorimer ; Körding, Konrad P ; Blohm, Gunnar</creatorcontrib><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.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1005142</identifier><identifier>PMID: 28081134</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Bayes Theorem ; Biology and Life Sciences ; Brain research ; Colleges &amp; universities ; Funding ; Humans ; Medical research ; Medicine and Health Sciences ; Models, Neurological ; Models, Statistical ; Pain ; Pain - physiopathology ; Pain Perception - physiology ; Perceptions ; Physical Sciences ; Psychological aspects ; Review ; Social Sciences</subject><ispartof>PLoS computational biology, 2017-01, Vol.13 (1), p.e1005142-e1005142</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Tabor A, Thacker MA, Moseley GL, Körding KP (2017) Pain: A Statistical Account. PLoS Comput Biol 13(1): e1005142. doi:10.1371/journal.pcbi.1005142</rights><rights>2017 Tabor et al 2017 Tabor et al</rights><rights>2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Tabor A, Thacker MA, Moseley GL, Körding KP (2017) Pain: A Statistical Account. PLoS Comput Biol 13(1): e1005142. doi:10.1371/journal.pcbi.1005142</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c661t-6983ee5340f8ab4d6b65eb83e2c9d01f3a3b2ecdc011570a16ab3c33f40242183</citedby><cites>FETCH-LOGICAL-c661t-6983ee5340f8ab4d6b65eb83e2c9d01f3a3b2ecdc011570a16ab3c33f40242183</cites><orcidid>0000-0002-8168-2052 ; 0000-0001-8408-4499</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1868997410/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1868997410?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,313,314,723,776,780,788,881,25731,27899,27901,27902,36989,36990,44566,53766,53768,74869</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28081134$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Blohm, Gunnar</contributor><creatorcontrib>Tabor, Abby</creatorcontrib><creatorcontrib>Thacker, Michael A</creatorcontrib><creatorcontrib>Moseley, G Lorimer</creatorcontrib><creatorcontrib>Körding, Konrad P</creatorcontrib><title>Pain: A Statistical Account</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><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.</description><subject>Bayes Theorem</subject><subject>Biology and Life Sciences</subject><subject>Brain research</subject><subject>Colleges &amp; universities</subject><subject>Funding</subject><subject>Humans</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Models, Neurological</subject><subject>Models, Statistical</subject><subject>Pain</subject><subject>Pain - physiopathology</subject><subject>Pain Perception - physiology</subject><subject>Perceptions</subject><subject>Physical Sciences</subject><subject>Psychological aspects</subject><subject>Review</subject><subject>Social Sciences</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqVkktv1DAQxyMEog_4BCBYiQscdvHEj8QckFYVj5UqQBTO1tiZLF5l422coPLt8XbTqkG9IB88Gv_mPw9Plj0HtgBewNtNGLoWm8XOWb8AxiSI_EF2DFLyecFl-fCOfZSdxLhhLJlaPc6O8pKVAFwcZ8--oW_fzZazix57H3vvsJktnQtD2z_JHtXYRHo63qfZz48ffpx9np9__bQ6W57PnVLQz5UuOZHkgtUlWlEpqyTZ5MudrhjUHLnNyVWOAciCISi03HFeC5aLHEp-mr086O6aEM3YVzRQqlLrQgBLxOpAVAE3Ztf5LXZ_TEBvrh2hWxvsUu0NGYtkheC8ckoKR0yjJrKaOStTMapOWu_HbIPdUuWo7TtsJqLTl9b_Muvw28ics0KoJPB6FOjC5UCxN1sfHTUNthSG67pBqDRfkdBX_6D3dzdSa0wN-LYOKa_bi5ql0OmvBdOQqMU9VDoVbb0LLdU--ScBbyYBienpql_jEKNZXXz_D_bLlBUH1nUhxo7q29kBM_vVvGnS7FfTjKuZwl7cnftt0M0u8r83f9wI</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Tabor, Abby</creator><creator>Thacker, Michael A</creator><creator>Moseley, G Lorimer</creator><creator>Körding, Konrad P</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-8168-2052</orcidid><orcidid>https://orcid.org/0000-0001-8408-4499</orcidid></search><sort><creationdate>20170101</creationdate><title>Pain: A Statistical Account</title><author>Tabor, Abby ; Thacker, Michael A ; Moseley, G Lorimer ; Körding, Konrad P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c661t-6983ee5340f8ab4d6b65eb83e2c9d01f3a3b2ecdc011570a16ab3c33f40242183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Bayes Theorem</topic><topic>Biology and Life Sciences</topic><topic>Brain research</topic><topic>Colleges &amp; universities</topic><topic>Funding</topic><topic>Humans</topic><topic>Medical research</topic><topic>Medicine and Health Sciences</topic><topic>Models, Neurological</topic><topic>Models, Statistical</topic><topic>Pain</topic><topic>Pain - physiopathology</topic><topic>Pain Perception - physiology</topic><topic>Perceptions</topic><topic>Physical Sciences</topic><topic>Psychological aspects</topic><topic>Review</topic><topic>Social Sciences</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tabor, Abby</creatorcontrib><creatorcontrib>Thacker, Michael A</creatorcontrib><creatorcontrib>Moseley, G Lorimer</creatorcontrib><creatorcontrib>Körding, Konrad P</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium &amp; Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health &amp; Medical Collection (Proquest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Database‎ (1962 - current)</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Biological Sciences</collection><collection>Computing Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Biological Science Database</collection><collection>ProQuest advanced technologies &amp; aerospace journals</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tabor, Abby</au><au>Thacker, Michael A</au><au>Moseley, G Lorimer</au><au>Körding, Konrad P</au><au>Blohm, Gunnar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pain: A Statistical Account</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2017-01-01</date><risdate>2017</risdate><volume>13</volume><issue>1</issue><spage>e1005142</spage><epage>e1005142</epage><pages>e1005142-e1005142</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28081134</pmid><doi>10.1371/journal.pcbi.1005142</doi><orcidid>https://orcid.org/0000-0002-8168-2052</orcidid><orcidid>https://orcid.org/0000-0001-8408-4499</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1553-7358
ispartof PLoS computational biology, 2017-01, Vol.13 (1), p.e1005142-e1005142
issn 1553-7358
1553-734X
1553-7358
language eng
recordid cdi_plos_journals_1868997410
source Publicly Available Content (ProQuest); PubMed Central
subjects Bayes Theorem
Biology and Life Sciences
Brain research
Colleges & universities
Funding
Humans
Medical research
Medicine and Health Sciences
Models, Neurological
Models, Statistical
Pain
Pain - physiopathology
Pain Perception - physiology
Perceptions
Physical Sciences
Psychological aspects
Review
Social Sciences
title Pain: A Statistical Account
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T07%3A35%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Pain:%20A%20Statistical%20Account&rft.jtitle=PLoS%20computational%20biology&rft.au=Tabor,%20Abby&rft.date=2017-01-01&rft.volume=13&rft.issue=1&rft.spage=e1005142&rft.epage=e1005142&rft.pages=e1005142-e1005142&rft.issn=1553-7358&rft.eissn=1553-7358&rft_id=info:doi/10.1371/journal.pcbi.1005142&rft_dat=%3Cgale_plos_%3EA493714091%3C/gale_plos_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c661t-6983ee5340f8ab4d6b65eb83e2c9d01f3a3b2ecdc011570a16ab3c33f40242183%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1868997410&rft_id=info:pmid/28081134&rft_galeid=A493714091&rfr_iscdi=true