Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma
•Decision-making mechanisms in substance use disorders (SUDs) remain poorly understood.•We used computational modeling to better understand these mechanisms.•SUD patients showed less precise action selection mechanisms than healthy subjects.•SUD patients also learned slower from negative outcomes th...
Gespeichert in:
Veröffentlicht in: | Drug and alcohol dependence 2020-10, Vol.215, p.108208-108208, Article 108208 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 108208 |
---|---|
container_issue | |
container_start_page | 108208 |
container_title | Drug and alcohol dependence |
container_volume | 215 |
creator | Smith, Ryan Schwartenbeck, Philipp Stewart, Jennifer L. Kuplicki, Rayus Ekhtiari, Hamed Paulus, Martin P. |
description | •Decision-making mechanisms in substance use disorders (SUDs) remain poorly understood.•We used computational modeling to better understand these mechanisms.•SUD patients showed less precise action selection mechanisms than healthy subjects.•SUD patients also learned slower from negative outcomes than healthy subjects.•This could help explain continued patterns of maladaptive choices in SUDs.
Substance use disorders (SUDs) are a major public health risk. However, mechanisms accounting for continued patterns of poor choices in the face of negative life consequences remain poorly understood.
We use a computational (active inference) modeling approach, combined with multiple regression and hierarchical Bayesian group analyses, to examine how treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 147) and healthy controls (HCs; N = 54) make choices to resolve uncertainty within a gambling task. A subset of SUDs (N = 49) and HCs (N = 51) propensity-matched on age, sex, and verbal IQ were also compared to replicate larger group findings.
Results indicate that: (a) SUDs show poorer task performance than HCs (p = 0.03, Cohen’s d = 0.33), with model estimates revealing less precise action selection mechanisms (p = 0.004, d = 0.43), a lower learning rate from losses (p = 0.02, d = 0.36), and a greater learning rate from gains (p = 0.04, d = 0.31); and (b) groups do not differ significantly in goal-directed information seeking.
Findings suggest a pattern of inconsistent behavior in response to positive outcomes in SUDs combined with a tendency to attribute negative outcomes to chance. Specifically, individuals with SUDs fail to settle on a behavior strategy despite sufficient evidence of its success. These learning impairments could help account for difficulties in adjusting behavior and maintaining optimal decision-making during and after treatment. |
doi_str_mv | 10.1016/j.drugalcdep.2020.108208 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7502502</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0376871620303732</els_id><sourcerecordid>2454517722</sourcerecordid><originalsourceid>FETCH-LOGICAL-c573t-ed0a8e93d0365404f7f906309ce753f59825027dc9cac0688aa80e4f55913ae3</originalsourceid><addsrcrecordid>eNqFkcFu1DAQhi0EokvhFZAlzlnGcRw7HJCgKlCpEpfeLdee7HqVxMFOQnkCXhunKQVOWJbGHv_zz8gfIZTBngGr3572Ls4H01mH476Eck2rEtQTsmNKNgVAVT8lO-CyLpRk9Rl5kdIJ8qobeE7OeKmAMcZ35OdVP0a0PiE1dvJhoAk73E4-X-bbNJnBIp2zwvkUosP4jl4u3uGabkO8L1yQdmji4IcD9f1ofOxxmBL9fsTsErplfZiOSPFu7ELE4j76KXt22PfmJXnWmi7hq4d4Tm4-Xd5cfCmuv36-uvhwXVgh-VSgA6Ow4Q54LSqoWtk2UHNoLErBW9GoUkApnW2ssVArZYwCrFohGsYN8nPyfrMd59senc0zRtPpMfrexB86GK__fRn8UR_ComW2zTsbvHkwiOHbjGnSpzDHIY-sy0pUgklZriq1qWwMKUVsHzsw0CtBfdJ_COqVoN4I5tLXf0_4WPgbWRZ83ASYv2nxGHWyfmXhfAY5aRf8_7v8AvpxtbE</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2454517722</pqid></control><display><type>article</type><title>Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma</title><source>Applied Social Sciences Index & Abstracts (ASSIA)</source><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><creator>Smith, Ryan ; Schwartenbeck, Philipp ; Stewart, Jennifer L. ; Kuplicki, Rayus ; Ekhtiari, Hamed ; Paulus, Martin P.</creator><creatorcontrib>Smith, Ryan ; Schwartenbeck, Philipp ; Stewart, Jennifer L. ; Kuplicki, Rayus ; Ekhtiari, Hamed ; Paulus, Martin P. ; Tulsa 1000 Investigators</creatorcontrib><description>•Decision-making mechanisms in substance use disorders (SUDs) remain poorly understood.•We used computational modeling to better understand these mechanisms.•SUD patients showed less precise action selection mechanisms than healthy subjects.•SUD patients also learned slower from negative outcomes than healthy subjects.•This could help explain continued patterns of maladaptive choices in SUDs.
Substance use disorders (SUDs) are a major public health risk. However, mechanisms accounting for continued patterns of poor choices in the face of negative life consequences remain poorly understood.
We use a computational (active inference) modeling approach, combined with multiple regression and hierarchical Bayesian group analyses, to examine how treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 147) and healthy controls (HCs; N = 54) make choices to resolve uncertainty within a gambling task. A subset of SUDs (N = 49) and HCs (N = 51) propensity-matched on age, sex, and verbal IQ were also compared to replicate larger group findings.
Results indicate that: (a) SUDs show poorer task performance than HCs (p = 0.03, Cohen’s d = 0.33), with model estimates revealing less precise action selection mechanisms (p = 0.004, d = 0.43), a lower learning rate from losses (p = 0.02, d = 0.36), and a greater learning rate from gains (p = 0.04, d = 0.31); and (b) groups do not differ significantly in goal-directed information seeking.
Findings suggest a pattern of inconsistent behavior in response to positive outcomes in SUDs combined with a tendency to attribute negative outcomes to chance. Specifically, individuals with SUDs fail to settle on a behavior strategy despite sufficient evidence of its success. These learning impairments could help account for difficulties in adjusting behavior and maintaining optimal decision-making during and after treatment.</description><identifier>ISSN: 0376-8716</identifier><identifier>EISSN: 1879-0046</identifier><identifier>DOI: 10.1016/j.drugalcdep.2020.108208</identifier><identifier>PMID: 32801113</identifier><language>eng</language><publisher>Ireland: Elsevier B.V</publisher><subject>Active inference ; Adult ; Bayes Theorem ; Bayesian analysis ; Behavior ; Cannabis ; Computational modeling ; Computer applications ; Decision making ; Directed exploration ; Drug addiction ; Drug use ; Explore-exploit dilemma ; Female ; Gambling ; Hallucinogens ; Health risks ; Health services utilization ; Help seeking behavior ; Humans ; Information seeking behavior ; Intelligence tests ; Learning ; Learning rate ; Male ; Marijuana ; Narcotics ; Opioids ; Problem-Based Learning ; Public health ; Regression analysis ; Sedative drugs ; Sedatives ; Stimulants ; Substance use ; Substance use disorder ; Substance use disorders ; Substance-Related Disorders - psychology ; Task performance ; Task Performance and Analysis ; Uncertainty ; Young Adult</subject><ispartof>Drug and alcohol dependence, 2020-10, Vol.215, p.108208-108208, Article 108208</ispartof><rights>2020 Elsevier B.V.</rights><rights>Copyright © 2020 Elsevier B.V. All rights reserved.</rights><rights>Copyright Elsevier Science Ltd. Oct 1, 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c573t-ed0a8e93d0365404f7f906309ce753f59825027dc9cac0688aa80e4f55913ae3</citedby><cites>FETCH-LOGICAL-c573t-ed0a8e93d0365404f7f906309ce753f59825027dc9cac0688aa80e4f55913ae3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0376871620303732$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,881,3537,27901,27902,30976,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32801113$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Smith, Ryan</creatorcontrib><creatorcontrib>Schwartenbeck, Philipp</creatorcontrib><creatorcontrib>Stewart, Jennifer L.</creatorcontrib><creatorcontrib>Kuplicki, Rayus</creatorcontrib><creatorcontrib>Ekhtiari, Hamed</creatorcontrib><creatorcontrib>Paulus, Martin P.</creatorcontrib><creatorcontrib>Tulsa 1000 Investigators</creatorcontrib><title>Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma</title><title>Drug and alcohol dependence</title><addtitle>Drug Alcohol Depend</addtitle><description>•Decision-making mechanisms in substance use disorders (SUDs) remain poorly understood.•We used computational modeling to better understand these mechanisms.•SUD patients showed less precise action selection mechanisms than healthy subjects.•SUD patients also learned slower from negative outcomes than healthy subjects.•This could help explain continued patterns of maladaptive choices in SUDs.
Substance use disorders (SUDs) are a major public health risk. However, mechanisms accounting for continued patterns of poor choices in the face of negative life consequences remain poorly understood.
We use a computational (active inference) modeling approach, combined with multiple regression and hierarchical Bayesian group analyses, to examine how treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 147) and healthy controls (HCs; N = 54) make choices to resolve uncertainty within a gambling task. A subset of SUDs (N = 49) and HCs (N = 51) propensity-matched on age, sex, and verbal IQ were also compared to replicate larger group findings.
Results indicate that: (a) SUDs show poorer task performance than HCs (p = 0.03, Cohen’s d = 0.33), with model estimates revealing less precise action selection mechanisms (p = 0.004, d = 0.43), a lower learning rate from losses (p = 0.02, d = 0.36), and a greater learning rate from gains (p = 0.04, d = 0.31); and (b) groups do not differ significantly in goal-directed information seeking.
Findings suggest a pattern of inconsistent behavior in response to positive outcomes in SUDs combined with a tendency to attribute negative outcomes to chance. Specifically, individuals with SUDs fail to settle on a behavior strategy despite sufficient evidence of its success. These learning impairments could help account for difficulties in adjusting behavior and maintaining optimal decision-making during and after treatment.</description><subject>Active inference</subject><subject>Adult</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Behavior</subject><subject>Cannabis</subject><subject>Computational modeling</subject><subject>Computer applications</subject><subject>Decision making</subject><subject>Directed exploration</subject><subject>Drug addiction</subject><subject>Drug use</subject><subject>Explore-exploit dilemma</subject><subject>Female</subject><subject>Gambling</subject><subject>Hallucinogens</subject><subject>Health risks</subject><subject>Health services utilization</subject><subject>Help seeking behavior</subject><subject>Humans</subject><subject>Information seeking behavior</subject><subject>Intelligence tests</subject><subject>Learning</subject><subject>Learning rate</subject><subject>Male</subject><subject>Marijuana</subject><subject>Narcotics</subject><subject>Opioids</subject><subject>Problem-Based Learning</subject><subject>Public health</subject><subject>Regression analysis</subject><subject>Sedative drugs</subject><subject>Sedatives</subject><subject>Stimulants</subject><subject>Substance use</subject><subject>Substance use disorder</subject><subject>Substance use disorders</subject><subject>Substance-Related Disorders - psychology</subject><subject>Task performance</subject><subject>Task Performance and Analysis</subject><subject>Uncertainty</subject><subject>Young Adult</subject><issn>0376-8716</issn><issn>1879-0046</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>7QJ</sourceid><recordid>eNqFkcFu1DAQhi0EokvhFZAlzlnGcRw7HJCgKlCpEpfeLdee7HqVxMFOQnkCXhunKQVOWJbGHv_zz8gfIZTBngGr3572Ls4H01mH476Eck2rEtQTsmNKNgVAVT8lO-CyLpRk9Rl5kdIJ8qobeE7OeKmAMcZ35OdVP0a0PiE1dvJhoAk73E4-X-bbNJnBIp2zwvkUosP4jl4u3uGabkO8L1yQdmji4IcD9f1ofOxxmBL9fsTsErplfZiOSPFu7ELE4j76KXt22PfmJXnWmi7hq4d4Tm4-Xd5cfCmuv36-uvhwXVgh-VSgA6Ow4Q54LSqoWtk2UHNoLErBW9GoUkApnW2ssVArZYwCrFohGsYN8nPyfrMd59senc0zRtPpMfrexB86GK__fRn8UR_ComW2zTsbvHkwiOHbjGnSpzDHIY-sy0pUgklZriq1qWwMKUVsHzsw0CtBfdJ_COqVoN4I5tLXf0_4WPgbWRZ83ASYv2nxGHWyfmXhfAY5aRf8_7v8AvpxtbE</recordid><startdate>20201001</startdate><enddate>20201001</enddate><creator>Smith, Ryan</creator><creator>Schwartenbeck, Philipp</creator><creator>Stewart, Jennifer L.</creator><creator>Kuplicki, Rayus</creator><creator>Ekhtiari, Hamed</creator><creator>Paulus, Martin P.</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</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>7QJ</scope><scope>7TK</scope><scope>7U7</scope><scope>C1K</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>5PM</scope></search><sort><creationdate>20201001</creationdate><title>Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma</title><author>Smith, Ryan ; Schwartenbeck, Philipp ; Stewart, Jennifer L. ; Kuplicki, Rayus ; Ekhtiari, Hamed ; Paulus, Martin P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c573t-ed0a8e93d0365404f7f906309ce753f59825027dc9cac0688aa80e4f55913ae3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Active inference</topic><topic>Adult</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Behavior</topic><topic>Cannabis</topic><topic>Computational modeling</topic><topic>Computer applications</topic><topic>Decision making</topic><topic>Directed exploration</topic><topic>Drug addiction</topic><topic>Drug use</topic><topic>Explore-exploit dilemma</topic><topic>Female</topic><topic>Gambling</topic><topic>Hallucinogens</topic><topic>Health risks</topic><topic>Health services utilization</topic><topic>Help seeking behavior</topic><topic>Humans</topic><topic>Information seeking behavior</topic><topic>Intelligence tests</topic><topic>Learning</topic><topic>Learning rate</topic><topic>Male</topic><topic>Marijuana</topic><topic>Narcotics</topic><topic>Opioids</topic><topic>Problem-Based Learning</topic><topic>Public health</topic><topic>Regression analysis</topic><topic>Sedative drugs</topic><topic>Sedatives</topic><topic>Stimulants</topic><topic>Substance use</topic><topic>Substance use disorder</topic><topic>Substance use disorders</topic><topic>Substance-Related Disorders - psychology</topic><topic>Task performance</topic><topic>Task Performance and Analysis</topic><topic>Uncertainty</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Smith, Ryan</creatorcontrib><creatorcontrib>Schwartenbeck, Philipp</creatorcontrib><creatorcontrib>Stewart, Jennifer L.</creatorcontrib><creatorcontrib>Kuplicki, Rayus</creatorcontrib><creatorcontrib>Ekhtiari, Hamed</creatorcontrib><creatorcontrib>Paulus, Martin P.</creatorcontrib><creatorcontrib>Tulsa 1000 Investigators</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Applied Social Sciences Index & Abstracts (ASSIA)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Drug and alcohol dependence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Smith, Ryan</au><au>Schwartenbeck, Philipp</au><au>Stewart, Jennifer L.</au><au>Kuplicki, Rayus</au><au>Ekhtiari, Hamed</au><au>Paulus, Martin P.</au><aucorp>Tulsa 1000 Investigators</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma</atitle><jtitle>Drug and alcohol dependence</jtitle><addtitle>Drug Alcohol Depend</addtitle><date>2020-10-01</date><risdate>2020</risdate><volume>215</volume><spage>108208</spage><epage>108208</epage><pages>108208-108208</pages><artnum>108208</artnum><issn>0376-8716</issn><eissn>1879-0046</eissn><abstract>•Decision-making mechanisms in substance use disorders (SUDs) remain poorly understood.•We used computational modeling to better understand these mechanisms.•SUD patients showed less precise action selection mechanisms than healthy subjects.•SUD patients also learned slower from negative outcomes than healthy subjects.•This could help explain continued patterns of maladaptive choices in SUDs.
Substance use disorders (SUDs) are a major public health risk. However, mechanisms accounting for continued patterns of poor choices in the face of negative life consequences remain poorly understood.
We use a computational (active inference) modeling approach, combined with multiple regression and hierarchical Bayesian group analyses, to examine how treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 147) and healthy controls (HCs; N = 54) make choices to resolve uncertainty within a gambling task. A subset of SUDs (N = 49) and HCs (N = 51) propensity-matched on age, sex, and verbal IQ were also compared to replicate larger group findings.
Results indicate that: (a) SUDs show poorer task performance than HCs (p = 0.03, Cohen’s d = 0.33), with model estimates revealing less precise action selection mechanisms (p = 0.004, d = 0.43), a lower learning rate from losses (p = 0.02, d = 0.36), and a greater learning rate from gains (p = 0.04, d = 0.31); and (b) groups do not differ significantly in goal-directed information seeking.
Findings suggest a pattern of inconsistent behavior in response to positive outcomes in SUDs combined with a tendency to attribute negative outcomes to chance. Specifically, individuals with SUDs fail to settle on a behavior strategy despite sufficient evidence of its success. These learning impairments could help account for difficulties in adjusting behavior and maintaining optimal decision-making during and after treatment.</abstract><cop>Ireland</cop><pub>Elsevier B.V</pub><pmid>32801113</pmid><doi>10.1016/j.drugalcdep.2020.108208</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0376-8716 |
ispartof | Drug and alcohol dependence, 2020-10, Vol.215, p.108208-108208, Article 108208 |
issn | 0376-8716 1879-0046 |
language | eng |
recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7502502 |
source | Applied Social Sciences Index & Abstracts (ASSIA); MEDLINE; Elsevier ScienceDirect Journals |
subjects | Active inference Adult Bayes Theorem Bayesian analysis Behavior Cannabis Computational modeling Computer applications Decision making Directed exploration Drug addiction Drug use Explore-exploit dilemma Female Gambling Hallucinogens Health risks Health services utilization Help seeking behavior Humans Information seeking behavior Intelligence tests Learning Learning rate Male Marijuana Narcotics Opioids Problem-Based Learning Public health Regression analysis Sedative drugs Sedatives Stimulants Substance use Substance use disorder Substance use disorders Substance-Related Disorders - psychology Task performance Task Performance and Analysis Uncertainty Young Adult |
title | Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T09%3A55%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Imprecise%20action%20selection%20in%20substance%20use%20disorder:%20Evidence%20for%20active%20learning%20impairments%20when%20solving%20the%20explore-exploit%20dilemma&rft.jtitle=Drug%20and%20alcohol%20dependence&rft.au=Smith,%20Ryan&rft.aucorp=Tulsa%201000%20Investigators&rft.date=2020-10-01&rft.volume=215&rft.spage=108208&rft.epage=108208&rft.pages=108208-108208&rft.artnum=108208&rft.issn=0376-8716&rft.eissn=1879-0046&rft_id=info:doi/10.1016/j.drugalcdep.2020.108208&rft_dat=%3Cproquest_pubme%3E2454517722%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2454517722&rft_id=info:pmid/32801113&rft_els_id=S0376871620303732&rfr_iscdi=true |