{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/78131"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/78131","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Cocaine Use Modulates Neural Prediction Error During Aversive Learning","abstract":"Cocaine use has contributed to 5 million individuals falling into the cycle of addiction. Prior research in cocaine dependence mainly focused on rewards. Losses also play a critical role in cocaine dependence as dependent individuals fail to avoid social, health, and economic losses even when they acknowledge them. However, dependent individuals are extremely adept at escaping negative states like withdrawal. To further understand whether cocaine use may contribute to dysfunctions in aversive learning, this paper uses fMRI and an aversive learning task to examine cocaine dependent individuals abstinent from cocaine use (C-) and using as usual (C+). Specifically of interest is the neural signal representing actual loss compared to the expected loss, better known as prediction error (δ), which individuals use to update future expectations. When abstinent (C-), dependent individuals exhibited higher positive prediction error (δ+) signal in their striatum than when they were using as usual. Furthermore, their striatal δ+ signal enhancements from drug abstinence were predicted by higher positive learning rate (α+) enhancements. However, no relationships were found between drug abstinence enhancements to negative learning rates (α±-) and negative prediction error (δ-) striatal signals. Abstinent (C-) individuals' striatal δ+ signal was predicted by longer drug use history, signifying possible relief learning adaptations with time. Lastly, craving measures, especially the desire to use cocaine and positive effects of cocaine, also positively correlated with C- individuals' striatal δ+ signal. This suggests possible relief learning adaptations in response to higher craving and withdrawal symptoms. Taken together, enhanced striatal δ+ signal when abstinent and adaptations in relief learning provide evidence in supporting dependent individuals' lack of aversive learning ability while using as usual and enhanced relief learning ability for the purpose of avoiding negative situations such as withdrawal, suggesting a neurocomputational mechanism that pushes the dependent individual to maintains dependence.","abstract_html":"Cocaine use has contributed to 5 million individuals falling into the cycle of addiction. Prior research in cocaine dependence mainly focused on rewards. Losses also play a critical role in cocaine dependence as dependent individuals fail to avoid social, health, and economic losses even when they acknowledge them. However, dependent individuals are extremely adept at escaping negative states like withdrawal. To further understand whether cocaine use may contribute to dysfunctions in aversive learning, this paper uses fMRI and an aversive learning task to examine cocaine dependent individuals abstinent from cocaine use (C-) and using as usual (C+). Specifically of interest is the neural signal representing actual loss compared to the expected loss, better known as prediction error (δ), which individuals use to update future expectations. When abstinent (C-), dependent individuals exhibited higher positive prediction error (δ+) signal in their striatum than when they were using as usual. Furthermore, their striatal δ+ signal enhancements from drug abstinence were predicted by higher positive learning rate (α+) enhancements. However, no relationships were found between drug abstinence enhancements to negative learning rates (α±-) and negative prediction error (δ-) striatal signals. Abstinent (C-) individuals' striatal δ+ signal was predicted by longer drug use history, signifying possible relief learning adaptations with time. Lastly, craving measures, especially the desire to use cocaine and positive effects of cocaine, also positively correlated with C- individuals' striatal δ+ signal. This suggests possible relief learning adaptations in response to higher craving and withdrawal symptoms. Taken together, enhanced striatal δ+ signal when abstinent and adaptations in relief learning provide evidence in supporting dependent individuals' lack of aversive learning ability while using as usual and enhanced relief learning ability for the purpose of avoiding negative situations such as withdrawal, suggesting a neurocomputational mechanism that pushes the dependent individual to maintains dependence.","abstract_has_math":false,"creators":["Wang, John Mujia"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Psychology","degree_department":"Psychology","school":null,"contributors":[],"advisors":[],"committee_chairs":["Chiu, Pearl H."],"committee_members":["Casas, Brooks","Cooper, Lee D.","Panneton, Robin K."],"year":2015,"date_issued":"2015-05-07","date_published":"2015-05-07","updated_at":"2026-07-22T22:20:23Z","subjects":["reinforcement learning","prediction error","cocaine","dopamine","fMRI"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-05202015-112918"],"render_values":[{"text":"etd-05202015-112918","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/78131","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Chiu, Pearl H."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Casas, Brooks","Cooper, Lee D.","Panneton, Robin K."]},{"key":"dc:contributor.department","label":"Department","values":["Psychology"]},{"key":"dc:creator","label":"Author","values":["Wang, John Mujia"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-06-13T19:44:11Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-06-13T19:44:11Z","2015-06-08"]},{"key":"dc:date.issued","label":"Date","values":["2015-05-07"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.dcmitype","label":"Dc Type Dcmitype","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["reinforcement learning","prediction error","cocaine","dopamine","fMRI"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-05202015-112918"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/78131"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Cocaine use has contributed to 5 million individuals falling into the cycle of addiction. Prior research in cocaine dependence mainly focused on rewards. Losses also play a critical role in cocaine dependence as dependent individuals fail to avoid social, health, and economic losses even when they acknowledge them. However, dependent individuals are extremely adept at escaping negative states like withdrawal. To further understand whether cocaine use may contribute to dysfunctions in aversive learning, this paper uses fMRI and an aversive learning task to examine cocaine dependent individuals abstinent from cocaine use (C-) and using as usual (C+). Specifically of interest is the neural signal representing actual loss compared to the expected loss, better known as prediction error (δ), which individuals use to update future expectations. When abstinent (C-), dependent individuals exhibited higher positive prediction error (δ+) signal in their striatum than when they were using as usual. Furthermore, their striatal δ+ signal enhancements from drug abstinence were predicted by higher positive learning rate (α+) enhancements. However, no relationships were found between drug abstinence enhancements to negative learning rates (α±-) and negative prediction error (δ-) striatal signals. Abstinent (C-) individuals' striatal δ+ signal was predicted by longer drug use history, signifying possible relief learning adaptations with time. Lastly, craving measures, especially the desire to use cocaine and positive effects of cocaine, also positively correlated with C- individuals' striatal δ+ signal. This suggests possible relief learning adaptations in response to higher craving and withdrawal symptoms. Taken together, enhanced striatal δ+ signal when abstinent and adaptations in relief learning provide evidence in supporting dependent individuals' lack of aversive learning ability while using as usual and enhanced relief learning ability for the purpose of avoiding negative situations such as withdrawal, suggesting a neurocomputational mechanism that pushes the dependent individual to maintains dependence."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Cocaine Use Modulates Neural Prediction Error During Aversive Learning"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Chiu, Pearl H."],"dc:contributor.committeemember":["Casas, Brooks","Cooper, Lee D.","Panneton, Robin K."],"dc:contributor.department":["Psychology"],"dc:creator":["Wang, John Mujia"],"dc:date.accessioned":["2017-06-13T19:44:11Z"],"dc:date.available":["2017-06-13T19:44:11Z","2015-06-08"],"dc:date.issued":["2015-05-07"],"dc:description.abstract":["Cocaine use has contributed to 5 million individuals falling into the cycle of addiction. Prior research in cocaine dependence mainly focused on rewards. Losses also play a critical role in cocaine dependence as dependent individuals fail to avoid social, health, and economic losses even when they acknowledge them. However, dependent individuals are extremely adept at escaping negative states like withdrawal. To further understand whether cocaine use may contribute to dysfunctions in aversive learning, this paper uses fMRI and an aversive learning task to examine cocaine dependent individuals abstinent from cocaine use (C-) and using as usual (C+). Specifically of interest is the neural signal representing actual loss compared to the expected loss, better known as prediction error (δ), which individuals use to update future expectations. When abstinent (C-), dependent individuals exhibited higher positive prediction error (δ+) signal in their striatum than when they were using as usual. Furthermore, their striatal δ+ signal enhancements from drug abstinence were predicted by higher positive learning rate (α+) enhancements. However, no relationships were found between drug abstinence enhancements to negative learning rates (α±-) and negative prediction error (δ-) striatal signals. Abstinent (C-) individuals' striatal δ+ signal was predicted by longer drug use history, signifying possible relief learning adaptations with time. Lastly, craving measures, especially the desire to use cocaine and positive effects of cocaine, also positively correlated with C- individuals' striatal δ+ signal. This suggests possible relief learning adaptations in response to higher craving and withdrawal symptoms. Taken together, enhanced striatal δ+ signal when abstinent and adaptations in relief learning provide evidence in supporting dependent individuals' lack of aversive learning ability while using as usual and enhanced relief learning ability for the purpose of avoiding negative situations such as withdrawal, suggesting a neurocomputational mechanism that pushes the dependent individual to maintains dependence."],"dc:description.degree":["Master of Science"],"dc:format.mimetype":["application/pdf"],"dc:identifier.other":["etd-05202015-112918"],"dc:identifier.uri":["http://hdl.handle.net/10919/78131"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["reinforcement learning","prediction error","cocaine","dopamine","fMRI"],"dc:title":["Cocaine Use Modulates Neural Prediction Error During Aversive Learning"],"dc:type":["Thesis"],"dc:type.dcmitype":["Text"],"thesis:degree_discipline":["Psychology"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:23Z"}