This study used the Valence-Expectancy Learning model, a mathematical cognitive model, to dissect young adult performance on the Iowa Gambling Task (IGT). Drinking behavior and monetary incentive were examined as predictors of performance on the IGT. No differences were found among groups when data were analyzed using traditional behavioral analyses. However, when the Expectancy-Valence Learning model was applied to the data, differences between groups were found related to attention and choice consistency. Importantly, the cognitive model was not a good fit for fifty-seven percent of the data, meaning that it did not succeed in explaining how the participants' choices were dependent on trial-by-trial feedback. This may indicate that many young adults in this study were not invested in the task.
","abstract_html":"<p>This study used the Valence-Expectancy Learning model, a mathematical cognitive model, to dissect young adult performance on the Iowa Gambling Task (IGT). Drinking behavior and monetary incentive were examined as predictors of performance on the IGT. No differences were found among groups when data were analyzed using traditional behavioral analyses. However, when the Expectancy-Valence Learning model was applied to the data, differences between groups were found related to attention and choice consistency. Importantly, the cognitive model was not a good fit for fifty-seven percent of the data, meaning that it did not succeed in explaining how the participants' choices were dependent on trial-by-trial feedback. This may indicate that many young adults in this study were not invested in the task.</p>","abstract_has_math":false,"creators":["Wagner, Lori Anne"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Peter J. Donovick","Matthew D. Johnson","Kenneth J. Kurtz"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009-01-01T08:00:00Z","date_published":"2009-01-01T08:00:00Z","updated_at":"2026-07-24T01:10:28Z","subjects":["Alcohol","Cognitive modeling","Decision-making","Expectancy-valence learning model","Iowa Gambling Task","Young adults","Clinical Psychology","Cognitive Psychology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://orb.binghamton.edu/dissertation_and_theses/5","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Peter J. Donovick","Matthew D. Johnson","Kenneth J. Kurtz"]},{"key":"dc:creator","label":"Author","values":["Wagner, Lori Anne"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-08-19T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Alcohol","Cognitive modeling","Decision-making","Expectancy-valence learning model","Iowa Gambling Task","Young adults","Clinical Psychology","Cognitive Psychology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://orb.binghamton.edu/dissertation_and_theses/5"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This study used the Valence-Expectancy Learning model, a mathematical cognitive model, to dissect young adult performance on the Iowa Gambling Task (IGT). Drinking behavior and monetary incentive were examined as predictors of performance on the IGT. No differences were found among groups when data were analyzed using traditional behavioral analyses. However, when the Expectancy-Valence Learning model was applied to the data, differences between groups were found related to attention and choice consistency. Importantly, the cognitive model was not a good fit for fifty-seven percent of the data, meaning that it did not succeed in explaining how the participants' choices were dependent on trial-by-trial feedback. This may indicate that many young adults in this study were not invested in the task.
"]},{"key":"dc:title","label":"Title","values":["Cognitive modeling analysis of decision-making processes in young adults at-risk and not at-risk for alcohol dependence"]}]}],"canonical_facts":{"dc:contributor":["Peter J. Donovick","Matthew D. Johnson","Kenneth J. Kurtz"],"dc:creator":["Wagner, Lori Anne"],"dc:date.available":["2016-08-19T07:00:00Z"],"dc:description.abstract":["This study used the Valence-Expectancy Learning model, a mathematical cognitive model, to dissect young adult performance on the Iowa Gambling Task (IGT). Drinking behavior and monetary incentive were examined as predictors of performance on the IGT. No differences were found among groups when data were analyzed using traditional behavioral analyses. However, when the Expectancy-Valence Learning model was applied to the data, differences between groups were found related to attention and choice consistency. Importantly, the cognitive model was not a good fit for fifty-seven percent of the data, meaning that it did not succeed in explaining how the participants' choices were dependent on trial-by-trial feedback. This may indicate that many young adults in this study were not invested in the task.
"],"dc:identifier":["https://orb.binghamton.edu/dissertation_and_theses/5"],"dc:subject":["Alcohol","Cognitive modeling","Decision-making","Expectancy-valence learning model","Iowa Gambling Task","Young adults","Clinical Psychology","Cognitive Psychology"],"dc:title":["Cognitive modeling analysis of decision-making processes in young adults at-risk and not at-risk for alcohol dependence"],"thesis:degree_discipline":["Psychology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T01:10:28Z"}