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However, the complexity of the <jats:styled-content style=\"fixed-case\">IGT<\/jats:styled-content> makes it difficult to attribute variation in performance to specific cognitive processes. Several cognitive models have been proposed for the <jats:styled-content style=\"fixed-case\">IGT<\/jats:styled-content> in an effort to address this problem, but currently no single model shows optimal performance for both short\u2010 and long\u2010term prediction accuracy and parameter recovery. Here, we propose the Outcome\u2010Representation Learning (<jats:styled-content style=\"fixed-case\">ORL<\/jats:styled-content>) model, a novel model that provides the best compromise between competing models. We test the performance of the <jats:styled-content style=\"fixed-case\">ORL<\/jats:styled-content> model on 393 subjects' data collected across multiple research sites, and we show that the <jats:styled-content style=\"fixed-case\">ORL<\/jats:styled-content> reveals distinct patterns of decision\u2010making in substance\u2010using populations. Our work highlights the importance of using multiple model comparison metrics to make valid inference with cognitive models and sheds light on learning mechanisms that play a role in underweighting of rare events.<\/jats:p>","DOI":"10.1111\/cogs.12688","type":"journal-article","created":{"date-parts":[[2018,10,6]],"date-time":"2018-10-06T08:45:08Z","timestamp":1538815508000},"page":"2534-2561","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["The Outcome\u2010Representation Learning Model: A Novel Reinforcement Learning Model of the Iowa Gambling Task"],"prefix":"10.1111","volume":"42","author":[{"given":"Nathaniel","family":"Haines","sequence":"first","affiliation":[{"name":"Department of Psychology The Ohio State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasmin","family":"Vassileva","sequence":"additional","affiliation":[{"name":"Department of Psychiatry Virginia Commonwealth University"},{"name":"Institute for Drug and Alcohol Studies Virginia Commonwealth University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woo\u2010Young","family":"Ahn","sequence":"additional","affiliation":[{"name":"Department of Psychology The Ohio State University"},{"name":"Department of Psychology Seoul National University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,10,5]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.04.096"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cobeha.2016.02.001"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/03640210802352992"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/bs.pbr.2015.07.032"},{"key":"e_1_2_8_6_1","article-title":"Revealing neuro\u2010computational mechanisms of reinforcement learning and decision\u2010making with the hBayesDM package","volume":"064287","author":"Ahn W.\u2010Y.","year":"2017","journal-title":"Computational Psychiatry"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1037\/a0020684"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2014.00849"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.drugalcdep.2016.02.008"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/bdm.443"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1037\/1040-3590.10.4.331"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0028-3932(02)00015-5"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/0010-0277(94)90018-3"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0028-3932(00)00136-6"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1037\/a0035823"},{"key":"e_1_2_8_16_1","unstructured":"Betancourt M. 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