Stochastic model predicts evolving preferences in the Iowa gambling task
Learning under uncertainty is a common task that people face in their daily life. This process relies on the cognitive ability to adjust behavior to environmental demands. Although the biological underpinnings of those cognitive processes have been extensively studied, there has been little work in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-12-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00167/full |
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| author | Miguel Angel eFuentes Miguel Angel eFuentes Miguel Angel eFuentes Claudio eLavin Claudio eLavin Claudio eLavin Luis Sebastian Contreras-Huerta Luis Sebastian Contreras-Huerta Hernan eMiguel Hernan eMiguel Eduardo eRosales Jubal Eduardo eRosales Jubal Eduardo eRosales Jubal |
| author_facet | Miguel Angel eFuentes Miguel Angel eFuentes Miguel Angel eFuentes Claudio eLavin Claudio eLavin Claudio eLavin Luis Sebastian Contreras-Huerta Luis Sebastian Contreras-Huerta Hernan eMiguel Hernan eMiguel Eduardo eRosales Jubal Eduardo eRosales Jubal Eduardo eRosales Jubal |
| author_sort | Miguel Angel eFuentes |
| collection | DOAJ |
| description | Learning under uncertainty is a common task that people face in their daily life. This process relies on the cognitive ability to adjust behavior to environmental demands. Although the biological underpinnings of those cognitive processes have been extensively studied, there has been little work in formal models seeking to capture the fundamental dynamic of learning under uncertainty. In the present work, we aimed to understand the basic cognitive mechanisms of outcome processing involved in decisions under uncertainty and to evaluate the relevance of previous experiences in enhancing learning processes within such uncertain context. We propose a formal model that emulates the behavior of people playing a well established paradigm (Iowa Gambling Task - IGT) and compare its outcome with a behavioral experiment. We further explored whether it was possible to emulate maladaptive behavior observed in clinical samples by modifying the model parameter which controls the update of expected outcomes distributions. Results showed that the performance of the model resembles the observed participant performance as well as IGT performance by healthy subjects described in the literature. Interestingly, the model converges faster than some subjects on the decks with higher net expected outcome. Furthermore, the modified version of the model replicated the trend observed in clinical samples performing the task. We argue that the basic cognitive component underlying learning under uncertainty can be represented as a differential equation that considers the outcomes of previous decisions for guiding the agent to an adaptive strategy. |
| first_indexed | 2024-12-11T22:06:12Z |
| format | Article |
| id | doaj.art-ac8dcdee33e540b2bb5f2aa2a81eec66 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-11T22:06:12Z |
| publishDate | 2014-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-ac8dcdee33e540b2bb5f2aa2a81eec662022-12-22T00:48:56ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882014-12-01810.3389/fncom.2014.00167105974Stochastic model predicts evolving preferences in the Iowa gambling taskMiguel Angel eFuentes0Miguel Angel eFuentes1Miguel Angel eFuentes2Claudio eLavin3Claudio eLavin4Claudio eLavin5Luis Sebastian Contreras-Huerta6Luis Sebastian Contreras-Huerta7Hernan eMiguel8Hernan eMiguel9Eduardo eRosales Jubal10Eduardo eRosales Jubal11Eduardo eRosales Jubal12Santa Fe InstituteInstituto de sistemas complejos de ValparaísoInstituto de Investigaciones Filosóficas and CONICETUniversidad Diego PortalesUniversidad Diego PortalesUniversidad Diego PortalesUniversidad Diego PortalesUniversidad Diego PortalesInstituto de Investigaciones Filosóficas and CONICETUniversidad de Buenos AiresJohannes Gutenberg University of MainzMax Planck Institute for Brain ResearchErnst-Strüngmann Institute (ESI) for Neuroscience, Cooperation with the Max Planck SocietyLearning under uncertainty is a common task that people face in their daily life. This process relies on the cognitive ability to adjust behavior to environmental demands. Although the biological underpinnings of those cognitive processes have been extensively studied, there has been little work in formal models seeking to capture the fundamental dynamic of learning under uncertainty. In the present work, we aimed to understand the basic cognitive mechanisms of outcome processing involved in decisions under uncertainty and to evaluate the relevance of previous experiences in enhancing learning processes within such uncertain context. We propose a formal model that emulates the behavior of people playing a well established paradigm (Iowa Gambling Task - IGT) and compare its outcome with a behavioral experiment. We further explored whether it was possible to emulate maladaptive behavior observed in clinical samples by modifying the model parameter which controls the update of expected outcomes distributions. Results showed that the performance of the model resembles the observed participant performance as well as IGT performance by healthy subjects described in the literature. Interestingly, the model converges faster than some subjects on the decks with higher net expected outcome. Furthermore, the modified version of the model replicated the trend observed in clinical samples performing the task. We argue that the basic cognitive component underlying learning under uncertainty can be represented as a differential equation that considers the outcomes of previous decisions for guiding the agent to an adaptive strategy.http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00167/fullDecision MakingLearningCategorizationuncertaintyIowa Gambling Taskstochastic |
| spellingShingle | Miguel Angel eFuentes Miguel Angel eFuentes Miguel Angel eFuentes Claudio eLavin Claudio eLavin Claudio eLavin Luis Sebastian Contreras-Huerta Luis Sebastian Contreras-Huerta Hernan eMiguel Hernan eMiguel Eduardo eRosales Jubal Eduardo eRosales Jubal Eduardo eRosales Jubal Stochastic model predicts evolving preferences in the Iowa gambling task Frontiers in Computational Neuroscience Decision Making Learning Categorization uncertainty Iowa Gambling Task stochastic |
| title | Stochastic model predicts evolving preferences in the Iowa gambling task |
| title_full | Stochastic model predicts evolving preferences in the Iowa gambling task |
| title_fullStr | Stochastic model predicts evolving preferences in the Iowa gambling task |
| title_full_unstemmed | Stochastic model predicts evolving preferences in the Iowa gambling task |
| title_short | Stochastic model predicts evolving preferences in the Iowa gambling task |
| title_sort | stochastic model predicts evolving preferences in the iowa gambling task |
| topic | Decision Making Learning Categorization uncertainty Iowa Gambling Task stochastic |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00167/full |
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