title: Stochastic model predicts evolving preferences in the Iowa gambling task.
abstract: 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.
authors.affiliation: Santa Fe Institute Santa Fe, NM, USA ; Instituto de Sistemas Complejos de Valparaíso Valparaíso, Chile ; Instituto de Investigaciones Filosóficas and CONICET, Sociedad Argentina de Análisis Filosófico (SADAF) Buenos Aires, Argentina.
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Fuentes
authors.affiliation: Facultad de Economía y Empresa, Centro de Neuroeconomía, Universidad Diego Portales Santiago, Chile ; Faculty of Psychology, Centre for the Study of Argumentation and Reasoning, Universidad Diego Portales Santiago, Chile ; Laboratory of Cognitive and Social Neuroscience (LaNCyS), UDP-INECO Foundation Core on Neuroscience, Universidad Diego Portales Santiago, Chile.
authors.forename: Claudio
authors.initials: C
authors.lastname: Lavín
authors.affiliation: Faculty of Psychology, Centre for the Study of Argumentation and Reasoning, Universidad Diego Portales Santiago, Chile ; Laboratory of Cognitive and Social Neuroscience (LaNCyS), UDP-INECO Foundation Core on Neuroscience, Universidad Diego Portales Santiago, Chile.
authors.forename: L Sebastián
authors.initials: LS
authors.lastname: Contreras-Huerta
authors.affiliation: Universidad de Buenos Aires, Sociedad Argentina de Análisis Filosófico (SADAF) Buenos Aires, Argentina.
authors.forename: Hernan
authors.initials: H
authors.lastname: Miguel
authors.affiliation: Department of Neurophysiology, Max Planck Institute for Brain Research Frankfurt am Main, Germany ; Ernst-Strüngmann Institute for Neuroscience in Cooperation with the Max Planck Society Frankfurt am Main, Germany ; Focus Program Translational Neurosciences, Institute for Microscopic Anatomy and Neurobiology, Johannes Gutenberg-University Mainz Mainz, Germany.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Rosales Jubal
keywords: Iowa gambling task; categorization; conceptual network; decision making; dynamic landscape; learning; stochastic; uncertainty