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dc.creatorLópez Guzmán, Silvia
dc.identifier.issnISBN: 979-846-6800
dc.description.abstractComputational models of impulsive decision-making, like temporal discounting, are widely used to study addiction. However, clinically validating a marker supposes developing methods that provide high accuracy and reliability. We first show that a modified model of temporal discounting incorporating individual-specific risk sensitivity - provides a more precise, unbiased, and reliable measure of impulsivity than the standard approach. Using this tool, and given the current opioid epidemic, we set out to investigate longitudinally whether discounting would signal relevant negative outcomes like drug use, relapse and dropout in patients undergoing treatment for opioid addiction. We found that changes in discount rates were related to increased drug use in patients, indicating a vulnerability to full relapse and treatment failure.
dc.relation.ispartofCognitive Computational Neuroscience, ISBN:979-846-6800 (2017); 2pp.
dc.sourceCognitive Computational Neuroscience
dc.titleA Precise Computational Measure of Impulsivity that Signals Relevant Outcomes in Opioid Addiction Treatment
dc.publisherCognitive Computational Neuroscience
dc.subject.keywordOpioid Addiction
dc.subject.keywordComputational Psychiatry
dc.subject.keywordDecision Making
dc.subject.keywordRisky Decision-Making
dc.subject.keywordDelay Discounting
dc.type.spaparte de Libro
dc.rights.accesoAbierto (Texto Completo)
dc.title.TranslatedTitleUna medida computacional precisa de la impulsividad que señala resultados relevantes en el tratamiento de la adicción a los opiáceos
dc.relation.citationTitleCognitive Computational Neuroscience

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