title: A model-based analysis of impulsivity using a slot-machine gambling paradigm.
abstract: Impulsivity plays a key role in decision-making under uncertainty. It is a significant contributor to problem and pathological gambling (PG). Standard assessments of impulsivity by questionnaires, however, have various limitations, partly because impulsivity is a broad, multi-faceted concept. What remains unclear is which of these facets contribute to shaping gambling behavior. In the present study, we investigated impulsivity as expressed in a gambling setting by applying computational modeling to data from 47 healthy male volunteers who played a realistic, virtual slot-machine gambling task. Behaviorally, we found that impulsivity, as measured independently by the 11th revision of the Barratt Impulsiveness Scale (BIS-11), correlated significantly with an aggregate read-out of the following gambling responses: bet increases (BIs), machines switches (MS), casino switches (CS), and double-ups (DUs). Using model comparison, we compared a set of hierarchical Bayesian belief-updating models, i.e., the Hierarchical Gaussian Filter (HGF) and Rescorla-Wagner reinforcement learning (RL) models, with regard to how well they explained different aspects of the behavioral data. We then examined the construct validity of our winning models with multiple regression, relating subject-specific model parameter estimates to the individual BIS-11 total scores. In the most predictive model (a three-level HGF), the two free parameters encoded uncertainty-dependent mechanisms of belief updates and significantly explained BIS-11 variance across subjects. Furthermore, in this model, decision noise was a function of trial-wise uncertainty about winning probability. Collectively, our results provide a proof of concept that hierarchical Bayesian models can characterize the decision-making mechanisms linked to the impulsive traits of an individual. These novel indices of gambling mechanisms unmasked during actual play may be useful for online prevention measures for at-risk players and future assessments of PG.
authors: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH Zurich) Zurich, Switzerland.
authors: Saee
authors: S
authors: Paliwal
authors: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH Zurich) Zurich, Switzerland.
authors: Frederike H
authors: FH
authors: Petzschner
authors: Max Plank Institute for Neurological Research Cologne, Germany.
authors: Anna Katharina
authors: AK
authors: Schmitz
authors: Max Plank Institute for Neurological Research Cologne, Germany.
authors: Marc
authors: M
authors: Tittgemeyer
authors: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH Zurich) Zurich, Switzerland ; Max Plank Institute for Neurological Research Cologne, Germany ; Laboratory for Social and Neural Systems Research (SNS), University of Zurich Zurich, Switzerland ; Wellcome Trust Centre for Neuroimaging, University College London London, UK.
authors: Klaas E
authors: KE
authors: Stephan
keywords: Barratt Impulsiveness Scale; Hierarchical Bayesian Model; Hierarchical Gaussian Filter; impulsivity; pathological gambling