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.
Highlights
Uncertainty is a fundamental aspect of human decision-making (Bland and Schaefer, 2012)
Perceptual variables We considered the three following pereptual variables which refer to binary trial outcomes and are summarized in Table 2: (i) Win/Loss Gross (WLG); this case treats real wins and fake wins as wins and near-misses as losses; (ii) Win/Loss Net (WLN); this option only considers real wins as wins and treats fake wins and near-misses as losses; (iii) Overlearn (OL), where real wins, fake wins and near-misses are all considered as wins
({bet increases (BIs)}, {BI, DU}, {BI, DU, casino switches (CS)}, {BI, DU, CS, machines switches (MS)}), we used Bayesian model selection (BMS). This enabled us to identify, for each of the perceptualresponse feature sets, the model with the highest posterior probability (Figure 6). These selected 12 models entered a second stage of model comparison, where we examined the construct validity of these models by testing how well their parameter estimates predicted the independent BIS-11 scores
Summary
Uncertainty is a fundamental aspect of human decision-making (Bland and Schaefer, 2012). One general framework for assessing decision-making under uncertainty is to view humans as Bayesian learners From this perspective, humans employ a generative model of sensory inputs to update beliefs about the state of the world and choose actions in order to minimize prediction errors (Knill and Pouget, 2004; Daunizeau et al, 2010; Friston et al, 2010). Humans employ a generative model of sensory inputs to update beliefs about the state of the world and choose actions in order to minimize prediction errors (Knill and Pouget, 2004; Daunizeau et al, 2010; Friston et al, 2010) When this predictive machinery breaks (due to disease or drugs), maladaptive behavior can arise. One interesting and clinically relevant case of potentially harmful aberrant behavior that arises is impulsivity, i.e., actions without deliberation or forethought, in the face of uncertainty (Dickman, 1993; Sharma et al, 2014)
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