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.

Subjective estimates of uncertainty during gambling and impulsivity after subthalamic deep brain stimulation for Parkinson’s disease

“…By allowing participants to vary their bet size, switch between slot machines and place double-or-nothing bets, we could estimate how participants not only inferred the trial-by-trial probability of winning, but also updated higherorder beliefs about the fluctuations (volatility) of a slot machine's winning probability. Similar to the rationale outlined in prior work, (Paliwal et al, 2014) we believe that a naturalistic paradigm engenders increased behavioural engagement, allowing us to quantify behaviour that has a higher fidelity to 'real world' impulsivity. Additionally, model-based estimates derived from the computational framework may afford us an individual profile of how each participant represented (and responded to) environmental uncertainty.…”

“…In prior work, we found that participant-specific parameter estimates encoding different aspects of uncertainty (such as an estimate of environmental uncertainty, the tonic volatility ) related to a clinical measure of impulsivity in a cohort of healthy individuals (Paliwal et al, 2014).…”

“…We employed a modified version of an established slot machine gambling paradigm validated in healthy controls (Paliwal et al, 2014). The task was designed to have standard features normally attributed to slot machines (colours, sounds, banners), and its features were designed to mirror the specifications of Swiss and German slot machines ( Figure 2).…”

“…If the longer-term outcomes of actions are not (or do not seem to be) predictable, prospective thinking may be replaced by seeking immediately available outcomes, a policy that would manifest behaviourally as impulsivity (Patton et al, 1995). More specifically, subjective uncertainty about environmental dynamics, or the longer-term consequences of actions, has been associated with a tendency to reduce reflection and long-term planning, and favour shortterm over long-term outcomes (Averbeck et al, 2014;FitzGerald et al, 2015;Paliwal et al, the rate at which they learn (update their beliefs). This learning rate can be linked to an agent's encoding of volatility Mathys et al, 2011;Mathys et al, 2014).…”

“…In this analysis, we employed a similar computational framework to that previously reported (Paliwal et al, 2014), applying a hierarchical Bayesian model (the Hierarchical Gaussian Filter, HGF) to behavioural data from thirty-eight participants with Parkinson's disease who played a virtual casino before and after subthalamic DBS. By allowing participants to vary their bet size, switch between slot machines and place double-or-nothing bets, we could estimate how participants not only inferred the trial-by-trial probability of winning, but also updated higherorder beliefs about the fluctuations (volatility) of a slot machine's winning probability.…”