Impulsivity and predictive control are associated with suboptimal action-selection and action-value learning in regular gamblers

Lim, Matthew Sheng Mian; Jocham, Gerhard; Hunt, Laurence T.; Behrens, Timothy E.J.; Rogers, Robert D.

doi:10.1080/14459795.2015.1078835

Cited by 14 publications

(10 citation statements)

References 62 publications

(104 reference statements)

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“…S3). Indeed, previous work using computational models to describe behavior in a binary-choice RL task found associations between high impulsivity, as measured by BIS-11, and two parameters encoding uncertaintydependent mechanisms of belief updates (Lim et al, 2015). In contrast, we did not find significant correlations of our fitted model parameters with impulsivity; that neither model could capture the specific effect of impulsivity suggests that both may lack some parameter specifically accounting for a noise vs reversal signal judgment.…”

Section: No Effect Of Impulsivity On a Priori Measurescontrasting

confidence: 75%

Impulsivity relates to multi-trial choice strategy in probabilistic reversal learning

Zou¹,

Lopez²,

Johnson³

et al. 2021

Preprint

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Impulsivity is defined as a trait-like tendency to engage in rash actions that are poorly thought out or expressed in an untimely manner. Previous research has found that impulsivity relates to deficits in decision making, in particular when it necessitates executive control or reward outcomes. Reinforcement learning (RL) relies on the ability to integrate valenced outcomes to make good decisions, and has recently be shown to often recruit executive function; as such, it is unsurprising that impulsivity has been studied in the context of RL. However, how impulsivity relates to the mechanisms of RL remains unclear. We aimed to investigate the relationship between impulsivity and learning in a reward-driven learning task with probabilistic feedback and reversal known to recruit executive function. Based on prior literature in clinical populations, we predicted that higher impulsivity would be associated with poorer performance on the task, driven by more frequent switching following unrewarded outcomes. Our results did not support this prediction, but more advanced, trial-history dependent analyses revealed specific effects of impulsivity on switching behavior following consecutive unrewarded trials. Computational modeling captured group-level behavior, but not impulsivity results. Our results support previous findings highlighting the importance of sensitivity to negative outcomes in understanding how impulsivity relates to learning, but indicate that this may stem from more complex strategies than usually considered in computational models of learning. This should be an important target for future research.

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Section: No Effect Of Impulsivity On a Priori Measurescontrasting

confidence: 75%

Impulsivity relates to multi-trial choice strategy in probabilistic reversal learning

Zou¹,

Lopez²,

Johnson³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…We, again, observed no relationship between the learning rates and either psychiatric trait suggesting impulsivity is not associated with poorer or exaggerated learning per se, but rather, it is linked to a more inconsistent learning over time 22,46 . We found a similar double dissociation in another task setting, in which participants received feedback for both (chosen and unchosen) stimuli (Supplementary Information).…”

Section: Distinct Associations Of Impulsivity and Compulsivity When Accounting For Learning Imprecisionscontrasting

confidence: 50%

Distinct computational mechanisms underlying cognitive flexibility deficits in impulsivity and compulsivity

Skvortsova

Hauser

2022

Preprint

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Cognitive flexibility, the ability to quickly adapt to changing environmental demands, is a hallmark of human behaviour, and is impaired across multiple psychiatric disorders. Especially compulsivity and impulsivity disorders have been linked to impaired adaptive learning and flexibility. Initial computational investigations suggested these distinct psychiatric dimensions suffer from the same underlying neurocognitive impairments, related to stochasticity during choice. However, a recent advance in computational neuroscience has demonstrated that imprecision in the learning process itself can account for a large portion of behavioural variability traditionally attributed to choice-stochasticity. Here, in a series of large-scale experiments using both lab-designed and gamified citizen-science tasks, we show that distinct computational markers are affected in compulsivity and impulsivity. Whilst impulsivity is tied to an imprecision in learning across valence domains, (hygiene-related) compulsivity is linked to choice-stochasticity. This double-dissociation demonstrates that distinct neurocomputational mechanisms can drive seemingly similar behavioural deficits, only dissociable using targeted computational approaches.

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“…State-dependent modulations of risk-attitude have been found impaired in problem gambling (Fujimoto et al, 2017). Similar impairments are observed in reversal learning, where gamblers make more perseveration errors following contingency reversals (de Ruiter et al, 2009;Boog et al, 2014), an effect that has been linked to maladaptive control beliefs about gambling outcomes, which might interfere with decision-making (Lim et al, 2015).…”

Section: Introductionmentioning

confidence: 76%

Attenuated Directed Exploration during Reinforcement Learning in Gambling Disorder

2021

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Gambling disorder (GD) is a behavioral addiction associated with impairments in value-based decision-making and behavioral flexibility and might be linked to changes in the dopamine system. Maximizing long-term rewards requires a flexible tradeoff between the exploitation of known options and the exploration of novel options for information gain. This explorationexploitation trade-off is thought to depend on dopamine neurotransmission. We hypothesized that human gamblers would show a reduction in directed (uncertainty-based) exploration, accompanied by changes in brain activity in a fronto-parietal exploration-related network. Twenty-three frequent, non-treatment seeking gamblers and twenty-three healthy matched controls (all male) performed a four-armed bandit task during functional magnetic resonance imaging (fMRI). Computational modeling using hierarchical Bayesian parameter estimation revealed signatures of directed exploration, random exploration, and perseveration in both groups. Gamblers showed a reduction in directed exploration, whereas random exploration and perseveration were similar between groups. Neuroimaging revealed no evidence for group differences in neural representations of basic task variables (expected value, prediction errors). Our hypothesis of reduced frontal pole (FP) recruitment in gamblers was not supported. Exploratory analyses showed that during directed exploration, gamblers showed reduced parietal cortex and substantia-nigra/ventral-tegmental-area activity. Cross-validated classification analyses revealed that connectivity in an exploration-related network was predictive of group status, suggesting that connectivity patterns might be more predictive of problem gambling than univariate effects. Findings reveal specific reductions of strategic exploration in gamblers that might be linked to altered processing in a fronto-parietal network and/or changes in dopamine neurotransmission implicated in GD.

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Impulsivity and predictive control are associated with suboptimal action-selection and action-value learning in regular gamblers

Cited by 14 publications

References 62 publications

Impulsivity relates to multi-trial choice strategy in probabilistic reversal learning

Impulsivity relates to multi-trial choice strategy in probabilistic reversal learning

Distinct computational mechanisms underlying cognitive flexibility deficits in impulsivity and compulsivity

Attenuated Directed Exploration during Reinforcement Learning in Gambling Disorder

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