ArticleDrug and alcohol dependence2020
A computational model of the Cambridge gambling task with applications to substance use disorders.
Article in Drug and alcohol dependence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 46 citations in OpenAlex.
- Decision-making and risk-taking as predictors of health risk behaviors in the Millennium Cohort Study.JCPP advances · 2026Article
- The longitudinal role of sleep on self-harm during adolescence: A birth cohort study.Journal of child psychology and psychiatry, and allied disciplines · 2026Article
- Neurobehavioural links from infant screen time to anxiety.EBioMedicine · 2026Article
- Better together - efficacy of combined physical and cognitive training in young adults for enhancing physical and cognitive performance: a randomized controlled trial.Frontiers in sports and active living · 2026Article
- Associations Between Steep Delay Discounting and Punishment Learning.Computational psychiatry (Cambridge, Mass.) · 2026Article
- Exploring risky decision-making dynamics during antidepressant treatment in major depressive disorder: a computational modeling approach.BMC psychiatry · 2025Article
- Hippocampal perineuronal net degradation identifies prefrontal and striatal circuits involved in schizophrenia-like changes in marmosets.Science advances · 2025Article
- Sleep problems, decision-making, and suicide attempts during adolescence: a longitudinal birth cohort study.Sleep advances : a journal of the Sleep Research Society · 2025Article
- Reliable affordances: A generative modeling approach for test-retest reliability of the affordances task.Behavior research methods · 2024Article
- Comparing attention, impulsivity, and executive functions between patients with opiate use disorder: Buprenorphine maintenance treatment versus active users, in comparison with healthy controls.Indian journal of psychiatry · 2024Article
- An explorative study on consequences of abuse on psychological wellbeing and cognitive outcomes in victims of gender-based violence.Frontiers in psychology · 2024Article
- The Effects of Cognitive Impulsivity on the Duration of Remission in Alcohol-Dependent Patients.Consortium psychiatricum · 2023Article
- Role of Affect and Risk-Benefit Perception on Reckless Betting: Prior Wins and Losses Both Lead to Risky Bets.Journal of gambling studies · 2022Article
- Enhancing the Psychometric Properties of the Iowa Gambling Task Using Full Generative Modeling.Computational psychiatry (Cambridge, Mass.) · 2022Article
- Increased decision latency in alcohol use disorder reflects altered resting-state synchrony in the anterior salience network.Scientific reports · 2021Article
- Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks.Psychological methods · 2021Article
- Changes in Loss Sensitivity During Treatment in Concurrent Disorders Inpatients: A Computational Model Approach to Assessing Risky Decision-Making.Frontiers in psychiatry · 2021Article
- Moving beyond Ordinary Factor Analysis in Studies of Personality and Personality Disorder: A Computational Modeling Perspective.Psychopathology · 2020Review
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundImpulsivity is central to all forms of externalizing psychopathology, including problematic substance use. The Cambridge Gambling task (CGT) is a popular neurocognitive task used to assess impulsivity in both clinical and healthy populations. However, the traditional methods of analysis in the CGT do not fully capture the multiple cognitive mechanisms that give rise to impulsive behavior, which can lead to underpowered and difficult-to-interpret behavioral measures.
objectivesThe current study presents the cognitive modeling approach as an alternative to traditional methods and assesses predictive and convergent validity across and between approaches.
methodsWe used hierarchical Bayesian modeling to fit a series of cognitive models to data from healthy controls (N = 124) and individuals with histories of substance use disorders (Heroin: N = 79; Amphetamine: N = 76; Polysubstance: N = 103; final total across groups N = 382). Using Bayesian model comparison, we identified the best fitting model, which was then used to identify differences in cognitive model parameters between groups.
resultsThe cognitive modeling approach revealed differences in quality of decision making and impulsivity between controls and individuals with substance use disorders that traditional methods alone did not detect. Crucially, convergent validity between traditional measures and cognitive model parameters was strong across all groups.
conclusionThe cognitive modeling approach is a viable method of measuring the latent mechanisms that give rise to choice behavior in the CGT, which allows for stronger statistical inferences and a better understanding of impulsive and risk-seeking behavior.
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