ArticleCognitive science2018
The Outcome-Representation Learning Model: A Novel Reinforcement Learning Model of the Iowa Gambling Task.
Article in Cognitive science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
32 citing papers in PubMed.
- Reward-specific learning parameters change across normative adolescent development and are blunted in youth with high risk for depression.Journal of child psychology and psychiatry, and allied disciplines · 2026Article
- Associations Between Steep Delay Discounting and Punishment Learning.Computational psychiatry (Cambridge, Mass.) · 2026Article
- Reward and punishment learning among people with a lifetime history of anxiety, depression, and substance use disorder.Cognitive, affective & behavioral neuroscience · 2025Article
- Negative affectivity drivers of impulsivity in opioid use disorder.Nature reviews. Psychology · 2025Article
- Forgetting phenomena in the Iowa Gambling Task: a new computational model among diverse participants.Frontiers in psychology · 2025Article
- Alcohol intoxication and negative mood similarly affect reward learning but not punishment learning in the Iowa gambling task.Judgment and decision making · 2025Article
- Do Human Reinforcement Learning Models Account for Key Experimental Choice Patterns in the Iowa Gambling Task?Computational brain & behavior · 2025Article
- Probabilistic Representation Differences between Decisions from Description and Decisions from Experience.Journal of Intelligence · 2024Article
- Test-retest reliability of the play-or-pass version of the Iowa Gambling Task.Cognitive, affective & behavioral neuroscience · 2024Article
- Modulation of dlPFC function and decision-making capacity by repetitive transcranial magnetic stimulation in methamphetamine use disorder.Translational psychiatry · 2024Article
- Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts.PLoS computational biology · 2024Article
- The influence of social feedback on reward learning in the Iowa gambling task.Frontiers in psychology · 2024Article
- From Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health Research.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2023Review
- Decision-making deficits in obsessive-compulsive disorder are associated with abnormality of recency and response consistency parameter in prospect valence learning model.Frontiers in psychiatry · 2023Article
- Impulsivity-related right superior frontal gyrus as a biomarker of internet gaming disorder.General psychiatry · 2023Article
- Computational models of exploration and exploitation characterise onset and efficacy of treatment in methamphetamine use disorder.Addiction biology · 2022Article
- Differences in Learning and Persistency Characterizing Behavior in Chronic Pain for the Iowa Gambling Task: Web-Based Laboratory-in-the-Field Study.Journal of medical Internet research · 2022Article
- Shuffle the Decks: Children Are Sensitive to Incidental Nonrandom Structure in a Sequential-Choice Task.Psychological science · 2022Article
- Enhancing the Psychometric Properties of the Iowa Gambling Task Using Full Generative Modeling.Computational psychiatry (Cambridge, Mass.) · 2022Article
- Using Methods From Computational Decision-making to Predict Nonadherence to Fitness Goals: Protocol for an Observational Study.JMIR research protocols · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The Iowa Gambling Task (IGT) is widely used to study decision-making within healthy and psychiatric populations. However, the complexity of the IGT makes it difficult to attribute variation in performance to specific cognitive processes. Several cognitive models have been proposed for the IGT in an effort to address this problem, but currently no single model shows optimal performance for both short- and long-term prediction accuracy and parameter recovery. Here, we propose the Outcome-Representation Learning (ORL) model, a novel model that provides the best compromise between competing models. We test the performance of the ORL model on 393 subjects' data collected across multiple research sites, and we show that the ORL reveals distinct patterns of decision-making in substance-using populations. Our work highlights the importance of using multiple model comparison metrics to make valid inference with cognitive models and sheds light on learning mechanisms that play a role in underweighting of rare events.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.