ArticlePLoS computational biology2019
Sequential exploration in the Iowa gambling task: Validation of a new computational model in a large dataset of young and old healthy participants.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 40 citations in OpenAlex.
- The dynamics of explore-exploit decisions suggest a threshold mechanism for reduced random exploration in older adults.PLoS computational biology · 2025Article
- Forgetting phenomena in the Iowa Gambling Task: a new computational model among diverse participants.Frontiers in psychology · 2025Article
- Do Human Reinforcement Learning Models Account for Key Experimental Choice Patterns in the Iowa Gambling Task?Computational brain & behavior · 2025Article
- Differential impacts of healthy cognitive aging on directed and random exploration.Psychology and aging · 2024Article
- Dynamic noise estimation: A generalized method for modeling noise fluctuations in decision-making.bioRxiv : the preprint server for biology · 2024Article
- Decomposing decision-making in preschoolers: Making decisions under ambiguity versus risk.PloS one · 2024Article
- Computational modeling of decision-making in substance abusers: testing Bechara's hypotheses.Frontiers in psychology · 2024Article
- Parietal stimulation reverses age-related decline in exploration, learning, and decision-making.bioRxiv : the preprint server for biology · 2023Article
- Introducing tomsup: Theory of mind simulations using Python.Behavior research methods · 2023Article
- Article
- Computational models of exploration and exploitation characterise onset and efficacy of treatment in methamphetamine use disorder.Addiction biology · 2022Article
- Attenuated Directed Exploration during Reinforcement Learning in Gambling Disorder.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Decision-making inflexibility in a reversal learning task is associated with severity of problem gambling symptoms but not with a diagnosis of substance use disorder.BMC psychology · 2020Article
- The Role of Emotional vs. Cognitive Intelligence in Economic Decision-Making Amongst Older Adults.Frontiers in neuroscience · 2020Article
- Deciphering Age Differences in Experience-Based Decision-Making: The Role of Sleep.Nature and science of sleep · 2020Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Iowa Gambling Task (IGT) is one of the most common paradigms used to assess decision-making and executive functioning in neurological and psychiatric disorders. Several reinforcement-learning (RL) models were recently proposed to refine the qualitative and quantitative inferences that can be made about these processes based on IGT data. Yet, these models do not account for the complex exploratory patterns which characterize participants' behavior in the task. Using a dataset of more than 500 subjects, we demonstrate the existence of sequential exploration in the IGT and we describe a new computational architecture disentangling exploitation, random exploration and sequential exploration in this large population of participants. The new Value plus Sequential Exploration (VSE) architecture provided a better fit than previous models. Parameter recovery, model recovery and simulation analyses confirmed the superiority of the VSE scheme. Furthermore, using the VSE model, we confirmed the existence of a significant reduction in directed exploration across lifespan in the IGT, as previously reported with other paradigms. Finally, we provide a user-friendly toolbox enabling researchers to easily and flexibly fit computational models on the IGT data, hence promoting reanalysis of the numerous datasets acquired in various populations of patients and contributing to the development of computational psychiatry.
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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.