ArticleScientific reports2020
Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Rapid and reliable computational markers of decision-making for predicting daily smoking behavior and smoking cessation treatment outcomes.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Task and Resting-State Functional Magnetic Resonance Imaging Modeling of Brain-Behavior Relationships in Developmental Cohorts.Biological psychiatry · 2026Review
- Can AI help with the hardest thing: pro health behavior change.NPJ cardiovascular health · 2026Review
- Comparing the reliability of individual differences for various measurement models in conflict tasks.Psychonomic bulletin & review · 2026Article
- Framing as a mechanism to overcome the temptation of bad habits.Frontiers in behavioral neuroscience · 2025Review
- Genomic structural equation study reveals links between anorexia nervosa and delay discounting and lack of perseverance but not other facets of impulsivity.Frontiers in psychiatry · 2025Article
- Delay discounting of rewards and losses, alcohol use, and the influence of socioeconomic factors: A cross-sectional online study in frequent drinkers.Alcohol, clinical & experimental research · 2024Article
- Improving the Reliability of Cognitive Task Measures: A Narrative Review.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2023Review
- A Bayesian perspective on severity: risky predictions and specific hypotheses.Psychonomic bulletin & review · 2023Review
- Adaptive Design Optimization for a Mnemonic Similarity Task.Journal of mathematical psychology · 2022Article
- Unifying Evidence on Delay Discounting: Open Task, Analysis Tutorial, and Normative Data from an Italian Sample.International journal of environmental research and public health · 2022Article
- A Model Guided Approach to Evoke Homogeneous Behavior During Temporal Reward and Loss Discounting.Frontiers in psychiatry · 2022Article
- Comparing Discounting of Potentially Real Rewards and Losses by Means of Functional Magnetic Resonance Imaging.Frontiers in systems neuroscience · 2022Article
- Model-based experimental manipulation of probabilistic behavior in interpretable behavioral latent variable models.Frontiers in neuroscience · 2022Article
- ADOpy: a python package for adaptive design optimization.Behavior research methods · 2021Article
- Computational optimization of associative learning experiments.PLoS computational biology · 2020Article
- Test-retest reliability of computational parameters versus manifest behavior for decisional flexibility in psychosis.Psychological assessmentArticle
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Authors and funding
8 authors.
Funding
Abstract
Machine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning. In three populations (college students, patients with a substance use disorder, and Amazon Mechanical Turk workers), we evaluated one such method, Bayesian adaptive design optimization (ADO), in the area of delay discounting by comparing its test-retest reliability, precision, and efficiency with that of a conventional staircase method. In all three populations tested, the results showed that ADO led to 0.95 or higher test-retest reliability of the discounting rate within 10-20 trials (under 1-2 min of testing), captured approximately 10% more variance in test-retest reliability, was 3-5 times more precise, and was 3-8 times more efficient than the staircase method. The ADO methodology provides efficient and precise protocols for measuring individual differences in delay discounting.
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