ArticleBehavior research methods2024
Test-retest reliability of reinforcement learning parameters.
Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Automatic value learning results in counterproductive human behavior.Nature communications · 2026Article
- The recoverability, reliability, and generalizability of reward processing parameters and relation to mental health symptoms.Psychological medicine · 2026Article
- State Optimism as a State-Dependent Prior About Rewarding Outcomes.Computational brain & behavior · 2026Article
- Reply to Cecchi and Palminteri: On the need to model temporal variation in learning rates.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Prefrontal and parieto-occipital neural signatures of evidence accumulation and response to computerised Cognitive Behavioural Therapy in depression.Npj mental health research · 2025Article
- Deep Learning Improves Parameter Estimation in Reinforcement Learning Models.bioRxiv : the preprint server for biology · 2025Article
- [Association of increased greater tubercle angle and critical shoulder angle with rotator cuff tears].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2025Article
- A task-invariant prior explains trial-by-trial active avoidance behaviour across gain and loss tasks.Communications psychology · 2025Article
- Sample size matters when estimating test-retest reliability of behaviour.Behavior research methods · 2025Article
- The Effect of Reward Magnitude on Different Types of Exploration in Human Reinforcement Learning.Computational brain & behavior · 2025Article
- Understanding learning through uncertainty and bias.Communications psychology · 2025Review
- Clarifying the reliability paradox: poor measurement reliability attenuates group differences.Frontiers in psychology · 2025Article
- Material hardship, not household income, predicts impaired punishment learning: a computational reinforcement learning perspective.Frontiers in psychology · 2025Article
- Consistency within change: Evaluating the psychometric properties of a widely used predictive-inference task.Behavior research methods · 2024Article
- Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts.PLoS computational biology · 2024Article
- Test-retest reliability of behavioral and computational measures of advice taking under volatility.PloS one · 2024Article
- Informing the treatment of social anxiety disorder with computational and neuroimaging data.Psychoradiology · 2024Article
- Reliability of gamified reinforcement learning in densely sampled longitudinal assessments.PLOS digital health · 2023Article
- Test-retest reliability of computational parameters versus manifest behavior for decisional flexibility in psychosis.Psychological assessmentArticle
Corrections and comments
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4 authors.
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Abstract
It has recently been suggested that parameter estimates of computational models can be used to understand individual differences at the process level. One area of research in which this approach, called computational phenotyping, has taken hold is computational psychiatry. One requirement for successful computational phenotyping is that behavior and parameters are stable over time. Surprisingly, the test-retest reliability of behavior and model parameters remains unknown for most experimental tasks and models. The present study seeks to close this gap by investigating the test-retest reliability of canonical reinforcement learning models in the context of two often-used learning paradigms: a two-armed bandit and a reversal learning task. We tested independent cohorts for the two tasks (N = 69 and N = 47) via an online testing platform with a between-test interval of five weeks. Whereas reliability was high for personality and cognitive measures (with ICCs ranging from .67 to .93), it was generally poor for the parameter estimates of the reinforcement learning models (with ICCs ranging from .02 to .52 for the bandit task and from .01 to .71 for the reversal learning task). Given that simulations indicated that our procedures could detect high test-retest reliability, this suggests that a significant proportion of the variability must be ascribed to the participants themselves. In support of that hypothesis, we show that mood (stress and happiness) can partly explain within-participant variability. Taken together, these results are critical for current practices in computational phenotyping and suggest that individual variability should be taken into account in the future development of the field.
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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 Socratic graph.