ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2020
Improving the Reliability of Computational Analyses: Model-Based Planning and Its Relationship With Compulsivity.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed, 103 citations in OpenAlex.
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- 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
- Threading the Needle: Practical Considerations for Merging Theory-Driven Computational Psychiatry With Data-Driven Analytics to Enhance Precision Health at Scale.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- Associations Between Steep Delay Discounting and Punishment Learning.Computational psychiatry (Cambridge, Mass.) · 2026Article
- Neural Bases of Affect-Based Impulsivity: A Decision Neuroscience Account.Computational psychiatry (Cambridge, Mass.) · 2026Review
- Attenuated learning rates for negative outcomes in substance use disorders: A replication and extension of prior longitudinal computational modeling results.Drug and alcohol dependence · 2026Article
- Computational characterization of metacognitive ability in subjective decision-making.bioRxiv : the preprint server for biology · 2025Article
- Impulsive adolescents exhibit inefficient processing and a low decision threshold when decoding facial expressions of emotions.Psychological medicine · 2025Article
- Signatures of Perseveration and Heuristic-Based Directed Exploration in Two-Step Sequential Decision Task Behaviour.Computational psychiatry (Cambridge, Mass.) · 2025Article
- Improving the Reliability of the Pavlovian Go/No-Go Task for Computational Psychiatry Research.Computational psychiatry (Cambridge, Mass.) · 2025Article
- Computational Mechanisms of Approach-Avoidance Conflict Predictively Differentiate Between Affective and Substance Use Disorders.Computational psychiatry (Cambridge, Mass.) · 2025Article
- Does the reliability of computational models truly improve with hierarchical modeling? Some recommendations and considerations for the assessment of model parameter reliability : Reliability of computational model parameters.Psychonomic bulletin & review · 2024Review
- Reinforcement-Learning-Informed Queries Guide Behavioral Change.Clinical psychological science : a journal of the Association for Psychological Science · 2024Article
- Revisiting the role of computational neuroimaging in the era of integrative neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Consistency within change: Evaluating the psychometric properties of a widely used predictive-inference task.Behavior research methods · 2024Article
- Language sentiment predicts changes in depressive symptoms.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Temporal stability of Bayesian belief updating in perceptual decision-making.Behavior research methods · 2024Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundComputational models show great promise in mapping latent decision-making processes onto dissociable neural substrates and clinical phenotypes. One prominent example in reinforcement learning is model-based planning, which specifically relates to transdiagnostic compulsivity. However, the reliability of computational model-derived measures such as model-based planning is unclear. Establishing reliability is necessary to ensure that such models measure stable, traitlike processes, as assumed in computational psychiatry. Although analysis approaches affect validity of reinforcement learning models and reliability of other task-based measures, their effect on reliability of reinforcement learning models of empirical data has not been systematically studied.
methodsWe first assessed within- and across-session reliability and effects of analysis approaches (model estimation, parameterization, and data cleaning) of measures of model-based planning in patients with compulsive disorders (n = 38). The analysis approaches affecting test-retest reliability were tested in 3 large generalization samples (healthy participants: n = 541 and 111; people with a range of compulsivity: n = 1413).
resultsAnalysis approaches greatly influenced reliability: reliability of model-based planning measures ranged from 0 (no concordance) to above 0.9 (acceptable for clinical applications). The largest influence on reliability was whether model-estimation approaches were robust and accounted for the hierarchical structure of estimated parameters. Improvements in reliability generalized to other datasets and greatly reduced the sample size needed to find a relationship between model-based planning and compulsivity in an independent dataset.
conclusionsThese results indicate that computational psychiatry measures such as model-based planning can reliably measure latent decision-making processes, but when doing so must assess the ability of methods to estimate complex models from limited data.
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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.