ArticleeLife2021
A new model of decision processing in instrumental learning tasks.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06091163 (A Randomised Controlled Trial Evaluating the Efficacy and Mechanisms of a Ketogenic Diet as an Adjunctive Treatment for People With Treatment-resistant Depression), which is not on this map. Cited by 33 papers.
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Randomised Controlled Trial Evaluating the Efficacy and Mechanisms of a Ketogenic Diet as an Adjunctive Treatment for People With Treatment-resistant Depression
Who cites it
33 citing papers in PubMed.
- Modeling decision dynamics disentangles working memory, cognitive control and reinforcement learning and reveals clinical differences.PLoS computational biology · 2026Article
- Quantifying the effects of prospective memory and ongoing task difficulty on capacity sharing and cognitive control.Memory & cognition · 2026Article
- HSSM: A Widely Applicable Toolbox for Hierarchical Bayesian Neurocognitive Modeling.bioRxiv : the preprint server for biology · 2026Article
- Improving translational insight using sequential sampling models in drug choice.Psychopharmacology · 2026Review
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- An illustrative guide to expressing cognitive theories using evidence accumulation modelling.Behavior research methods · 2026Article
- Joint Cognitive Models Reveal Sources of Robust Individual Differences in Conflict Processing.Computational brain & behavior · 2026Article
- The diffusion model's drift rate parameter primarily reflects efficiency, rather than speed, of evidence accumulation.Psychonomic bulletin & review · 2026Article
- Bayesian hierarchical cognitive modeling with the EMC2 package.Behavior research methods · 2026Article
- How cortico-basal ganglia-thalamic subnetworks can shift decision policies to increase reward rate.PLoS computational biology · 2025Article
- Joint models reveal human subcortical underpinnings of choice and learning behavior.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application.Computational brain & behavior · 2025Article
- Parameter identifiability in evidence-accumulation models: The effect of error rates on the diffusion decision model and the linear ballistic accumulator.Psychonomic bulletin & review · 2025Article
- Bayesian Workflow for Generative Modeling in Computational Psychiatry.Computational psychiatry (Cambridge, Mass.) · 2025Article
- EZ-CDM: Fast, simple, robust, and accurate estimation of circular diffusion model parameters.Psychonomic bulletin & review · 2024Review
- Test-retest reliability of reinforcement learning parameters.Behavior research methods · 2024Article
- Joint modeling of choices and reaction times based on Bayesian contextual behavioral control.PLoS computational biology · 2024Article
- The effects of non-diagnostic information on confidence and decision making.Memory & cognition · 2024Article
- The autocorrelated Bayesian sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times.Psychological review · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Learning and decision-making are interactive processes, yet cognitive modeling of error-driven learning and decision-making have largely evolved separately. Recently, evidence accumulation models (EAMs) of decision-making and reinforcement learning (RL) models of error-driven learning have been combined into joint RL-EAMs that can in principle address these interactions. However, we show that the most commonly used combination, based on the diffusion decision model (DDM) for binary choice, consistently fails to capture crucial aspects of response times observed during reinforcement learning. We propose a new RL-EAM based on an advantage racing diffusion (ARD) framework for choices among two or more options that not only addresses this problem but captures stimulus difficulty, speed-accuracy trade-off, and stimulus-response-mapping reversal effects. The RL-ARD avoids fundamental limitations imposed by the DDM on addressing effects of absolute values of choices, as well as extensions beyond binary choice, and provides a computationally tractable basis for wider applications.
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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.