ArticleComputational brain & behavior2024
Joint Modelling of Latent Cognitive Mechanisms Shared Across Decision-Making Domains.
Article in Computational brain & behavior, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Random-effects psychophysics for studying individual differences in perception and cognition.Psychonomic bulletin & review · 2026Review
- An illustrative guide to expressing cognitive theories using evidence accumulation modelling.Behavior research methods · 2026Article
- The diffusion model's drift rate parameter primarily reflects efficiency, rather than speed, of evidence accumulation.Psychonomic bulletin & review · 2026Article
- From symptom-based heterogeneity to mechanism-based profiling in youth ADHD: the promise of computational psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- 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
- Dissociable Contributions of Goal-Relevant Evidence and Goal-Irrelevant Familiarity to Individual and Developmental Differences in Conflict Recognition.Cognitive science · 2024Article
- Flexible adaptation of task-positive brain networks predicts efficiency of evidence accumulation.Communications biology · 2024Article
- Building bridges between brain and behavior: An open-source toolbox for joint modeling with fMRI.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
8 authors.
Funding
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Abstract
Decision-making behavior is often understood using the framework of evidence accumulation models (EAMs). Nowadays, EAMs are applied to various domains of decision-making with the underlying assumption that the latent cognitive constructs proposed by EAMs are consistent across these domains. In this study, we investigate both the extent to which the parameters of EAMs are related between four different decision-making domains and across different time points. To that end, we make use of the novel joint modelling approach, that explicitly includes relationships between parameters, such as covariances or underlying factors, in one combined joint model. Consequently, this joint model also accounts for measurement error and uncertainty within the estimation of these relations. We found that EAM parameters were consistent between time points on three of the four decision-making tasks. For our between-task analysis, we constructed a joint model with a factor analysis on the parameters of the different tasks. Our two-factor joint model indicated that information processing ability was related between the different decision-making domains. However, other cognitive constructs such as the degree of response caution and urgency were only comparable on some domains.
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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.