ReviewPsychonomic bulletin & review2021
The interpretation of behavior-model correlations in unidentified cognitive models.
Review in Psychonomic bulletin & review, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Modeling the cognitive processes of accepting clinical decision support.Quarterly journal of experimental psychology (2006) · 2026Article
- An illustrative guide to expressing cognitive theories using evidence accumulation modelling.Behavior research methods · 2026Article
- A drift-diffusion model of temporal generalization outperforms existing models and captures modality differences and learning effects.Behavior research methods · 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
- Interpretation of individual differences in computational neuroscience using a latent input approach.Developmental cognitive neuroscience · 2025Review
- One Standard for All: Uniform Scale for Comparing Individuals and Groups in Hierarchical Bayesian Evidence Accumulation Modeling.Journal of cognition · 2024Article
- Joint Modelling of Latent Cognitive Mechanisms Shared Across Decision-Making Domains.Computational brain & behavior · 2024Article
- The Curve of Learning With and Without Instructions.Journal of cognition · 2024Article
- The Bayesian Mutation Sampler Explains Distributions of Causal Judgments.Open mind : discoveries in cognitive science · 2023Article
- Capturing Dynamic Performance in a Cognitive Model: Estimating ACT-R Memory Parameters With the Linear Ballistic Accumulator.Topics in cognitive science · 2022Article
- Article
- Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model.Frontiers in psychology · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rise of computational modeling in the past decade has led to a substantial increase in the number of papers that report parameter estimates of computational cognitive models. A common application of computational cognitive models is to quantify individual differences in behavior by estimating how these are expressed in differences in parameters. For these inferences to hold, models need to be identified, meaning that one set of parameters is most likely, given the behavior under consideration. For many models, model identification can be achieved up to a scaling constraint, which means that under the assumption that one parameter has a specific value, all remaining parameters are identified. In the current note, we argue that this scaling constraint implies a strong assumption about the cognitive process that the model is intended to explain, and warn against an overinterpretation of the associative relations found in this way. We will illustrate these points using signal detection theory, reinforcement learning models, and the linear ballistic accumulator model, and provide suggestions for a clearer interpretation of modeling results.
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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.