ArticleBehavior research methods2018
Using Bayesian regression to test hypotheses about relationships between parameters and covariates in cognitive models.
Article in Behavior research methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Comparing descriptive and theoretical models of decision-making under uncertainty and their relation to socioeconomic factors.PloS one · 2025Article
- Using cognitive modeling to examine the effects of competition on strategy and effort in races and tournaments.Psychonomic bulletin & review · 2023Article
- Article
- Perceptual Decision-Making in Children: Age-Related Differences and EEG Correlates.Computational brain & behavior · 2021Article
- Improving the Reliability of Computational Analyses: Model-Based Planning and Its Relationship With Compulsivity.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2020Article
- Moving beyond Ordinary Factor Analysis in Studies of Personality and Personality Disorder: A Computational Modeling Perspective.Psychopathology · 2020Review
- On the importance of avoiding shortcuts in applying cognitive models to hierarchical data.Behavior research methods · 2018Article
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Authors and funding
3 authors.
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Abstract
An important tool in the advancement of cognitive science are quantitative models that represent different cognitive variables in terms of model parameters. To evaluate such models, their parameters are typically tested for relationships with behavioral and physiological variables that are thought to reflect specific cognitive processes. However, many models do not come equipped with the statistical framework needed to relate model parameters to covariates. Instead, researchers often revert to classifying participants into groups depending on their values on the covariates, and subsequently comparing the estimated model parameters between these groups. Here we develop a comprehensive solution to the covariate problem in the form of a Bayesian regression framework. Our framework can be easily added to existing cognitive models and allows researchers to quantify the evidential support for relationships between covariates and model parameters using Bayes factors. Moreover, we present a simulation study that demonstrates the superiority of the Bayesian regression framework to the conventional classification-based approach.
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