ArticleBehavior research methods2018
On the importance of avoiding shortcuts in applying cognitive models to hierarchical data.
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 45 papers.
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45 citing papers in PubMed.
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- Acute isolation is associated with increased reward seeking and reward learning in human adolescents.Communications psychology · 2025Article
- A tutorial for estimating Bayesian hierarchical mixture models for visual working memory tasks: Introducing the Bayesian Measurement Modeling (bmm) package for R.Behavior research methods · 2025Article
- The Effect of Reward Magnitude on Different Types of Exploration in Human Reinforcement Learning.Computational brain & behavior · 2025Article
- Troubleshooting Bayesian cognitive models.Psychological methods · 2025Article
- Partially Observable Predictor Models for Identifying Cognitive Markers.Computational brain & behavior · 2025Article
- Acute effects of Δ9-tetrahydrocannabinol on computational measures of neurocognitive processes are related to recent cannabis use among adolescents and young adults.Frontiers in adolescent medicine · 2025Article
- Modeling Decision-Making in Schizophrenia: Associations Between Computationally Derived Risk Propensity and Self-Reported Risk Perception.Schizophrenia bulletin · 2024Article
- Meta-learning of human motor adaptation via the dorsal premotor cortex.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Integrating word-form representations with global similarity computation in recognition memory.Psychonomic bulletin & review · 2024Article
- Assessing the Longitudinal Associations Between Decision-Making Processes and Attention Problems in Early Adolescence.Research on child and adolescent psychopathology · 2024Article
- Does context recollection depend on the base-rate of contextual features?Cognitive processing · 2024Article
- Prior probability biases perceptual choices by modulating the accumulation rate, rather than the baseline, of decision evidence.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Reliability of gamified reinforcement learning in densely sampled longitudinal assessments.PLOS digital health · 2023Article
- Reinforcement learning establishes a minimal metacognitive process to monitor and control motor learning performance.Nature communications · 2023Article
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4 authors.
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Abstract
Psychological experiments often yield data that are hierarchically structured. A number of popular shortcut strategies in cognitive modeling do not properly accommodate this structure and can result in biased conclusions. To gauge the severity of these biases, we conducted a simulation study for a two-group experiment. We first considered a modeling strategy that ignores the hierarchical data structure. In line with theoretical results, our simulations showed that Bayesian and frequentist methods that rely on this strategy are biased towards the null hypothesis. Secondly, we considered a modeling strategy that takes a two-step approach by first obtaining participant-level estimates from a hierarchical cognitive model and subsequently using these estimates in a follow-up statistical test. Methods that rely on this strategy are biased towards the alternative hypothesis. Only hierarchical models of the multilevel data lead to correct conclusions. Our results are particularly relevant for the use of hierarchical Bayesian parameter estimates in cognitive modeling.
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