ArticleJournal of mathematical psychology2017
Approaches to Analysis in Model-based Cognitive Neuroscience.
Article in Journal of mathematical psychology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 75 papers.
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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.
Exploring the Effects of Spinal Cord Stimulation in Parkinson's Disease.
Long term effects of spinal cord stimulation in patients with parkinson's disease open label extension to h-49023
Who cites it
75 citing papers in PubMed.
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- Estimating and learning personality traits of and from women with borderline personality disorder.Borderline personality disorder and emotion dysregulation · 2026Article
- Getting blood from a stone: Improving brain-behavior inferences without brain data.Imaging neuroscience (Cambridge, Mass.) · 2026Article
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- Efficiency of evidence accumulation as a formal model-based measure of task-general executive functioning in adolescents.Journal of psychopathology and clinical science · 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
- 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
- FrAMBI: A Software Framework for Auditory Modeling Based on Bayesian Inference.Neuroinformatics · 2025Article
- Credibility revolution: pursuing a balanced and sustainable approach, without dogmas, without magic elixirs.Frontiers in psychology · 2025Article
- Dissociable Contributions of Goal-Relevant Evidence and Goal-Irrelevant Familiarity to Individual and Developmental Differences in Conflict Recognition.Cognitive science · 2024Article
- A tutorial on fitting joint models of M/EEG and behavior to understand cognition.Behavior research methods · 2024Article
- Flexible adaptation of task-positive brain networks predicts efficiency of evidence accumulation.Communications biology · 2024Article
- "The eyes are the window to the representation": Linking gaze to memory precision and decision weights in object discrimination tasks.Psychological review · 2024Article
- Different components of cognitive-behavioral therapy affect specific cognitive mechanisms.Science advances · 2024Article
- Article
15 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Our understanding of cognition has been advanced by two traditionally nonoverlapping and non-interacting groups. Mathematical psychologists rely on behavioral data to evaluate formal models of cognition, whereas cognitive neuroscientists rely on statistical models to understand patterns of neural activity, often without any attempt to make a connection to the mechanism supporting the computation. Both approaches suffer from critical limitations as a direct result of their focus on data at one level of analysis (cf. Marr, 1982), and these limitations have inspired researchers to attempt to combine both neural and behavioral measures in a cross-level integrative fashion. The importance of solving this problem has spawned several entirely new theoretical and statistical frameworks developed by both mathematical psychologists and cognitive neuroscientists. However, with each new approach comes a particular set of limitations and benefits. In this article, we survey and characterize several approaches for linking brain and behavioral data. We organize these approaches on the basis of particular cognitive modeling goals: (1) using the neural data to constrain a behavioral model, (2) using the behavioral model to predict neural data, and (3) fitting both neural and behavioral data simultaneously. Within each goal, we highlight a few particularly successful approaches for accomplishing that goal, and discuss some applications. Finally, we provide a conceptual guide to choosing among various analytic approaches in performing model-based cognitive neuroscience.
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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.