ArticleNature human behaviour2023
Modelling human behaviour in cognitive tasks with latent dynamical systems.
Article in Nature human behaviour, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 32 citations in OpenAlex.
- Feature interference underlies a neuronal basis for the behavioral cost of task uncertainty.Nature neuroscience · 2026Article
- Cognitive flexibility versus stability via activation-based and weight-based adaptations.Communications psychology · 2026Article
- Modeling of control over task switching and cross-task interference supports a two-dimensional model of cognitive stability and flexibility.Psychonomic bulletin & review · 2025Review
- Mental graphs structure the storage and retrieval of visuomotor associations.Nature human behaviour · 2025Article
- Data-driven equation discovery reveals nonlinear reinforcement learning in humans.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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- Convergent neural dynamical systems for task control in artificial networks and human brains.bioRxiv : the preprint server for biology · 2025Article
- Article
- Limitation of switching sensory information flow in flexible perceptual decision making.Nature communications · 2025Article
- Flexible inference for animal learning rules using neural networks.Advances in neural information processing systems · 2025Article
- Contextual control demands determine whether stability and flexibility trade off against each other.Attention, perception & psychophysics · 2024Article
- Centering cognitive neuroscience on task demands and generalization.Nature neuroscience · 2024Review
- Humans reconfigure target and distractor processing to address distinct task demands.Psychological review · 2024Article
- Harnessing the flexibility of neural networks to predict dynamic theoretical parameters underlying human choice behavior.PLoS computational biology · 2024Article
- Enhancing experimental design through Bayes factor design analysis: insights from multi-armed bandit tasks.Wellcome open research · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Response time data collected from cognitive tasks are a cornerstone of psychology and neuroscience research, yet existing models of these data either make strong assumptions about the data-generating process or are limited to modelling single trials. We introduce task-DyVA, a deep learning framework in which expressive dynamical systems are trained to reproduce sequences of response times observed in data from individual human subjects. Models fitted to a large task-switching dataset captured subject-specific behavioural differences with high temporal precision, including task-switching costs. Through perturbation experiments and analyses of the models' latent dynamics, we find support for a rational account of switch costs in terms of a stability-flexibility trade-off. Thus, our framework can be used to discover interpretable cognitive theories that explain how the brain dynamically gives rise to behaviour.
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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.