ArticleNature human behaviour2024
Dynamic computational phenotyping of human cognition.
Article in Nature human behaviour, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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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.
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Who cites it
29 citing papers in PubMed.
- Automatic value learning results in counterproductive human behavior.Nature communications · 2026Article
- The recoverability, reliability, and generalizability of reward processing parameters and relation to mental health symptoms.Psychological medicine · 2026Article
- Article
- Autism-associated learning patterns show reduced credit assignment to outcome-irrelevant features.Translational psychiatry · 2026Article
- Cognitive modeling of real-world behavior for understanding mental health.Trends in cognitive sciences · 2026Review
- State Optimism as a State-Dependent Prior About Rewarding Outcomes.Computational brain & behavior · 2026Article
- Inhibitory Control, Shifting, and Working Memory Updating Domains form Cognitive Phenotypes in Non-human Primates.bioRxiv : the preprint server for biology · 2026Article
- Day-to-day fluctuations in cognitive precision predict the domain-general intention-behavior gap.Science advances · 2026Article
- Large-scale experimental investigation of the reliability of confidence measures.Communications psychology · 2025Article
- Development and Piloting of Co.Ge.: A Web-Based Digital Platform for Generative and Clinical Cognitive Assessment.Journal of personalized medicine · 2025Article
- Default mode network connectivity predicts individual differences in long-term forgetting: Evidence for storage degradation, not retrieval failure.PLoS computational biology · 2025Article
- Deep Learning Improves Parameter Estimation in Reinforcement Learning Models.bioRxiv : the preprint server for biology · 2025Article
- A Bayesian hierarchical model of trial-to-trial fluctuations in decision criterion.PLoS computational biology · 2025Article
- Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application.Computational brain & behavior · 2025Article
- A task-invariant prior explains trial-by-trial active avoidance behaviour across gain and loss tasks.Communications psychology · 2025Article
- Disentangling sources of variability in decision-making.Nature reviews. Neuroscience · 2025Review
- Day-to-day fluctuations in motivation drive effort-based decision-making.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Understanding learning through uncertainty and bias.Communications psychology · 2025Review
- Unraveling latent cognitive, metacognitive, strategic, and affective processes underlying children's problem-solving using Bayesian cognitive modeling.bioRxiv : the preprint server for biology · 2025Article
- Transcranial direct current stimulation alleviates the negative impact of immediate and chronic social exclusion on intertemporal decision-making.Social cognitive and affective neuroscience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational phenotyping has emerged as a powerful tool for characterizing individual variability across a variety of cognitive domains. An individual's computational phenotype is defined as a set of mechanistically interpretable parameters obtained from fitting computational models to behavioural data. However, the interpretation of these parameters hinges critically on their psychometric properties, which are rarely studied. To identify the sources governing the temporal variability of the computational phenotype, we carried out a 12-week longitudinal study using a battery of seven tasks that measure aspects of human learning, memory, perception and decision making. To examine the influence of state effects, each week, participants provided reports tracking their mood, habits and daily activities. We developed a dynamic computational phenotyping framework, which allowed us to tease apart the time-varying effects of practice and internal states such as affective valence and arousal. Our results show that many phenotype dimensions covary with practice and affective factors, indicating that what appears to be unreliability may reflect previously unmeasured structure. These results support a fundamentally dynamic understanding of cognitive variability within an individual.
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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 Socratic graph.