ReviewNature reviews. Neuroscience2025
Disentangling sources of variability in decision-making.
Review in Nature reviews. Neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
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Who cites it
8 citing papers in PubMed.
- Less systematic decision-making during delay discounting, but not discounting rate, is robustly linked to psychopathology.Journal of psychopathology and clinical science · 2026Article
- Perceptual Decision Efficiency and Optimal Sleep Quality Are Associated with Female College Soccer Injury Avoidance.Brain sciences · 2026Article
- Improving translational insight using sequential sampling models in drug choice.Psychopharmacology · 2026Review
- Quantifying uncertainty in drift diffusion models of decision making under temporal dependence and parameter variability.bioRxiv : the preprint server for biology · 2026Article
- Stochastic choice drives variability in patch foraging decisions in humans and rats.Communications psychology · 2026Article
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- Perceptual Decision Efficiency Is Modifiable and Associated with Decreased Musculoskeletal Injury Risk Among Female College Soccer Players.Brain sciences · 2025Article
- A Bayesian hierarchical model of trial-to-trial fluctuations in decision criterion.PLoS computational biology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Even the most highly-trained observers presented with identical choice-relevant stimuli will reliably exhibit substantial trial-to-trial variability in the timing and accuracy of their choices. Despite being a pervasive feature of choice behaviour and a prominent phenotype for numerous clinical disorders, the capability to disentangle the sources of such intra-individual variability (IIV) remains limited. In principle, computational models of decision-making offer a means of parsing and estimating these sources, but methodological limitations have prevented this potential from being fully realized in practice. In this Review, we first discuss current limitations of algorithmic models for understanding variability in decision-making behaviour. We then highlight recent advances in behavioural paradigm design, novel analyses of cross-trial behavioural and neural dynamics, and the development of neurally grounded computational models that are now making it possible to link distinct components of IIV to well-defined neural processes. Taken together, we demonstrate how these methods are opening up new avenues for systematically analysing the neural origins of IIV, paving the way for a more refined, holistic understanding of decision-making in health and disease.
Indexed as
Identifiers
40114010What OpenQuestion holds
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.