ArticleNature communications2024
Computational processes of simultaneous learning of stochasticity and volatility in humans.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Methamphetamine-induced adaptation of learning rate dynamics depend on baseline performance.eLife · 2025Trial
- The compositionality continuum as a principle for studying the neural basis of intelligence.Nature neuroscience · 2026Review
- Autonomy in learning: Predictability modulates the beneficial effect of choice on memory.Memory & cognition · 2026Article
- Understanding anxiety through uncertainty quantification.British journal of psychology (London, England : 1953) · 2026Article
- Inferring the causes of noise from binary outcomes: A normative theory of learning under uncertainty.Psychological review · 2026Article
- Dissociating volatility and stochasticity reveals transdiagnostic computational signatures of psychopathology.bioRxiv : the preprint server for biology · 2026Article
- Early Life Stress Affects Human Decision Making by Increasing Expectations of Volatility.Developmental science · 2026Article
- Predicting continuous outcomes: Some new tests of associative approaches to contingency learning.PLoS computational biology · 2026Article
- Environmental uncertainty shapes human effort learning.PLoS biology · 2026Article
- Computational origins of cortical brain circuits for social cognition.Nature reviews. Neuroscience · 2026Review
- Affective valence as a computational signal for learning value.Communications psychology · 2026Article
- Excessive Flexibility? Recurrent Neural Networks Can Accommodate Individual Differences in Reinforcement Learning Through In-Context Adaptation.Computational brain & behavior · 2026Article
- Addressing low statistical power in computational modelling studies in psychology and neuroscience.Nature human behaviour · 2026Article
- Article
- Neural Dynamics of Social Cognition: A Single-Trial Computational Analysis of Learning Under Uncertainty.Human brain mapping · 2026Article
- Computational signatures of uncertainty are reflected in motor cortex excitatory neurochemistry.Nature communications · 2025Article
- Inferring learning rules during de novo task learning.bioRxiv : the preprint server for biology · 2025Article
- The role of affective states in computational psychiatry.The international journal of neuropsychopharmacology · 2025Review
- Computational processes of simultaneous learning of stochasticity and volatility in humans.Nature communications · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Making adaptive decisions requires predicting outcomes, and this in turn requires adapting to uncertain environments. This study explores computational challenges in distinguishing two types of noise influencing predictions: volatility and stochasticity. Volatility refers to diffusion noise in latent causes, requiring a higher learning rate, while stochasticity introduces moment-to-moment observation noise and reduces learning rate. Dissociating these effects is challenging as both increase the variance of observations. Previous research examined these factors mostly separately, but it remains unclear whether and how humans dissociate them when they are played off against one another. In two large-scale experiments, through a behavioral prediction task and computational modeling, we report evidence of humans dissociating volatility and stochasticity solely based on their observations. We observed contrasting effects of volatility and stochasticity on learning rates, consistent with statistical principles. These results are consistent with a computational model that estimates volatility and stochasticity by balancing their dueling effects.
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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.