ArticlePLoS computational biology2020
A simple model for learning in volatile environments.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Methamphetamine-induced adaptation of learning rate dynamics depend on baseline performance.eLife · 2025Trial
- Hippocampal-cingulate dynamics in the human brain link reinforcement-learning and memory.bioRxiv : the preprint server for biology · 2026Article
- Reward-induced endogenous pain inhibition scales with action-outcome certainty in humans.Pain · 2026Article
- Inferring the causes of noise from binary outcomes: A normative theory of learning under uncertainty.Psychological review · 2026Article
- Article
- 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
- Modeling human synchronization to rhythmic patterns with varying statistical regularities.Acta psychologica · 2026Article
- Hybrid neural-cognitive models reveal how memory shapes human reward learning.Nature human behaviour · 2026Article
- Environmental uncertainty shapes human effort learning.PLoS biology · 2026Article
- The Impact of Prior Beliefs about Volatility on Adaptive Behavior.Journal of cognition · 2026Article
- Neural Dynamics of Social Cognition: A Single-Trial Computational Analysis of Learning Under Uncertainty.Human brain mapping · 2026Article
- Human Strategy Adaptation in Reinforcement Learning Resembles Policy Gradient Ascent.bioRxiv : the preprint server for biology · 2025Article
- Impaired Adaptive Learning in Chronic Pain Contributes to Apathy.medRxiv : the preprint server for health sciences · 2025Article
- Distinct Computational Mechanisms of Uncertainty Processing Explain Opposing Exploratory Behaviors in Anxiety and Apathy.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- A nonlinear relationship between prediction errors and learning rates in human reinforcement-learning.PLoS computational biology · 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
- Volatility-driven learning in human infants.Science advances · 2025Article
- Stochastic decisions support optimal foraging of volatile environments, and are disrupted by anxiety.Cognitive, affective & behavioral neuroscience · 2025Article
- Further perceptions of probability: Accurate, stepwise updating is contingent on prior information about the task and the response mode.Psychonomic bulletin & review · 2025Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Sound principles of statistical inference dictate that uncertainty shapes learning. In this work, we revisit the question of learning in volatile environments, in which both the first and second-order statistics of observations dynamically evolve over time. We propose a new model, the volatile Kalman filter (VKF), which is based on a tractable state-space model of uncertainty and extends the Kalman filter algorithm to volatile environments. The proposed model is algorithmically simple and encompasses the Kalman filter as a special case. Specifically, in addition to the error-correcting rule of Kalman filter for learning observations, the VKF learns volatility according to a second error-correcting rule. These dual updates echo and contextualize classical psychological models of learning, in particular hybrid accounts of Pearce-Hall and Rescorla-Wagner. At the computational level, compared with existing models, the VKF gives up some flexibility in the generative model to enable a more faithful approximation to exact inference. When fit to empirical data, the VKF is better behaved than alternatives and better captures human choice data in two independent datasets of probabilistic learning tasks. The proposed model provides a coherent account of learning in stable or volatile environments and has implications for decision neuroscience research.
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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.