ArticleScience advances2023
Adaptive tuning of human learning and choice variability to unexpected uncertainty.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Inferring the causes of noise from binary outcomes: A normative theory of learning under uncertainty.Psychological review · 2026Article
- Environmental uncertainty shapes human effort learning.PLoS biology · 2026Article
- Sensory integration, temporal prediction, and rule discovery reflect interdependent inference processes.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Dual process impairments in reinforcement learning and working memory systems underlie learning deficits in physiological anxiety.PLoS computational biology · 2025Article
- Fluctuations in Sequential Many-Alternative Decisions Reveal Strategies Beyond Immediate Reward Maximisation.Journal of cognition · 2025Article
- Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks.Science advances · 2024Article
- Computational processes of simultaneous learning of stochasticity and volatility in humans.Nature communications · 2024Article
- Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space.Science advances · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human value-based decisions are notably variable under uncertainty. This variability is known to arise from two distinct sources: variable choices aimed at exploring available options and imprecise learning of option values due to limited cognitive resources. However, whether these two sources of decision variability are tuned to their specific costs and benefits remains unclear. To address this question, we compared the effects of expected and unexpected uncertainty on decision-making in the same reinforcement learning task. Across two large behavioral datasets, we found that humans choose more variably between options but simultaneously learn less imprecisely their values in response to unexpected uncertainty. Using simulations of learning agents, we demonstrate that these opposite adjustments reflect adaptive tuning of exploration and learning precision to the structure of uncertainty. Together, these findings indicate that humans regulate not only how much they explore uncertain options but also how precisely they learn the values of these options.
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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.