ArticlePLoS computational biology2019
Confidence resets reveal hierarchical adaptive learning in humans.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Perceiving the uncertain: predictive coding and individual differences in the dynamics of multistable perception.Cognitive processing · 2026Review
- Hidden State Inference or Continuous Belief Updating during a Dynamic Visuomotor Skill.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Beyond discretization: why cognitive science should embrace continuity.Frontiers in psychology · 2026Article
- Comparing object lifting kinematics and the size-weight illusion between physical reality and virtual reality.Attention, perception & psychophysics · 2025Article
- Counteracting uncertainty: exploring the impact of anxiety on updating predictions about environmental states.Biological cybernetics · 2025Article
- Touch-driven advantages in reaction time but not in performance in a cross-sensory comparison of reinforcement learning.Heliyon · 2025Article
- Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks.Science advances · 2024Article
- Learning and metacognition under volatility in GD: Lower learning rates and distorted coupling between action and confidence.Journal of behavioral addictions · 2024Article
- Two Determinants of Dynamic Adaptive Learning for Magnitudes and Probabilities.Open mind : discoveries in cognitive science · 2024Article
- Neural and computational underpinnings of biased confidence in human reinforcement learning.Nature communications · 2023Article
- Probabilistic integration of preceding responses explains response bias in perceptual decision making.iScience · 2023Article
- Task-evoked pupillary responses track precision-weighted prediction errors and learning rate during interceptive visuomotor actions.Scientific reports · 2022Article
- Motivational signals disrupt metacognitive signals in the human ventromedial prefrontal cortex.Communications biology · 2022Article
- Models of Dynamic Belief Updating in Psychosis-A Review Across Different Computational Approaches.Frontiers in psychiatry · 2022Review
- Article
- Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making.PLoS computational biology · 2021Article
- Neural surprise in somatosensory Bayesian learning.PLoS computational biology · 2021Article
- Using the past to estimate sensory uncertainty.eLife · 2020Article
- Robust valence-induced biases on motor response and confidence in human reinforcement learning.Cognitive, affective & behavioral neuroscience · 2020Article
- Brain dynamics for confidence-weighted learning.PLoS computational biology · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hierarchical processing is pervasive in the brain, but its computational significance for learning under uncertainty is disputed. On the one hand, hierarchical models provide an optimal framework and are becoming increasingly popular to study cognition. On the other hand, non-hierarchical (flat) models remain influential and can learn efficiently, even in uncertain and changing environments. Here, we show that previously proposed hallmarks of hierarchical learning, which relied on reports of learned quantities or choices in simple experiments, are insufficient to categorically distinguish hierarchical from flat models. Instead, we present a novel test which leverages a more complex task, whose hierarchical structure allows generalization between different statistics tracked in parallel. We use reports of confidence to quantitatively and qualitatively arbitrate between the two accounts of learning. Our results support the hierarchical learning framework, and demonstrate how confidence can be a useful metric in learning theory.
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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.