ArticleNature communications2017
Feature-based learning improves adaptability without compromising precision.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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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.
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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
46 citing papers in PubMed.
- Article
- Automatic value learning results in counterproductive human behavior.Nature communications · 2026Article
- Autism-associated learning patterns show reduced credit assignment to outcome-irrelevant features.Translational psychiatry · 2026Article
- Neurocomputational modeling of rule abstraction and memorization during probabilistic stimulus-reward learning.iScience · 2026Article
- Sex differences in task engagement and lapse rate during reward learning.Oxford open neuroscience · 2026Article
- How working memory and reinforcement learning interact when avoiding punishment and pursuing reward concurrently.Journal of experimental psychology. General · 2025Article
- An Information-Theoretic Framework for Understanding Learning and Choice Under Uncertainty.Entropy (Basel, Switzerland) · 2025Article
- Schemas, reinforcement learning and the medial prefrontal cortex.Nature reviews. Neuroscience · 2025Review
- Contributions of Attention to Learning in Multidimensional Reward Environments.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Neural Mechanisms Underlying Robust Target Selection in Response to Microstimulation of the Oculomotor System.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Semantic Anchors Facilitate Task Encoding in Continual Learning.Open mind : discoveries in cognitive science · 2025Article
- Harnessing multi-output machine learning approach and dynamical observables from network structure to optimize COVID-19 intervention strategies.Biology methods & protocols · 2025Article
- Noradrenergic alpha-2a receptor stimulation enhances prediction error signaling and updating of attention sets in anterior cingulate cortex and striatum.Nature communications · 2024Article
- Integration of Theory and Practice in Medical Morphology Curriculum in Postgraduate Training: A Flipped Classroom and Case-based Learning Exercise.Current medical science · 2023Article
- Mechanisms of adjustments to different types of uncertainty in the reward environment across mice and monkeys.Cognitive, affective & behavioral neuroscience · 2023Article
- Humans combine value learning and hypothesis testing strategically in multi-dimensional probabilistic reward learning.PLoS computational biology · 2022Article
- Explicit knowledge of task structure is a primary determinant of human model-based action.Nature human behaviour · 2022Article
- Selective particle attention: Rapidly and flexibly selecting features for deep reinforcement learning.Neural networks : the official journal of the International Neural Network Society · 2022Article
- Model-based learning retrospectively updates model-free values.Scientific reports · 2022Article
- Computational models of adaptive behavior and prefrontal cortex.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Learning from reward feedback is essential for survival but can become extremely challenging with myriad choice options. Here, we propose that learning reward values of individual features can provide a heuristic for estimating reward values of choice options in dynamic, multi-dimensional environments. We hypothesize that this feature-based learning occurs not just because it can reduce dimensionality, but more importantly because it can increase adaptability without compromising precision of learning. We experimentally test this hypothesis and find that in dynamic environments, human subjects adopt feature-based learning even when this approach does not reduce dimensionality. Even in static, low-dimensional environments, subjects initially adopt feature-based learning and gradually switch to learning reward values of individual options, depending on how accurately objects' values can be predicted by combining feature values. Our computational models reproduce these results and highlight the importance of neurons coding feature values for parallel learning of values for features and objects.
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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.