ReviewAnnual review of neuroscience2021
Human Representation Learning.
Review in Annual review of neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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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Who cites it
31 citing papers in PubMed.
- Structuring the World: Statistical Learning From the Top and From the Bottom.The European journal of neuroscience · 2026Review
- Article
- Attention and learning strategies reveal distinct dimensions of psychiatric diseases.Research square · 2026Article
- The effects of task similarity during representation learning in brains and neural networks.Nature communications · 2025Article
- Dynamically shifting from compositional to conjunctive brain representations supports cognitive task learning.Nature communications · 2025Article
- Attentional Mechanisms for Learning Feature Combinations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Feature-based reward learning shapes human social learning strategies.Nature human behaviour · 2025Article
- Task-relevant representational spaces in human memory traces.PLoS computational biology · 2025Article
- Collective cooperative intelligence.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Humans learn generalizable representations through efficient coding.Nature communications · 2025Article
- Free recall is shaped by inference and scaffolded by event structure.Communications psychology · 2025Article
- Contributions of Attention to Learning in Multidimensional Reward Environments.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
- Functional Neurological Disorder and Autism Spectrum Disorder: A Complex and Potentially Significant Relationship.Brain and behavior · 2024Review
- Adaptive empathic response selection is sensitive to multiple dimensions of social interaction.Communications psychology · 2024Article
- An inductive bias for slowly changing features in human reinforcement learning.PLoS computational biology · 2024Article
- The Ubiquity of Time in Latent-cause Inference.Journal of cognitive neuroscience · 2024Article
- Social uncertainty in the digital world.Trends in cognitive sciences · 2024Article
- Latent-state and model-based learning in PTSD.Trends in neurosciences · 2024Review
- The successor representation subserves hierarchical abstraction for goal-directed behavior.PLoS computational biology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The central theme of this review is the dynamic interaction between information selection and learning. We pose a fundamental question about this interaction: How do we learn what features of our experiences are worth learning about? In humans, this process depends on attention and memory, two cognitive functions that together constrain representations of the world to features that are relevant for goal attainment. Recent evidence suggests that the representations shaped by attention and memory are themselves inferred from experience with each task. We review this evidence and place it in the context of work that has explicitly characterized representation learning as statistical inference. We discuss how inference can be scaled to real-world decisions by approximating beliefs based on a small number of experiences. Finally, we highlight some implications of this inference process for human decision-making in social environments.
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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.