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Human Representation Learning
Open accessAbstract
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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“Without direct access to what is causing advent sensory information, meaning is derived from past events that seem similar to the current state of the body and world.136”
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Metadata
- Authors
- Radulescu, A., Shin, Y. S., & Niv, Y.
- Affiliations
- Princeton University (US)
- Venue
- Annual Review of Neuroscience
- Publisher
- Annual Reviews
- Published
- 2021-07-08
- Volume
- 44
- Issue
- 1
- Pages
- 253-273
- ISSN
- 0147-006X (ISSN-L)
- 1545-4126
- PMC
- PMC9707489
- OpenAlex
- W3138639721
- Cited by
- 63
- References
- 158
- Citations by year
- 2026: 3 · 2025: 17 · 2024: 21 · 2023: 11 · 2022: 10 · 2021: 1
- FWCI
- 1.59
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-sa
- Locations
- Annual Review of Neuroscience (publishedVersion, cc-by-sa)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R01MH119511, R21MH120798)
- Army Research Office (W911NF-14-1, W911NF1410101, W911NF)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Keywords
- representation learning, attention, memory, statistical inference, information selection, belief updating, human decision-making, social decision-making, goal-directed learning, experiential learning
- MeSH terms
- Attention, Cognition, Humans, Learning
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