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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”
From A new science of emotion: implications for functional neurological disorder (2022) · cites this paper as [136]
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Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,093
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,043
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
Reinforcement Learning: An Introduction
Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,825
Representation Learning with Contrastive Predictive Coding
Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv · doi:10.48550/arxiv.1807.03748 · Cited by 6,242
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
Research works printed but not linked: 8
- Bellman R., 1957, J. Math. Mech., 6, 679
- Callaway F, 2019, PsyArXiv
- Jones M, 2010, Proceedings of the 32nd Annual Meeting of the Cognitive Science Society 2010, 1, 1258
- Kleiman-Weiner M, 2016, Proceedings of the 38th Annual Conference of the Cognitive Science Society
- Mildner J, 2018, PsyArXiv
- Radulescu A, 2020, From heuristic to optimal models in naturalistic visual search
- Sitzmann V, 2019, Advances in Neural Information Processing Systems 32, 1121
- Vélez N, 2020, Curr. Opin. Behav. Sci
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Self-citation
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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