期刊
CURRENT OPINION IN NEUROBIOLOGY
卷 58, 期 -, 页码 167-174出版社
CURRENT BIOLOGY LTD
DOI: 10.1016/j.conb.2019.08.004
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资金
- National Eye Institute of the US National Institutes of Health [R01EY020851]
- National Science Foundation [1265480]
- Simons Foundation [543033]
- Direct For Social, Behav & Economic Scie
- Division Of Behavioral and Cognitive Sci [1265480] Funding Source: National Science Foundation
A strong preference for novelty emerges in infancy and is prevalent across the animal kingdom. When incorporated into reinforcement-based machine learning algorithms, visual novelty can act as an intrinsic reward signal that vastly increases the efficiency of exploration and expedites learning, particularly in situations where external rewards are difficult to obtain. Here we review parallels between recent developments in novelty-driven machine learning algorithms and our understanding of how visual novelty is computed and signaled in the primate brain. We propose that in the visual system, novelty representations are not configured with the principal goal of detecting novel objects, but rather with the broader goal of flexibly generalizing novelty information across different states in the service of driving novelty-based learning.
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