期刊
TRENDS IN COGNITIVE SCIENCES
卷 20, 期 8, 页码 629-640出版社
ELSEVIER SCIENCE LONDON
DOI: 10.1016/j.tics.2016.06.003
关键词
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资金
- National Institutes of Health [DC-009209-12]
- National Science Foundation (NSF) Career Award [1554488]
- NSF workshop award 'Quantitative Theories of Learning Memory, and Prediction' [BCS-1430087]
- Direct For Mathematical & Physical Scien
- Division Of Physics [1554488] Funding Source: National Science Foundation
A core question in cognitive science concerns how humans acquire and represent knowledge about their environments. To this end, quantitative theories of learning processes have been formalized in an attempt to explain and predict changes in brain and behavior. We connect here statistical learning approaches in cognitive science, which are rooted in the sensitivity of learners to local distributional regularities, and network science approaches to characterizing global patterns and their emergent properties. We focus on innovative work that describes how learning is influenced by the topological properties underlying sensory input. The confluence of these theoretical approaches and this recent empirical evidence motivate the importance of scaling-up quantitative approaches to learning at both the behavioral and neural levels.
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