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Trends Cogn Sci. 2016 Aug;20(8):629-640. doi: 10.1016/j.tics.2016.06.003. Epub 2016 Jun 29.

Local Patterns to Global Architectures: Influences of Network Topology on Human Learning.

Author information

1
Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: ekaruza@sas.upenn.edu.
2
Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.
3
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.

Abstract

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.

KEYWORDS:

complex systems; network science; statistical learning

PMID:
27373349
PMCID:
PMC4970514
DOI:
10.1016/j.tics.2016.06.003
[Indexed for MEDLINE]
Free PMC Article

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