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

Elisabeth A. Karuza, Sharon L. Thompson-Schill, Danielle S. Bassett

Research output: Contribution to journalReview articlepeer-review

43 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)629-640
Number of pages12
JournalTrends in Cognitive Sciences
Volume20
Issue number8
DOIs
StatePublished - Aug 1 2016

All Science Journal Classification (ASJC) codes

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience

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