Local Patterns to Global Architectures: Influences of Network Topology on Human Learning. Issue 8 (August 2016)

• Record Type: Journal Article
• Authors: Karuza, Elisabeth A.; Thompson-Schill, Sharon L.; Bassett, Danielle S.
• Is Part Of: Trends in cognitive sciences. Volume 20:Issue 8(2016)
• Page Start: 629
• Page End: 640
• Publication Date: 2016-08
• Subjects: complex systems -- network science -- statistical learning; Cognitive science -- Periodicals; Cognitive neuroscience -- Periodicals; 153.05
• DOI: 10.1016/j.tics.2016.06.003
• Languages: English
• ISSNs: 1364-6613

Abstract

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. Trends: Descriptive analytical approaches indicate that diverse facets of the environment adhere to a complex network structure. Recent advances offer insight into how learners might acquire and access network representations. Specifically, higher-order topological properties of networks have been shown to facilitate learning. Emerging neuroimaging techniques construe the brain itself as complex system, revealing how network dynamics support learning. We suggest that network science approaches are compatible with statistical learning approaches to knowledge acquisition. That is, local statistical regularities extracted from sensory input form the building blocks of complex network structures. Broader architectural properties of network structures might then explain learning effects beyond sensitivity to local statistical information.