title: Local Patterns to Global Architectures: Influences of Network Topology on Human Learning.
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.
authors.affiliation: Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: ekaruza@sas.upenn.edu.
authors.forename: Elisabeth A
authors.initials: EA
authors.lastname: Karuza
authors.affiliation: Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Sharon L
authors.initials: SL
authors.lastname: Thompson-Schill
authors.affiliation: Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Danielle S
authors.initials: DS
authors.lastname: Bassett
mesh_terms: D001921:Brain / Q000502:physiology*; D019336:Cognitive Science; D006801:Humans; D007858:Learning; D008959:Models, Neurological
keywords: complex systems; network science; statistical learning