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A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior.

Authors
  • Bassett, Danielle S1
  • Mattar, Marcelo G2
  • 1 Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: [email protected]
  • 2 Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Type
Published Article
Journal
Trends in cognitive sciences
Publication Date
Apr 01, 2017
Volume
21
Issue
4
Pages
250–264
Identifiers
DOI: 10.1016/j.tics.2017.01.010
PMID: 28259554
Source
Medline
License
Unknown

Abstract

Humans adapt their behavior to their external environment in a process often facilitated by learning. Efforts to describe learning empirically can be complemented by quantitative theories that map changes in neurophysiology to changes in behavior. In this review we highlight recent advances in network science that offer a sets of tools and a general perspective that may be particularly useful in understanding types of learning that are supported by distributed neural circuits. We describe recent applications of these tools to neuroimaging data that provide unique insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills, forming a network neuroscience of human learning. While promising, the tools have yet to be linked to the well-formulated models of behavior that are commonly utilized in cognitive psychology. We argue that continued progress will require the explicit marriage of network approaches to neuroimaging data and quantitative models of behavior.

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