Disentangling narrow and coarse semantic networks in the brain: The role of computational models of word meaning

Benjamin Schloss, Ping Li

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

There has been a recent boom in research relating semantic space computational models to fMRI data, in an effort to better understand how the brain represents semantic information. In the first study reported here, we expanded on a previous study to examine how different semantic space models and modeling parameters affect the abilities of these computational models to predict brain activation in a data-driven set of 500 selected voxels. The findings suggest that these computational models may contain distinct types of semantic information that relate to different brain areas in different ways. On the basis of these findings, in a second study we conducted an additional exploratory analysis of theoretically motivated brain regions in the language network. We demonstrated that data-driven computational models can be successfully integrated into theoretical frameworks to inform and test theories of semantic representation and processing. The findings from our work are discussed in light of future directions for neuroimaging and computational research.

Original languageEnglish (US)
Pages (from-to)1582-1596
Number of pages15
JournalBehavior research methods
Volume49
Issue number5
DOIs
StatePublished - Oct 1 2017

Fingerprint

Semantics
Space Simulation
Brain
Aptitude
Research
Neuroimaging
Language
Magnetic Resonance Imaging
Computational Model
Word Meaning
Semantic Network
Semantic Information
Semantic Space
Data-driven

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Psychology (miscellaneous)
  • Psychology(all)

Cite this

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Disentangling narrow and coarse semantic networks in the brain : The role of computational models of word meaning. / Schloss, Benjamin; Li, Ping.

In: Behavior research methods, Vol. 49, No. 5, 01.10.2017, p. 1582-1596.

Research output: Contribution to journalArticle

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