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

Article

DOI: 10.3758/s13428-016-0807-0

Cite this article as:
Schloss, B. & Li, P. Behav Res (2016). doi:10.3758/s13428-016-0807-0

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.

Keywords

LSA HAL Semantic space models Coarse semantic coding fMRI 

Copyright information

© Psychonomic Society, Inc. 2016

Authors and Affiliations

  1. 1.Department of Psychology and Center for Brain, Behavior and CognitionPennsylvania State UniversityState CollegeUSA