Structural similarities between brain and linguistic data provide evidence of semantic relations in the brain.

ABSTRACT: This paper presents a new method of analysis by which structural similarities between brain data and linguistic data can be assessed at the semantic level. It shows how to measure the strength of these structural similarities and so determine the relatively better fit of the brain data with one semantic model over another. The first model is derived from WordNet, a lexical database of English compiled by language experts. The second is given by the corpus-based statistical technique of latent semantic analysis (LSA), which detects relations between words that are latent or hidden in text. The brain data are drawn from experiments in which statements about the geography of Europe were presented auditorily to participants who were asked to determine their truth or falsity while electroenceph

SUBMITTER: Crangle CE 

PROVIDER: S-EPMC3682999 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature