A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes

• Published in: PloS one Vol. 5; no. 1; p. e8622
• Main Authors: Just, Marcel Adam, Cherkassky, Vladimir L., Aryal, Sandesh, Mitchell, Tom M.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.01.2010; Public Library of Science (PLoS)
• Subjects: Adult; Artificial intelligence; Brain; Brain - physiology; Brain mapping; Cortex; Factor analysis; Factor Analysis, Statistical; Female; Functional magnetic resonance imaging; Humans; Language; Learning algorithms; Machine learning; Magnetic Resonance Imaging; Male; Malus; Medical imaging; Neural coding; Neuroimaging; Neuroscience; Neuroscience/Cognitive Neuroscience; Neuroscience/Experimental Psychology; Psychology; Semantic analysis; Semantics; Shelters; Studies; Tool use; United States > US; Pittsburgh Pennsylvania; Pennsylvania
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0008622

This article describes the discovery of a set of biologically-driven semantic dimensions underlying the neural representation of concrete nouns, and then demonstrates how a resulting theory of noun representation can be used to identify simple thoughts through their fMRI patterns. We use factor analysis of fMRI brain imaging data to reveal the biological representation of individual concrete nouns like apple, in the absence of any pictorial stimuli. From this analysis emerge three main semantic factors underpinning the neural representation of nouns naming physical objects, which we label manipulation, shelter, and eating. Each factor is neurally represented in 3-4 different brain locations that correspond to a cortical network that co-activates in non-linguistic tasks, such as tool use pantomime for the manipulation factor. Several converging methods, such as the use of behavioral ratings of word meaning and text corpus characteristics, provide independent evidence of the centrality of these factors to the representations. The factors are then used with machine learning classifier techniques to show that the fMRI-measured brain representation of an individual concrete noun like apple can be identified with good accuracy from among 60 candidate words, using only the fMRI activity in the 16 locations associated with these factors. To further demonstrate the generativity of the proposed account, a theory-based model is developed to predict the brain activation patterns for words to which the algorithm has not been previously exposed. The methods, findings, and theory constitute a new approach of using brain activity for understanding how object concepts are represented in the mind.