Literature DB >> 17651011

Neural correlates of concreteness in semantic categorization.

Penny M Pexman1, Ian S Hargreaves, Jodi D Edwards, Luke C Henry, Bradley G Goodyear.   

Abstract

In some contexts, concrete words (CARROT) are recognized and remembered more readily than abstract words (TRUTH). This concreteness effect has historically been explained by two theories of semantic representation: dual-coding [Paivio, A. Dual coding theory: Retrospect and current status. Canadian Journal of Psychology, 45, 255-287, 1991] and context-availability [Schwanenflugel, P. J. Why are abstract concepts hard to understand? In P. J. Schwanenflugel (Ed.), The psychology of word meanings (pp. 223-250). Hillsdale, NJ: Erlbaum, 1991]. Past efforts to adjudicate between these theories using functional magnetic resonance imaging have produced mixed results. Using event-related functional magnetic resonance imaging, we reexamined this issue with a semantic categorization task that allowed for uniform semantic judgments of concrete and abstract words. The participants were 20 healthy adults. Functional analyses contrasted activation associated with concrete and abstract meanings of ambiguous and unambiguous words. Results showed that for both ambiguous and unambiguous words, abstract meanings were associated with more widespread cortical activation than concrete meanings in numerous regions associated with semantic processing, including temporal, parietal, and frontal cortices. These results are inconsistent with both dual-coding and context-availability theories, as these theories propose that the representations of abstract concepts are relatively impoverished. Our results suggest, instead, that semantic retrieval of abstract concepts involves a network of association areas. We argue that this finding is compatible with a theory of semantic representation such as Barsalou's [Barsalou, L. W. Perceptual symbol systems. Behavioral & Brain Sciences, 22, 577-660, 1999] perceptual symbol systems, whereby concrete and abstract concepts are represented by similar mechanisms but with differences in focal content.

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Year:  2007        PMID: 17651011     DOI: 10.1162/jocn.2007.19.8.1407

Source DB:  PubMed          Journal:  J Cogn Neurosci        ISSN: 0898-929X            Impact factor:   3.225


  35 in total

1.  Neural representation of abstract and concrete concepts: a meta-analysis of neuroimaging studies.

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2.  Distributed cell assemblies for general lexical and category-specific semantic processing as revealed by fMRI cluster analysis.

Authors:  Friedemann Pulvermüller; Ferath Kherif; Olaf Hauk; Bettina Mohr; Ian Nimmo-Smith
Journal:  Hum Brain Mapp       Date:  2009-12       Impact factor: 5.038

3.  How a hobby can shape cognition: visual word recognition in competitive Scrabble players.

Authors:  Ian S Hargreaves; Penny M Pexman; Lenka Zdrazilova; Peter Sargious
Journal:  Mem Cognit       Date:  2012-01

4.  Converging evidence from fMRI and aphasia that the left temporoparietal cortex has an essential role in representing abstract semantic knowledge.

Authors:  Laura M Skipper-Kallal; Dan Mirman; Ingrid R Olson
Journal:  Cortex       Date:  2015-05-09       Impact factor: 4.027

5.  Semantic richness effects in lexical decision: The role of feedback.

Authors:  Melvin J Yap; Gail Y Lim; Penny M Pexman
Journal:  Mem Cognit       Date:  2015-11

Review 6.  The neural and computational bases of semantic cognition.

Authors:  Matthew A Lambon Ralph; Elizabeth Jefferies; Karalyn Patterson; Timothy T Rogers
Journal:  Nat Rev Neurosci       Date:  2016-11-24       Impact factor: 34.870

Review 7.  Language as a disruptive technology: abstract concepts, embodiment and the flexible mind.

Authors:  Guy Dove
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2018-08-05       Impact factor: 6.237

8.  A brain-based account of "basic-level" concepts.

Authors:  Andrew James Bauer; Marcel Adam Just
Journal:  Neuroimage       Date:  2017-08-19       Impact factor: 6.556

9.  ERP measures of partial semantic knowledge: left temporal indices of skill differences and lexical quality.

Authors:  Gwen A Frishkoff; Charles A Perfetti; Chris Westbury
Journal:  Biol Psychol       Date:  2008-05-13       Impact factor: 3.251

10.  Neural systems for reading aloud: a multiparametric approach.

Authors:  William W Graves; Rutvik Desai; Colin Humphries; Mark S Seidenberg; Jeffrey R Binder
Journal:  Cereb Cortex       Date:  2009-11-17       Impact factor: 5.357

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