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Cited by 367

The DOI matches its Crossref record; 223 of the 223 references it cites were found in a registry or an index.

Toward a brain-based componential semantic representation

Binder, Jeffrey R., Conant, Lisa L., Humphries, Colin J., Fernandino, Leonardo, Simons, Stephen B., Aguilar, Mario, Desai, Rutvik H.·2016·Cognitive Neuropsychology
DOI 10.1080/02643294.2016.1147426
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Abstract

Componential theories of lexical semantics assume that concepts can be represented by sets of features or attributes that are in some sense primitive or basic components of meaning. The binary features used in classical category and prototype theories are problematic in that these features are themselves complex concepts, leaving open the question of what constitutes a primitive feature. The present availability of brain imaging tools has enhanced interest in how concepts are represented in brains, and accumulating evidence supports the claim that these representations are at least partly "embodied" in the perception, action, and other modal neural systems through which concepts are experienced. In this study we explore the possibility of devising a componential model of semantic representation based entirely on such functional divisions in the human brain. We propose a basic set of approximately 65 experiential attributes based on neurobiological considerations, comprising sensory, motor, spatial, temporal, affective, social, and cognitive experiences. We provide normative data on the salience of each attribute for a large set of English nouns, verbs, and adjectives, and show how these attribute vectors distinguish a priori conceptual categories and capture semantic similarity. Robust quantitative differences between concrete object categories were observed across a large number of attribute dimensions. A within- versus between-category similarity metric showed much greater separation between categories than representations derived from distributional (latent semantic) analysis of text. Cluster analyses were used to explore the similarity structure in the data independent of a priori labels, revealing several novel category distinctions. We discuss how such a representation might deal with various longstanding problems in semantic theory, such as feature selection and weighting, representation of abstract concepts, effects of context on semantic retrieval, and conceptual combination. In contrast to componential models based on verbal features, the proposed representation systematically relates semantic content to large-scale brain networks and biologically plausible accounts of concept acquisition.

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Metadata

Authors
Binder, J. R., Conant, L. L., Humphries, C. J., Fernandino, L., Simons, S. B., Aguilar, M., & Desai, R. H.
Venue
Cognitive Neuropsychology
Publisher
Informa UK Limited
Published
2016-05-18
Volume
33
Issue
3-4
Pages
130-174
ISSN
  • 0264-3294 (ISSN-L)
  • 1464-0627
DOI
10.1080/02643294.2016.1147426
OpenAlex
W2434184590
Cited by
367
References
223
FWCI
37.62
Type
journal article
Language
en
Open access
closed

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