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Metaphor Interpretation as Embodied Simulation

Open access
GIBBS, RAYMOND W.·2006·Mind <html_ent glyph="@amp;" ascii="&"/> Language

Abstract

Abstract: Cognitive theories of metaphor understanding are typically described in terms of the mappings between different kinds of abstract, schematic, disembodied knowledge. My claim in this paper is that part of our ability to make sense of metaphorical language, both individual utterances and extended narratives, resides in the automatic construction of a simulation whereby we imagine performing the bodily actions referred to in the language. Thus, understanding metaphorical expressions like ‘grasp a concept’ or ‘get over’ an emotion involve simulating what it must be like to engage in these specific activities, even though these actions are, strictly speaking, impossible to physically perform. This process of building a simulation, one that is fundamentally embodied in being constrained by past and present bodily experiences, has specific consequences for how verbal metaphors are understood, and how cognitive scientists, more generally, characterize the nature of metaphorical language and thought.

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Crossref · 10.1111/j.1468-0017.2006.00285.x

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Metadata

Authors
GIBBS, R. W.
Venue
Mind <html_ent glyph="@amp;" ascii="&"/> Language
Publisher
Wiley
Published
2006-06
Volume
21
Issue
3
Pages
434-458
ISSN
  • 0268-1064 (ISSN-L)
  • 1468-0017
OpenAlex
W1968310932
Cited by
631
References
2
FWCI
7.66
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Metaphor Interpretation as Embodied Simulation. https://app.cochat.ai/papers/10.1111/j.1468-0017.2006.00285.x
The paper
GIBBS, R. W. (2006). Metaphor Interpretation as Embodied Simulation. Mind <html_ent glyph="@amp;" ascii="&"/> Language. https://doi.org/10.1111/j.1468-0017.2006.00285.x

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.