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. 2022 Jul;43(10):3195-3206.
doi: 10.1002/hbm.25844. Epub 2022 Mar 28.

Similarities and differences in the neural representations of abstract concepts across English and Mandarin

Robert Vargas  1 , Marcel Adam Just  1
Affiliations

Similarities and differences in the neural representations of abstract concepts across English and Mandarin

Robert Vargas et al. Hum Brain Mapp. 2022 Jul.

Abstract

Recent research suggests there is a neural organization for representing abstract concepts that is common across English speakers. To investigate the possible role of language on the representation of abstract concepts, multivariate pattern analytic (MVPA) techniques were applied to fMRI data to compare the neural representations of 28 individual abstract concepts between native English and Mandarin speakers. Factor analyses of the activation patterns of the 28 abstract concepts from both languages characterized this commonality in terms of a set of four underlying neurosemantic dimensions, indicating the degree to which a concept is verbally represented, internal to the person, contains social content, and is rule-based. These common semantic dimensions (factors) underlying the 28 concepts provided a sufficient basis for reliably identifying the individual abstract concepts from their neural signature in the other language with a mean rank accuracy of 0.65 (p < .001). Although the neural dimensions used for representing abstract concepts are common across languages, differences in the meaning of some individual concepts can be accommodated in terms of differential salience of particular dimensions. These semantic dimensions constitute a set of neurocognitive resources for abstract concept representations within a larger set of regions responsible for general semantic processing.

Keywords: MVPA; abstract concepts; cross language; fMRI; semantic.

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Conflict of interest statement

The authors declare no competing interests.

Figures

FIGURE 1
FIGURE 1
Locations for five interpretable factor dimensions from Mandarin‐specific analysis. These spheres were specified using the centroids of clusters of voxels (containing a minimum of 10 voxels) with high loadings (>0.4) on each of the factors
FIGURE 2
FIGURE 2
Locations for five interpretable factor dimensions from combined‐language analysis. These spheres were specified using the centroids of clusters of voxels (containing a minimum of 10 voxels) with high loadings (>0.4) on each of the factors
See this image and copyright information in PMC

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