A social-semantic-working-memory account for two canonical language areas

Author:

Zhang Guangyao1,Xu Yangwen2,Wang Xiuyi1,Li Jixing3,Shi Weiting1,Bi Yanchao4,Lin Nan1

Affiliation:

1. Institute of Psychology, Chinese Academy of Sciences

2. University of Trento

3. City University of Hong Kong

4. Beijing Normal University

Abstract

Abstract Language and social cognition are traditionally studied as separate cognitive domains, yet accumulative studies reveal overlapping neural correlates at the left ventral temporoparietal junction (vTPJ) and lateral anterior temporal lobe (lATL), which have been attributed to sentence processing and social concept activation. We propose a common cognitive component underlying both effects -- social-semantic working memory. We confirmed two key predictions of our hypothesis using fMRI: First, the left vTPJ and lATL showed sensitivity to sentences only when the sentences conveyed social meaning.; second, these regions showed persistent social-semantic-selective activity after the linguistic stimuli disappeared. We additionally found that both regions were sensitive to the socialness of nonlinguistic stimuli and were more tightly connected with the social-semantic-processing areas than with the sentence-processing areas. The converging evidence indicates the social-semantic-working-memory function of the left vTPJ and lATL and challenges the general-semantic and/or syntactic accounts for the neural activity of these regions.

Publisher

Research Square Platform LLC

Reference105 articles.

1. Definition and characterization of an extended social-affective default network;Amft M;Brain Structure & Function,2015

2. Neural representation of social concepts: a coordinate-based meta-analysis of fMRI studies;Arioli M;Brain Imaging & Behavior,2021

3. A fast diffeomorphic image registration algorithm;Ashburner J;NeuroImage,2007

4. Unified segmentation;Ashburner J;NeuroImage,2005

5. Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R.H.B., Singmann, H., Dai, B., Scheipl, F., Grothendieck, G., Green, P., Fox, J., Bauer, A., & Krivitsky., P.N. (2014). lme4: Linear Mixed-Effects Models Using ‘Eigen’ and S4 Classes. R package version 1.1–30. https://github.com/lme4/lme4/

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