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Lin N, Zhang X, Wang X, Wang S. The organization of the semantic network as reflected by the neural correlates of six semantic dimensions. BRAIN AND LANGUAGE 2024; 250:105388. [PMID: 38295716 DOI: 10.1016/j.bandl.2024.105388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 03/03/2024]
Abstract
Multiple sensory-motor and non-sensory-motor dimensions have been proposed for semantic representation, but it remains unclear how the semantic system is organized along them in the human brain. Using naturalistic fMRI data and large-scale semantic ratings, we investigated the overlaps and dissociations between the neural correlates of six semantic dimensions: vision, motor, socialness, emotion, space, and time. Our findings revealed a more complex semantic atlas than what is predicted by current neurobiological models of semantic representation. Brain regions that are selectively sensitive to specific semantic dimensions were found both within and outside the brain networks assumed to represent multimodal general and/or abstract semantics. Overlaps between the neural correlates of different semantic dimensions were mainly found inside the default mode network, concentrated in the left anterior superior temporal gyrus and angular gyrus, which have been proposed as two connector hubs that bridge the multimodal experiential semantic system and the language-supported semantic system.
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Affiliation(s)
- Nan Lin
- CAS Key Laboratory of Behavioural Sciences, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
| | - Xiaohan Zhang
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xiuyi Wang
- CAS Key Laboratory of Behavioural Sciences, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Shaonan Wang
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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Zhang G, Xu Y, Wang X, Li J, Shi W, Bi Y, Lin N. A social-semantic working-memory account for two canonical language areas. Nat Hum Behav 2023; 7:1980-1997. [PMID: 37735521 DOI: 10.1038/s41562-023-01704-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/22/2023] [Indexed: 09/23/2023]
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 the left 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 functional MRI. 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 non-linguistic 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.
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Affiliation(s)
- Guangyao Zhang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Yangwen Xu
- Center for Mind/Brain Sciences, University of Trento, Trento, Italy
| | - Xiuyi Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Jixing Li
- Department of Linguistics and Translation, City University of Hong Kong, Hong Kong SAR, China
| | - Weiting Shi
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Nan Lin
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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Wang S, Zhang Y, Shi W, Zhang G, Zhang J, Lin N, Zong C. A large dataset of semantic ratings and its computational extension. Sci Data 2023; 10:106. [PMID: 36823158 PMCID: PMC9950052 DOI: 10.1038/s41597-023-01995-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 01/31/2023] [Indexed: 02/25/2023] Open
Abstract
Evidence from psychology and cognitive neuroscience indicates that the human brain's semantic system contains several specific subsystems, each representing a particular dimension of semantic information. Word ratings on these different semantic dimensions can help investigate the behavioral and neural impacts of semantic dimensions on language processes and build computational representations of language meaning according to the semantic space of the human cognitive system. Existing semantic rating databases provide ratings for hundreds to thousands of words, which can hardly support a comprehensive semantic analysis of natural texts or speech. This article reports a large database, the Six Semantic Dimension Database (SSDD), which contains subjective ratings for 17,940 commonly used Chinese words on six major semantic dimensions: vision, motor, socialness, emotion, time, and space. Furthermore, using computational models to learn the mapping relations between subjective ratings and word embeddings, we include the estimated semantic ratings for 1,427,992 Chinese and 1,515,633 English words in the SSDD. The SSDD will aid studies on natural language processing, text analysis, and semantic representation in the brain.
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Affiliation(s)
- Shaonan Wang
- National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yunhao Zhang
- National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Weiting Shi
- CAS Key Laboratory of Behavioural Sciences, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Guangyao Zhang
- CAS Key Laboratory of Behavioural Sciences, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Jiajun Zhang
- National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Nan Lin
- CAS Key Laboratory of Behavioural Sciences, Institute of Psychology, Beijing, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
| | - Chengqing Zong
- National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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Pexman PM, Diveica V, Binney RJ. Social semantics: the organization and grounding of abstract concepts. Philos Trans R Soc Lond B Biol Sci 2023; 378:20210363. [PMID: 36571120 PMCID: PMC9791475 DOI: 10.1098/rstb.2021.0363] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
concepts, like justice and friendship, are central features of our daily lives. Traditionally, abstract concepts are distinguished from other concepts in that they cannot be directly experienced through the senses. As such, they pose a challenge for strongly embodied models of semantic representation that assume a central role for sensorimotor information. There is growing recognition, however, that it is possible for meaning to be 'grounded' via cognitive systems, including those involved in processing language and emotion. In this article, we focus on the specific proposal that social significance is a key feature in the representation of some concepts. We begin by reviewing recent evidence in favour of this proposal from the fields of psycholinguistics and neuroimaging. We then discuss the limited extent to which there is consensus about the definition of 'socialness' and propose essential next steps for research in this domain. Taking one such step, we describe preliminary data from an unprecedented large-scale rating study that can help determine how socialness is distinct from other facets of word meaning. We provide a backdrop of contemporary theories regarding semantic representation and social cognition and highlight important predictions for both brain and behaviour. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.
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Affiliation(s)
- Penny M. Pexman
- Department of Psychology and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada, T2N 1N4
| | - Veronica Diveica
- School of Human and Behavioural Sciences, Bangor University, Bangor LL57 2AS, UK
| | - Richard J. Binney
- School of Human and Behavioural Sciences, Bangor University, Bangor LL57 2AS, UK
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Coexistence of the social semantic effect and non-semantic effect in the default mode network. Brain Struct Funct 2023; 228:321-339. [PMID: 35394555 DOI: 10.1007/s00429-022-02476-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 02/23/2022] [Indexed: 01/07/2023]
Abstract
Neuroimaging studies have found both semantic and non-semantic effects in the default mode network (DMN), leading to an intense debate on the role of the DMN in semantic processes. Four different views have been proposed: (1) the general semantic view holds that the DMN contains several hub regions supporting general semantic processes; (2) the non-semantic view holds that the semantic effects observed in the DMN (especially the ventral angular gyrus) are confounded by difficulty and do not reflect semantic processing per se; (3) the multifunction view holds that the same areas in the DMN can support both semantic and non-semantic functions; and (4) the multisystem view holds that the DMN contains multiple subnetworks supporting different aspects of semantic processes separately. Using an fMRI experiment, we found that in one of the subnetworks of the DMN, called the social semantic network, all areas showed social semantic activation and difficulty-induced deactivation. The distributions of two non-semantic effects, that is, difficulty-induced and task-induced deactivations, showed dissociation in the DMN. In the bilateral angular gyri, the ventral subdivisions showed social semantic activation independent of difficulty, while the dorsal subdivisions showed no semantic effect but difficulty-induced activation. Our findings provide two insights into the semantic and non-semantic functions of the DMN, which are consistent with both the multisystem and multifunction views: first, the same areas of the DMN can support both social semantic and non-semantic functions; second, similar to the multiple semantic effects of the DMN, the non-semantic effects also vary across its subsystems.
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Zhang G, Xu Y, Zhang M, Wang S, Lin N. The brain network in support of social semantic accumulation. Soc Cogn Affect Neurosci 2021; 16:393-405. [PMID: 33433627 PMCID: PMC7990071 DOI: 10.1093/scan/nsab003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 12/07/2020] [Accepted: 01/11/2021] [Indexed: 11/14/2022] Open
Abstract
Some studies have indicated that a specific 'social semantic network' represents the social meanings of words. However, studies of the comprehension of complex materials, such as sentences and narratives, have indicated that the same network supports the online accumulation of connected semantic information. In this study, we examined the hypothesis that this network does not simply represent the social meanings of words but also accumulates connected social meanings from texts. We defined the social semantic network by conducting a meta-analysis of previous studies on social semantic processing and then examined the effects of social semantic accumulation using a functional Magnetic Resonance Imaging (fMRI) experiment. Two important findings were obtained. First, the social semantic network showed a stronger social semantic effect in sentence and narrative reading than in word list reading, indicating the amplitude of social semantic activation can be accumulated in the network. Second, the activation of the social semantic network in sentence and narrative reading can be better explained by the holistic social-semantic-richness rating scores of the stimuli than by those of the constitutive words, indicating the social semantic contents can be integrated in the network. These two findings convergently indicate that the social semantic network supports the accumulation of connected social meanings.
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Affiliation(s)
- Guangyao Zhang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yangwen Xu
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento 38123, Italy.,International School for Advanced Studies (SISSA), Trieste 34136, Italy
| | - Meimei Zhang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
| | - Shaonan Wang
- National Laboratory of Pattern Recognition, CASIA, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Nan Lin
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
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Arioli M, Gianelli C, Canessa N. Neural representation of social concepts: a coordinate-based meta-analysis of fMRI studies. Brain Imaging Behav 2020; 15:1912-1921. [DOI: 10.1007/s11682-020-00384-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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