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Decomposing the cognitive structure of human social intelligence

Poster No:

644

Submission Type:

Abstract Submission

Authors:

Siyi Li¹, Guoqiu Chen¹, Yin Wang¹

Institutions:

¹State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, Beijing

First Author:

Siyi Li
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing

Co-Author(s):

Guoqiu Chen
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing

Yin Wang
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing

Introduction:

Social intelligence encompasses the cognitive skills and knowledge essential for navigating social environments, playing a critical role in individual survival, social competitiveness, and interpersonal relationships. The complex, abstract, and multifaceted nature of social intelligence, combined with the dynamic and flexible characteristics of human social behaviors, has left research in this area underexplored. A comprehensive theoretical framework and effective assessment tools for social intelligence are yet to be established, nor is there a clear understanding of its relationship with other domains such as general cognitive abilities (e.g., spatial processing, reasoning), meta-cognition, emotional traits (e.g., emotional intelligence, depression), values, and personality traits.

Methods:

Here, we examined 20 components of social intelligence, including theory of mind, person perception, empathy, and prosocial tendencies. By conducting extensive research, local adaptation, item development, and reliability testing, a comprehensive set of 71 social cognition tasks and scales, as well as criterion measures (e.g., social network size, social adaptability, autistic traits), and 31 general cognition tests was developed. Using a within-subject design, 524 participants completed the task set, resulting in 301 indicators for each participant. Hierarchical clustering, exploratory factor analysis (EFA) and network analysis were applied to explore the underlying structures. 50 subjects participated in both behavioral tasks and naturalistic stimuli MRI scanning to explore the brain mechanisms.

Results:

First, clustering results revealed distinct psychological spaces for experiments and surveys, indicating that different measures potentially assess different facets of social intelligence. Besides, surveys demonstrated greater internal consistency and higher inter-item correlations, but also displayed significantly higher predictive validity than experimental measures. Second, EFA focusing on 64 survey indicators revealed five primary factors: integrity, self-consciousness, moral disengagement, detachment and moral foundations. Network analysis demonstrated stronger correlations between social intelligence, personality traits, emotional factors, and metacognitive abilities. Finally, through multilayer network analysis, we identified mapping relationships between specific behaviors and brain mechanisms, shedding light on how social intelligence is represented in neural activity.

·Factor structure of survey-based measures identified through exploratory factor analysis (EFA).

Conclusions:

This study provides evidence of distinct ontologies in measurements; The identified five-factor structure (integrity, self-consciousness, moral disengagement, detachment, and moral foundations) offers a comprehensive framework for understanding the cognitive components underlying social intelligence. Furthermore, the observed network interactions between social intelligence, personality, emotional factors, and metacognitive abilities underscore the need for a holistic approach in both research and clinical assessment. Finally, the multilayer network analysis linking behaviors to neural mechanisms provides a novel pathway for exploring the neural representations of social intelligence, offering potential applications in clinical settings and facilitating the discovery of developmental trajectories.

Emotion, Motivation and Social Neuroscience:

Self Processes ²

Social Cognition ¹

Modeling and Analysis Methods:

fMRI Connectivity and Network Modeling

Task-Independent and Resting-State Analysis

Novel Imaging Acquisition Methods:

Multi-Modal Imaging

Keywords:

Cognition

^1|2Indicates the priority used for review

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Please indicate below if your study was a "resting state" or "task-activation” study.

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Task-activation

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was this research conducted in the United States?

Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

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Please indicate which methods were used in your research:

Behavior

Functional MRI

For human MRI, what field strength scanner do you use?

3.0T

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