Poster No:
641
Submission Type:
Abstract Submission
Authors:
Mingzhe Zhang1, Yuxing Yang1, Deng Pan1, Yijie Zhang1, Jingkai Li1, Yinyin Zang2, Rui Jing1, Wu Li1, Xi Yu1, Qiandong Wang1, Yin Wang1
Institutions:
1Beijing Normal University, Beijing, China, 2Peking University, Beijing, China
First Author:
Co-Author(s):
Deng Pan
Beijing Normal University
Beijing, China
Jingkai Li
Beijing Normal University
Beijing, China
Rui Jing
Beijing Normal University
Beijing, China
Wu Li
Beijing Normal University
Beijing, China
Xi Yu
Beijing Normal University
Beijing, China
Yin Wang
Beijing Normal University
Beijing, China
Introduction:
Humans possess an impressive ability to understand social relationships. We can form a stable judgment even with a brief glance of people interacting. During this short period, individuals not only collect simple social features like intimacy but also engage in top-down social inference based on prior relational knowledge. An important unanswered question is how and why this intricate mental process affects the way people perceive human relationships. Here, we conducted 4 studies that aims to investigate the neurocomputational of basic social features and higher-level conceptual knowledge of individuals' perceptual processes.
Methods:
In study 1, each participant (N = 50) completed four tasks while watching social relationship images: free-viewing task, relationship judgment task, closeness and equality evaluating task. We also collected each participant's eye-movement and relational knowledge data. In study 2, we collected fMRI and eye-tracking data simultaneously (N = 40) using a similar paradigm to study 1. In study 3, we adapted the paradigm from Study 1 to investigate the development of social knowledge inference in infants (N = 20), children with autism (N = 50), and typically developing children (N = 50). In study 4, we collected eye-tracking data from macaques (N = 2) as they freely observed inter-personal relationships images and inter-macaque relationships images.
Results:
Study 1 demonstrate that both the bottom-up social features and top-down social knowledge of human relationships impact individuals' gaze patterns. The influence of social features on perception is automatic and intuitive, whereas knowledge affects perception only when individuals actively engage with it. In study 2, we applied GLM, RSA and decoding methods to identify brain regions involved in processing different aspects of interpersonal relationships. For instance, brain regions such as the IPL represented equality, the insula represented closeness, and the ATL reflected prior relational knowledge. Eye movement control was associated with the FEF. Furthermore, using PPI and DCM, we found that the pSTS acts as a central hub for processing social information. The functional connectivity between pSTS and specific brain areas increases when individuals access different aspects of social knowledge, facilitating the transmission of information to the FEF for eye movement control. In study 3, our findings showed that as social knowledge accumulates (Infant-Autism-Typical Development-Adult), its top-down influence on eye movement patterns gradually increases. In study 4, we found that macaques' social knowledge significantly predicted their gaze patterns when observing inter-macaque relationships, but not when viewing inter-personal relationships. This result underscores the specificity of prior knowledge in influencing eye movement patterns.
Conclusions:
In summary, our results offer insights into whether, how, and why social features and conceptual knowledge jointly shape perception. Past research focused on physical features, basic cognitive abilities (such as attention), or simple social attributes (like faces) influencing eye perception. Our study not only shows that social cognition in human relationships significantly influences perceptual patterns but also investigates its neurocomputational mechanisms, exploring associated developmental and pathological implications.
Emotion, Motivation and Social Neuroscience:
Social Cognition 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Multivariate Approaches
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Perception, Attention and Motor Behavior:
Perception: Visual
Keywords:
FUNCTIONAL MRI
Perception
Social Interactions
Vision
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.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
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.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
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Yes
Please indicate which methods were used in your research:
Functional MRI
Behavior
Other, Please specify
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Eye Tracking
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Other, Please list
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Nilearn
Provide references using APA citation style.
not applicable.
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