Poster No:
640
Submission Type:
Abstract Submission
Authors:
Yijie Zhang1, MingZhe Zhang1, Yin Wang1
Institutions:
1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
First Author:
Yijie Zhang
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
Co-Author(s):
MingZhe Zhang
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
Yin Wang
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
Introduction:
There are two sides to every issue. Personal attitudes are not set in stone but updated continuously in response to new information, persuasive arguments, or cognitive dissonance, reflecting the changing nature of belief (Verplanken & Orbell, 2022). While research on attitude change mostly examines collective behaviors over time (Charlesworth & Banaji, 2022), especially through the lens of political polarization (Leong et al., 2020; Van Baar et al., 2021; Van Baar & FeldmanHall, 2022), the neurocognitive processes underlying individuals' shifts between different viewpoints, termed attitude flexibility, remain a significant and unresolved question (Lorenz et al., 2021). In this work, we sought to investigate the behavioral and neural patterns underlying attitude flexibility and test how these patterns relate to individual real-life social interactions.
Methods:
We collected naturalistic fMRI data from 61 students (in an undergraduate program) as they watched video clips from 7 debaters, each lasting 3 min, on the topic 'Can humans fall in love with artificial intelligence'. The clips were presented in an alternating sequence of con and pro sides, followed by a 2 min resting-state scan. During the viewing, participants self-reported their agreement in real-time on a scale from -10 to 10, where negative scores indicated support for the opposing side and positive scores indicated support for the pro side. After scanning, participants provided subjective cognitive and emotional ratings for all arguments presented by the debaters, enabling us to quantify the arguments' strength and model neural signatures over time.
Results:
Results showed that (1) Participants' attitude trajectories significantly aligned with debaters' arguments, demonstrating notable fluctuations in agreement levels that paralleled the presented pro and con perspectives. (2) We developed an fMRI-based predictive model of self-stance, agreement, and emotional valence, successfully distinguishing neural patterns associated with varying levels of support and emotional responses (Kohoutová et al., 2020). Moreover, we decoded final stance ratings from resting-state brain activity, demonstrating the predictive power of baseline neural signatures. (3) Participants' reports on their traits, prior knowledge, and personal experiences related to the debate topic revealed that higher cognitive flexibility and more extensive topic-related knowledge were significantly linked to more dynamic attitude transformations. (4) Based on participants' reports of their social networks within the program, we found that neural signatures of flexibility predict the number of real-life social contacts and the ability to maintain friendships, highlighting the ecological predictive validity of attitude flexibility in social settings.
Conclusions:
Our study demonstrates that attitude flexibility is associated with distinct neural signatures and cognitive traits, and it effectively predicts real-life social interactions. These results advance our understanding of the neurocognitive mechanisms driving dynamic attitude changes and emphasize the importance of flexibility in social connectivity.
Emotion, Motivation and Social Neuroscience:
Social Cognition 1
Social Interaction
Higher Cognitive Functions:
Higher Cognitive Functions Other
Modeling and Analysis Methods:
Classification and Predictive Modeling
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Social Interactions
Other - social attitude; attitude-behavior relation; naturalistic neuroimaging; brain decoding
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.
Resting state
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?
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?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
Behavior
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Other, Please list
-
fmriprep; mriqc; nilearn; brainiak; nltools
Provide references using APA citation style.
1. Charlesworth, T., & Banaji, M. (2022). Patterns of implicit and explicit attitudes: IV. Change and stability from 2007 to 2020. Psychological Science, 33, 95679762210842.
2. Kohoutová, L., Heo, J., Cha, S., Lee, S., Moon, T., Wager, T. D., & Woo, C.-W. (2020). Toward a unified framework for interpreting machine-learning models in neuroimaging. Nature Protocols, 15(4), 1399–1435.
3. Leong, Y. C., Chen, J., Willer, R., & Zaki, J. (2020). Conservative and liberal attitudes drive polarized neural responses to political content. Proceedings of the National Academy of Sciences of the United States of America, 117(44), 27731–27739.
4. Lorenz, J., Neumann, M., & Schröder, T. (2021). Individual attitude change and societal dynamics: Computational experiments with psychological theories. Psychological Review, 128(4), 623–642.
5. Van Baar, J. M., & FeldmanHall, O. (2022). The polarized mind in context: Interdisciplinary approaches to the psychology of political polarization. American Psychologist, 77(3), 394–408.
6. Van Baar, J. M., Halpern, D. J., & FeldmanHall, O. (2021). Intolerance of uncertainty modulates brain-to-brain synchrony during politically polarized perception. Proceedings of the National Academy of Sciences, 118(20), e2022491118.
7. Verplanken, B., & Orbell, S. (2022). Attitudes, Habits, and Behavior Change. Annual Review of Psychology, 73(1), 327–352.
No