Intrinsic neural architecture of implicit self-evaluation bivalence: Evidence from resting-state ALFF and dynamic
Kun Shi1, Xiangyi Chen1, Xiao Luo1
1Faculty of Psychology, Southwest University, Chongqing, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China.
Behavioural Brain Research
|April 16, 2026
Summary
Implicit self-evaluation bivalence, holding both positive and negative implicit self-views, involves distinct neural patterns. This study reveals altered brain connectivity in self-referential and motor networks, suggesting a network-based understanding of the implicit self.
Area of Science:
- Neuroscience
- Psychology
- Cognitive Science
Background:
- Implicit self-esteem is often considered unidimensional.
- Emerging research indicates implicit self-evaluation bivalence, where individuals hold both positive and negative implicit self-views simultaneously.
Purpose of the Study:
- To investigate the neural architecture of implicit self-evaluation bivalence.
- To explore regional brain activity and functional connectivity associated with bivalence using resting-state fMRI.
Main Methods:
- Resting-state functional magnetic resonance imaging (fMRI) was employed in 101 healthy adults.
- A multilevel framework integrating regional activity (ALFF) and static/dynamic functional connectivity was used.
- Analysis focused on associations between bivalence levels and neural measures.
Main Results:
- Higher bivalence correlated with reduced low-frequency fluctuations in the right angular gyrus, left precuneus, right precentral gyrus, and left supplementary motor area.
- Bivalence was linked to stronger static connectivity between the angular gyrus and right middle orbitofrontal cortex.
- Increased dynamic connectivity between the angular gyrus and default-mode/somatosensory regions, and reduced temporal variability in precuneus-supplementary motor area connectivity were observed.
Conclusions:
- Implicit self-evaluation bivalence is associated with a distinct intrinsic neural architecture.
- Findings suggest altered static and dynamic coupling within self-referential and motor systems.
- This research supports a network-based model of the implicit self, moving beyond unidimensional views.
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