Enhanced bodily states of fear facilitates bias perception of fearful faces
- Authors
- Jung, Won-Mo; Lee, Ye-Seul; Lee, In-Seon; Wallraven, Christian; Ryu, Yeonhee; Chae, Younbyoung
- Issue Date
- 23-11월-2020
- Publisher
- BMC
- Keywords
- Anterior cingulate cortex; Emotional face; Extrastriate body area; Fear; Interoception
- Citation
- MOLECULAR BRAIN, v.13, no.1
- Indexed
- SCIE
SCOPUS
- Journal Title
- MOLECULAR BRAIN
- Volume
- 13
- Number
- 1
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/51464
- DOI
- 10.1186/s13041-020-00674-6
- ISSN
- 1756-6606
- Abstract
- We investigated whether enhanced interoceptive bodily states of fear would facilitate recognition of the fearful faces. Participants performed an emotional judgment task after a bodily imagery task inside a functional magnetic resonance imaging scanner. In the bodily imagery task, participants were instructed to imagine feeling the bodily sensations of two specific somatotopic patterns: a fear-associated bodily sensation (FBS) or a disgust-associated bodily sensation (DBS). They were shown faces expressing various levels of fearfulness and disgust and instructed to classify the facial expression as fear or disgust. We found a stronger bias favoring the "fearful face" under the congruent FBS condition than under the incongruent DBS condition. The brain response to fearful versus intermediate faces increased in the fronto-insular-temporal network under the FBS condition, but not the DBS condition. The fearful face elicited activity in the anterior cingulate cortex and extrastriate body area under the FBS condition relative to the DBS condition. Furthermore, functional connectivity between the anterior cingulate cortex/extrastriate body area and the fronto-insular-temporal network was modulated according to the specific bodily sensation. Our findings suggest that somatotopic patterns of bodily sensation provide informative access to the collective visceral state in the fear processing via the fronto-insular-temporal network.
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Collections - Graduate School > Department of Artificial Intelligence > 1. Journal Articles
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