Important and ground breaking work from Yan et al. on our social brain pathways:
Significance
We
present evidence for a third “social” brain pathway dedicated to
processing dynamic social signals—specifically facial expressions—for
behavioral emotion categorization. Using generative technology, we
isolated facial movements (action units, AUs) while controlling static
face identity features. Participants viewed and categorized these
emotion-specific facial models while we tracked their brain activity
using MEG. Results revealed a functional pathway from the occipital
cortex to MT, bank of the STS, and STG that selectively represents,
communicates, and integrates dynamic AUs, filtering out static identity
features. By precisely controlling stimulus features, our method
provides a transparent “glass box” view of neural mechanisms. This
reproducible approach establishes a foundation in computational social
neuroscience, linking stimuli, brain processing, and social perception
behaviors.
Abstract
Emerging
theories in cognitive neuroscience propose a third brain pathway
dedicated to processing biological motion, alongside the established
ventral and dorsal pathways. However, its role in computing dynamic
social signals for behavior remains uncharted. Here, participants (N =
10) actively categorized dynamic facial expressions synthesized by a
generative model and displayed on different face identities—as “happy,”
“surprise,” “fear,” “anger,” “disgust,” “sad”—while we recorded their
MEG responses. Using representational interaction measures that link
facial features with MEG activity and categorization behavior, we
identified within each participant a functional social pathway extending
from the occipital cortex to the superior temporal gyrus. This pathway
selectively represents, communicates, and integrates facial movements
that are essential for the behavioral categorization of emotion, while
task-irrelevant identity features are filtered out in the occipital
cortex. Our findings uncover how the third pathway selectively computes
complex dynamic social signals for emotion categorization in individual
participants, offering computational insights into the dynamics of
neural activity.
No comments:
Post a Comment