Biology subjects

Garrod, O.

Publications and source records attributed to Garrod, O..

2 recordsLinked to original sources

bioRxiv2024

The Brain Computes Dynamic Facial Movements for Emotion Categorization Using a Third Pathway

Recent theories suggest a new brain pathway dedicated to processing social movement is involved in understanding emotions from biological motion, beyond the well-known ventral and dorsal pathways. However, how this social pathway functions as a network that computes dynamic biological motion signals for perceptual behavior is unchartered. Here, we used a generative model of important facial movements that participants (N = 10) categorized as "happy," "surprise," "fear," "anger," "disgust," "sad" while we recorded their MEG brain responses. Using new representational interaction measures (between facial features, MEGt source, and behavioral responses), we reveal per participant a functional social pathway extending from occipital cortex to superior temporal gyrus. Its MEG sources selectively represent, communicate and compose facial movements to disambiguate emotion categorization behavior, while occipital cortex swiftly filters out task-irrelevant identity-defining face shape features. Our findings reveal how social pathway selectively computes complex dynamic social signals to categorize emotions in individual participants.

neuroscience↗

bioRxiv2023

Neural representation strength of predicted category features biases decision behavior

Theories of prediction-for-perception propose that the brain predicts the information contents of upcoming stimuli to facilitate their perceptual categorization. A mechanistic understanding should therefore address where, when, and how the brain predicts the stimulus features that change behavior. However, typical approaches do not address these predicted stimulus features. Instead, multivariate classifiers are trained to contrast the bottom-up patterns of neural activity between two stimulus categories. These classifiers then quantify top-down predictions as reactivations of the category contrast. However, a category-contrast cannot quantify the features reactivated for each category-which might be from either category, or both. To study the predicted category-features, we randomly sampled features of stimuli that afford two categorical perceptions and trained multivariate classifiers to discriminate the features specific to each. In a cueing design, we show where, when and how trial-by-trial category-feature reactivation strength directly biases decision behavior, transforming our conceptual and mechanistic understanding of prediction-for-perception.

neuroscience↗