Statistical pattern recognition reveals shared neural signatures for displaying and recognizing specific facial expressions. Issue 8 (2nd October 2020)

• Record Type: Journal Article
• Authors: Volynets, Sofia; Smirnov, Dmitry; Saarimäki, Heini; Nummenmaa, Lauri
• Is Part Of: Social cognitive and affective neuroscience. Volume 15:Issue 8(2020)
• Page Start: 803
• Page End: 813
• Publication Date: 2020-10-02
• Subjects: emotion -- facial expression -- pattern recognition -- fMRI; Neurosciences -- Periodicals; Cognitive neuroscience -- Periodicals; Neuropsychology -- Periodicals; 612.8205
• DOI: 10.1093/scan/nsaa110
• Languages: English
• ISSNs: 1749-5016

Abstract

Abstract: Human neuroimaging and behavioural studies suggest that somatomotor 'mirroring' of seen facial expressions may support their recognition. Here we show that viewing specific facial expressions triggers the representation corresponding to that expression in the observer's brain. Twelve healthy female volunteers underwent two separate fMRI sessions: one where they observed and another where they displayed three types of facial expressions (joy, anger and disgust). Pattern classifier based on Bayesian logistic regression was trained to classify facial expressions (i) within modality (trained and tested with data recorded while observing or displaying expressions) and (ii) between modalities (trained with data recorded while displaying expressions and tested with data recorded while observing the expressions). Cross-modal classification was performed in two ways: with and without functional realignment of the data across observing/displaying conditions. All expressions could be accurately classified within and also across modalities. Brain regions contributing most to cross-modal classification accuracy included primary motor and somatosensory cortices. Functional realignment led to only minor increases in cross-modal classification accuracy for most of the examined ROIs. Substantial improvement was observed in the occipito-ventral components of the core system for facial expression recognition. Altogether these results support the embodied emotion recognition model and show that expression-specific somatomotor neural signatures could support facial expression recognition.