A1 Refereed original research article in a scientific journal
Statistical pattern recognition reveals shared neural signatures for displaying and recognizing specific facial expressions
Authors: Volynets Sofia, Smirnov Dmitry, Saarimäki Heini, Nummenmaa Lauri
Publisher: OXFORD UNIV PRESS
Publication year: 2020
Journal: Social Cognitive and Affective Neuroscience
Journal name in source: SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE
Journal acronym: SOC COGN AFFECT NEUR
Volume: 15
Issue: 8
First page : 803
Last page: 813
Number of pages: 11
ISSN: 1749-5016
eISSN: 1749-5024
DOI: https://doi.org/10.1093/scan/nsaa110
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/51055376
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.
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