Bodily maps of emotions are culturally universal.

Social Cognitive and Affective Neuroscience

et al. 2020

Self Cite

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.

Section: Discussionsupporting

confidence: 56%

Statistical pattern recognition reveals shared neural signatures for displaying and recognizing specific facial expressions

Volynets

Смирнов

Social Cognitive and Affective Neuroscience

et al. 2020

Self Cite

“…Altogether these data support the models of embodied emotion recognition, which propose that perceiving emotion involves perceptual, somatovisceral, and motoric re-experiencing of the relevant emotion in oneself, and one possible mechanism for that is reactivating modality-specific brain areas without actual behavioural output (Niedenthal 2007). Indeed, behavioural studies have provided that a wide variety of emotions are represented in embodied somatosensory format (Nummenmaa et al, 2014; 2017; Volynets et al, in press), and the present study shows how such embodied signatures of emotions can also contribute to recognizing others’ expressions.…”

Section: Discussionsupporting

confidence: 56%

Statistical Pattern Recognition Reveals Shared Neural Signatures for Displaying and Recognizing Specific Facial Expressions

Volynets

Smirnov

et al. 2019

Preprint

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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 basic 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.

“…Significant arousal‐dependent effects were also observed in the SMA, primary left SI, SII, and premotor cortices. Prior studies have established that different emotions are associated with discernible activation patterns in SI (Saarimäki et al, ), possibly reflecting specific emotion‐related changes in bodily states (Nummenmaa, Glerean, Hari, & Hietanen, ; Nummenmaa, Hari, Hietanen, & Glerean, ; Volynets, Glerean, Hietanen, Hari, & Nummenmaa, ). The presently observed emotion‐dependent synchronization of somatosensory and motor brain areas likely reflects engaging in similar bodily perspective across the speakers and listeners.…”

Section: Discussionmentioning

confidence: 99%

Emotions amplify speaker–listener neural alignment

Smirnov

Glerean

et al. 2019

Human Brain Mapping

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Individuals often align their emotional states during conversation. Here, we reveal how such emotional alignment is reflected in synchronization of brain activity across speakers and listeners. Two “speaker” subjects told emotional and neutral autobiographical stories while their hemodynamic brain activity was measured with functional magnetic resonance imaging (fMRI). The stories were recorded and played back to 16 “listener” subjects during fMRI. After scanning, both speakers and listeners rated the moment‐to‐moment valence and arousal of the stories. Time‐varying similarity of the blood‐oxygenation‐level‐dependent (BOLD) time series was quantified by intersubject phase synchronization (ISPS) between speaker–listener pairs. Telling and listening to the stories elicited similar emotions across speaker–listener pairs. Arousal was associated with increased speaker–listener neural synchronization in brain regions supporting attentional, auditory, somatosensory, and motor processing. Valence was associated with increased speaker–listener neural synchronization in brain regions involved in emotional processing, including amygdala, hippocampus, and temporal pole. Speaker–listener synchronization of subjective feelings of arousal was associated with increased neural synchronization in somatosensory and subcortical brain regions; synchronization of valence was associated with neural synchronization in parietal cortices and midline structures. We propose that emotion‐dependent speaker–listener neural synchronization is associated with emotional contagion, thereby implying that listeners reproduce some aspects of the speaker's emotional state at the neural level.