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      Facial EMG Responses to Emotional Expressions Are Related to Emotion Perception Ability

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          Abstract

          Although most people can identify facial expressions of emotions well, they still differ in this ability. According to embodied simulation theories understanding emotions of others is fostered by involuntarily mimicking the perceived expressions, causing a “reactivation” of the corresponding mental state. Some studies suggest automatic facial mimicry during expression viewing; however, findings on the relationship between mimicry and emotion perception abilities are equivocal. The present study investigated individual differences in emotion perception and its relationship to facial muscle responses - recorded with electromyogram (EMG) - in response to emotional facial expressions. N° = °269 participants completed multiple tasks measuring face and emotion perception. EMG recordings were taken from a subsample ( N° = °110) in an independent emotion classification task of short videos displaying six emotions. Confirmatory factor analyses of the m. corrugator supercilii in response to angry, happy, sad, and neutral expressions showed that individual differences in corrugator activity can be separated into a general response to all faces and an emotion-related response. Structural equation modeling revealed a substantial relationship between the emotion-related response and emotion perception ability, providing evidence for the role of facial muscle activation in emotion perception from an individual differences perspective.

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          Most cited references19

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          Configural information in facial expression perception.

          Composite facial expressions were prepared by aligning the top half of one expression (e.g., anger) with the bottom half of another (e.g., happiness). Experiment 1 shows that participants are slower to identify the expression in either half of these composite images relative to a "noncomposite" control condition in which the 2 halves are misaligned. This parallels the composite effect for facial identity (A. W. Young, D. Hellawell, & D. C. Hay, 1987), and like its identity counterpart, the effect is disrupted by inverting the stimuli (Experiment 2). Experiment 3 shows that no composite effect is found when the top and bottom sections contain different models' faces posing the same expression; this serves to exclude many nonconfigural interpretations of the composite effect (e.g., that composites are more "attention-grabbing" than noncomposites). Finally, Experiment 4 demonstrates that the composite effects for identity and expression operate independently of one another.
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            The simulating social mind: the role of the mirror neuron system and simulation in the social and communicative deficits of autism spectrum disorders.

            The mechanism by which humans perceive others differs greatly from how humans perceive inanimate objects. Unlike inanimate objects, humans have the distinct property of being "like me" in the eyes of the observer. This allows us to use the same systems that process knowledge about self-performed actions, self-conceived thoughts, and self-experienced emotions to understand actions, thoughts, and emotions in others. The authors propose that internal simulation mechanisms, such as the mirror neuron system, are necessary for normal development of recognition, imitation, theory of mind, empathy, and language. Additionally, the authors suggest that dysfunctional simulation mechanisms may underlie the social and communicative deficits seen in individuals with autism spectrum disorders.
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              Simulationist models of face-based emotion recognition.

              Recent studies of emotion mindreading reveal that for three emotions, fear, disgust, and anger, deficits in face-based recognition are paired with deficits in the production of the same emotion. What type of mindreading process would explain this pattern of paired deficits? The simulation approach and the theorizing approach are examined to determine their compatibility with the existing evidence. We conclude that the simulation approach offers the best explanation of the data. What computational steps might be used, however, in simulation-style emotion detection? Four alternative models are explored: a generate-and-test model, a reverse simulation model, a variant of the reverse simulation model that employs an "as if" loop, and an unmediated resonance model.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                28 January 2014
                : 9
                : 1
                : e84053
                Affiliations
                [1 ]Department of Psychology, Humboldt Universität zu Berlin, Berlin, Germany
                [2 ]Department of Psychology, University Ulm, Ulm, Germany
                The University of Queensland, Australia
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JK AH GR OW WS. Performed the experiments: GR JK. Analyzed the data: JK. Contributed reagents/materials/analysis tools: AH GR WS OW. Wrote the paper: JK.

                Article
                PONE-D-13-34396
                10.1371/journal.pone.0084053
                3904816
                24489647
                a4556b03-3541-4d98-ab0d-56371d031bc0
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 August 2013
                : 11 November 2013
                Page count
                Pages: 10
                Funding
                This research was supported by the German Research Foundation, Grant No. WI2667/2-4 to Oliver Wilhelm and Werner Sommer. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine
                Diagnostic Medicine
                Clinical Neurophysiology
                Electromyography
                Mental Health
                Psychology
                Behavior
                Emotions
                Social Psychology
                Social and Behavioral Sciences
                Psychology
                Behavior
                Emotions
                Psychometrics
                Psychophysics
                Social Psychology

                Uncategorized
                Uncategorized

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