Sensorimotor simulation and emotion processing: Impairing facial action increases semantic retrieval demands

Joshua D. Davis , Piotr Winkielman , Seana Coulson

Abstract

Sensorimotor models suggest that understanding the emotional content of a face recruits a simulation process in which a viewer partially reproduces the facial expression in their own sensorimotor system. An important prediction of these models is that disrupting simulation should make emotion recognition more difficult. Here we used electroencephalogram (EEG) and facial electromyogram (EMG) to investigate how interfering with sensorimotor signals from the face influences the real-time processing of emotional faces. EEG and EMG were recorded as healthy adults viewed emotional faces and rated their valence. During control blocks, participants held a conjoined pair of chopsticks loosely between their lips. During interference blocks, participants held the chopsticks horizontally between their teeth and lips to generate motor noise on the lower part of the face. This noise was confirmed by EMG at the zygomaticus. Analysis of EEG indicated that faces expressing happiness or disgust�lower f
Author Joshua D. Davis
Joshua D. Davis,,
-
, Piotr Winkielman (Wydział Psychologii)
Piotr Winkielman,,
- Wydział Psychologii
, Seana Coulson
Seana Coulson,,
-
Journal seriesCognitive Affective & Behavioral Neuroscience, ISSN 1530-7026, (A 40 pkt)
Issue year2017
Vol17
No3
Pages652-664
Publication size in sheets0.6
Keywords in EnglishEmotion, ERP, Embodied cognition, 2017, No terms assigned, 2017
ASJC Classification2802 Behavioral Neuroscience; 2805 Cognitive Neuroscience
URL http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-10262-001&lang=pl&site=ehost-live
Languageen angielski
File
davis-winkielman-coulson_faces-n400_CABN2017.pdf 1.24 MB
Additional file
Oświadczenie_winkielman-CABN-2017 (1).pdf 89.49 KB
Score (nominal)40
Score sourcejournalList
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2017 = 1.03; WoS Impact Factor: 2017 = 2.565 (2) - 2017=3.413 (5)
Citation count*20 (2020-10-21)
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