Efficient recognition of facial expressions does not require motor simulation
What mechanisms underlie facial expression recognition? A popular hypothesis holds that efficient facial expression recognition cannot be achieved by visual analysis alone but additionally requires a mechanism of motor simulation — an unconscious, covert imitation of the observed facial postures and movements. Here, we first discuss why this hypothesis does not necessarily follow from extant empirical evidence. Next, we report experimental evidence against the central premise of this view: we demonstrate that individuals can achieve normotypical efficient facial expression recognition despite a congenital absence of relevant facial motor representations and, therefore, unaided by motor simulation. This underscores the need to reconsider the role of motor simulation in facial expression recognition.
Data and stimulus materials are publicly available and can be accessed on the Open Science Framework platform (https://osf.io/8t4fv/?view_only=85c15cafe5d94bb6a5cff2f09a6ef56d)
Data from: Efficient recognition of facial expressions does not require motor simulationOpen Science Framework, DOI 10.17605/OSF.IO/8T4FV.
Article and author information
Harvard University's Mind, Brain and Behavior Interfaculty Initiative
- Alfonso Caramazza
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Human subjects: The study was approved by the local Ethical committee at UCLouvain (Registration # B403201629166). Written informed consents were obtained from all participants prior to the study, and after the nature and possible consequences of the studies were explained.
- Richard B Ivry, University of California, Berkeley, United States
- Received: December 22, 2019
- Accepted: May 3, 2020
- Accepted Manuscript published: May 4, 2020 (version 1)
- Version of Record published: May 12, 2020 (version 2)
© 2020, Vannuscorps et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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