Abstract
Neuroimaging studies have shown mirror system (MS) activation when participants infer internal states e.g. emotions, intentions or beliefs (known as ‘mentalizing’) from others’ actions. However, the exact role of the MS in mentalizing tasks is unknown. Dysfunctional MS activation may underlie mentalizing deficits experienced by adults with autism spectrum disorder (ASD). This study investigated the timing of MS activity when inferring intentions in order to delineate between existing models of MS involvement. Single-pulse transcranial magnetic stimulation (TMS) was applied to the primary motor cortex at different time points during the observation of hand actions whilst participants inferred intentions (mentalizing task) and performed a non-mentalizing task. Electromyographic activity in the contralateral hand was used as an indirect measure of MS activity. Greater corticospinal activity was found during the mentalizing task than the non-mentalizing task, but only at the end of observed actions, suggesting late MS involvement in processing intentions. Enhanced corticospinal activity was not related to autistic traits or behavioural performance suggesting the MS has a more automatic role in processing others’ intentions, irrespective of mentalizing ability. Our results extend current knowledge of MS activation when mentalizing, allowing initial delineation between different models of MS involvement in mentalizing.
Original language | English |
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Pages (from-to) | 109-117 |
Number of pages | 9 |
Journal | Brain research |
Volume | 1700 |
Early online date | 19 Jul 2018 |
DOIs | |
Publication status | Published - 1 Dec 2018 |
Bibliographical note
©2018 Published by Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.Keywords
- Action
- TRANSCRANIAL MAGNETIC STIMULATION
- MIRROR NEURONS
- INTENTION
- AUTISM
Datasets
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TMS MEP mentalizing timing experiment
Barraclough, N. (Creator) & Cole, E. J. (Creator), University of York, 1 Oct 2017
DOI: 10.15124/1ba2da13-e393-4643-8460-a9257ff7ae42
Dataset