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A Supramodal Neural Network for Speech and Gesture Semantics: An fMRI Study

Overview of attention for article published in PLOS ONE, November 2012
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Title
A Supramodal Neural Network for Speech and Gesture Semantics: An fMRI Study
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0051207
Pubmed ID
Authors

Benjamin Straube, Antonia Green, Susanne Weis, Tilo Kircher

Abstract

In a natural setting, speech is often accompanied by gestures. As language, speech-accompanying iconic gestures to some extent convey semantic information. However, if comprehension of the information contained in both the auditory and visual modality depends on same or different brain-networks is quite unknown. In this fMRI study, we aimed at identifying the cortical areas engaged in supramodal processing of semantic information. BOLD changes were recorded in 18 healthy right-handed male subjects watching video clips showing an actor who either performed speech (S, acoustic) or gestures (G, visual) in more (+) or less (-) meaningful varieties. In the experimental conditions familiar speech or isolated iconic gestures were presented; during the visual control condition the volunteers watched meaningless gestures (G-), while during the acoustic control condition a foreign language was presented (S-). The conjunction of the visual and acoustic semantic processing revealed activations extending from the left inferior frontal gyrus to the precentral gyrus, and included bilateral posterior temporal regions. We conclude that proclaiming this frontotemporal network the brain's core language system is to take too narrow a view. Our results rather indicate that these regions constitute a supramodal semantic processing network.

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Geographical breakdown

Country Count As %
Japan 1 <1%
Finland 1 <1%
Spain 1 <1%
Germany 1 <1%
Unknown 110 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 25%
Researcher 25 22%
Student > Bachelor 9 8%
Professor > Associate Professor 7 6%
Student > Master 6 5%
Other 19 17%
Unknown 19 17%
Readers by discipline Count As %
Psychology 31 27%
Neuroscience 23 20%
Medicine and Dentistry 11 10%
Computer Science 4 4%
Linguistics 4 4%
Other 13 11%
Unknown 28 25%