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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
Published on: August 9, 2024
Rapid pre-attentive processing of a famous speaker: Electrophysiological effects of Angela Merkel's voice
Paula Rinke1, Tatjana Schmidt2, Kjartan Beier3
1Research Group Phonetics, Institute of German Linguistics, Philipps-University Marburg, Germany; Center for Mind, Brain & Behavior, Universities of Marburg & Gießen, Germany.
Abstract:
The recognition of human speakers by their voices is a remarkable cognitive ability. Previous research has established a voice area in the right temporal cortex involved in the integration of speaker-specific acoustic features. This integration appears to occur rapidly, especially in case of familiar voices. However, the exact time course of this process is less well understood. To this end, we here investigated the automatic change detection response of the human brain while listening to the famous voice of German chancellor Angela Merkel, embedded in the context of acoustically matched voices. A classic passive oddball paradigm contrasted short word stimuli uttered by Merkel with word stimuli uttered by two unfamiliar female speakers. Electrophysiological voice processing indices from 21 participants were quantified as mismatch negativities (MMNs) and P3a differences. Cortical sources were approximated by variable resolution electromagnetic tomography. The results showed amplitude and latency effects for both MMN and P3a: The famous (familiar) voice elicited a smaller but earlier MMN than the unfamiliar voices. The P3a, by contrast, was both larger and later for the familiar than for the unfamiliar voices. Familiar-voice MMNs originated from right-hemispheric regions in temporal cortex, overlapping with the temporal voice area, while unfamiliar-voice MMNs stemmed from left superior temporal gyrus. These results suggest that the processing of a very famous voice relies on pre-attentive right temporal processing within the first 150 ms of the acoustic signal. The findings further our understanding of the neural dynamics underlying familiar voice processing.
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