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On the Time Course of Vocal Emotion Recognition

Overview of attention for article published in PLOS ONE, November 2011
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Title
On the Time Course of Vocal Emotion Recognition
Published in
PLOS ONE, November 2011
DOI 10.1371/journal.pone.0027256
Pubmed ID
Authors

Marc D. Pell, Sonja A. Kotz

Abstract

How quickly do listeners recognize emotions from a speaker's voice, and does the time course for recognition vary by emotion type? To address these questions, we adapted the auditory gating paradigm to estimate how much vocal information is needed for listeners to categorize five basic emotions (anger, disgust, fear, sadness, happiness) and neutral utterances produced by male and female speakers of English. Semantically-anomalous pseudo-utterances (e.g., The rivix jolled the silling) conveying each emotion were divided into seven gate intervals according to the number of syllables that listeners heard from sentence onset. Participants (n = 48) judged the emotional meaning of stimuli presented at each gate duration interval, in a successive, blocked presentation format. Analyses looked at how recognition of each emotion evolves as an utterance unfolds and estimated the "identification point" for each emotion. Results showed that anger, sadness, fear, and neutral expressions are recognized more accurately at short gate intervals than happiness, and particularly disgust; however, as speech unfolds, recognition of happiness improves significantly towards the end of the utterance (and fear is recognized more accurately than other emotions). When the gate associated with the emotion identification point of each stimulus was calculated, data indicated that fear (M = 517 ms), sadness (M = 576 ms), and neutral (M = 510 ms) expressions were identified from shorter acoustic events than the other emotions. These data reveal differences in the underlying time course for conscious recognition of basic emotions from vocal expressions, which should be accounted for in studies of emotional speech processing.

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Geographical breakdown

Country Count As %
Germany 2 <1%
Netherlands 1 <1%
Israel 1 <1%
Mexico 1 <1%
Argentina 1 <1%
Japan 1 <1%
United States 1 <1%
Luxembourg 1 <1%
Unknown 211 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 52 24%
Researcher 38 17%
Student > Master 21 10%
Student > Bachelor 20 9%
Student > Postgraduate 15 7%
Other 39 18%
Unknown 35 16%
Readers by discipline Count As %
Psychology 77 35%
Engineering 18 8%
Agricultural and Biological Sciences 18 8%
Neuroscience 17 8%
Linguistics 14 6%
Other 33 15%
Unknown 43 20%