Literature DB >> 11515959

Dynamic properties influence the perception of facial expressions.

M Kamachi1, V Bruce, S Mukaida, J Gyoba, S Yoshikawa, S Akamatsu.   

Abstract

Two experiments were conducted to investigate the role played by dynamic information in identifying facial expressions of emotion. Dynamic expression sequences were created by generating and displaying morph sequences which changed the face from neutral to a peak expression in different numbers of intervening intermediate stages, to create fast (6 frames), medium (26 frames), and slow (101 frames) sequences. In experiment 1, participants were asked to describe what the person shown in each sequence was feeling. Sadness was more accurately identified when slow sequences were shown. Happiness, and to some extent surprise, was better from faster sequences, while anger was most accurately detected from the sequences of medium pace. In experiment 2 we used an intensity-rating task and static images as well as dynamic ones to examine whether effects were due to total time of the displays or to the speed of sequence. Accuracies of expression judgments were derived from the rated intensities and the results were similar to those of experiment 1 for angry and sad expressions (surprised and happy were close to ceiling). Moreover, the effect of display time was found only for dynamic expressions and not for static ones, suggesting that it was speed, not time, which was responsible for these effects. These results suggest that representations of basic expressions of emotion encode information about dynamic as well as static properties.

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Year:  2001        PMID: 11515959     DOI: 10.1068/p3131

Source DB:  PubMed          Journal:  Perception        ISSN: 0301-0066            Impact factor:   1.490


  29 in total

1.  A search advantage for faces learned in motion.

Authors:  Karin S Pilz; Ian M Thornton; Heinrich H Bülthoff
Journal:  Exp Brain Res       Date:  2005-12-06       Impact factor: 1.972

2.  Sex differences in perception of emotion intensity in dynamic and static facial expressions.

Authors:  Cezary Biele; Anna Grabowska
Journal:  Exp Brain Res       Date:  2006-01-26       Impact factor: 1.972

3.  Neural processing of dynamic emotional facial expressions in psychopaths.

Authors:  Jean Decety; Laurie Skelly; Keith J Yoder; Kent A Kiehl
Journal:  Soc Neurosci       Date:  2014-02       Impact factor: 2.083

4.  Slowing down presentation of facial movements and vocal sounds enhances facial expression recognition and induces facial-vocal imitation in children with autism.

Authors:  Carole Tardif; France Lainé; Mélissa Rodriguez; Bruno Gepner
Journal:  J Autism Dev Disord       Date:  2006-10-07

5.  The efficiency of dynamic and static facial expression recognition.

Authors:  Jason M Gold; Jarrett D Barker; Shawn Barr; Jennifer L Bittner; W Drew Bromfield; Nicole Chu; Roy A Goode; Doori Lee; Michael Simmons; Aparna Srinath
Journal:  J Vis       Date:  2013-04-25       Impact factor: 2.240

Review 6.  Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements.

Authors:  Lisa Feldman Barrett; Ralph Adolphs; Stacy Marsella; Aleix M Martinez; Seth D Pollak
Journal:  Psychol Sci Public Interest       Date:  2019-07

7.  Exposure to the self-face facilitates identification of dynamic facial expressions: influences on individual differences.

Authors:  Yuan Hang Li; Nim Tottenham
Journal:  Emotion       Date:  2012-12-03

Review 8.  Alexithymia and the processing of emotional facial expressions (EFEs): systematic review, unanswered questions and further perspectives.

Authors:  Delphine Grynberg; Betty Chang; Olivier Corneille; Pierre Maurage; Nicolas Vermeulen; Sylvie Berthoz; Olivier Luminet
Journal:  PLoS One       Date:  2012-08-23       Impact factor: 3.240

9.  The MPI facial expression database--a validated database of emotional and conversational facial expressions.

Authors:  Kathrin Kaulard; Douglas W Cunningham; Heinrich H Bülthoff; Christian Wallraven
Journal:  PLoS One       Date:  2012-03-15       Impact factor: 3.240

10.  Natural facial motion enhances cortical responses to faces.

Authors:  Johannes Schultz; Karin S Pilz
Journal:  Exp Brain Res       Date:  2009-02-11       Impact factor: 1.972

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