Literature DB >> 21427208

Is there a dynamic advantage for facial expressions?

Chiara Fiorentini1, Paolo Viviani.   

Abstract

Some evidence suggests that it is easier to identify facial expressions (FEs) shown as dynamic displays than as photographs (dynamic advantage hypothesis). Previously, this has been tested by using dynamic FEs simulated either by morphing a neutral face into an emotional one or by computer animations. For the first time, we tested the dynamic advantage hypothesis by using high-speed recordings of actors' FEs. In the dynamic condition, stimuli were graded blends of two recordings (duration: 4.18 s), each describing the unfolding of an expression from neutral to apex. In the static condition, stimuli (duration: 3 s) were blends of just the apex of the same recordings. Stimuli for both conditions were generated by linearly morphing one expression into the other. Performance was estimated by a forced-choice task asking participants to identify which prototype the morphed stimulus was more similar to. Identification accuracy was not different between conditions. Response times (RTs) measured from stimulus onset were shorter for static than for dynamic stimuli. Yet, most responses to dynamic stimuli were given before expressions reached their apex. Thus, with a threshold model, we tested whether discriminative information is integrated more effectively in dynamic than in static conditions. We did not find any systematic difference. In short, neither identification accuracy nor RTs supported the dynamic advantage hypothesis.

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Year:  2011        PMID: 21427208     DOI: 10.1167/11.3.17

Source DB:  PubMed          Journal:  J Vis        ISSN: 1534-7362            Impact factor:   2.240


  22 in total

1.  Words are a context for mental inference.

Authors:  Nicole Betz; Katie Hoemann; Lisa Feldman Barrett
Journal:  Emotion       Date:  2019-01-10

2.  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

3.  Younger and Older Users' Recognition of Virtual Agent Facial Expressions.

Authors:  Jenay M Beer; Cory-Ann Smarr; Arthur D Fisk; Wendy A Rogers
Journal:  Int J Hum Comput Stud       Date:  2015-03-01       Impact factor: 3.632

Review 4.  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

5.  Psychophysical measures of sensitivity to facial expression of emotion.

Authors:  Michelle Marneweck; Andrea Loftus; Geoff Hammond
Journal:  Front Psychol       Date:  2013-02-20

6.  Variation in the X-linked EFHC2 gene is associated with social cognitive abilities in males.

Authors:  Carla M Startin; Chiara Fiorentini; Michelle de Haan; David H Skuse
Journal:  PLoS One       Date:  2015-06-24       Impact factor: 3.240

7.  Independence of face identity and expression processing: exploring the role of motion.

Authors:  Karen Lander; Natalie Butcher
Journal:  Front Psychol       Date:  2015-03-13

8.  The recognition of facial expressions of emotion in deaf and hearing individuals.

Authors:  Helen Rodger; Junpeng Lao; Chloé Stoll; Anne-Raphaëlle Richoz; Olivier Pascalis; Matthew Dye; Roberto Caldara
Journal:  Heliyon       Date:  2021-05-15

9.  Time pressure inhibits dynamic advantage in the classification of facial expressions of emotion.

Authors:  Zhongqing Jiang; Wenhui Li; Guillermo Recio; Ying Liu; Wenbo Luo; Doufei Zhang; Dan Sun
Journal:  PLoS One       Date:  2014-06-18       Impact factor: 3.240

Review 10.  Recognition of facial expressions by alcoholic patients: a systematic literature review.

Authors:  Mariana Fortunata Donadon; Flávia de Lima Osório
Journal:  Neuropsychiatr Dis Treat       Date:  2014-09-05       Impact factor: 2.570

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