September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Spatial frequency utilization during the recognition of static, dynamic and dynamic random facial expressions.
Author Affiliations
  • Marie-Pier Plouffe Demers
    Département de Psychoéducation et Psychologie, Université du Québec en Outaouais
    Centre de Recherche en Neuropsychologie et Cognition, Département de Psychologie, Université de Montréal
  • Camille Saumure Régimbald
    Département de Psychoéducation et Psychologie, Université du Québec en Outaouais
    Centre de Recherche en Neuropsychologie et Cognition, Département de Psychologie, Université de Montréal
  • Daniel Fiset
    Département de Psychoéducation et Psychologie, Université du Québec en Outaouais
    Centre de Recherche en Neuropsychologie et Cognition, Département de Psychologie, Université de Montréal
  • Caroline Blais
    Département de Psychoéducation et Psychologie, Université du Québec en Outaouais
    Centre de Recherche en Neuropsychologie et Cognition, Département de Psychologie, Université de Montréal
Journal of Vision August 2017, Vol.17, 826. doi:10.1167/17.10.826
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      Marie-Pier Plouffe Demers, Camille Saumure Régimbald, Daniel Fiset, Caroline Blais; Spatial frequency utilization during the recognition of static, dynamic and dynamic random facial expressions.. Journal of Vision 2017;17(10):826. doi: 10.1167/17.10.826.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Previous studies have revealed that dynamic facial expressions (DFE) are better recognized than static facial expressions (SFE; Ambada et al., 2005). We have recently demonstrated that DFE can be recognized while fixating less on the features, and relying more on lower spatial frequencies (SF), than with SFE (Saumure et al., VSS2016). Since biological motion can be processed in extrafoveal vision (Gurnsey et al., 2008), the information provided by the motion in DFE may decrease the need to fixate the features and extract higher SF. This hypothesis would predict for dynamic-random facial expressions (D-RFE) created by altering the biological motion of the original DFE (i.e randomized frames) to be processed similarly to SFE. In this experiment, SF utilization of 27 participants was measured with SFE, DFE and D-RFE using SF Bubbles (Willenbockel et al., 2010). Participants categorized pictures and videos (block design) of the six basic facial expressions and neutrality, presented for a duration of 450 ms. SF tunings were obtained by conducting a multiple regression analysis on the SF filters and accuracies across trials. Statistical thresholds were found with the Stat4Ci (Chauvin et al., 2005). SF bands peaking at 16.6 cycles per face (cpf), 14 cpf, and 15.6 cpf were found with SFE, DFE and D-RFE, respectively (ZCrit=2.84, p< 0.05). Low SFs (3.2 to 4.2 cpf) were significantly more utilized with D-RFE than with SFE; and mid-to-high SFs (>18.6; 18.9 to 36.8 cpf) were significantly more utilized with SFE than with D-RFE and DFE respectively (ZCrit=3.09, p< 0.025). A marginal trend also indicated a higher utilization of low SF with DFE than with SFE (Zdynamic-static=2.57). These results suggest reliance on lower SF even when biological motion was altered.

Meeting abstract presented at VSS 2017

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