Abstract
Facial expressions of emotion play a key role in social interactions. While in everyday life, their dynamic and transient nature calls for a fast processing of the visual information they contain, a majority of studies investigating the visual processes underlying their recognition have focused on their static display. The present study aimed to gain a better understanding of these processes while using more ecological dynamic facial expressions. In two experiments, we directly compared the spatial frequency (SF) tuning during the recognition of static and dynamic facial expressions. Experiment 1 revealed a shift toward lower SFs for dynamic expressions in comparison to static ones. Experiment 2 was designed to verify if changes in SF tuning curves were specific to the presence of emotional information in motion by comparing the SF tuning profiles for static, dynamic, and shuffled dynamic expressions. Results showed a similar shift toward lower SFs for shuffled expressions, suggesting that the difference found between dynamic and static expressions might not be linked to informative motion per se but to the presence of motion regardless its nature.
Highlights
In social settings, the human face represents one of the richest nonverbal sources of information
A dynamic advantage was found for most facial expressions: anger [t(19) −8.7049; p < 0.001; 95% CI (−10.30 to −6.31%)], fear [t(19) −3.3401; p = 0.0034; 95% CI (−6.77 to −1.55%)], sadness [t(19) −5.2577; p < 0.001; 95% CI (−11.94 to −5.14%)], and surprise [t(19) −7.4219; p < 0.001; 95% CI (−10.94 to −6.13%)]
Experiment 2 aimed at verifying if the difference observed in the spatial frequencies (SF) tuning is related to the presence of informative motion in dynamic expressions
Summary
The human face represents one of the richest nonverbal sources of information. It is an essential skill for humans to continually monitor the facial expressions of others in order to appropriately tailor their behavior throughout social interactions. A majority of studies investigating the visual processes underlying facial emotion recognition have relied on static pictures displaying facial emotions at their apex (i.e., highest intensity). The present study was aimed at gaining a better understanding of this process by investigating the mechanisms subtending this important endeavor, using more ecological dynamic facial expressions. We were interested in utilization of spatial frequencies (SF), considered the “atom” upon which primary visual cortex neurons base their world representation (DeValois and DeValois, 1990), during recognition of static and dynamic facial expressions. Lower SFs code coarser visual information, such as global face shape or facial feature location, while higher SFs code finer visual information, such as facial feature shape or details like wrinkles
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