October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Spatial frequencies for detection of pain facial expressions revealed by reverse correlation
Author Affiliations
  • Joel Guerette
    Universite du Quebec en Outaouais
    Universite du Quebec a Montreal
  • Isabelle Charbonneau
    Universite du Quebec en Outaouais
  • Francis Gingras
    Universite du Quebec en Outaouais
    Universite du Quebec a Montreal
  • Caroline Blais
    Universite du Quebec en Outaouais
  • Stephanie Cormier
    Universite du Quebec en Outaouais
  • Daniel Fiset
    Universite du Quebec en Outaouais
Journal of Vision October 2020, Vol.20, 1384. doi:https://doi.org/10.1167/jov.20.11.1384
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      Joel Guerette, Isabelle Charbonneau, Francis Gingras, Caroline Blais, Stephanie Cormier, Daniel Fiset; Spatial frequencies for detection of pain facial expressions revealed by reverse correlation. Journal of Vision 2020;20(11):1384. doi: https://doi.org/10.1167/jov.20.11.1384.

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

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Abstract

The ability to detect pain facial expressions is a crucial step before help can be provided. Because of the biological importance of this skill, it is plausible to expect that an observer can detect that expression even from a relatively large distance. Accordingly, in VSS2019, we presented a study showing that pain facial expression detection relies on low spatial frequencies (SF; Guérette et al., 2019); low SF are available from farther away than high SF. These results were obtained using posed facial expressions, with a method that involves repeating the same stimuli. In the present study, we used Reverse Correlation (Mangini & Biederman, 2004) to verify in which SF the mental representation of pain facial expressions are encoded. This method has the advantage of revealing the expectations about the appearance of an expression, and the latter may be closer to spontaneous expressions encountered in day-to-day social interactions. On each trial, a neutral face was used as background stimulus, on which sinusoidal white noise was added. Participants were asked to choose which of two noisy faces better represented a target emotion. Three target emotion conditions were used: pain, fear, and happiness. Fear and happiness are respectively considered the most similar and dissimilar expressions to pain (Wang et al., 2015). Mental representations of pain involved SF ranging from 1.13 to 12.3 cycles per face (cpf), peaking at 3.78 cpf. Fear and happiness relied on a similar range of SF (4.17 and 3.63 cpf, respectively). These results show that low SF are encoded in mental representations of pain facial expressions. This finding is congruent with previous findings that accurate detection of pain relies on low SF, and add evidence to the idea that pain expressions are communicated in a way to be detected from far away and using coarse visual information.

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