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Angry facial expressions bias gender categorization in children and adults: behavioral and computational evidence.

Bayet L, Pascalis O, Quinn PC, Lee K, Gentaz É, Tanaka JW - Front Psychol (2015)

Bottom Line: Angry faces are perceived as more masculine by adults.Based on several computational simulations of gender categorization (Experiment 3), we further conclude that (1) the angry-male bias results, at least partially, from a strategy of attending to facial features or their second-order relations when categorizing face gender, and (2) any single choice of computational representation (e.g., Principal Component Analysis) is insufficient to assess resemblances between face categories, as different representations of the very same faces suggest different bases for the angry-male bias.Taken together, the evidence suggests considerable stability in the interaction between some facial dimensions in social categorization that is present prior to the onset of formal schooling.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire de Psychologie et Neurocognition, University of Grenoble-Alps Grenoble, France ; Laboratoire de Psychologie et Neurocognition, Centre National de la Recherche Scientifique Grenoble, France.

ABSTRACT
Angry faces are perceived as more masculine by adults. However, the developmental course and underlying mechanism (bottom-up stimulus driven or top-down belief driven) associated with the angry-male bias remain unclear. Here we report that anger biases face gender categorization toward "male" responding in children as young as 5-6 years. The bias is observed for both own- and other-race faces, and is remarkably unchanged across development (into adulthood) as revealed by signal detection analyses (Experiments 1-2). The developmental course of the angry-male bias, along with its extension to other-race faces, combine to suggest that it is not rooted in extensive experience, e.g., observing males engaging in aggressive acts during the school years. Based on several computational simulations of gender categorization (Experiment 3), we further conclude that (1) the angry-male bias results, at least partially, from a strategy of attending to facial features or their second-order relations when categorizing face gender, and (2) any single choice of computational representation (e.g., Principal Component Analysis) is insufficient to assess resemblances between face categories, as different representations of the very same faces suggest different bases for the angry-male bias. Our findings are thus consistent with stimulus-and stereotyped-belief driven accounts of the angry-male bias. Taken together, the evidence suggests considerable stability in the interaction between some facial dimensions in social categorization that is present prior to the onset of formal schooling.

No MeSH data available.


Related in: MedlinePlus

Sensitivity and male bias for gender categorization in Experiments 1 (adults) and 2 (children). Female faces were used as “signal” class. Each star represents a significant difference between angry and smiling faces (paired Student t-tests, p < 0.05, uncorrected). Top: Sensitivity for Caucasian (A) and Chinese (B) faces. Bottom: Male bias for Caucasian (C) and Chinese (D) faces.
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Figure 3: Sensitivity and male bias for gender categorization in Experiments 1 (adults) and 2 (children). Female faces were used as “signal” class. Each star represents a significant difference between angry and smiling faces (paired Student t-tests, p < 0.05, uncorrected). Top: Sensitivity for Caucasian (A) and Chinese (B) faces. Bottom: Male bias for Caucasian (C) and Chinese (D) faces.

Mentions: Sensitivity was greatly reduced in Chinese faces (η2 = 0.38, i.e., a large effect), replicating the other-race effect for gender categorization (O'Toole et al., 1996). Angry expressions reduced sensitivity in Caucasian but not Chinese faces (Figures 3A,B). Male bias was high overall, also replicating the finding by O'Toole et al. (1996). Here, in addition, we found that (1) the male bias was significantly enhanced for Chinese faces (η2 = 0.35, another large effect), and (2) angry expressions also enhanced the male bias, as predicted, in Caucasian and Chinese faces (η2 = 0.17, a moderate effect)—although to a lesser extent in the latter (Figures 3C,D). Since Emotion affects the male bias but not sensitivity in Chinese faces, it follows that the effect of Emotion on the male bias is not solely mediated by its effect on sensitivity.


Angry facial expressions bias gender categorization in children and adults: behavioral and computational evidence.

Bayet L, Pascalis O, Quinn PC, Lee K, Gentaz É, Tanaka JW - Front Psychol (2015)

Sensitivity and male bias for gender categorization in Experiments 1 (adults) and 2 (children). Female faces were used as “signal” class. Each star represents a significant difference between angry and smiling faces (paired Student t-tests, p < 0.05, uncorrected). Top: Sensitivity for Caucasian (A) and Chinese (B) faces. Bottom: Male bias for Caucasian (C) and Chinese (D) faces.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4374394&req=5

Figure 3: Sensitivity and male bias for gender categorization in Experiments 1 (adults) and 2 (children). Female faces were used as “signal” class. Each star represents a significant difference between angry and smiling faces (paired Student t-tests, p < 0.05, uncorrected). Top: Sensitivity for Caucasian (A) and Chinese (B) faces. Bottom: Male bias for Caucasian (C) and Chinese (D) faces.
Mentions: Sensitivity was greatly reduced in Chinese faces (η2 = 0.38, i.e., a large effect), replicating the other-race effect for gender categorization (O'Toole et al., 1996). Angry expressions reduced sensitivity in Caucasian but not Chinese faces (Figures 3A,B). Male bias was high overall, also replicating the finding by O'Toole et al. (1996). Here, in addition, we found that (1) the male bias was significantly enhanced for Chinese faces (η2 = 0.35, another large effect), and (2) angry expressions also enhanced the male bias, as predicted, in Caucasian and Chinese faces (η2 = 0.17, a moderate effect)—although to a lesser extent in the latter (Figures 3C,D). Since Emotion affects the male bias but not sensitivity in Chinese faces, it follows that the effect of Emotion on the male bias is not solely mediated by its effect on sensitivity.

Bottom Line: Angry faces are perceived as more masculine by adults.Based on several computational simulations of gender categorization (Experiment 3), we further conclude that (1) the angry-male bias results, at least partially, from a strategy of attending to facial features or their second-order relations when categorizing face gender, and (2) any single choice of computational representation (e.g., Principal Component Analysis) is insufficient to assess resemblances between face categories, as different representations of the very same faces suggest different bases for the angry-male bias.Taken together, the evidence suggests considerable stability in the interaction between some facial dimensions in social categorization that is present prior to the onset of formal schooling.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire de Psychologie et Neurocognition, University of Grenoble-Alps Grenoble, France ; Laboratoire de Psychologie et Neurocognition, Centre National de la Recherche Scientifique Grenoble, France.

ABSTRACT
Angry faces are perceived as more masculine by adults. However, the developmental course and underlying mechanism (bottom-up stimulus driven or top-down belief driven) associated with the angry-male bias remain unclear. Here we report that anger biases face gender categorization toward "male" responding in children as young as 5-6 years. The bias is observed for both own- and other-race faces, and is remarkably unchanged across development (into adulthood) as revealed by signal detection analyses (Experiments 1-2). The developmental course of the angry-male bias, along with its extension to other-race faces, combine to suggest that it is not rooted in extensive experience, e.g., observing males engaging in aggressive acts during the school years. Based on several computational simulations of gender categorization (Experiment 3), we further conclude that (1) the angry-male bias results, at least partially, from a strategy of attending to facial features or their second-order relations when categorizing face gender, and (2) any single choice of computational representation (e.g., Principal Component Analysis) is insufficient to assess resemblances between face categories, as different representations of the very same faces suggest different bases for the angry-male bias. Our findings are thus consistent with stimulus-and stereotyped-belief driven accounts of the angry-male bias. Taken together, the evidence suggests considerable stability in the interaction between some facial dimensions in social categorization that is present prior to the onset of formal schooling.

No MeSH data available.


Related in: MedlinePlus