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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

Reaction times for gender categorization in Experiments 1 (adults) and 2 (children). Only reaction times from correct trials are included. Each star represents a significant difference between angry and smiling faces (paired Student t-tests, p < 0.05, uncorrected). Top: Caucasian (A) and Chinese (B) female faces. Bottom: Caucasian (C) and Chinese (D) male faces.
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Figure 2: Reaction times for gender categorization in Experiments 1 (adults) and 2 (children). Only reaction times from correct trials are included. Each star represents a significant difference between angry and smiling faces (paired Student t-tests, p < 0.05, uncorrected). Top: Caucasian (A) and Chinese (B) female faces. Bottom: Caucasian (C) and Chinese (D) male faces.

Mentions: A Race-by-Gender-by-Emotion three-way interaction was significant in the best LMM of adult inverse reaction times (Table 1). It stemmed from (1) a significant Race-by-Emotion effect on male [χ2(2) = 6.48, p = 0.039] but not female faces [χ2(2) = 4.20, p = 0.123], due to an effect of Emotion on Chinese male faces [χ2(2) = 8.87, p = 0.012] but not Caucasian male faces [χ2(2) = 2.49, p = 0.288]; and (2) a significant Race-by-Gender effect on neutral [χ2(1) = 4.24, p = 0.039] but not smiling [χ2(1) = 3.31, p = 0.069] or angry [χ2(1) = 0.14, p = 0.706] faces. The former Race-by-Emotion effect on male faces was expected and corresponds to a ceiling effect on the reaction times to Caucasian male faces. The latter Race-by-Gender effect on neutral faces was unexpected and stemmed from an effect of Race in female [χ2(1) = 7.91, p = 0.005] but not male neutral faces [χ2(1) = 0.28, p = 0.600] along with the converse effect of Gender on Chinese [χ2(1) = 5.16, p = 0.023] but not Caucasian neutral faces [χ2(1) = 0.03, p = 0.872]. Indeed, reaction time for neutral female Chinese faces was relatively long, akin to that for angry female Chinese faces (Figure 2B) and unlike that for neutral female Caucasian faces (Figure 2A). Since there was no hypothesis regarding this effect, it will not be discussed further.


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)

Reaction times for gender categorization in Experiments 1 (adults) and 2 (children). Only reaction times from correct trials are included. Each star represents a significant difference between angry and smiling faces (paired Student t-tests, p < 0.05, uncorrected). Top: Caucasian (A) and Chinese (B) female faces. Bottom: Caucasian (C) and Chinese (D) male faces.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Reaction times for gender categorization in Experiments 1 (adults) and 2 (children). Only reaction times from correct trials are included. Each star represents a significant difference between angry and smiling faces (paired Student t-tests, p < 0.05, uncorrected). Top: Caucasian (A) and Chinese (B) female faces. Bottom: Caucasian (C) and Chinese (D) male faces.
Mentions: A Race-by-Gender-by-Emotion three-way interaction was significant in the best LMM of adult inverse reaction times (Table 1). It stemmed from (1) a significant Race-by-Emotion effect on male [χ2(2) = 6.48, p = 0.039] but not female faces [χ2(2) = 4.20, p = 0.123], due to an effect of Emotion on Chinese male faces [χ2(2) = 8.87, p = 0.012] but not Caucasian male faces [χ2(2) = 2.49, p = 0.288]; and (2) a significant Race-by-Gender effect on neutral [χ2(1) = 4.24, p = 0.039] but not smiling [χ2(1) = 3.31, p = 0.069] or angry [χ2(1) = 0.14, p = 0.706] faces. The former Race-by-Emotion effect on male faces was expected and corresponds to a ceiling effect on the reaction times to Caucasian male faces. The latter Race-by-Gender effect on neutral faces was unexpected and stemmed from an effect of Race in female [χ2(1) = 7.91, p = 0.005] but not male neutral faces [χ2(1) = 0.28, p = 0.600] along with the converse effect of Gender on Chinese [χ2(1) = 5.16, p = 0.023] but not Caucasian neutral faces [χ2(1) = 0.03, p = 0.872]. Indeed, reaction time for neutral female Chinese faces was relatively long, akin to that for angry female Chinese faces (Figure 2B) and unlike that for neutral female Caucasian faces (Figure 2A). Since there was no hypothesis regarding this effect, it will not be discussed further.

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