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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

Computational models. (A) Overall model specification. Each model had an unsupervised learning step (either PCA, ICA) followed by a supervised learning step (logistic regression or SVM). (B) Training, cross validation and test workflow. Stimuli were partitioned into a training set and a test set. Variables used in further analysis were the Leave-One-Out Cross-validation (LOOCV) accuracy, the test accuracy, and the log-odds at training. Human ratings were obtained in the control study (Supplementary Material).
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Figure 4: Computational models. (A) Overall model specification. Each model had an unsupervised learning step (either PCA, ICA) followed by a supervised learning step (logistic regression or SVM). (B) Training, cross validation and test workflow. Stimuli were partitioned into a training set and a test set. Variables used in further analysis were the Leave-One-Out Cross-validation (LOOCV) accuracy, the test accuracy, and the log-odds at training. Human ratings were obtained in the control study (Supplementary Material).

Mentions: Analyses were run in Matlab 7.9.0529. The raw stimuli were used to train different classifiers (Figure 4A). The stimuli were divided into a training set and a test set that were used separately to obtain different measures of gender categorization accuracy (Figure 4B). Several models and set partitions were implemented to explore different types of training and representations (Table 7; Figure 4A).


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)

Computational models. (A) Overall model specification. Each model had an unsupervised learning step (either PCA, ICA) followed by a supervised learning step (logistic regression or SVM). (B) Training, cross validation and test workflow. Stimuli were partitioned into a training set and a test set. Variables used in further analysis were the Leave-One-Out Cross-validation (LOOCV) accuracy, the test accuracy, and the log-odds at training. Human ratings were obtained in the control study (Supplementary Material).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Computational models. (A) Overall model specification. Each model had an unsupervised learning step (either PCA, ICA) followed by a supervised learning step (logistic regression or SVM). (B) Training, cross validation and test workflow. Stimuli were partitioned into a training set and a test set. Variables used in further analysis were the Leave-One-Out Cross-validation (LOOCV) accuracy, the test accuracy, and the log-odds at training. Human ratings were obtained in the control study (Supplementary Material).
Mentions: Analyses were run in Matlab 7.9.0529. The raw stimuli were used to train different classifiers (Figure 4A). The stimuli were divided into a training set and a test set that were used separately to obtain different measures of gender categorization accuracy (Figure 4B). Several models and set partitions were implemented to explore different types of training and representations (Table 7; Figure 4A).

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