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An investigation of the effect of race-based social categorization on adults' recognition of emotion.

ABSTRACT: Emotion recognition is important for social interaction and communication, yet previous research has identified a cross-cultural emotion recognition deficit: Recognition is less accurate for emotions expressed by individuals from a cultural group different than one's own. The current study examined whether social categorization based on race, in the absence of cultural differences, influences emotion recognition in a diverse context. South Asian and White Canadians in the Greater Toronto Area completed an emotion recognition task that required them to identify the seven basic emotional expressions when posed by members of the same two groups, allowing us to tease apart the contributions of culture and social group membership. Contrary to our hypothesis, there was no mutual in-group advantage in emotion recognition: Participants were not more accurate at recognizing emotions posed by their respective racial in-groups. Both groups were more accurate at recognizing expressions when posed by South Asian faces, and White participants were more accurate overall compared to South Asian participants. These results suggest that in a diverse environment, categorization based on race alone does not lead to the creation of social out-groups in a way that negatively impacts emotion recognition.

SUBMITTER: Reyes BN

PROVIDER: S-EPMC5825022 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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An investigation of the effect of race-based social categorization on adults' recognition of emotion.

Reyes B Nicole BN Segal Shira C SC Moulson Margaret C MC

PloS one 20180223 2

Emotion recognition is important for social interaction and communication, yet previous research has identified a cross-cultural emotion recognition deficit: Recognition is less accurate for emotions expressed by individuals from a cultural group different than one's own. The current study examined whether social categorization based on race, in the absence of cultural differences, influences emotion recognition in a diverse context. South Asian and White Canadians in the Greater Toronto Area co ...[more]

PMID: 29474367

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Project description:Emotion recognition is key to interpersonal communication and to human-machine interaction. Body expression may contribute to emotion recognition, but most past studies focused on a few motions, limiting accurate recognition. Moreover, emotions in most previous research were acted out, resulting in non-natural motion, which is unapplicable in reality. We present an approach for emotion recognition based on body motion in naturalistic settings, examining authentic emotions, natural movement, and a broad collection of motion parameters. A lab experiment using 24 participants manipulated participants' emotions using pretested movies into five conditions: happiness, relaxation, fear, sadness, and emotionally-neutral. Emotion was manipulated within subjects, with fillers in between and a counterbalanced order. A motion capture system measured posture and motion during standing and walking; a force plate measured center of pressure location. Traditional statistics revealed nonsignificant effects of emotions on most motion parameters; only 7 of 229 parameters demonstrate significant effects. Most significant effects are in parameters representing postural control during standing, which is consistent with past studies. Yet, the few significant effects suggest that it is impossible to recognize emotions based on a single motion parameter. We therefore developed machine learning models to classify emotions using a collection of parameters, and examined six models: k-nearest neighbors, decision tree, logistic regression, and the support vector machine with radial base function and linear and polynomial functions. The decision tree using 25 parameters provided the highest average accuracy (45.8%), more than twice the random guess for five conditions, which advances past studies demonstrating comparable accuracies, due to our naturalistic setting. This research suggests that machine learning models are valuable for emotion recognition in reality and lays the foundation for further progress in emotion recognition models, informing the development of recognition devices (e.g., depth camera), to be used in home-setting human-machine interactions.

Dataset Information

An investigation of the effect of race-based social categorization on adults' recognition of emotion.

Publications

An investigation of the effect of race-based social categorization on adults' recognition of emotion.

Similar Datasets

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