Dataset Information

Emotion recognition associated with polymorphism in oxytocinergic pathway gene ARNT2.

ABSTRACT: The ability to correctly understand the emotional expression of another person is essential for social relationships and appears to be a partly inherited trait. The neuropeptides oxytocin and vasopressin have been shown to influence this ability as well as face processing in humans. Here, recognition of the emotional content of faces and voices, separately and combined, was investigated in 492 subjects, genotyped for 25 single nucleotide polymorphisms (SNPs) in eight genes encoding proteins important for oxytocin and vasopressin neurotransmission. The SNP rs4778599 in the gene encoding aryl hydrocarbon receptor nuclear translocator 2 (ARNT2), a transcription factor that participates in the development of hypothalamic oxytocin and vasopressin neurons, showed an association that survived correction for multiple testing with emotion recognition of audio-visual stimuli in women (n?=?309). This study demonstrates evidence for an association that further expands previous findings of oxytocin and vasopressin involvement in emotion recognition.

SUBMITTER: Hovey D

PROVIDER: S-EPMC5827350 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Emotion recognition associated with polymorphism in oxytocinergic pathway gene ARNT2.

Hovey Daniel D Henningsson Susanne S Cortes Diana S DS Bänziger Tanja T Zettergren Anna A Melke Jonas J Fischer Håkan H Laukka Petri P Westberg Lars L

Social cognitive and affective neuroscience 20180201 2

The ability to correctly understand the emotional expression of another person is essential for social relationships and appears to be a partly inherited trait. The neuropeptides oxytocin and vasopressin have been shown to influence this ability as well as face processing in humans. Here, recognition of the emotional content of faces and voices, separately and combined, was investigated in 492 subjects, genotyped for 25 single nucleotide polymorphisms (SNPs) in eight genes encoding proteins impo ...[more]

PMID: 29194499

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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

Emotion recognition associated with polymorphism in oxytocinergic pathway gene ARNT2.

Publications

Emotion recognition associated with polymorphism in oxytocinergic pathway gene ARNT2.

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