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Impairments in decoding facial and vocal emotional expressions in high functioning autistic adults and adolescents

机译:在高功劳的自闭症成人和青少年解码面部和声乐情绪表达的损伤

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The present investigation shows that gender of stimuli, age, and emotional categories affects the ability of adults and adolescent with Autistic Spectrum Conditions (ASC) to decode facial and vocal emotional expressions. A total of 60 subjects participated to the research: 15 ASC and 15 control adolescents aged between 10-14 years; and 15 ASC and 15 control young adults aged between 20-24 years. Their tasks consisted in decoding: a) 24 adults and 24 children contemporary facial emotional expressions of happiness, sadness, anger, fear, surprise, and disgust; and b) 20 adult's vocal emotional expressions of the same abovementioned emotions (except disgust). Significant differences were observed between ASC and typically developed peers. The data suggest that gender, type (voices or faces) of stimuli, and participants' age affect the emotion recognition process making difficult the definition of a common and shared pattern of emotional expression's recognition compliance among autistic and control groups. These results suggest that efficient and effective e-health technologies need to be able to learn and adapt to user individual traits and subjective needs to offer personalized assistance and support.
机译:本研究表明,刺激,年龄和情绪类别的性别会影响成人和青少年与自闭症频谱条件(ASC)解码面部和声乐情绪表达的能力。共有60名受试者参加了研究:15岁和15名控制青少年在10-14岁之间;和15名ASC和15名40至24岁之间的年轻人。他们的任务组成了解码:a)24个成年人和24名儿童当代面部情感表达的幸福,悲伤,愤怒,恐惧,惊喜和厌恶; b)20位成年人的声乐情绪表达相同的上述情感(除了厌恶之外)。 ASC与通常开发的同龄人之间观察到显着差异。这些数据表明,刺激的性别,类型(声音或面部)和参与者的年龄会影响情绪识别过程,使得自闭症和对照组中的情感表达式识别符合性的共同和共同模式的定义难以实现。这些结果表明,有效且有效的电子卫生技术需要能够学习和适应用户个人特征和主观需求来提供个性化的援助和支持。

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