Atypical face perception has been associated with the socio-communicative difficulties that characterize autism spectrum disorder (ASD). Growing evidence, however, suggests that a widespread impairment in face perception is not as common as once thought. One important issue arising with the interpretation of this literature is the relationship between face processing and a more general perceptual tendency to focus on local rather than global information. Previous work has demonstrated that when discriminating faces presented from the same view, older adolescents and adults with ASD perform similarly to typically developing individuals. When faces are presented from different views, however, they perform more poorly-specifically, when access to local cues is minimized. In this study, we assessed the cross-sectional development of face identity discrimination across viewpoint using same- and different-view conditions in children and adolescents with and without ASD. Contrary to the findings in adults, our results revealed that all participants experienced greater difficulty identifying faces from different views than from same views, and demonstrated similar age-expected improvements in performance across tasks. These results suggest that differences in face discrimination across views may only emerge beyond the age of 15 years in ASD.
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http://dx.doi.org/10.1167/17.1.38 | DOI Listing |
Front Comput Neurosci
December 2024
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of Korea.
Facial emotion recognition (FER) can serve as a valuable tool for assessing emotional states, which are often linked to mental health. However, mental health encompasses a broad range of factors that go beyond facial expressions. While FER provides insights into certain aspects of emotional well-being, it can be used in conjunction with other assessments to form a more comprehensive understanding of an individual's mental health.
View Article and Find Full Text PDFActa Psychol (Amst)
December 2024
Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Affective Theory of Mind (ToM) is the ability to understand other peoples' emotional states and feelings. Several studies showed impaired affective ToM abilities in people with Parkinson's disease (PD). However, most studies tested this ability by using single-stimulus modality tasks (visual cues).
View Article and Find Full Text PDFComput Methods Programs Biomed
December 2024
School of Communication and Information Engineering, Shanghai University, 200444, Shanghai, China. Electronic address:
Background And Objectives: In the current global health landscape, there is an increasing demand for rapid and accurate assessment of mental states. Traditional assessment methods typically rely on face-to-face interactions, which are not only time-consuming but also highly subjective. Addressing this issue, this study aims to develop a client-server-based, non-contact multimodal emotion and behavior recognition system to enhance the efficiency and accuracy of mental state assessments.
View Article and Find Full Text PDFACS Sens
December 2024
College of Integrated Circuits, Taiyuan University of Technology, Taiyuan 030024, China.
By analyzing facial features to perform expression recognition and health monitoring, facial perception plays a pivotal role in noninvasive, real-time disease diagnosis and prevention. Current perception routes are limited by structural complexity and the necessity of a power supply, making timely and accurate monitoring difficult. Herein, a self-powered poly(vinyl alcohol)-gellan gum-glycerol thermogalvanic gel patch enabling facial perception is developed for monitoring emotions and atypical pathological states.
View Article and Find Full Text PDFIn unsupervised transfer learning for medical image segmentation, where existing algorithms face the challenge of error propagation due to inaccessible source domain data. In response to this scenario, source-free domain transfer algorithm with reduced style sensitivity (SFDT-RSS) is designed. SFDT-RSS initially pre-trains the source domain model by using the generalization strategy and subsequently adapts the pre-trained model to target domain without accessing source data.
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