Predicting Autistic Traits Using Eye Movement during Visual Perspective Taking and Facial Emotion Identification.

Autistic traits are broad in severity and difficult to measure quantitatively. Quantitative measurement would be helpful in determining the effectiveness of training and therapy for autistic traits. The development of eye-tracking technology has made it easier to understand autistic traits. Previous works showed that autistic traits can be predicted from eye movements in the facial emotion identification task. It may be possible to measure autistic traits more accurately than the only prediction from facial emotion identification ability. In this study, we used a visual perspective taking task. The results showed that the Social Responsiveness Scale-2 score, which is associated with autistic traits, was predicted at 0.414 in Spearman's correlation coefficient by using eye movements obtained from the two tasks.

The Validation of Automated Social Skills Training in Members of the General Population Over 4 Weeks: Comparative Study.

BACKGROUND: Social skills training by human trainers is a well-established method of teaching appropriate social and communication skills and strengthening social self-efficacy. Specifically, human social skills training is a fundamental approach to teaching and learning the rules of social interaction. However, it is cost-ineffective and offers low accessibility, since the number of professional trainers is limited. A conversational agent is a system that can communicate with a human being in a natural language. We proposed to overcome the limitations of current social skills training with conversational agents. Our system is capable of speech recognition, response selection, and speech synthesis and can also generate nonverbal behaviors. We developed a system that incorporated automated social skills training that completely adheres to the training model of Bellack et al through a conversational agent. OBJECTIVE: This study aimed to validate the training effect of a conversational agent-based social skills training system in members of the general population during a 4-week training session. We compare 2 groups (with and without training) and hypothesize that the trained group's social skills will improve. Furthermore, this study sought to clarify the effect size for future larger-scale evaluations, including a much larger group of different social pathological phenomena. METHODS: For the experiment, 26 healthy Japanese participants were separated into 2 groups, where we hypothesized that group 1 (system trained) will make greater improvement than group 2 (nontrained). System training was done as a 4-week intervention where the participants visit the examination room every week. Each training session included social skills training with a conversational agent for 3 basic skills. We evaluated the training effect using questionnaires in pre- and posttraining evaluations. In addition to the questionnaires, we conducted a performance test that required the social cognition and expression of participants in new role-play scenarios. Blind ratings by third-party trainers were made by watching recorded role-play videos. A nonparametric Wilcoxson Rank Sum test was performed for each variable. Improvement between pre- and posttraining evaluations was used to compare the 2 groups. Moreover, we compared the statistical significance from the questionnaires and ratings between the 2 groups. RESULTS: Of the 26 recruited participants, 18 completed this experiment: 9 in group 1 and 9 in group 2. Those in group 1 achieved significant improvement in generalized self-efficacy (P=.02; effect size r=0.53). We also found a significant decrease in state anxiety presence (P=.04; r=0.49), measured by the State-Trait Anxiety Inventory (STAI). For ratings by third-party trainers, speech clarity was significantly strengthened in group 1 (P=.03; r=0.30). CONCLUSIONS: Our findings reveal the usefulness of the automated social skills training after a 4-week training period. This study confirms a large effect size between groups on generalized self-efficacy, state anxiety presence, and speech clarity.

Eye-movement analysis on facial expression for identifying children and adults with neurodevelopmental disorders.

Experienced psychiatrists identify people with autism spectrum disorder (ASD) and schizophrenia (Sz) through interviews based on diagnostic criteria, their responses, and various neuropsychological tests. To improve the clinical diagnosis of neurodevelopmental disorders such as ASD and Sz, the discovery of disorder-specific biomarkers and behavioral indicators with sufficient sensitivity is important. In recent years, studies have been conducted using machine learning to make more accurate predictions. Among various indicators, eye movement, which can be easily obtained, has attracted much attention and various studies have been conducted for ASD and Sz. Eye movement specificity during facial expression recognition has been studied extensively in the past, but modeling taking into account differences in specificity among facial expressions has not been conducted. In this paper, we propose a method to detect ASD or Sz from eye movement during the Facial Emotion Identification Test (FEIT) while considering differences in eye movement due to the facial expressions presented. We also confirm that weighting using the differences improves classification accuracy. Our data set sample consisted of 15 adults with ASD and Sz, 16 controls, and 15 children with ASD and 17 controls. Random forest was used to weight each test and classify the participants as control, ASD, or Sz. The most successful approach used heat maps and convolutional neural networks (CNN) for eye retention. This method classified Sz in adults with 64.5% accuracy, ASD in adults with up to 71.0% accuracy, and ASD in children with 66.7% accuracy. Classifying of ASD result was significantly different (p<.05) by the binomial test with chance rate. The results show a 10% and 16.7% improvement in accuracy, respectively, compared to a model that does not take facial expressions into account. In ASD, this indicates that modeling is effective, which weights the output of each image.