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.

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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.

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 professional trainers are limited. In our previous work, we attempted to automate social skills training by developing a conversational agent that taught social skills through interaction. Long-term validation of such an automated training system is needed. OBJECTIVE This study aims to validate the training effect of a social skills training system during a four-week randomized controlled pilot trial. We compare two groups (with and without training) and hypothesize that trained people will improve their social skills and overcome their social anxiety to a greater extent than the non-trained group. Furthermore, this study seeks to clarify the optimal sample size and effect size for future larger-scale evaluations. METHODS For the study, 26 Japanese participants are separated into the two groups, where we hypothesize that Group 1 (with training) will make greater improvement than Group 2 (without training). In addition to questionnaires, we conduct a Role-play Performance Test that requires the social cognition and expression of participants in new role-play scenarios. Blind ratings by third-party trainers are made by watching recorded role-play videos. A non-parametric Wilcoxson rank sum test is performed for each variable. Improvement between pre- and post-training evaluations are used to compare the two groups. Moreover, we compare the statistical significance from the questionnaires and ratings between the two groups. RESULTS Out of 26 recruited participants, 18 individuals completed this experiment: 9 people in Group 1 and 9 in Group 2. Those in Group 1 achieved significant improvement in generalized self-efficacy (P=0.02). We also found a significant decrease in state anxiety presence (P=0.04) measured by the State-Trait Anxiety Inventory. For ratings by third-party trainers, clarity of speech was significantly strengthened in Group 1 (P=0.03). CONCLUSIONS Our findings reveal the usefulness of the social skills training system after a one-month training period. This study confirms a large effect size between groups, thus indicating the need for further larger-scale evaluations.