Mouthwash prevents bad breath and sterilizes bacteria that cause dental caries and periodontal disease, so it provides important means for improving oral hygiene in our daily life. Along with active ingredients such as bactericides, mouthwash contains a variety of aromas to mask the bitterness of base ingredients and increase palatability. However, given that numerous aroma studies have demonstrated various physiological efficacies of aromas on autonomic/central nervous systems, it is no wonder that aroma mouthwash has some sort of physiological impact when it is used. In this study, we investigated the effect of aroma in mouthwash on peripheral and cardiac autonomic nervous system activity. The experiment was carried out in a within-subject design wherein 20 healthy women under 5 conditions, which are 4 types of mouthwashes: peppermint, bergamot + peppermint, orange + peppermint, and lavender + peppermint, and water as a control. Participants performed a 20-minute calculation task as a cognitive stressor, and then rinsing by a mouthwash or water. We evaluated the recovery period from acute stress response for 20 minutes after the task. As a result, it was observed that a mouthwash with citrus flavor had a relaxing effect in terms of subjective scores and recovering from physiological stress response. The results may illustrate a potential benefit of using aroma mouthwash.

In recent years, cross-modal effect in which perceptions interact with each other has been drawing attention. In the case of cross-modal effect between vision and taste, the effect of the angularity of shapes on taste has been widely studied while there has been little research on the other features of shapes. Previous research have shown that the emotional valence arisen from visual perception causes cross-modal effect between vision and taste. Therefore, this study focuses on the complexity of shapes as a visual stimulus to influence emotional valence and aims to confirm the cross-modal effect induced by its sensation. First, based on previous research, the hypotheses about the effects of the complexity of shapes on taste were made. Second, by using particle swarm optimization algorithm, closed curve shapes were generated based on curvature entropy, a quantitative index of the complexity of shapes, which indicates the randomness of curvature transition. Third, cup holders, which had these closed curve shapes on their sides, were created by using a 3D printer. Finally, by comparing the tastes of orange juice in these cup holders, the effect of the complexity of shapes on the perception of sweetness, sourness and intensity was confirmed. The results suggest that the complexity of shapes controlled by curvature entropy weakens the perception of sweetness whereas it enhances that of sourness and intensity. This finding can be used for reducing sugar intake in bottle packaging.

An objective assessment method for schizophrenia is highly needed. For this purpose, this study examined the emotional characteristics of facial stiffness, one of the indices for estimating patients’ flat affect states using average faces. First, we conducted an experiment to extract psychological evaluations of facial stiffness in 14 Japanese schizophrenia. 12 medical experts rated the facial stiffness of the patients in 127 videos using a rating program. Meanwhile, their gaze data were collected. In addition, 11 average faces of 14 patients and other one of 14 healthy subjects were created in order to extract the physical characteristics of facial stiffness. The results of the emotional analysis showed that the average faces of the healthy subjects and patients were different proportions of 8 emotions. The faces of healthy subjects were "calm" with a confidence level of 99%. On the contrary, the patient's face contained 15% to 65% of emotions other than "calm," such as "anger" and "sad. In images in which the emotion of "anger" was easily identified, the gazing points tended to be concentrated around the eyes. On the other hand, images in which the gazing points were widely distributed may have been difficult to identify because they were not typical emotional expressions. Therefore, it is suggested that there are two types of criteria for judging facial stiffness in schizophrenia: the emotional expression of "anger" and a mixture of features of "anger," "sad," and "calm.

In recent years, significant research has been conducted on the use of deep learning for prediction of facial attractiveness. These studies are expected to have various applications such as recommendation systems and face beautification. Therefore, it is crucial to improve the prediction accuracy. In this study, to improve the accuracy of facial attractiveness prediction, several convolutional neural network-based models were built using sex-specific datasets. Then, their accuracies were compared. The results showed that VGG19 and VGG16 had the highest accuracies for the male and female face datasets, respectively. A detailed confirmation of the factors necessary for prediction is expected to contribute to the construction of models based on human perceptual characteristics. These models maybe utilized in various engineering applications.