Due to the distribution of various deceptive techniques in our daily environments, we must consider a way of educating interaction literacy, i.e., enabling trust calibration and appropriate responsive action through critical thinking and observations of interacting partners. For this purpose, in this study, we conducted an experiment where a social robot deliberately deceives participants under low-risk and non-harmful tasks (such as consent form readings) in both face-to-face and online survey settings, as an analogy of an evacuation drill or inoculation theory. We developed a semi-autonomous robot system where the robot provides information with or without deceptive strategies to attempt to either mislead (with deception) or encourage careful reading (without deception) of the consent form. After the tasks, the participants were informed of the robot’s role and evaluated their impressions of it. Additionally, following the laboratory experiment, participants voluntarily completed a follow-up online survey and participated in a similar consent form reading task online. The experimental results showed that the participants were more deceived due to the robot’s deceptive strategies, but their negative impressions of the robot were limited. Moreover, the participants who were deceived during the first task were also deceived in the second task. This suggests that our approach can be used as a promising method for identifying individuals who are easily deceived by others in non-harmful situations, thus aiding in the development of interaction literacy programs.

Using multiple robots in presentations more effectively attracts customers and provides information to them than a single robot. Many robotics researchers have employed this approach to achieve effective robot-presentation systems in real environments. Although these systems implicitly equalize the amount of speech in the conversational content among multiple robots, it remains unknown whether that strategy is actually important. For example, the effects of the second robot that provides only back-channel responses have not yet been investigated. Unveiling the relationships between the presentation effects and the amount of speech in multiple robot presentations is undoubtedly crucial for designing the content of multiple robots’ conversations. For this purpose, we conducted a video-based web-survey whose findings suggest that using multiple robots for product recommendations increases their commercial attractiveness. The results showed some advantages by equalizing the amount of speech compared to biasing that amount in presentations.

Head-tilting motions are social signals that play a key role in communication. In human-robot interaction, researchers have found that this motion increases the observer’s feelings of kawaii toward a robot. Kawaii is a Japanese word meaning cute and adorable, and the feeling of kawaii refers to such affectionate, tender feelings toward an object. The feeling of kawaii has proven to be crucial for designing socially acceptable robots. However, the impact of the head-tilting speed of social robots on the observer’s kawaii feelings remains unclear because previous research mainly used static images. This study determined the optimal head-tilting speed and direction to deepen the observer’s feeling of kawaii toward a robot. Our findings found that participants reported feeling more kawaii toward a robot when it tilted its head to its right within 0.5 or 1 second. Our results also showed that gestures associated with head tilting are critical for robot behavior design.

Due to the current worldwide COVID-19 pandemic, such measures as social distancing is being promulgated to reduce community spread. Unfortunately, such steps greatly limit physical touching, and this touch starvation increases people's stress. We address this problem by focusing on touch interactions with oneself: self-touch. We developed a system that consists of a touch sensor and a supportive voice function to encourage self-touch behaviors. We experimentally evaluated whether our developed system increased self-touch behaviors and investigated its stress-buffering effects under stressful settings. The experimental results showed that the system increased self-touch behaviors, although only male participants showed significant stress-buffering effects.

Interactive robots participating in our daily lives should have the fundamental ability to socially communicate with humans. In this paper, we propose a mechanism for two social communication abilities: forming long-term relationships and estimating friendly relationships among people. The mechanism for long-term relationships is based on three principles of behavior design. The robot we developed, Robovie, is able to interact with children in the same way as children do. Moreover, the mechanism is designed for long-term interaction along the following three design principles: (1) it calls children by name using radio frequency identification tags; (2) it adapts its interactive behaviors for each child based on a pseudo development mechanism; and (3) it confides its personal matters to the children who have interacted with the robot for an extended period of time. Regarding the estimation of friendly relationships, the robot assumes that people who spontaneously behave as a group together are friends. Then, by identifying each person in the interacting group around the robot, it estimates the relationships between them. We conducted a two-month field trial at an elementary school. An interactive humanoid robot, Robovie, was placed in a classroom at the school. The results of the field trial revealed that the robot successfully continued interacting with many children for two months, and seemed to have established friendly relationships with them. In addition, it demonstrated reasonable performance in identifying friendships among children. We believe that these results demonstrate the potential of current interactive robots to establish social relationships with humans in our daily lives.

This paper presents a useful tactile display method that can control multiple tactile sensations such as roughness, pressure, and friction sensations using simple vibratory stimulations. Our concepts are based on two points: frequency range selection for making selective stimulation on different tactile receptors types and timing control of the stimulation in response to hand movements. The selective stimulations were realized by selecting reactive frequencies of vibratory stimulation based on temporal response characteristics of tactile receptors. For representing roughness sensation, vibrating frequencies were modulated in response to hand velocity considered as a temporal coding perception of FA I type receptor. Two reactive frequencies were also selected for representing pressure sensation and friction sensation corresponding to SA I and FA II type receptors respectively. A wearable tactile display using ICPF (ionic conducting polymer gel film) actuators verified our proposed methods. Finally, we conducted a total texture feeling display combined with our methods for roughness, pressure, and friction sensations in a parameterized manner. Comparison with real clothes showed that some combinations of multiple tactile sensations could express texture feels of the expected materials.

This paper reports our novel approach to developing a social robot. The developed robot is able to identify relationships among humans from their physical behaviors as it establishes long-term relationships with humans. This interactive humanoid robot attracts humans to interact with it and, as a result, induces them to perform their group behaviors in front of it. The robot recognizes friendly relationships among humans by simultaneously identifying each person in the interacting group. We conducted a two-month experiment in an elementary school. As a result, the robot successfully continued friendly interaction with many children for the two months, and demonstrated reasonable performance in identifying friendships among children. We believe this ability to maintain friendly relationships with humans and to identify human relationships is essential to behaving socially.

A new technique of fusing genetic algorithms with Fuzzy ARTMAP is proposed. This method selects the appropriate autoregressive model order for EEG signals and consequently classifies these signals into their respective different mental tasks. The experimental results show that this method outperforms other statistical autoregressive model order selection methods like Akaike Information Criterion, Final Prediction Error and reflection coefficient.

Reports a way to visualize the active position of the brain, and an improvement method to estimate the dipole of the MEG (magnetoencephalogram), which is the central part of the activation. In the past, at the Medical School of Osaka University, a method was used where an early-stage position was set manually and only its neighborhood was searched, but that method is less precise. On the other hand, our new method automatically estimates the dipole position and its moment for all positions of the brain. As a result, the measurement accuracy of the dipole position and moment was improved, and the position and moment derived from this calculation is medically adequate.

We propose and examine methods for analyzing an EEG (electroencephalogram) during music listening in particular based on information flow combined with the source derivation (SD) method. The relationship between EEG and music cognition is also discussed. Music can be regarded as input to the brain system which influences human mentality. Since music cognition has many emotional aspects, it is expected that the EEG recorded during music listening may reflect the electrical activity of brain regions related to those emotional aspects. The brain system consists of dozens of functional subsystems and we agree with the hypothesis that their cooperative process enables high level recognition such as music listening. From the point of view of informational neurobiology, we extracted the spatial and temporal frequency patterns from the EEG. First, the SD method was applied to the raw data of EEGs as pre-processing. The SD method is a powerful technique to avoid noises which are evoked and overlapped from other regions of the brain. We improved this method for realizing higher accuracy. Then, we calculated the information flow between EEG channels by directional coherence analysis. In this paper, we especially focused on the directional coherence analysis and spatial patterns of the brain's activated area in response to the music structure. Several observations have shown that there were differences in activated areas between relaxing and music listening conditions.