The current study investigated how robot tutors influence brain activity during child-robot interaction (CRI) for learning of second language vocabulary. We gathered EEG signals from two groups of children; 1) Robot group (N=21) who listened to a storytelling social robot and learned French words, and 2) Display group (N=20) who listened to the same story in the French language mediated by only a computer screen. To measure learning-induced changes in the brain, functional connectivity analysis was conducted on EEG signals, which quantifies the communication between brain regions during the learning phase. Results showed a significantly higher functional brain connectivity for the Robot group in the theta frequency band, which has been previously associated with language functions in neuroscientific literature. Our results provide neurophysiological evidence for the benefit of robot tutors in second language learning in children.

Mindfulness is the state of paying attention to the present moment on purpose and meditation is the technique to obtain this state. This study aims to develop a robot assistant that facilitates mindfulness training by means of a Brain-Computer Interface (BCI) system. To achieve this goal, we collected EEG signals from two groups of subjects engaging in a meditative vs. non-meditative human-robot interaction (HRI) and evaluated cerebral hemispheric asymmetry, which is recognized as a well-defined indicator of emotional states. Moreover, using self-reported affective states, we strived to explain asymmetry changes based on pre- and post-experiment mood alterations. We found that unlike earlier meditation studies, the fronto-central activations in alpha and theta frequency bands were not influenced by robot-guided mindfulness practice, however there was a significantly greater right-sided activity in the occipital gamma band of Meditation group, which is attributed to increased sensory awareness and open monitoring. In addition, there was a significant main effect of Time on participant's self-reported affect, indicating an improved mood after interaction with the robot regardless of the interaction type. Our results suggest that EEG responses during robot-guided meditation hold promise in real-time detection and neurofeedback of mindful state to the user, however the experienced neurophysiological changes may differ based on the meditation practice and recruited tools. This study is the first to report EEG changes during mindfulness practice with a robot. We believe that our findings driven from an ecologically valid setting, can be used in development of future BCI systems that are integrated with social robots for health applications.

In this paper we present a lab study on robot abuse by children. 61 Japanese children of ages 7-9 interacted individually with Robovie, a social robot, in a context that promoted children's free disruptive behaviors towards the robot. We compared the robot's use of an adaptation of a parental discipline strategy, the so-called love-withdrawal technique, to a similar set of robot behaviors that lacked any specific strategy (neutral condition). The main insight we gained was that perhaps we should better not focus on general robot behaviors to try to fit all children, but rather, we should adapt the robot behaviors to children's individual differences. For instance, we found that the love-withdrawal-based strategy was significantly more effective in children of age 8-9 than on children of 7.

People's responses to a hypnosis intervention is diverse and unpredictable. A system that predicts user's level of susceptibility from their electroencephalography (EEG) signals can be helpful in clinical hypnotherapy sessions. In this paper, we extracted differential entropy (DE) of the recorded EEGs from two groups of subjects with high and low hypnotic susceptibility and built a support vector machine on these DE features for the classification of susceptibility trait. Moreover, we proposed a clustering-based feature refinement strategy to improve the estimation of such trait. Results showed a high classification performance in detection of subjects' level of susceptibility before and during hypnosis. Our results suggest the usefulness of this classifier in development of future Bel systems applied in the domain of therapy and healthcare.

In our previous study, we developed Pedagogical Agent with Gaze Interaction (PAGI), an anthropomorphic animated pedagogical agent that engages in gaze interaction with students. Using PAGI, we revealed that temporal contingency from virtual agents facilitate learning (temporal contingency effect), and proposed two hypotheses that may explain the result; 1) temporal contingency reduces extraneous cognitive load related to visual search, 2) temporal contingency prime social stance in learners which enhances learning. To assess more deeply into this matter, we tested two critical features of the agent, saliency and socialness. Two arrow shaped agents, of which differed in saliency, were employed. Apart from the appearance of the agents, the experimental design was identical to the previous study. University students learned words of a foreign language, with temporally contingent agent or recorded version of the agent, which played pre-recorded sessions from the contingent agents. From the result we gained evidence supporting the second hypothesis. Non-social agents did not trigger temporal contingency effect.

In this study, we investigate the two effects of joint attention, building relationships and sharing recognition, in group interaction consisting of two robots and one person. The building relationship is focused on entrainment resulting from the two robots' gazing at one person, while the sharing recognition refers to an original effect of the joint attention in a group interaction featuring the shared focus of individuals on one object. Results of experiments on the building relationships aspect showed that when two robots gazed at a person, he/she tended to be more immersed in communication with the robots. In the case of sharing recognition, when the two robots gazed at the target synchronously, the person could share it more correctly than when it was done asynchronously.

Characteristics of the human visual field are well known to be different in central (fovea) and peripheral areas. Existing computational models of visual saliency, however, do not take into account this biological evidence. The existing models compute visual saliency uniformly over the retina and, thus, have difficulty in accurately predicting the next gaze (fixation) point. This paper proposes to incorporate human visual field characteristics into visual saliency, and presents a computational model for producing such a saliency map. Our model integrates image features obtained by bottom-up computation in such a way that weights for the integration depend on the distance from the current gaze point where the weights are optimally learned using actual saccade data. The experimental results using a large number of fixation/saccade data with wide viewing angles demonstrate the advantage of our saliency map, showing that it can accurately predict the point where one looks next.

This paper investigates human behavior around an interactive robot at a science museum. To develop a communication robot that works in daily environments, it is important to investigate the available information from a robot about people's behavior. Such information will enable the robot to predict people's behavior so that the robot can optimize its interactive behavior. We analyzed visitor behavior toward a simple interactive robot exhibited at a science museum in relation to information from sound level and range sensors. We discovered factors that influence the way people approach, maintain distance, and interact both physically and verbally with the robot. This enabled us to extract meaningful information from the sensory information and apply it to communication robots.

Coding can contribute to robot design by suggesting behavioral benchmarks. These, however, depend on the level of analysis. In illustration, semi-formalized rules are used to investigate child-robot encounters. By using behavior-level codes, we extract information about how children use the robot. This leads to findings about longitudinal changes in how children evaluate its behaviors. Children, we find, use the robot as a social mediator - to prompt synchronized social events. By focusing on a behavioral level, coding can benefit designers of robots, software and sensors

Interactive agents such as pet robots or adaptive speech interface systems that require forming a mutual adaptation process with users should have two competences. One of these is recognizing reward information from users' expressed paralanguage information, and the other is informing the learning system about the users by means of that reward information. The purpose of this study was to clarify the specific contents of reward information and the actual mechanism of a learning system by observing how 2 persons could create a smooth speech communication, such as that between owners and their pets. A communication experiment was conducted to observe how human participants create smooth communication through acquiring meaning from utterances in languages they did not understand. Then, based on experimental results, a meaning-acquisition model that considers the following 2 assumptions was constructed: (a) To achieve a mutual adaptive relationship with users, the model needs to induce users' adaptation and to exploit this induced adaptation to recognize the meanings of a user's speech sounds; and (b) to recognize users' utterances through trial-and-error interaction regardless of the language used, the model should focus on prosodic information in speech sounds, rather than on the phoneme information on which most past interface studies have focused. The results confirmed that the proposed model could recognize the meanings of users' verbal commands by using participants' adaptations to the model for its meaning-acquisition process. However, this phenomenon was observed only when an experimenter gave the participants appropriate instructions equivalent to catchphrases that helped users learn how to use and interact intuitively with the model. Thus, this suggested the need for a subsequent study to discover how to induce the participants' adaptations or natural behaviors without giving these kinds of instructions.

This study investigated what effect physical constraints have on the interpretation of demonstrative pronouns when a user navigates a robot. For this investigation, a robot navigation environment called Spondia-II was develope, and an experiment conducted. It is known that the interpretation of demonstrative pronouns requires information about not only the situation (or context) but also the speaker's viewpoint during a dialogue. The results of the experiment suggest that physical constraints do affect the user's viewpoint, especially when a user utters a demonstrative pronoun while navigating the robot. In actual fact, the user alters the use of demonstrative pronouns according to the change in the user's viewpoint. It is also suggested that the user and the robot share the same viewpoint during the physical interaction.

This paper describes communication load reduction on task negotiation with contract net protocol (CNP) for multiple autonomous mobile robots. For controlling multiple robots, CNP is useful, but the broadcast of task announcement messages on CNP tends to consume much communication load. In order to overcome this problem, the authors have developed a system called LEMMING which learns proper addresses for the task announcement messages with case-based reasoning. However, the learning method used in LEMMING sometimes caused inefficient task execution. In this paper, we propose an extension of LEMMING with message interception which enables the system to execute tasks more efficiently by eavesdropping on leaked message.

Intelligent robots need to share knowledge with human beings for flexible interaction. However, the gap between low‐level sensory data and abstract human knowledge makes it difficult to preencode robot behavior against human's various complex demands. This article presents a way of enabling robots to learn abstract concepts from sensory and perceptual data. In order to overcome the gap between the low‐level sensory data and higher level concept description, a method called feature abstraction is used. Feature abstraction dynamically defines abstract sensors from primitive sensory devices and makes it possible to learn appropriate sensory‐motor constraints. This method has been implemented on a real mobile robot as a learning system called Acorn‐II. Acorn‐II was evaluated with some empirical results and it was shown that the system can learn some abstract concepts more accurately than other existing systems.

Describes LEMMING, a learning system for multiple mobile robot environments. LEMMING extends the idea of the broadcast-based contract net protocol with CBR (case-based reasoning). In terms of the CBR mechanism, LEMMING can learn to select appropriate robots for a given task. Thus, LEMMING makes it possible to reduce the waste of communication resources and the trouble of processing irrelevant messages. The paper evaluates LEMMING with some experiments and shows that LEMMING can deal with task negotiation more effectively than a broadcast-based contract net system.