The effect of conversation on altruism: A comparative study with different media and generations.

Despite the overwhelming evidence of climate change and its effects on future generations, most individuals are still hesitant to make environmental changes that would especially benefit future generations. In this study, we investigate whether dialogue can influence people's altruistic behavior toward future generations of humans, and how it may be affected by participant age and the appearance of the conversation partner. We used a human, an android robot called Telenoid, and a speaker as representatives of future generations. Participants were split among an old age group and a young age group and were randomly assigned to converse with one of the aforementioned representatives. We asked the participants to play a round of the Dictator Game with the representative they were assigned, followed by an interactive conversation and another round of the Dictator Game in order to gauge their level of altruism. The results show that, on average, participants gave more money after having an interactive conversation, and that older adults tend to give more money than young adults. There were no significant differences between the three representatives. The results show that empathy might have been the most important factor in the increase in altruistic behavior for all participants.

Improved Glycemic Control through Robot-Assisted Remote Interview for Outpatients with Type 2 Diabetes: A Pilot Study.

Background and Objectives: Our research group developed a robot-assisted diabetes self-management monitoring system to support Certified Diabetes Care and Education Specialists (CDCESs) in tracking the health status of patients with type 2 diabetes (T2D). This study aimed to evaluate the impact of this system on glycemic control and to identify suitable candidates for its use. Materials and Methods: After obtaining written informed consent from all participants with T2D, the CDCESs conducted remote interviews with the patients using RoBoHoN. All participants completed a questionnaire immediately after the experiment. HbA1c was assessed at the time of the interview and two months later, and glycemic control status was categorized as either "Adequate" or "Inadequate" based on the target HbA1c levels outlined in the guidelines for adult and elderly patients with type 2 diabetes by the Japan Diabetes Society. Patients who changed their medication regimens within the two months following the interview were excluded from the study. Results: The clinical characteristics of the 28 eligible patients were as follows: 67.9 ± 14.8 years old, 23 men (69%), body mass index (24.7 ± 4.9 kg/m2), and HbA1c levels 7.16 ± 1.11% at interview and two months later. Glycemic control status (GCS) was Adequate (A) to Inadequate (I): 1 case; I to A: 7 cases; A to A good: 14 cases; I to I: 6 cases (p-value = 0.02862 by Chi-square test). Multiple regression analyses showed that Q1 (Did RoBoHoN speak clearly?) and Q7 (Was RoBoHoN's response natural?) significantly contributed to GCS, indicating that the naturalness of the responses did not impair the robot-assisted interviews. The results suggest that to improve the system in the future, it is more beneficial to focus on the content of the conversation rather than pursuing superficial naturalness in the responses. Conclusions: This study demonstrated the efficacy of a robot-assisted diabetes management system that can contribute to improved glycemic control.

Social robot for older adults with cognitive decline: a preliminary trial.

The number of older adults living alone is rapidly increasing. Loneliness in older adults not only degrade their quality of life but also causes troubles such as heavy burden on the medical staff, especially when cognitive decline is present. Social robots could be used in several ways to reduce such problems. As a first step towards this goal, we introduced conversation robots into the homes of older adults with cognitive decline to evaluate the robot's availability and acceptance during several months. The study involved two steps, one for evaluating the robustness of the proposed robotic system, and the second one to examine the long-term acceptance of social robots by older adults with cognitive decline living alone. Our data shows that after several weeks of human-robot interaction, the participants continued to use the robot and successfully integrated them into their lives. These results open the possibility of further research involving how sustained interaction can be achieved, as well as which factors contributed to the acceptance of the robot.

Neuromagnetic Decoding of Simultaneous Bilateral Hand Movements for Multidimensional Brain-Machine Interfaces.

To provide multidimensional control, we describe the first reported decoding of bilateral hand movements by using single-trial magnetoencephalography signals as a new approach to enhance a user's ability to interact with a complex environment through a multidimensional brain-machine interface. Ten healthy participants performed or imagined four types of bilateral hand movements during neuromagnetic measurements. By applying a support vector machine (SVM) method to classify the four movements regarding the sensor data obtained from the sensorimotor area, we found the mean accuracy of a two-class classification using the amplitudes of neuromagnetic fields to be particularly suitable for real-time applications, with accuracies comparable to those obtained in previous studies involving unilateral movement. The sensor data from over the sensorimotor cortex showed discriminative time-series waveforms and time-frequency maps in the bilateral hemispheres according to the four tasks. Furthermore, we used four-class classification algorithms based on the SVM method to decode all types of bilateral movements. Our results provided further proof that the slow components of neuromagnetic fields carry sufficient neural information to classify even bilateral hand movements and demonstrated the potential utility of decoding bilateral movements for engineering purposes such as multidimensional motor control.

Android Feedback-Based Training Modulates Sensorimotor Rhythms During Motor Imagery.

EEG-based brain computer interface (BCI) systems have demonstrated potential to assist patients with devastating motor paralysis conditions. However, there is great interest in shifting the BCI trend toward applications aimed at healthy users. Although BCI operation depends on technological factors (i.e., EEG pattern classification algorithm) and human factors (i.e., how well the person can generate good quality EEG patterns), it is the latter that is least investigated. In order to control a motor imagery-based BCI, users need to learn to modulate their sensorimotor brain rhythms by practicing motor imagery using a classical training protocol with an abstract visual feedback. In this paper, we investigate a different BCI training protocol using a human-like android robot (Geminoid HI-2) to provide realistic visual feedback. The proposed training protocol addresses deficiencies of the classical approach and takes the advantage of body-abled user capabilities. Experimental results suggest that android feedback-based BCI training improves the modulation of sensorimotor rhythms during motor imagery task. Moreover, we discuss how the influence of body ownership transfer illusion toward the android might have an effect on the modulation of event-related desynchronization/synchronization activity.

BMI control of a third arm for multitasking.

Brain-machine interface (BMI) systems have been widely studied to allow people with motor paralysis conditions to control assistive robotic devices that replace or recover lost function but not to extend the capabilities of healthy users. We report an experiment in which healthy participants were able to extend their capabilities by using a noninvasive BMI to control a human-like robotic arm and achieve multitasking. Experimental results demonstrate that participants were able to reliably control the robotic arm with the BMI to perform a goal-oriented task while simultaneously using their own arms to do a different task. This outcome opens possibilities to explore future human body augmentation applications for healthy people that not only enhance their capability to perform a particular task but also extend their physical capabilities to perform multiple tasks simultaneously.

Subthalamic nucleus detects unnatural android movement.

An android, i.e., a realistic humanoid robot with human-like capabilities, may induce an uncanny feeling in human observers. The uncanny feeling about an android has two main causes: its appearance and movement. The uncanny feeling about an android increases when its appearance is almost human-like but its movement is not fully natural or comparable to human movement. Even if an android has human-like flexible joints, its slightly jerky movements cause a human observer to detect subtle unnaturalness in them. However, the neural mechanism underlying the detection of unnatural movements remains unclear. We conducted an fMRI experiment to compare the observation of an android and the observation of a human on which the android is modelled, and we found differences in the activation pattern of the brain regions that are responsible for the production of smooth and natural movement. More specifically, we found that the visual observation of the android, compared with that of the human model, caused greater activation in the subthalamic nucleus (STN). When the android's slightly jerky movements are visually observed, the STN detects their subtle unnaturalness. This finding suggests that the detection of unnatural movements is attributed to an error signal resulting from a mismatch between a visual input and an internal model for smooth movement.

Removal of proprioception by BCI raises a stronger body ownership illusion in control of a humanlike robot.

Body ownership illusions provide evidence that our sense of self is not coherent and can be extended to non-body objects. Studying about these illusions gives us practical tools to understand the brain mechanisms that underlie body recognition and the experience of self. We previously introduced an illusion of body ownership transfer (BOT) for operators of a very humanlike robot. This sensation of owning the robot's body was confirmed when operators controlled the robot either by performing the desired motion with their body (motion-control) or by employing a brain-computer interface (BCI) that translated motor imagery commands to robot movement (BCI-control). The interesting observation during BCI-control was that the illusion could be induced even with a noticeable delay in the BCI system. Temporal discrepancy has always shown critical weakening effects on body ownership illusions. However the delay-robustness of BOT during BCI-control raised a question about the interaction between the proprioceptive inputs and delayed visual feedback in agency-driven illusions. In this work, we compared the intensity of BOT illusion for operators in two conditions; motion-control and BCI-control. Our results revealed a significantly stronger BOT illusion for the case of BCI-control. This finding highlights BCI's potential in inducing stronger agency-driven illusions by building a direct communication between the brain and controlled body, and therefore removing awareness from the subject's own body.

The Importance of Visual Feedback Design in BCIs; from Embodiment to Motor Imagery Learning.

Brain computer interfaces (BCIs) have been developed and implemented in many areas as a new communication channel between the human brain and external devices. Despite their rapid growth and broad popularity, the inaccurate performance and cost of user-training are yet the main issues that prevent their application out of the research and clinical environment. We previously introduced a BCI system for the control of a very humanlike android that could raise a sense of embodiment and agency in the operators only by imagining a movement (motor imagery) and watching the robot perform it. Also using the same setup, we further discovered that the positive bias of subjects' performance both increased their sensation of embodiment and improved their motor imagery skills in a short period. In this work, we studied the shared mechanism between the experience of embodiment and motor imagery. We compared the trend of motor imagery learning when two groups of subjects BCI-operated different looking robots, a very humanlike android's hands and a pair of metallic gripper. Although our experiments did not show a significant change of learning between the two groups immediately during one session, the android group revealed better motor imagery skills in the follow up session when both groups repeated the task using the non-humanlike gripper. This result shows that motor imagery skills learnt during the BCI-operation of humanlike hands are more robust to time and visual feedback changes. We discuss the role of embodiment and mirror neuron system in such outcome and propose the application of androids for efficient BCI training.

Can We Talk through a Robot As if Face-to-Face? Long-Term Fieldwork Using Teleoperated Robot for Seniors with Alzheimer's Disease.

This work presents a case study on fieldwork in a group home for the elderly with dementia using a teleoperated robot called Telenoid. We compared Telenoid-mediated and face-to-face conditions with three residents with Alzheimer's disease (AD). The result indicates that two of the three residents with moderate AD showed a positive reaction to Telenoid. Both became less nervous while communicating with Telenoid from the time they were first introduced to it. Moreover, they started to use more body gestures in the face-to-face condition and more physical interactions in the Telenoid-mediated condition. In this work, we present all the results and discuss the possibilities of using Telenoid as a tool to provide opportunities for seniors to communicate over the long term.

Intimacy in Phone Conversations: Anxiety Reduction for Danish Seniors with Hugvie.

There is a lack of physical contact in current telecommunications such as text messaging and Internet access. To challenge the limitation and re-embody telecommunication, researchers have attempted to introduce tactile stimulation to media and developed huggable devices. Previous experiments in Japan showed that a huggable communication technology, i.e., Hugvie decreased stress level of its female users. In the present experiment in Denmark, we aim to investigate (i) whether Hugvie can decrease stress cross-culturally, i.e., Japanese vs. Danish participants (ii), investigate whether gender plays a role in this psychological effect (stress reduction) and (iii) if there is a preference of this type of communication technology (Hugvie vs. a regular telephone). Twenty-nine healthy elderly participated (15 female and 14 male, M = 64.52 years, SD = 5.67) in Jutland, Denmark. The participants filled out questionnaires including State-Trait Anxiety Inventory, NEO Five Factor Inventory (NEO-FFI), and Becks Depression Inventory, had a 15 min conversation via phone or Hugvie and were interviewed afterward. They spoke with an unknown person of opposite gender during the conversation; the same two conversation partners were used during the experiment and the Phone and Hugvie groups were equally balanced. There was no baseline difference between the Hugvie and Phone groups on age or anxiety or depression scores. In the Hugvie group, there was a statistically significant reduction on state anxiety after meeting Hugvie (p = 0.013). The change in state anxiety for the Hugvie group was positively correlated with openness (r = 0.532, p = 0.041) as measured by the NEO-FFI. This indicates that openness to experiences may increase the chances of having an anxiety reduction from being with Hugvie. Based on the results, we see that personality may affect the participants' engagement and benefits from Hugvie. We discuss the implications of the results and further elaborations.

Persistence of the uncanny valley: the influence of repeated interactions and a robot's attitude on its perception.

The uncanny valley theory proposed by Mori has been heavily investigated in the recent years by researchers from various fields. However, the videos and images used in these studies did not permit any human interaction with the uncanny objects. Therefore, in the field of human-robot interaction it is still unclear what, if any, impact an uncanny-looking robot will have in the context of an interaction. In this paper we describe an exploratory empirical study using a live interaction paradigm that involved repeated interactions with robots that differed in embodiment and their attitude toward a human. We found that both investigated components of the uncanniness (likeability and eeriness) can be affected by an interaction with a robot. Likeability of a robot was mainly affected by its attitude and this effect was especially prominent for a machine-like robot. On the other hand, merely repeating interactions was sufficient to reduce eeriness irrespective of a robot's embodiment. As a result we urge other researchers to investigate Mori's theory in studies that involve actual human-robot interaction in order to fully understand the changing nature of this phenomenon.

Effect of biased feedback on motor imagery learning in BCI-teleoperation system.

Feedback design is an important issue in motor imagery BCI systems. Regardless, to date it has not been reported how feedback presentation can optimize co-adaptation between a human brain and such systems. This paper assesses the effect of realistic visual feedback on users' BCI performance and motor imagery skills. We previously developed a tele-operation system for a pair of humanlike robotic hands and showed that BCI control of such hands along with first-person perspective visual feedback of movements can arouse a sense of embodiment in the operators. In the first stage of this study, we found that the intensity of this ownership illusion was associated with feedback presentation and subjects' performance during BCI motion control. In the second stage, we probed the effect of positive and negative feedback bias on subjects' BCI performance and motor imagery skills. Although the subject specific classifier, which was set up at the beginning of experiment, detected no significant change in the subjects' online performance, evaluation of brain activity patterns revealed that subjects' self-regulation of motor imagery features improved due to a positive bias of feedback and a possible occurrence of ownership illusion. Our findings suggest that in general training protocols for BCIs, manipulation of feedback can play an important role in the optimization of subjects' motor imagery skills.

Humanlike robot hands controlled by brain activity arouse illusion of ownership in operators.

Operators of a pair of robotic hands report ownership for those hands when they hold image of a grasp motion and watch the robot perform it. We present a novel body ownership illusion that is induced by merely watching and controlling robot's motions through a brain machine interface. In past studies, body ownership illusions were induced by correlation of such sensory inputs as vision, touch and proprioception. However, in the presented illusion none of the mentioned sensations are integrated except vision. Our results show that during BMI-operation of robotic hands, the interaction between motor commands and visual feedback of the intended motions is adequate to incorporate the non-body limbs into one's own body. Our discussion focuses on the role of proprioceptive information in the mechanism of agency-driven illusions. We believe that our findings will contribute to improvement of tele-presence systems in which operators incorporate BMI-operated robots into their body representations.

Building artificial humans to understand humans.

If we could build an android as a very humanlike robot, how would we humans distinguish a real human from an android? The answer to this question is not so easy. In human-android interaction, we cannot see the internal mechanism of the android, and thus we may simply believe that it is a human. This means that a human can be defined from two perspectives: one by organic mechanism and the other by appearance. Further, the current rapid progress in artificial organs makes this distinction confusing. The approach discussed in this article is to create artificial humans with humanlike appearances. The developed artificial humans, an android and a geminoid, can be used to improve understanding of humans through psychological and cognitive tests conducted using the artificial humans. We call this new approach to understanding humans android science.