The proteus effect on human pain perception through avatar muscularity and gender factors.

The Proteus effect, which occurs when using an avatar in virtual reality, influences user behavior, changes attitudes, and improves physical performance. Here, we show that human pain perception can be alleviated by the Proteus effect. To investigate the pain alleviation effect of using an avatar in a virtual environment, we conducted two experiments using a head-mounted display and a thermal pain stimulator to induce acute pain. The first experiment involved 20 adult participants, while the second experiment involved 44 adult participants. Experimental results show that participants reported significantly lower pain scores (15.982% reduction), as measured by the Pain Assessment Scale (PAS), when using a muscular avatar than when using a normal avatar. The experiments also revealed several significant gender factors. For example, participants reported significantly lower pain scores when using a gender-congruent avatar. In addition, the use of a muscular avatar was particularly effective for male participants. In contrast, female participants consistently reported lower pain scores when using the avatar regardless of its body type (muscular/normal). To further our understanding, we also measured participants' gender-related pain stereotypes using the Gender Role Expectations of Pain (GREP) questionnaire, as well as participants' sense of embodiment. The results of these questionnaires are consistent with the results of the PAS, suggesting possible relationships between stereotypes and the Proteus effect on pain perception, and between the degree of immersion in an avatar and the user's perception of pain.

A wearable soft robot that can alleviate the pain and fear of the wearer.

Social soft robotics may provide a new solution for alleviating human pain and fear. Here, we introduce a hand-held soft robot that can be clenched by the wearer. The robot comprises small airbags that can be inflated to provide the wearer with a feeling of being clenched. We then conducted an in-depth study of 66 adults who participated in a pain research protocol using thermal stimulation to investigate the effect of wearing the robot on pain perception and fear of injections. Pain assessment scale scores for perceived pain decreased significantly [Formula: see text] when participants wore the robot compared with the baseline condition in which the robot was not worn. In addition, the saliva test results showed a downward trend in oxytocin level when the robot provided the wearer with haptic feedback via the inflation of the internal airbags in response to the wearer's clench. Furthermore, the negative psychological state of participants, as measured using the positive and negative affect scale, improved significantly when wearing the robot. We also revealed that the salivary cortisol level, an indicator of stress, decreased significantly across all participants at the end of the experiment. In addition, participants' fear of injections was significantly improved after participation in the experiment. These results suggest that the wearable soft robot may alleviate the human perception of pain and fear in during medical treatments, such as vaccinations.

Weight Shift Movements of a Social Mediator Robot Make It Being Recognized as Serious and Suppress Anger, Revenge and Avoidance Motivation of the User.

Humans can become aggressive during text messaging. To maintain a healthy interpersonal relationship through text messaging, our negative mental states, such as anger, have to be well-controlled. This paper discusses the use of a handheld social robot deployed as a mediator in text messaging between humans. The robot is equipped with a movable weight inside its body. By controlling the movement of the internal weight during the time when the robot speaks out messages received from a human sender, we hypothesize that the psychological state of a receiver who holds the robot can be affected (for example, he/she will listen to the messages more seriously). In a controlled study (n = 94), in which participants were manipulated to be frustrated by using a context scenario, we studied the effect of three dialogue scripts with/without weight shifts. Results showed that introducing weight shifts together with the robot speech suppressed on average 23% of the user's anger. However, only 3.5% of the anger was suppressed when the weight shifts were not applied. Additionally, in cases where the robot showed empathy to the user in words with weight shifts, the user's revenge urge was successfully reduced by 22%. There was almost no effect confirmed when the weight shifts were not applied. A similar effect was also found in avoidance motivation: 15% of the avoidance motivation was reduced if weight shifts were applied. The reductions in revenge and avoidance motivation are considered important factors for human forgiveness. Therefore, our findings provide experimental evidence that weight shifts can be an effective expression modality for mediator robots, from the perspective of not only suppressing the user's anger but also by inducing forgiveness during messaging.

Correction: MSPM: A modularized and scalable multi-agent reinforcement learning-based system for financial portfolio management.

[This corrects the article DOI: 10.1371/journal.pone.0263689.].

MSPM: A modularized and scalable multi-agent reinforcement learning-based system for financial portfolio management.

Financial portfolio management (PM) is one of the most applicable problems in reinforcement learning (RL) owing to its sequential decision-making nature. However, existing RL-based approaches rarely focus on scalability or reusability to adapt to the ever-changing markets. These approaches are rigid and unscalable to accommodate the varying number of assets of portfolios and increasing need for heterogeneous data input. Also, RL agents in the existing systems are ad-hoc trained and hardly reusable for different portfolios. To confront the above problems, a modular design is desired for the systems to be compatible with reusable asset-dedicated agents. In this paper, we propose a multi-agent RL-based system for PM (MSPM). MSPM involves two types of asynchronously-updated modules: Evolving Agent Module (EAM) and Strategic Agent Module (SAM). An EAM is an information-generating module with a Deep Q-network (DQN) agent, and it receives heterogeneous data and generates signal-comprised information for a particular asset. An SAM is a decision-making module with a Proximal Policy Optimization (PPO) agent for portfolio optimization, and it connects to multiple EAMs to reallocate the corresponding assets in a financial portfolio. Once been trained, EAMs can be connected to any SAM at will, like assembling LEGO blocks. With its modularized architecture, the multi-step condensation of volatile market information, and the reusable design of EAM, MSPM simultaneously addresses the two challenges in RL-based PM: scalability and reusability. Experiments on 8-year U.S. stock market data prove the effectiveness of MSPM in profit accumulation by its outperformance over five different baselines in terms of accumulated rate of return (ARR), daily rate of return (DRR), and Sortino ratio (SR). MSPM improves ARR by at least 186.5% compared to constant rebalanced portfolio (CRP), a widely-used PM strategy. To validate the indispensability of EAM, we back-test and compare MSPMs on four different portfolios. EAM-enabled MSPMs improve ARR by at least 1341.8% compared to EAM-disabled MSPMs.

Social robots for education: A review.

Social robots can be used in education as tutors or peer learners. They have been shown to be effective at increasing cognitive and affective outcomes and have achieved outcomes similar to those of human tutoring on restricted tasks. This is largely because of their physical presence, which traditional learning technologies lack. We review the potential of social robots in education, discuss the technical challenges, and consider how the robot's appearance and behavior affect learning outcomes.

When a robot is social: spatial arrangements and multimodal semiotic engagement in the practice of social robotics.

Social roboticists design their robots to function as social agents in interaction with humans and other robots. Although we do not deny that the robot's design features are crucial for attaining this aim, we point to the relevance of spatial organization and coordination between the robot and the humans who interact with it. We recover these interactions through an observational study of a social robotics laboratory and examine them by applying a multimodal interactional analysis to two moments of robotics practice. We describe the vital role of roboticists and of the group of preverbal infants, who are involved in a robot's design activity, and we argue that the robot's social character is intrinsically related to the subtleties of human interactional moves in laboratories of social robotics. This human involvement in the robot's social agency is not simply controlled by individual will. Instead, the human-machine couplings are demanded by the situational dynamics in which the robot is lodged.

Socialization between toddlers and robots at an early childhood education center.

A state-of-the-art social robot was immersed in a classroom of toddlers for >5 months. The quality of the interaction between children and robots improved steadily for 27 sessions, quickly deteriorated for 15 sessions when the robot was reprogrammed to behave in a predictable manner, and improved in the last three sessions when the robot displayed again its full behavioral repertoire. Initially, the children treated the robot very differently than the way they treated each other. By the last sessions, 5 months later, they treated the robot as a peer rather than as a toy. Results indicate that current robot technology is surprisingly close to achieving autonomous bonding and socialization with human toddlers for sustained periods of time and that it could have great potential in educational settings assisting teachers and enriching the classroom environment.