Pivotal Response Treatment with and without robot-assistance for children with autism: a randomized controlled trial.

Pivotal response treatment (PRT) is a promising intervention focused on improving social communication skills in children with autism spectrum disorder (ASD). Since robots potentially appeal to children with ASD and may contribute to their motivation for social interaction, this exploratory randomized controlled trial (RCT) was conducted comparing PRT (PRT and robot-assisted PRT) with treatment-as-usual (TAU). Seventy-three children (PRT: n = 25; PRT + robot: n = 25; TAU: n = 23) with ASD, aged 3-8 years were assessed at baseline, after 10 and 20 weeks of intervention, and at 3-month follow-up. There were no significant group differences on parent- and teacher-rated general social-communicative skills and blindly rated global functioning directly after treatment. However, at follow-up largest gains were observed in robot-assisted PRT compared to other groups. These results suggest that robot-assistance may contribute to intervention efficacy for children with ASD when using game scenarios for robot-child interaction during multiple sessions combined with motivational components of PRT. This trial is registered at https://www.trialregister.nl/trial/4487 ; NL4487/NTR4712 (2014-08-01).

Personalizing HRI in Musical Instrument Practicing: The Influence of Robot Roles (Evaluative Versus Nonevaluative) on the Child's Motivation for Children in Different Learning Stages.

Learning to play a musical instrument involves skill learning and requires long-term practicing to reach expert levels. Research has already proven that the assistance of a robot can improve children's motivation and performance during practice. In an earlier study, we showed that the specific role (evaluative role versus nonevaluative role) the robot plays can determine children's motivation and performance. In the current study, we argue that the role of the robot has to be different for children in different learning stages (musical instrument expertise levels). Therefore, this study investigated whether children in different learning stages would have higher motivation when assisted by a robot in different supporting roles (i.e., evaluative role versus nonevaluative role). We conducted an empirical study in a real practice room of a music school with 31 children who were at different learning stages (i.e., beginners, developing players, and advanced players). In this study, every child practiced for three sessions: practicing alone, assisted by the evaluative robot, or assisted by the nonevaluative robot (in a random order). We measured motivation by using a questionnaire and analyzing video data. Results showed a significant interaction between condition (i.e., alone, evaluative robot, and nonevaluative robot) and learning stage groups indicating that children in different learning stage groups had different levels of motivation when practicing alone or with an evaluative or nonevaluative robot. More specifically, beginners had higher persistence when practicing with the nonevaluative robot, while advanced players expressed higher motivation after practicing with a robot than alone, but no difference was found between the two robot roles. Exploratory results also indicated that gender might have an interaction effect with the robot roles on child's motivation in music practice with social robots. This study offers more insight into the child-robot interaction and robot role design in musical instrument learning. Specifically, our findings shed light on personalization in HRI, that is, from adapting the role of the robot to the characteristics and the development level of the user.

Self-initiations in young children with autism during Pivotal Response Treatment with and without robot assistance.

LAY ABSTRACT: The initiation of social interaction is often defined as a core deficit of autism spectrum disorder. Optimizing these self-initiations is therefore a key component of Pivotal Response Treatment, an established intervention for children with autism spectrum disorder. However, little is known about the development of self-initiations during intervention and whether this development can be facilitated by robot assistance within Pivotal Response Treatment. The aim of this study was to (1) investigate the effect of Pivotal Response Treatment and robot-assisted Pivotal Response Treatment on self-initiations (functional and social) of young children with autism spectrum disorder over the course of intervention and (2) explore the relation between development in self-initiations and additional gains in general social-communicative skills. Forty-four children with autism spectrum disorder (aged 3-8 years) were included in this study. Self-initiations were assessed during parent-child interaction videos of therapy sessions and coded by raters who did not know which treatment (Pivotal Response Treatment or robot-assisted Pivotal Response Treatment) the child received. General social-communicative skills were assessed before start of the treatment, after 10 and 20 weeks of intervention and 3 months after the treatment was finalized. Results showed that self-initiations increased in both treatment groups, with the largest improvements in functional self-initiations in the group that received robot-assisted Pivotal Response Treatment. Increased self-initiations were related to higher parent-rated social awareness 3 months after finalizing the treatment.

Effects of the Level of Interactivity of a Social Robot and the Response of the Augmented Reality Display in Contextual Interactions of People with Dementia.

The well-being of people with dementia (PWD) living in long-term care facilities is hindered due to disengagement and social isolation. Animal-like social robots are increasingly used in dementia care as they can provide companionship and engage PWD in meaningful activities. While most previous human-robot interaction (HRI) research studied engagement independent from the context, recent findings indicate that the context of HRI sessions has an impact on user engagement. This study aims to explore the effects of contextual interactions between PWD and a social robot embedded in the augmented responsive environment. Three experimental conditions were compared: reactive context-enhanced robot interaction; dynamic context-enhanced interaction with a static robot; a control condition with only the dynamic context presented. Effectiveness evaluations were performed with 16 participants using four observational rating scales on observed engagement, affective states, and apathy related behaviors. Findings suggested that the higher level of interactivity of a social robot and the interactive contextualized feedback helped capture and maintain users' attention during engagement; however, it did not significantly improve their positive affective states. Additionally, the presence of either a static or a proactive robot reduced apathy-related behaviors by facilitating purposeful activities, thus, motivating behavioral engagement.

Adherence and acceptability of a robot-assisted Pivotal Response Treatment protocol for children with autism spectrum disorder.

The aim of this study is to present a robot-assisted therapy protocol for children with ASD based on the current state-of-the-art in both ASD intervention research and robotics research, and critically evaluate its adherence and acceptability based on child as well as parent ratings. The robot-assisted therapy was designed based on motivational components of Pivotal Response Treatment (PRT), a highly promising and feasible intervention focused at training "pivotal" (key) areas such as motivation for social interaction and self-initiations, with the goal of establishing collateral gains in untargeted areas of functioning and development, affected by autism spectrum disorders. Overall, children (3-8 y) could adhere to the robot-assisted therapy protocol (Mean percentage of treatment adherence 85.5%), showed positive affect ratings after therapy sessions (positive in 86.6% of sessions) and high robot likability scores (high in 79.4% of sessions). Positive likability ratings were mainly given by school-aged children (H(1) = 7.91, p = .005) and related to the movements, speech and game scenarios of the robot. Parent ratings on the added value of the robot were mainly positive (Mean of 84.8 on 0-100 scale), while lower parent ratings were related to inflexibility of robot behaviour.

Effects of Robot Facial Characteristics and Gender in Persuasive Human-Robot Interaction.

The growing interest in social robotics makes it relevant to examine the potential of robots as persuasive agents and, more specifically, to examine how robot characteristics influence the way people experience such interactions and comply with the persuasive attempts by robots. The purpose of this research is to identify how the (ostensible) gender and the facial characteristics of a robot influence the extent to which people trust it and the psychological reactance they experience from its persuasive attempts. This paper reports a laboratory study where SociBot™, a robot capable of displaying different faces and dynamic social cues, delivered persuasive messages to participants while playing a game. In-game choice behavior was logged, and trust and reactance toward the advisor were measured using questionnaires. Results show that a robotic advisor with upturned eyebrows and lips (features that people tend to trust more in humans) is more persuasive, evokes more trust, and less psychological reactance compared to one displaying eyebrows pointing down and lips curled downwards at the edges (facial characteristics typically not trusted in humans). Gender of the robot did not affect trust, but participants experienced higher psychological reactance when interacting with a robot of the opposite gender. Remarkably, mediation analysis showed that liking of the robot fully mediates the influence of facial characteristics on trusting beliefs and psychological reactance. Also, psychological reactance was a strong and reliable predictor of trusting beliefs but not of trusting behavior. These results suggest robots that are intended to influence human behavior should be designed to have facial characteristics we trust in humans and could be personalized to have the same gender as the user. Furthermore, personalization and adaptation techniques designed to make people like the robot more may help ensure they will also trust the robot.

Directing Attention Through Gaze Hints Improves Task Solving in Human-Humanoid Interaction.

In this paper, we report an experimental study designed to examine how participants perceive and interpret social hints from gaze exhibited by either a robot or a human tutor when carrying out a matching task. The underlying notion is that knowing where an agent is looking at provides cues that can direct attention to an object of interest during the activity. In this regard, we asked human participants to play a card matching game in the presence of either a human or a robotic tutor under two conditions. In one case, the tutor gave hints to help the participant find the matching cards by gazing toward the correct match, in the other case, the tutor only looked at the participants and did not give them any help. The performance was measured based on the time and the number of tries taken to complete the game. Results show that gaze hints (helping tutor) made the matching task significantly easier (fewer tries) with the robot tutor. Furthermore, we found out that the robots' gaze hints were recognized significantly more often than the human tutor gaze hints, and consequently, the participants performed significantly better with the robot tutor. The reported study provides new findings towards the use of non-verbal gaze hints in human-robot interaction, and lays out new design implications, especially for robot-based educative interventions.

A Hierarchical Bayesian Model for Crowd Emotions.

Estimation of emotions is an essential aspect in developing intelligent systems intended for crowded environments. However, emotion estimation in crowds remains a challenging problem due to the complexity in which human emotions are manifested and the capability of a system to perceive them in such conditions. This paper proposes a hierarchical Bayesian model to learn in unsupervised manner the behavior of individuals and of the crowd as a single entity, and explore the relation between behavior and emotions to infer emotional states. Information about the motion patterns of individuals are described using a self-organizing map, and a hierarchical Bayesian network builds probabilistic models to identify behaviors and infer the emotional state of individuals and the crowd. This model is trained and tested using data produced from simulated scenarios that resemble real-life environments. The conducted experiments tested the efficiency of our method to learn, detect and associate behaviors with emotional states yielding accuracy levels of 74% for individuals and 81% for the crowd, similar in performance with existing methods for pedestrian behavior detection but with novel concepts regarding the analysis of crowds.

Simulated trust: a cheap social learning strategy.

Animals use heuristic strategies to determine from which conspecifics to learn socially. This leads to directed social learning. Directed social learning protects them from copying non-adaptive information. So far, the strategies of animals, leading to directed social learning, are assumed to rely on (possibly indirect) inferences about the demonstrator's success. As an alternative to this assumption, we propose a strategy that only uses self-established estimates of the pay-offs of behavior. We evaluate the strategy in a number of agent-based simulations. Critically, the strategy's success is warranted by the inclusion of an incremental learning mechanism. Our findings point out new theoretical opportunities to regulate social learning for animals. More broadly, our simulations emphasize the need to include a realistic learning mechanism in game-theoretic studies of social learning strategies, and call for re-evaluation of previous findings.

Event based self-supervised temporal integration for multimodal sensor data.

A method for synergistic integration of multimodal sensor data is proposed in this paper. This method is based on two aspects of the integration process: (1) achieving synergistic integration of two or more sensory modalities, and (2) fusing the various information streams at particular moments during processing. Inspired by psychophysical experiments, we propose a self-supervised learning method for achieving synergy with combined representations. Evidence from temporal registration and binding experiments indicates that different cues are processed individually at specific time intervals. Therefore, an event-based temporal co-occurrence principle is proposed for the integration process. This integration method was applied to a mobile robot exploring unfamiliar environments. Simulations showed that integration enhanced route recognition with many perceptual similarities; moreover, they indicate that a perceptual hierarchy of knowledge about instant movement contributes significantly to short-term navigation, but that visual perceptions have bigger impact over longer intervals.