Building Long-Term Human–Robot Relationships: Examining Disclosure, Perception and Well-Being Across Time

While interactions with social robots are novel and exciting for many people, one concern is the extent to which people’s behavioural and emotional engagement might be sustained across time, since during initial interactions with a robot, its novelty is especially salient. This challenge is particularly noteworthy when considering interactions designed to support people’s well-being, with limited evidence (or empirical exploration) of social robots’ capacity to support people’s emotional health over time. Accordingly, our aim here was to examine how long-term repeated interactions with a social robot affect people’s self-disclosure behaviour toward the robot, their perceptions of the robot, and how such sustained interactions influence factors related to well-being. We conducted a mediated long-term online experiment with participants conversing with thesocial robot Pepper 10 times over 5 weeks. We found that people self-disclose increasingly more to a social robot over time, and report the robot to be more social and competent over time. Participants’ moods also improved after talking to the robot, and across sessions, they found the robot’s responses increasingly comforting as well as reported feeling less lonely. Finally, our results emphasize that when the discussion frame was supposedly more emotional (in this case, framing questions in the context of the COVID-19 pandemic), participants reported feeling lonelier and more stressed. These results set the stage forsituating social robots as conversational partners and provide crucial evidence for their potential inclusion in interventions supporting people’s emotional health through encouraging self-disclosure

An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives

Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. Previous surveys on the topic primarily consider papers published in either computer science or medicine, leading to a divide in understanding and hindering the sharing of beneficial knowledge between both domains. To bridge this gap, we conduct a comprehensive literature review using the PRISMA framework, reviewing 534 papers published in both computer science and medicine. Our systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques. We find that computer science papers focus on LLM techniques and evaluating response quality using automated metrics with little attention to the application while medical papers use rule-based conversational agents and outcome metrics to measure the health outcomes of participants. Based on our findings on transparency, ethics, and cultural heterogeneity in this review, we provide a few recommendations to help bridge the disciplinary divide and enable the cross-disciplinary development of mental health conversational agents.Comment: Accepted in EMNLP 2023 Main Conference, camera read

Human-Machine Communication: Complete Volume. Volume 1

This is the complete volume of HMC Volume 1

Alexa as an Active Listener: How Backchanneling Can Elicit Self-Disclosure and Promote User Experience

Active listening is a well-known skill applied in human communication to build intimacy and elicit self-disclosure to support a wide variety of cooperative tasks. When applied to conversational UIs, active listening from machines can also elicit greater self-disclosure by signaling to the users that they are being heard, which can have positive outcomes. However, it takes considerable engineering effort and training to embed active listening skills in machines at scale, given the need to personalize active-listening cues to individual users and their specific utterances. A more generic solution is needed given the increasing use of conversational agents, especially by the growing number of socially isolated individuals. With this in mind, we developed an Amazon Alexa skill that provides privacy-preserving and pseudo-random backchanneling to indicate active listening. User study (N = 40) data show that backchanneling improves perceived degree of active listening by smart speakers. It also results in more emotional disclosure, with participants using more positive words. Perception of smart speakers as active listeners is positively associated with perceived emotional support. Interview data corroborate the feasibility of using smart speakers to provide emotional support. These findings have important implications for smart speaker interaction design in several domains of cooperative work and social computing.Comment: To appear in Proceedings of the ACM on Human-Computer Interaction (PACM HCI). The paper will be presented in CSCW 2022 (https://cscw.acm.org/2022

A Psychological Need-Fulfillment Perspective for Designing Social Robots that Support Well-Being

This conceptual paper presents a novel framework for the design and study of social robots that support well-being. Building upon the self-determination theory and the associated Motivation, Engagement, and Thriving in User Experience (METUX) model, this paper argues that users’ psychological basic needs for autonomy, competence, and relatedness should be put at the center of social robot design. These basic needs are essential to people’s psychological well-being, engagement, and self-motivation. However, current literature offers limited insights into how human–robot interactions are related to users’ experiences of the satisfaction of their basic psychological needs and thus, to their well-being and flourishing. We propose that a need-fulfillment perspective could be an inspiring lens for the design of social robots, including socially assistive robots. We conceptualize various ways in which a psychological need-fulfillment perspective may be incorporated into future human–robot interaction research and design, ranging from the interface level to the specific tasks performed by a robot or the user’s behavior supported by the robot. The paper discusses the implications of the framework for designing social robots that promote well-being, as well as the implications for future research

Socially assistive robots : the specific case of the NAO

Numerous researches have studied the development of robotics, especially socially assistive robots (SAR), including the NAO robot. This small humanoid robot has a great potential in social assistance. The NAO robot’s features and capabilities, such as motricity, functionality, and affective capacities, have been studied in various contexts. The principal aim of this study is to gather every research that has been done using this robot to see how the NAO can be used and what could be its potential as a SAR. Articles using the NAO in any situation were found searching PSYCHINFO, Computer and Applied Sciences Complete and ACM Digital Library databases. The main inclusion criterion was that studies had to use the NAO robot. Studies comparing it with other robots or intervention programs were also included. Articles about technical improvements were excluded since they did not involve concrete utilisation of the NAO. Also, duplicates and articles with an important lack of information on sample were excluded. A total of 51 publications (1895 participants) were included in the review. Six categories were defined: social interactions, affectivity, intervention, assisted teaching, mild cognitive impairment/dementia, and autism/intellectual disability. A great majority of the findings are positive concerning the NAO robot. Its multimodality makes it a SAR with potential

Enabling autistic sociality: unrealised potentials in two-sided social interaction

Research on autism, which is defined as a life-long developmental disability affecting social interaction, has focussed predominantly on how autistic individuals perceive and interact with others with less emphasis on the perspectives of their interactional partners. Yet autistic viewpoints have highlighted how other people are part of a two-way breakdown in interaction originating from differences between people rather than the deficit of any one individual, a phenomenon known as the double empathy problem. A gap therefore exists in the literature in terms of understanding how autistic sociality (i.e. the range of social opportunities possible for a given individual on the spectrum) is shaped by different interactional partners. This thesis examines the double empathy problem in three interactional contexts. Study 1 examines relationships between autistic people and their family members through focussing on perspective-taking, the ability to impute mental states to others. In light of prior research where autistic abilities have been assessed using abstract scenarios, Study 1 implements a two-way measure of perspective-taking which considers both sides of 22 real-life relationships (n=44) consisting of autistic adults and their family members, to understand how autistic people are seen by familiar others as well as vice versa. It uses a mixed-methods approach, where members of each dyad were individually asked about 12 topics, providing quantitative scores and qualitative explanation of their rating of Self, their rating of their partner, and their predicted rating by their partner. Comparison of perspectives provided a means for detecting misunderstandings and their underlying rationale. The contribution of Study 1 is that it shows perspective-taking is two-sided: family members can be biased in underestimating the perspective-taking of their autistic relatives, while autistic adults are aware of being negatively viewed despite disagreeing with such views. Study 2 examines interactions between autistic adults (n=30) partaking in a naturally occurring activity of video-gaming at a charity. It is a qualitative study using participant observation, with each conversational turn systematically rated in terms of coherence, affect and symmetry to identify the key features of neurodivergent intersubjectivity, the process through which autistic people build shared understanding in their own non-normative ways. The contribution of Study 2 is to identify two forms of neurodivergent intersubjectivity which enable shared understanding to be achieved, but which have traditionally been viewed as undesirable from a normative social viewpoint: a generous assumption of common ground that, when understood, lead to rapid rapport, and, when not understood, resulted in potentially disruptive utterances; and a low demand for coordination that ameliorated many challenges associated with disruptive turns. Study 3 examines interactions involving lay people (n=256) who believe they are interacting with an autistic partner through an online collaborative game, when in fact they are playing with an intelligent virtual agent (IVA) who behaves the same way for all participants. Its contribution is methodological as it develops a new application for simulating interactions in experimental research called Dyad3D. Study 3 uses Dyad3D to explore how disclosure of an autism diagnosis by the IVA affects social perception and social behaviour in comparison to a disclosure of dyslexia and a condition where there is no diagnostic disclosure. Combined with a post-game questionnaire, Study 3 triangulates self-reported (quantitative rating scales and qualitative explanation) and behavioural measures (quantitative scores of actions within the game) to understand the interplay of positive and negative discrimination elicited through using the label of autism. It highlights that diagnostic disclosure of autism leads to significant positive bias in social perception when compared to a disclosure of dyslexia or a no disclosure condition; yet participants are not as helpful towards the autistic IVA as they think they are, indicating a potential bias in helping behaviour. The thesis takes an abductive methodological approach which integrates with a wider call for a more participatory model of research in the study of autism. Abduction is a form of reasoning which involves the iterative development of a hypothesis that holds the best explanatory scope for the underlying phenomena observed. It is inherently aligned with a participatory model of research because abduction involves the ongoing exploration of ideas that may originate from multiple sources (i.e. interactions with autistic people as well as research outputs). Taking a more holistic approach to the development of knowledge with autistic people which recognises the legitimacy of different claims to knowledge is important, because prior research in the field has often failed to critically reflect on researcherparticipant positionality and the principals underlying the development of research agenda. For this reason, the thesis details the participatory activities which surround and interconnect with the development of the three empirical studies. Overall the thesis contributes to understanding autistic sociality as a dynamic, interactionally shaped process. It reasons that autistic people have unrealised social potential, both in terms of imagining other perspectives (Study 1) and coordinating with others (Study 2). However, such social potential may not be easily recognised by other non-autistic people who may be biased in their assumptions about autism (Study 1 and Study 3). Consequently, the evidence presented in this thesis helps to explain some of the processes that underscore the double empathy problems reported in literature, including poor mental health (because autistic people are aware that they are misunderstood by others, see Study 1), employment prospects (because autistic social potential is under-recognised by others, see Study 1 and 3), and quality of life (because neurotypical standards of communication are not compatible with neurodivergent forms of intersubjectivity, see Study 2). The thesis therefore makes suggestions for how we design enabling environments which are sensitive to the dynamic factors that can enable autistic sociality to flourish