Trust is a key part of any social interaction, whether that be between humans, or humans interacting with different artificial agents. This paper investigates how an agent’s repeated incongruence failure might impact users’ trust. We augment a previously published human-robot interaction study (Nesset et al., 2023), by replacing the robot condition with a human actor. Here, we explore how users’ trust can be impacted by repeated failure depending on the agent involved and how to best repair trust once the failures take place. Our study found a significant decrease in users’ trust when a human makes an incongruence failure, but not when this failure was repeated, regardless of the repair strategy implemented. When comparing this to the previous robot condition, we found a significant difference in the trust measured in the human and the robot condition. Additionally, the repair strategy used had a significant effect on the users’ trust when the robot repeated its failure but not when the actor did. Our findings contribute to research on broken trust with repeated failures and highlight the importance of including a human comparison to better understand research findings in human-robot interactions.

Trust repair strategies are an important part of human-robot interaction. In this study, we investigate how repeated failures impact users’ trust and how we might mitigate them. Specifically, we look at different repair strategies in the form of apologies, with additional features to them such as warnings and promises. Through an online study, we explore these repair strategies for repeated failures in the form of robot incongruence, where there is a mismatch of verbal and non-verbal information given by the robot. Our results show that such incongruent robot behaviour has a significant overall negative impact on participants’ trust. We found that the robot making a promise, and then breaking it, results in a significant decrease in participants’ trust, when compared to a general apology as a repair strategy. These findings contribute to the research on trust repair strategies and, additionally, shed light on how robot failures, in the form of incongruences, impact participants’ trust.

Trust between humans and robots is a complex, multifaceted phenomenon and measuring it subjectively and reliably is challenging. It is also context dependent and so choosing the right tool for a specific study can prove difficult. This paper aims to evaluate various trust measures and compare them in terms of sensitivity to changes in trust. This is done by comparing two validated trust questionnaires (TAS and MDMT) and one single item assessment in a COVID-19 triage scenario. We found that trust measures are equivalent in terms of sensitivity to changes in trust. Furthermore, the study showed that trust could be measured similarly through a single item assessment in comparison with other lengthier scales, in scenarios with distinct breaks in trust. This finding would be of use for experiments where lengthy questionnaires are not appropriate, such as those in the wild.

The ability to impute mental states to oneself or others, or Theory of Mind (ToM), has been intrinsically linked to trust between humans. However, less is known about how a robot mimicking ToM affects users’ trust and behaviour. We explore this through an online study, where we compare three robot personas in a cooperative maze navigation task: one neutral, one that explains its reasoning in technical terms, and one that mimics ToM. We show that ToM influences human decision-making behaviour and trust in a way that makes it more appropriate with respect to the competencies of the robot. This is key for human-robot collaboration and adoption of robotics moving forward.

We focus on affective architecture issues relating to the generation of expressive facial behaviour, critique approaches that treat expressive behaviour as only a mirror of internal state rather than as also a social signal and discuss the advantages of combining the two approaches. Using the FAtiMA architecture, we analyse the requirements for generating expressive behavior as social signals at both reactive and cognitive levels. We discuss how facial expressions can be generated in a dynamic fashion. We propose generic architectural mechanisms to meet these requirements based on an explicit mind-body loop and Theory of Mind (ToM) processing. A illustrative scenario is given.

Social robots are being developed as a form of social skills training for individual's with an autism spectrum condition (ASC). Effective training will therefore require the social signals produced by a robot to be contingent with people's knowledge and expectations of social cognition and behaviour. Designing recognisable facial expressions is an important part of this challenge; ensuring interactions are more believable and motivating. This design process requires - amongst other factors - consideration of how culture and native language affects social signal processing. In this experiment participants offered a full-bodied robot (named 'Alyx') food items to which Alyx reacted autonomously, producing either an approving or disapproving expression. Participant's responded to these expressions (i.e. the robots social signals) by indicating whether Alyx liked or disliked the food. Task performance was examined both quantitatively (response time and accuracy) and qualitatively (participant's reactionary expressions). The results revealed significant cultural differences, as non-native English speakers were less accurate at interpreting expressions, but also a similar response trend between these groups. Qualitative analysis supported the notion that Alyx's expressions were not universally understood. These findings are discussed in the context of social skills training.

This article examines the educational efficacy of a learning environment in which children diagnosed with Autism Spectrum Conditions (ASC) engage in social interactions with an artificially intelligent (AI) virtual agent and where a human practitioner acts in support of the interactions. A multi-site intervention study in schools across the UK was conducted with 29 children with ASC and learning difficulties, aged 4--14 years old. For reasons related to data completeness and amount of exposure to the AI environment, data for 15 children was included in the analysis. The analysis revealed a significant increase in the proportion of social responses made by ASC children to human practitioners. The number of initiations made to human practitioners and to the virtual agent by the ASC children also increased numerically over the course of the sessions. However, due to large individual differences within the ASC group, this did not reach significance. Although no evidence of transfer to the real-world post-test was shown, anecdotal evidence of classroom transfer was reported. The work presented in this article offers an important contribution to the growing body of research in the context of AI technology design and use for autism intervention in real school contexts. Specifically, the work highlights key methodological challenges and opportunities in this area by leveraging interdisciplinary insights in a way that (i) bridges between educational interventions and intelligent technology design practices, (ii) considers the design of technology as well as the design of its use (context and procedures) on par with one another, and (iii) includes design contributions from different stakeholders, including children with and without ASC diagnosis, educational practitioners, and researchers.

This paper outlines a demonstration of the work carried out in the SoCoRo project investigating how far a neuro-typical population recognises facial expressions on a non-naturalistic robot face that are designed to show approval and disapproval. RFID-tagged objects are presented to an Emys robot head (called Alyx) and Alyx reacts to each with a facial expression. Participants are asked to put the object in a box marked 'Like' or 'Dislike'. This study is being extended to include assessment of participants' Autism Quotient using a validated questionnaire as a step towards using a robot to help train high-functioning adults with an Autism Spectrum Disorder in social signal recognition.

Brain Quest is an active smart phone game designed to promote both physical activity and executive function in 10-11 year old children. This paper describes the user centred design process which involved a team of psychologists, HCI experts, physical activity specialists and thirty four children over a period of 18 months. Results of two preliminary studies are promising, suggesting that Brain Quest is enjoyable, promotes moderate physical activity and has the potential to provide cognitive scaffolding of the key executive function (EF) skill of multitasking.