Where the action is

Dickerson, Paul; Robins, Ben; Dautenhahn, Kerstin

doi:10.1075/is.14.2.07dic

Cited by 21 publications

(3 citation statements)

References 15 publications

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“…We thereby draw on theories from social and communication psychology, ethology, linguistics, and cognitive science in order to understand the most important accomplishments of human cognition that lay the ground for communication. We would like to stress that -although it will not be discussed at the core of this paper -sociolinguistic approaches (Dickerson et al, 2013;Broz et al, 2014;Rollet and Clavel, 2020) play an important role for the improvement of current conversational systems.…”

Section: How Humans Communicate and Cooperate In Dialogmentioning

confidence: 99%

Revisiting Human-Agent Communication: The Importance of Joint Co-construction and Understanding Mental States

Kopp

Krämer

2021

Front. Psychol.

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The study of human-human communication and the development of computational models for human-agent communication have diverged significantly throughout the last decade. Yet, despite frequently made claims of “super-human performance” in, e.g., speech recognition or image processing, so far, no system is able to lead a half-decent coherent conversation with a human. In this paper, we argue that we must start to re-consider the hallmarks of cooperative communication and the core capabilities that we have developed for it, and which conversational agents need to be equipped with: incremental joint co-construction and mentalizing. We base our argument on a vast body of work on human-human communication and its psychological processes that we reason to be relevant and necessary to take into account when modeling human-agent communication. We contrast those with current conceptualizations of human-agent interaction and formulate suggestions for the development of future systems.

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Section: How Humans Communicate and Cooperate In Dialogmentioning

confidence: 99%

Revisiting Human-Agent Communication: The Importance of Joint Co-construction and Understanding Mental States

Kopp

Krämer

2021

Front. Psychol.

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“…Any spontaneous interactions directed toward the school staff member were not annotated, as they were asked not to be actively involved in the intervention. Potentially relevant interactions can occur at any point during the session, not only during the child-robot interaction (Dickerson et al, 2013). We therefore annotated the whole recording, which included parts where the robot was still covered by a blanket, as well as when the adult and child were engaged in free-play.…”

Section: Coding Schemementioning

confidence: 99%

Differences in Spontaneous Interactions of Autistic Children in an Interaction With an Adult and Humanoid Robot

et al. 2020

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Robots are promising tools for promoting engagement of autistic children in interventions and thereby increasing the amount of learning opportunities. However, designing deliberate robot behavior aimed at engaging autistic children remains challenging. Our current understanding of what interactions with a robot, or facilitated by a robot, are particularly motivating to autistic children is limited to qualitative reports with small sample sizes. Translating insights from these reports to design is difficult due to the large individual differences among autistic children in their needs, interests, and abilities. To address these issues, we conducted a descriptive study and report on an analysis of how 31 autistic children spontaneously interacted with a humanoid robot and an adult within the context of a robot-assisted intervention, as well as which individual characteristics were associated with the observed interactions. For this analysis, we used video recordings of autistic children engaged in a robot-assisted intervention that were recorded as part of the DE-ENIGMA database. The results showed that the autistic children frequently engaged in exploratory and functional interactions with the robot spontaneously, as well as in interactions with the adult that were elicited by the robot. In particular, we observed autistic children frequently initiating interactions aimed at making the robot do a certain action. Autistic children with stronger language ability, social functioning, and fewer autism spectrum-related symptoms, initiated more functional interactions with the robot and more robot-elicited interactions with the adult. We conclude that the children's individual characteristics, in particular the child's language ability, can be indicative of which types of interaction they are more likely to find interesting. Taking these into account for the design of deliberate robot behavior, coupled with providing more autonomy over the robot's behavior to the autistic children, appears promising for promoting engagement and facilitating more learning opportunities.

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“…Any spontaneous interactions directed towards the school staff member were not annotated, as they were asked not to be actively involved in the intervention. Potentially relevant interactions can occur at any point during the session, not only during the child-robot interaction (Dickerson et al, 2013). We therefore annotated the whole recording, which included parts where the robot was still covered by a blanket, as well as when the adult and child were engaged in free-play.…”

Section: Coding Schemementioning

confidence: 99%

Understanding and facilitating predictability for engagement in learning

Schadenberg

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A novel tool for delivering learning content to autistic children in a manner that keeps the children engaged, is tailored towards their strengths, and improves learning gains"; this is the promise of using robots to enhance interventions for autistic children. While robots have been found to pique the children's interest and improve engagement in interventions, designing robots to sustain long-term engagement that leads to learning is difficult. The children are very different from each other in how autism affects the development of their cognitive, language, and intellectual ability, which needs to be taken into account for the child-robot interaction. How this can and should be done is still an open question -one that will be addressed in this dissertation.In the first part of this dissertation, I describe our research in developing a novel robot-assisted intervention for teaching the basics of emotion recognition to autistic children. Our research was part of the EU-project DE-ENIGMA. A key research question that we address is how we can design a robot-assisted intervention to engage autistic children in learning. To address this broad research question, we report a descriptive study where autistic children interacted with our initial prototype of the "DE-ENIGMA robot-assisted intervention". In this study, we report on how the children's individual differences are correlated with how the children interacted within the intervention. Furthermore, we conducted a literature study to assess the user needs and user requirements that are relevant for developing robot-assisted interventions for autistic children, and describe how we developed the DE-ENIGMA intervention.The second part of this dissertation is related to a concept that is central to autism: predictability. Autistic children are believed to generally favour predictable environments, and contemporary Bayesian accounts of autism place the inability to effectively deal with unpredictability at the core of the condition. Because robots are programmable, we could, in theory, program them to be highly predictable. By doing so, we could address this need for predictability and possibly improve the engagement of autistic children to the intervention. In fact, the highly predictable nature of robots is a commonly used argument for why robots may be promising tools for those working with autistic children. Predictability, however, is poorly defined in current literature and lacks an operationalisation that we can use for manipulating a robot's predictability. We therefore provide a novel formalisation and operationalisation of predictability, and how it relates to human-robot interaction, based on the predictive processing framework. Furthermore, we report on two experimental studies where we investigated the effect of a robot's predictability on the studies' participants. In viii | Abstract one study, we specifically look at people's social perception of a robot in relation to its predictability. In the other study, we investigated the effect of a robot's predic...

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Where the action is

Cited by 21 publications

References 15 publications

Revisiting Human-Agent Communication: The Importance of Joint Co-construction and Understanding Mental States

Revisiting Human-Agent Communication: The Importance of Joint Co-construction and Understanding Mental States

Differences in Spontaneous Interactions of Autistic Children in an Interaction With an Adult and Humanoid Robot

Understanding and facilitating predictability for engagement in learning

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