The ALICO corpus: analysing the active listener

Visual bodily signals as core devices for coordinating minds in interaction

The view put forward here is that visual bodily signals play a core role in human communication and the coordination of minds. Critically, this role goes far beyond referential and propositional meaning. The human communication system that we consider to be the explanandum in the evolution of language thus is not spoken language. It is, instead, a deeply multimodal, multilayered, multifunctional system that developed-and survived-owing to the extraordinary flexibility and adaptability that it endows us with. Beyond their undisputed iconic power, visual bodily signals (manual and head gestures, facial expressions, gaze, torso movements) fundamentally contribute to key pragmatic processes in modern human communication. This contribution becomes particularly evident with a focus that includes non-iconic manual signals, non-manual signals and signal combinations. Such a focus also needs to consider meaning encoded not just via iconic mappings, since kinematic modulations and interaction-bound meaning are additional properties equipping the body with striking pragmatic capacities. Some of these capacities, or its precursors, may have already been present in the last common ancestor we share with the great apes and may qualify as early versions of the components constituting the hypothesized interaction engine. This article is part of the theme issue 'Revisiting the human 'interaction engine': comparative approaches to social action coordination'.

Modeling Feedback in Interaction With Conversational Agents—A Review

Intelligent agents interacting with humans through conversation (such as a robot, embodied conversational agent, or chatbot) need to receive feedback from the human to make sure that its communicative acts have the intended consequences. At the same time, the human interacting with the agent will also seek feedback, in order to ensure that her communicative acts have the intended consequences. In this review article, we give an overview of past and current research on how intelligent agents should be able to both give meaningful feedback toward humans, as well as understanding feedback given by the users. The review covers feedback across different modalities (e.g., speech, head gestures, gaze, and facial expression), different forms of feedback (e.g., backchannels, clarification requests), and models for allowing the agent to assess the user's level of understanding and adapt its behavior accordingly. Finally, we analyse some shortcomings of current approaches to modeling feedback, and identify important directions for future research.

Multimodal User Feedback During Adaptive Robot-Human Presentations

Feedback is an essential part of all communication, and agents communicating with humans must be able to both give and receive feedback in order to ensure mutual understanding. In this paper, we analyse multimodal feedback given by humans towards a robot that is presenting a piece of art in a shared environment, similar to a museum setting. The data analysed contains both video and audio recordings of 28 participants, and the data has been richly annotated both in terms of multimodal cues (speech, gaze, head gestures, facial expressions, and body pose), as well as the polarity of any feedback (negative, positive, or neutral). We train statistical and machine learning models on the dataset, and find that random forest models and multinomial regression models perform well on predicting the polarity of the participants' reactions. An analysis of the different modalities shows that most information is found in the participants' speech and head gestures, while much less information is found in their facial expressions, body pose and gaze. An analysis of the timing of the feedback shows that most feedback is given when the robot makes pauses (and thereby invites feedback), but that the more exact timing of the feedback does not affect its meaning.

Coordinated Collaboration and Nonverbal Social Interactions: A Formal and Functional Analysis of Gaze, Gestures, and Other Body Movements in a Contemporary Dance Improvisation Performance

This study presents a microanalysis of what information performers "give" and "give off" to each other via their bodies during a contemporary dance improvisation. We compare what expert performers and non-performers (sufficiently trained to successfully perform) do with their bodies during a silent, multiparty improvisation exercise, in order to identify any differences and to provide insight into nonverbal communication in a less conventional setting. The coordinated collaboration of the participants (two groups of six) was examined in a frame-by-frame analysis focusing on all body movements, including gaze shifts as well as the formal and functional movement units produced in the head-face, upper-, and lower-body regions. The Methods section describes in detail the annotation process and inter-rater agreement. The results of this study indicate that expert performers during the improvisation are in "performance mode" and have embodied other social cognitive strategies and skills (e.g., endogenous orienting, gaze avoidance, greater motor control) that the non-performers do not have available. Expert performers avoid using intentional communication, relying on information to be inferentially communicated in order to coordinate collaboratively, with silence and stillness being construed as meaningful in that social practice and context. The information that expert performers produce is quantitatively less (i.e., producing fewer body movements) and qualitatively more inferential than intentional compared to a control group of non-performers, which affects the quality of the performance.