"C׳Mon dude!": Users adapt their behaviour to a robotic agent with an attention model. Issue 80 (August 2015)

• Record Type: Journal Article
• Authors: Cavedon, Lawrence; Kroos, Christian; Herath, Damith; Burnham, Denis; Bishop, Laura; Leung, Yvonne; Stevens, Catherine J.
• Is Part Of: International journal of human-computer studies. Issue 80(2015)
• Page Start: 14
• Page End: 23
• Publication Date: 2015-08
• Subjects: Human–robot interaction -- Attention model -- Social interaction -- Evaluation -- Engagement; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2015.02.012
• Languages: English
• ISSNs: 1071-5819

Abstract

Abstract: Social cues facilitate engagement between interaction participants, whether they be two (or more) humans or a human and an artificial agent such as a robot. Previous work specific to human–agent/robot interaction has demonstrated the efficacy of implemented social behaviours, such as eye-gaze or facial gestures, for demonstrating the illusion of engagement and positively impacting interaction with a human. We describe the implementation of THAMBS, The Thinking Head Attention Model and Behavioural System, which is used to model attention controlling how a virtual agent reacts to external audio and visual stimuli within the context of an interaction with a human user. We evaluate the efficacy of THAMBS for a virtual agent mounted on a robotic platform in a controlled experimental setting, and collect both task- and behavioural-performance variables, along with self-reported ratings of engagement. Our results show that human subjects noticeably engaged more often, and in more interesting ways, with the robotic agent when THAMBS was activated, indicating that even a rudimentary display of attention by the robot elicits significantly increased attention by the human. Back-channelling had less of an effect on user behaviour. THAMBS and back-channelling did not interact and neither had an effect on self-report ratings. Our results concerning THAMBS hold implications for the design of successful human–robot interactive behaviours. Highlights: New approach to Modelling Attention incorporating input from video and audio channels. Attention Model (THAMBS) elicits greater engagement from users when activated. Users exhibit more pronounced turn-taking when THAMBS is activated. Results suggest implications for the design of successful interactive robot behaviours.