"C'Mon dude!": Users adapt their behaviour to a robotic agent with an attention model

Lawrence Cavedon, Christian Kroos, Damith Herath, Denis Burnham, Laura Bishop, Yvonne Leung, Catherine Stevens

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
9 Downloads (Pure)


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
Original languageEnglish
Pages (from-to)14-23
Number of pages10
JournalInternational Journal of Human-Computer Studies
Publication statusPublished - Aug 2015
Externally publishedYes


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