“C'Mon dude!”: Users adapt their behaviour to a robotic agent with an attention model
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 fac...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
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Elsevier
2015
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/40529 |
| _version_ | 1848755895809343488 |
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| author | Cavedon, L. Kroos, Christian Herath, D. Burnham, D. Bishop, L. Leung, Y. Stevens, C. |
| author_facet | Cavedon, L. Kroos, Christian Herath, D. Burnham, D. Bishop, L. Leung, Y. Stevens, C. |
| author_sort | Cavedon, L. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:03:35Z |
| format | Journal Article |
| id | curtin-20.500.11937-40529 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:03:35Z |
| publishDate | 2015 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-405292017-09-13T14:08:49Z “C'Mon dude!”: Users adapt their behaviour to a robotic agent with an attention model Cavedon, L. Kroos, Christian Herath, D. Burnham, D. Bishop, L. Leung, Y. Stevens, C. Evaluation Social interaction Human–robot interaction Engagement Attention model 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. 2015 Journal Article http://hdl.handle.net/20.500.11937/40529 10.1016/j.ijhcs.2015.02.012 Elsevier restricted |
| spellingShingle | Evaluation Social interaction Human–robot interaction Engagement Attention model Cavedon, L. Kroos, Christian Herath, D. Burnham, D. Bishop, L. Leung, Y. Stevens, C. “C'Mon dude!”: Users adapt their behaviour to a robotic agent with an attention model |
| title | “C'Mon dude!”: Users adapt their behaviour to a robotic agent with an attention model |
| title_full | “C'Mon dude!”: Users adapt their behaviour to a robotic agent with an attention model |
| title_fullStr | “C'Mon dude!”: Users adapt their behaviour to a robotic agent with an attention model |
| title_full_unstemmed | “C'Mon dude!”: Users adapt their behaviour to a robotic agent with an attention model |
| title_short | “C'Mon dude!”: Users adapt their behaviour to a robotic agent with an attention model |
| title_sort | “c'mon dude!”: users adapt their behaviour to a robotic agent with an attention model |
| topic | Evaluation Social interaction Human–robot interaction Engagement Attention model |
| url | http://hdl.handle.net/20.500.11937/40529 |