TY  - JOUR
U1  - Wissenschaftlicher Artikel
A1  - Pfeiffer, Thies
A1  - Mitev, Nikolina
A1  - Renner, Patrick
A1  - Staudte, Maria
T1  - Towards efficient human–machine collaboration: effects of gaze-driven feedback and engagement on performance
JF  - Cognitive Research: Principles and Implications
N2  - Referential success is crucial for collaborative task-solving in shared environments. In face-to-face interactions, humans, therefore, exploit speech, gesture, and gaze to identify a specific object. We investigate if and how the gaze behavior of a human interaction partner can be used by a gaze-aware assistance system to improve referential success. Specifically, our system describes objects in the real world to a human listener using on-the-fly speech generation. It continuously interprets listener gaze and implements alternative strategies to react to this implicit feedback. We used this system to investigate an optimal strategy for task performance: providing an unambiguous, longer instruction right from the beginning, or starting with a shorter, yet ambiguous instruction. Further, the system provides gaze-driven feedback, which could be either underspecified (“No, not that one!”) or contrastive (“Further left!”). As expected, our results show that ambiguous instructions followed by underspecified feedback are not beneficial for task performance, whereas contrastive feedback results in faster interactions. Interestingly, this approach even outperforms unambiguous instructions (manipulation between subjects). However, when the system alternates between underspecified and contrastive feedback to initially ambiguous descriptions in an interleaved manner (within subjects), task performance is similar for both approaches. This suggests that listeners engage more intensely with the system when they can expect it to be cooperative. This, rather than the actual informativity of the spoken feedback, may determine the efficiency of information uptake and performance.
KW  - Listener gaze
KW  - Multimodal systems
KW  - Human–computer interaction
KW  - Natural language generation
KW  - Referential success
Y1  - 2018
SN  - 2365-7464
SS  - 2365-7464
U6  - https://doi.org/10.1186/s41235-018-0148-x
DO  - https://doi.org/10.1186/s41235-018-0148-x
VL  - 3
SP  - 1
EP  - 16
S1  - 16
ER  -