September 2019
Volume 19, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2019
Visuo-motor adaptation during interaction with a user-adaptive system
Author Affiliations & Notes
  • Priscilla Balestrucci
    Applied Cognitive Psychology, Faculty for Computer Science, Engineering, and Psychology, Ulm University, Ulm, Germany
  • Marc O. Ernst
    Applied Cognitive Psychology, Faculty for Computer Science, Engineering, and Psychology, Ulm University, Ulm, Germany
Journal of Vision September 2019, Vol.19, 187a. doi:
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      Priscilla Balestrucci, Marc O. Ernst; Visuo-motor adaptation during interaction with a user-adaptive system. Journal of Vision 2019;19(10):187a. doi:

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      © ARVO (1962-2015); The Authors (2016-present)

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User-adaptive systems are a current trend in technological development. Such systems are designed to sense the user’s status based on ongoing interaction and automatically change certain features (e.g. content, interface, or interaction capabilities) in order to provide a targeted, personalized experience. In this scenario, users are likely to adapt to the evolving characteristics of the interaction (Burge et al., 2008), changing their own behavior to correctly interact with such systems and thereby leading to dynamics of mutual adaptation between human and machine. We investigated such mutual adaptation dynamics within a visuo-motor adaptation paradigm. Participants were instructed to perform fast pointing movements on a graphic tablet as accurately as possible, while also seeking to minimize the error between target and feedback location on a screen in front of them. The feedback location reflected the pointing performance of the user according to the underlying tablet-to-screen mapping, which changed systematically over time due to the introduction of a step offset. Concurrently, an adaptive algorithm corrected the feedback location according to an estimation of the participant’s error, thus contributing to the reduction of the displayed error over trials. In different experimental conditions, the extent of such contributions varied systematically, and we measured the adaptive performance of the human-machine system as a whole, as well as the underlying motor performance of participants. The greater the correction introduced by the adaptive algorithm, the more effective was the joint system in reducing visual error after the introduction of the step offset. On the other hand, when considering human’s motor behavior alone, the pointing error did not decrease, but tended to increase over time with higher contributions from the algorithm. Our findings indicate that, in order to obtain desired outcomes from interactions with user-adaptive technology, the sensorimotor mechanisms underlying such interactions must be considered.

Acknowledgement: The current study was funded by the German Research Foundation (DFG) within SFB/Transregio 161 

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