An adaptive model of gaze-based selection

Xiuli Chen, Aditya Acharya, Antti Oulasvirta, Andrew Howes

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

6 Citations (Scopus)
67 Downloads (Pure)


Gaze-based selection has received signifcant academic attention over a number of years. While advances have been made, it is possible that further progress could be made if there were a deeper understanding of the adaptive nature of the mechanisms that guide eye movement and vision. Control of eye movement typically results in a sequence of movements (saccades) and fxations followed by a dwell' at a target and a selection. To shed light on how these sequences are planned, this paper presents a computational model of the control of eye movements in gaze-based selection.We formulate the model as an optimal sequential planning problem bounded by the limits of the human visual and motor systems and use reinforcement learning to approximate optimal solutions. The model accurately replicates earlier results on the efects of target size and distance and captures a number of other aspects of performance. The model can be used to predict number of fxations and duration required to make a gaze-based selection. The future development of the model is discussed.

Original languageEnglish
Title of host publicationCHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Number of pages11
ISBN (Electronic)9781450380966
Publication statusPublished - 6 May 2021
MoE publication typeA4 Article in a conference publication
EventACM SIGCHI Annual Conference on Human Factors in Computing Systems - Virtual, Online, Yokohama, Japan
Duration: 8 May 202113 May 2021


ConferenceACM SIGCHI Annual Conference on Human Factors in Computing Systems
Abbreviated titleACM CHI
Internet address


  • Adaptive model
  • Computational rationality
  • Gaze-based selection
  • Reinforcement learning


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