An adaptive model of gaze-based selection

Xiuli Chen, Aditya Acharya, Antti Oulasvirta, Andrew Howes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Gaze-based selection has received significant 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 cognitive mechanisms that guide eye movement and vision. Control of eye movement typically results in a sequence of movements (saccades) and fixations 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 a 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 effects of target size and distance and captures a number of other aspects of performance. The model can be used to predict number of fixations and duration required to make a gaze-based selection. The future development of the model is discussed.
Original languageEnglish
Title of host publicationCHI '21
Subtitle of host publicationProceedings of the 2021 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
Number of pages11
ISBN (Print)9781450380966
Publication statusPublished - May 2021
EventCHI '21: CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths - Virtual, Virtual, Online, Japan
Duration: 8 May 202113 May 2021

Publication series

NameCHI: Conference on Human Factors in Computing Systems


ConferenceCHI '21: CHI Conference on Human Factors in Computing Systems
Abbreviated titleCHI 2021
CityVirtual, Online
Internet address


  • , computational rationality
  • Reinforcement learning
  • adaptive model
  • gaze-based selection


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