An Adaptive Model of Gaze-based Selection


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 adaptive nature of the 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 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 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.

Xiuli Chen
Aalto University, Espoo, Finland
Aditya Acharya
Aalto University, Espoo, Espoo, Finland
Antti Oulasvirta
Aalto University, Espoo, Finland
Andrew Howes
Aalto University, Espoo, Finland




会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (

セッション: Computational Physical Interaction

[A] Paper Room 02, 2021-05-10 17:00:00~2021-05-10 19:00:00 / [B] Paper Room 02, 2021-05-11 01:00:00~2021-05-11 03:00:00 / [C] Paper Room 02, 2021-05-11 09:00:00~2021-05-11 11:00:00
Paper Room 02
12 件の発表
2021-05-10 17:00:00
2021-05-10 19:00:00