An adaptive model of gaze-based selection

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

Standard

An adaptive model of gaze-based selection. / Chen, Xiuli; Acharya, Aditya; Oulasvirta, Antti.

CHI Conference on Human Factors in Computing Systems (CHI ’21). Association for Computing Machinery , 2021.

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

Harvard

Chen, X, Acharya, A & Oulasvirta, A 2021, An adaptive model of gaze-based selection. in CHI Conference on Human Factors in Computing Systems (CHI ’21). Association for Computing Machinery .

APA

Chen, X., Acharya, A., & Oulasvirta, A. (Accepted/In press). An adaptive model of gaze-based selection. In CHI Conference on Human Factors in Computing Systems (CHI ’21) Association for Computing Machinery .

Vancouver

Chen X, Acharya A, Oulasvirta A. An adaptive model of gaze-based selection. In CHI Conference on Human Factors in Computing Systems (CHI ’21). Association for Computing Machinery . 2021

Author

Chen, Xiuli ; Acharya, Aditya ; Oulasvirta, Antti. / An adaptive model of gaze-based selection. CHI Conference on Human Factors in Computing Systems (CHI ’21). Association for Computing Machinery , 2021.

Bibtex

@inproceedings{13f4852b4ebb4666ba74558f9949e4fb,
title = "An adaptive model of gaze-based selection",
abstract = "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 {\textquoteleft}dwell{\textquoteright} 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.",
author = "Xiuli Chen and Aditya Acharya and Antti Oulasvirta",
year = "2021",
month = may,
day = "8",
language = "English",
booktitle = "CHI Conference on Human Factors in Computing Systems (CHI {\textquoteright}21)",
publisher = "Association for Computing Machinery ",

}

RIS

TY - GEN

T1 - An adaptive model of gaze-based selection

AU - Chen, Xiuli

AU - Acharya, Aditya

AU - Oulasvirta, Antti

PY - 2021/5/8

Y1 - 2021/5/8

N2 - 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.

AB - 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.

M3 - Conference contribution

BT - CHI Conference on Human Factors in Computing Systems (CHI ’21)

PB - Association for Computing Machinery

ER -