Projects per year
Abstract
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 language | English |
---|---|
Title of host publication | CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems |
Publisher | ACM |
Number of pages | 11 |
ISBN (Electronic) | 9781450380966 |
DOIs | |
Publication status | Published - 6 May 2021 |
MoE publication type | A4 Article in a conference publication |
Event | ACM SIGCHI Annual Conference on Human Factors in Computing Systems - Virtual, Online, Yokohama, Japan Duration: 8 May 2021 → 13 May 2021 https://chi2021.acm.org/ |
Conference
Conference | ACM SIGCHI Annual Conference on Human Factors in Computing Systems |
---|---|
Abbreviated title | ACM CHI |
Country/Territory | Japan |
City | Yokohama |
Period | 08/05/2021 → 13/05/2021 |
Internet address |
Keywords
- Adaptive model
- Computational rationality
- Gaze-based selection
- Reinforcement learning
Fingerprint
Dive into the research topics of 'An adaptive model of gaze-based selection'. Together they form a unique fingerprint.-
Human Automata: Simulator-based Methods for Collaborative AI
Oulasvirta, A., Dayama, N., Hegemann, L., Laine, M., Santala, S., Nioche, A., Shiripour, M., Putkonen, A. & Kylmälä, J.
01/01/2020 → 31/12/2023
Project: Academy of Finland: Other research funding
-
Bayesian Artefact Design
Oulasvirta, A., Dayama, N., Hassinen, H., Leiva, L., Laine, M., Putkonen, A., Liao, Y., Peng, Z., Shin, J., Todi, K. & Nioche, A.
01/09/2018 → 31/08/2023
Project: Academy of Finland: Other research funding
-
FCAI: Finnish Center for Artificial Intelligence
01/01/2019 → 31/12/2022
Project: Academy of Finland: Other research funding