Gaze-based analysis of areas of interest (AOI) is widely used in information visualisation research to understand how people explore visualisations or assess the quality of visualisations concerning key characteristics such as memorability. However, nearby AOIs in visualisations amplify the uncertainty caused by the gaze estimation error, which strongly influences the mapping between gaze samples or fixations and different AOIs. We contribute a novel investigation into gaze uncertainty and quantify its impact on AOI-based analysis on visualisations using two novel metrics: the Flipping Candidate Rate (FCR) and Hit Any AOI Rate (HAAR). Our analysis of 40 real-world visualisations, including human gaze and AOI annotations, shows that uncertainty commonly appears in visualisations, which significantly impacts the analysis conducted in AOI-based studies. Moreover, we analysed four visualisation types and found that bar and scatter plots are commonly designed in a way that causes more uncertainty than line and pie plots in gaze-based analysis.
%0 Conference Paper
%1 wang2022impact
%A Wang, Yao
%A Koch, Maurice
%A Bâce, Mihai
%A Weiskopf, Daniel
%A Bulling, Andreas
%B 2022 Symposium on Eye Tracking Research and Applications
%D 2022
%I ACM
%K a01 visus:bullinas visus:weiskopf sfbtrr161 from:christinawarren a07 visus:kochme 2022 visus:wangyo visus
%P 1–6
%R 10.1145/3517031.3531166
%T Impact of Gaze Uncertainty on AOIs in Information Visualisations
%U https://dl.acm.org/doi/abs/10.1145/3517031.3531166
%X Gaze-based analysis of areas of interest (AOI) is widely used in information visualisation research to understand how people explore visualisations or assess the quality of visualisations concerning key characteristics such as memorability. However, nearby AOIs in visualisations amplify the uncertainty caused by the gaze estimation error, which strongly influences the mapping between gaze samples or fixations and different AOIs. We contribute a novel investigation into gaze uncertainty and quantify its impact on AOI-based analysis on visualisations using two novel metrics: the Flipping Candidate Rate (FCR) and Hit Any AOI Rate (HAAR). Our analysis of 40 real-world visualisations, including human gaze and AOI annotations, shows that uncertainty commonly appears in visualisations, which significantly impacts the analysis conducted in AOI-based studies. Moreover, we analysed four visualisation types and found that bar and scatter plots are commonly designed in a way that causes more uncertainty than line and pie plots in gaze-based analysis.
@inproceedings{wang2022impact,
abstract = {Gaze-based analysis of areas of interest (AOI) is widely used in information visualisation research to understand how people explore visualisations or assess the quality of visualisations concerning key characteristics such as memorability. However, nearby AOIs in visualisations amplify the uncertainty caused by the gaze estimation error, which strongly influences the mapping between gaze samples or fixations and different AOIs. We contribute a novel investigation into gaze uncertainty and quantify its impact on AOI-based analysis on visualisations using two novel metrics: the Flipping Candidate Rate (FCR) and Hit Any AOI Rate (HAAR). Our analysis of 40 real-world visualisations, including human gaze and AOI annotations, shows that uncertainty commonly appears in visualisations, which significantly impacts the analysis conducted in AOI-based studies. Moreover, we analysed four visualisation types and found that bar and scatter plots are commonly designed in a way that causes more uncertainty than line and pie plots in gaze-based analysis.},
added-at = {2022-06-07T09:19:00.000+0200},
author = {Wang, Yao and Koch, Maurice and B{\^a}ce, Mihai and Weiskopf, Daniel and Bulling, Andreas},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/22e06cce757e503c5d9d6f0f037813118/sfbtrr161},
booktitle = {2022 Symposium on Eye Tracking Research and Applications},
doi = {10.1145/3517031.3531166},
interhash = {ca541127cc768f8a3261a625f98ea772},
intrahash = {2e06cce757e503c5d9d6f0f037813118},
keywords = {a01 visus:bullinas visus:weiskopf sfbtrr161 from:christinawarren a07 visus:kochme 2022 visus:wangyo visus},
month = {6},
pages = {1–6},
publisher = {ACM},
timestamp = {2022-06-07T07:19:00.000+0200},
title = {Impact of Gaze Uncertainty on AOIs in Information Visualisations},
url = {https://dl.acm.org/doi/abs/10.1145/3517031.3531166},
year = 2022
}
