Title
A Study of Perceptual and Cognitive Models Applied to Prediction of Eye Gaze within Statistical Graphs
Abstract
ABSTRACT In theory, visual saliency in a graph should be used to draw attention to its most important component(s). Thus salience is commonly viewed both as a basis for predicting where graph readers are likely to look, and as a core design technique for emphasizing what a reader is intended to see among competing elements in a given chart or plot. We briefly review models, metrics, and applicable theories as they pertain to graphs. We then introduce new saliency models based on perceptual and cognitive theories that, to our knowledge, have not been previously applied to models for viewing statistical graphics. The resulting frameworks can be broadly classified as bottom-up perceptual models or top-down cognitive models. We report the results of evaluating these new theory-informed approaches on gaze data collected for statistical graphs and for more general information visualizations. Interestingly, the new models fare no better than previous ones. We review the experience, noting why we expected these hypotheses to be effective, and discuss how and why their performance did not match our aspirations. We suggest directions for future research that may help to clarify some of the issues raised.
Year
DOI
Venue
2020
10.1145/3385955.3407931
SAP
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Mark A. Livingston139933.58
Laura E. Matzen291.89
Andre Harrison3164.34
Alex Lulushi400.34
Mikaila Daniel500.34
Megan Dass600.34
Derek Brock7204.00
Jonathan W. Decker8717.60