Shifting Focus with HCEye: Exploring the Dynamics of Visual Highlighting and Cognitive Load on User Attention and Saliency Prediction-Reference-Cited by-同舟云学术

Shifting Focus with HCEye: Exploring the Dynamics of Visual Highlighting and Cognitive Load on User Attention and Saliency Prediction

Published:2024-05-20 Issue:ETRA Volume:8 Page:1-18
ISSN:2573-0142
Container-title:Proceedings of the ACM on Human-Computer Interaction
language:en
Short-container-title:Proc. ACM Hum.-Comput. Interact.

Author:

Das Anwesha¹^ORCID,Wu Zekun¹^ORCID,Skrjanec Iza²^ORCID,Feit Anna Maria¹^ORCID

Affiliation:

1. Saarland Informatics Campus, Saarland University, Saarbrücken, Saarland, Germany

2. Language Science and Technology, Saarland University, Saarbrücken, Saarland, Germany

Abstract

Visual highlighting can guide user attention in complex interfaces. However, its effectiveness under limited attentional capacities is underexplored. This paper examines the joint impact of visual highlighting (permanent and dynamic) and dual-task-induced cognitive load on gaze behaviour. Our analysis, using eye-movement data from 27 participants viewing 150 unique webpages reveals that while participants' ability to attend to UI elements decreases with increasing cognitive load, dynamic adaptations (i.e., highlighting) remain attention-grabbing. The presence of these factors significantly alters what people attend to and thus what is salient. Accordingly, we show that state-of-the-art saliency models increase their performance when accounting for different cognitive loads. Our empirical insights, along with our openly available dataset, enhance our understanding of attentional processes in UIs under varying cognitive (and perceptual) loads and open the door for new models that can predict user attention while multitasking.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3655610

Reference61 articles.

1. Tobii AB. [n. d.]. Tobii Pro Lab. https://www.tobii.com/

2. Fitting Linear Mixed-Effects Models Usinglme4

3. James Baumeister, Seung Youb Ssin, Neven AM ElSayed, Jillian Dorrian, David P Webb, James A Walsh, Timothy M Simon, Andrew Irlitti, Ross T Smith, Mark Kohler, et al. 2017. Cognitive cost of using augmented reality displays. IEEE transactions on visualization and computer graphics 23, 11 (2017), 2378--2388.

4. Zoya Bylinskii, Tilke Judd, Aude Oliva, Antonio Torralba, and Frédo Durand. 2018. What do different evaluation metrics tell us about saliency models? IEEE transactions on pattern analysis and machine intelligence 41, 3 (2018), 740--757.

5. How are learning strategies reflected in the eyes? Combining results from self-reports and eye-tracking