mlsanityData to 7 Oct 2026

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

arXiv 2404.14232 ↗First posted 22 Apr 2024cs.HC cs.AIcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 10 of its 60 references that are in the map. First posted on arXiv 22 Apr 2024.

Builds on

10 of 60 references.

References · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2016What do different evaluation metrics tell us about saliency models?cited by 184
  4. 2019TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detectioncited by 58
  5. 2015DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixationscited by 47
  6. 2016A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detectioncited by 23
  7. 2021Temporal-Spatial Feature Pyramid for Video Saliency Detectioncited by 22
  8. 2020Tidying Deep Saliency Prediction Architecturescited by 20
  9. 2021Understanding Visual Saliency in Mobile User Interfacescited by 13
  10. 2020Detecting Relevance during Decision-Making from Eye Movements for UI Adaptationcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025Are explainable AI (XAI) evaluation strategies aligned? Comparing subjective, objective, and mathematical evaluation measures using saliency mapscited by 4
  2. 2026When AI Persuades: Adversarial Explanation Attacks on Human Trust in AI-Assisted Decision Makingcited by 2
  3. 2025DTFSal: Audio-Visual Dynamic Token Fusion for Video Saliency Predictioncited by 1
  4. 2025Assessing Medical Training Skills via Eye and Head Movementscited by 0
  5. 2025Planning Ahead with RSA: Efficient Signalling in Dynamic Environments by Projecting User Awareness across Future Timestepscited by 0
  6. 2026Intelligent support for Human Oversight: Integrating Reinforcement Learning with Gaze Simulation to Personalize Highlightingcited by 0