Mining attention distribution paradigm: Discover gaze patterns and their association rules behind the visual image. (March 2023)

• Record Type: Journal Article
• Authors: Yu, Weiwei; Zhao, Feng; Ren, Zhijun; Jin, Dian; Yang, Xinliang; Zhang, Xiaokun
• Is Part Of: Computer methods and programs in biomedicine. Volume 230(2023)
• Publication Date: 2023-03
• Subjects: Visual attention distribution -- Pattern extraction -- Data mining -- Eye movement -- Gaze sequence interpretation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107330
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Discover attention distribution regularizes behind eye movement. Mining explicitly expressed gaze frequent patterns. Quantitatively analyze the association rules between gaze patterns. Mining method associating the eye-tracking data with scenes data. Abstract: Background and Objective: Attention allocation reflects the way of humans filtering and organizing the information. On one hand, different task scenarios seriously affect human's rule of attention distribution, on the other hand, visual attention reflecting the cognitive and psychological process. Most of the previous studies on visual attention allocation are based on cognitive models, predicted models, or statistical analysis of eye movement data or visual images, however, these methods are inadequate to provide an inside view of gaze behavior to reveal the attention distribution pattern within scenario context. Moreover, they seldom study the association rules of these patterns. Therefore, we adopted the big data mining approach to discover the paradigm of visual attention distribution. Methods: We applied the data mining method to extract the gaze patterns to discover the regularities of attention distribution behavior within the scenario context. The proposed method consists of three components, tasks scenario segmented and clustered, gaze pattern mining, and association rule of frequent pattern mining. Results: The proposed approach is tested on the operation platform. The complex operation task is simultaneously segmented and clustered with the TICC-based method and evaluated by the BCI index. The operator's eye movement frequent patterns and their association rule are discovered. The results demonstrate that our method can associate the eye-tracking data with the task-oriented scene data. Discussion: The proposed method provides the benefits of being able to explicitly express and quantitatively analyze people's visual attention patterns. The proposed method can not only be applied in the field of aerospace medicine and aviation psychology, but also can likely be applied to computer-aided diagnosis and follow-up tool for neurological disease and cognitive impairment related disease, such as ADHD (Attention Deficit Hyperactivity Disorder), neglect syndrome, social attention differences in ASD (Autism spectrum disorder).