Knowledge extraction from pointer movements and its application to detect uncertainty. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Cepeda, Catia; Dias, Maria Camila; Rindlisbacher, Dina; Gamboa, Hugo; Cheetham, Marcus
• Is Part Of: Heliyon. Volume 7:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: Human-computer interaction -- Pointer-tracking -- Mouse movement dynamics -- Decision uncertainty -- On-line survey -- Spatio-temporal features -- Machine learning; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2020.e05873
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: Pointer-tracking methods can capture a real-time trace at high spatio-temporal resolution of users' pointer interactions with a graphical user interface. This trace is potentially valuable for research on human-computer interaction (HCI) and for investigating perceptual, cognitive and affective processes during HCI. However, little research has reported spatio-temporal pointer features for the purpose of tracking pointer movements in on-line surveys. In two studies, we identified a set of pointer features and movement patterns and showed that these can be easily distinguished. In a third study, we explored the feasibility of using patterns of interactive pointer movements, or micro-behaviours, to detect response uncertainty. Using logistic regression and k-fold cross-validation in model training and testing, the uncertainty model achieved an estimated performance accuracy of 81%. These findings suggest that micro-behaviours provide a promising approach toward developing a better understanding of the relationship between the dynamics of pointer movements and underlying perceptual, cognitive and affective psychological mechanisms. Abstract : Human-computer interaction; Pointer-tracking; Mouse movement dynamics; Decision uncertainty; On-line survey; Spatio-temporal features; Machine learning