Semantic distance of icons: Impact on user cognitive performance and a new model for semantic distance classification

Abstract

With the continuous development of human-computer interaction technologies and the widespread use of graphical interfaces, icons that represent various objects and functions have a particularly important role. This study investigates the effect of semantic distance of icons on cognitive performance through an eye-movement-based experiment which involves a visual search for icons. The findings show that the semantic distance of icons has a significant effect on cognitive performance. A higher cognitive performance is found with semantically close icons which can better capture user attention. In addition, we use eye-movement indicators that are highly correlated with semantic distance, including mean pupil diameter, mean gaze duration and initial gaze time of an AOI, and analyze the objective relationship between these three eye-movement indicators and the semantic distance of icons to establish a dataset. The dataset is used as input for a Gradient Boosting Decision Tree (GBDT), which is a machine learning-based method for classifying the semantic distance of the icons in this study. The output of the GBDT model is classifying the semantic distance as far and close, and the experimental results show that the accuracy of the model reaches 84.28% after a comparison with other types of classifiers, which is in good agreement with the experimental results. Therefore, the model can address the relevant application requirements and simplify the evaluation process of icons to a certain extent, which has great significance in the field of icon design.