Eye-controlled interaction in VR offers intuitive, hands-free control, but reducing false triggers such as the “Midas touch” remains challenging. This study evaluates target-assisted fixation (TA-Fixation) under varying fields of view (FOVs) and target densities. Experiment 1 compares TA-Fixation with standard fixation and gaze gesture techniques, showing that TA-Fixation balances accuracy and efficiency. However, in smaller FOVs and higher target densities, both fixation and TA-Fixation exhibit reduced accuracy, highlighting the relative advantage than gaze gestures. Experiment 2 explores how to optimize TA-Fixation in high-density scenarios, focusing on the effects of the relative position and size of the assisted-trigger object (AO) on performance based on Fitts's Law. The results indicate that smaller sizes and greater distances improve accuracy but decrease efficiency, while smaller distances and medium sizes offer a better trade-off. This study provides both theoretical and practical support and optimization strategies for gaze input techniques.

Gaze interaction in virtual reality (VR) offers promising advantages in speed and hands-free operation. However, selecting a suitable selection mechanism in VR interfaces with different visual encodings remains a challenge. This research compares two visual input techniques and explores specific visual elements, including field of view (FOV), design style, and number of targets, with the goal of alleviating these problems. The pilot study identifies two search stages, and it also demonstrates a significant effect of FOV on search time. In the formal experiment, a gaze-based trigger experiment is employed to examine the selection performance of fixation and smooth pursuit. The results reveal that optimizing FOV and number of targets improves the triggering efficiency. Furthermore, fixation outperforms pursuit in terms of triggering time and accuracy under the same conditions, while cognitive load during triggering process remains relatively similar. These findings are expected to propose design recommendations to enhance fixation and pursuit within VR interfaces.

Eye-tracking technology presents the potential to enable more personalized audio commentaries. However, previous research has focused on the technical aspects of gaze-based audio commentary systems. In this study, we proposed three methods of gaze-based audio commentaries based on the eye-controlled interaction paradigms and developed three prototypes: implicit, explicit, and implicit & explicit. Results from a controlled study (N = 45) in virtual museums exhibiting ancient paintings indicated that the explicit method obtained better performance in terms of commentary experience, commentary quality, and intrinsic motivation, which were largely long-term. All three methods of gaze-based audio commentaries somewhat hindered users’ acquisition of audio information, but they facilitated the acquisition of visual information. Among them, the implicit method supported users in learning the details of visual information, while the explicit method aided in learning the position and dimension of visual information. Nevertheless, they lacked long-term value for users’ retention of the information.

Interface icons are often scaled to adapt to different displays in cross-device collaborations. However, adaptive scaling of icons may cause perceptual bias in how icon arrays are visually perceived, which reduces usability and coherent user experience. This article presents an empirical study that evaluates the perceptual bias in the consistency of icon spacing and size caused by adaptive scaling. Then the impact of various visual features of icons (i.e., the border shape, polarity, and composition) on the perceptual bias are investigated. In this study, we found that cross-device scaling of icons causes a perceptual bias in the consistency of icon spacing and size. The shape of the icon border has a significant difference in the perceived spacing of icons, and the bias of the round shape is smaller than that of the square shape. Moreover, changing the icon polarity can affect the perceptual bias of consistency in icon size. These findings are expected to propose scaling recommendations for improving the visual consistency of icon arrays across.

Edges crossing and nodes overlapping have a significant effect on the users’ recognition and comprehension of network diagrams. In this study, we propose a visual evaluation method for users’ cognition of network diagrams. First, this method carries out a set of cognitive experiments to collect the user’s cognitive performance that affects the variables, including accuracy and response time. The user’s pupil diameter is measured through an eye tracker to reflect their cognitive load. Second, the significance test points out the visual features as independent variables and then establishes an evaluation regression model. The experimental results show that the number of edges, edge length, node visual interference, and edge occlusion contribute to the evaluation models of response time, and edge occlusion and the number of node connections contribute to the accuracy model. Finally, these evaluation models demonstrate good predictability when assessing users’ cognition of network diagrams and provide practical recommendations for their use.