This study investigated the effect of layout order on the complexity of the dashboard interface based on screen-based eye trackers. By simplifying and abstracting dashboard interfaces and incorporating subjective ratings (symmetry and unity calculations), we successfully manipulated the levels of complexity and layout order of the interface materials. Using four types of eye movement data (total fixation count, total gaze duration, scanning paths, and hotspot maps) and behavioral data, we compared participants’ visual search behavior on interfaces with different layout orders and complexity levels. Experiment 1 revealed a significant interaction between layout order and interface complexity, with participants performing significantly better in the high-level layout order condition. Experiment 2 confirmed that the position of the core chart plays a crucial role in users’ visual search behavior and that the optimal layout order for the dashboard is to place the core chart on the left side of the interface’s horizontal axis, with partial symmetry in the no-core chart areas. This study highlights the effectiveness of eye-tracking techniques in user interface design research and provides valuable insights into optimizing dashboard interface design. Designers should adopt the design principle of “order is more” in addition to “less is more” and consider designing the core chart in the left-center position.

Autonomous systems’ “black-box” nature impedes user trust and adoption. To investigate explainable visualizations’ impact on trust and cognitive states, we conducted a within-subjects study with 29 participants performing high-fidelity driving tasks across three transparency conditions: black-box, standard, and enhanced visualization. Multimodal data analysis revealed that enhanced visualization significantly increased perceived usefulness by 28.5% (p < 0.001), improved functional trust, and decreased average pupil diameter by 15.3% (p < 0.05), indicating lower cognitive load. The black-box condition elicited minimal visual exploration, lowest subjective ratings, and “out-of-the-loop” behaviors. Fixation duration showed no significant difference between standard and enhanced conditions. These findings demonstrate that well-designed visualizations enable balanced trust calibration and cognitive efficiency, advocating “meaningful transparency” as a core design principle for effective human–machine collaboration in autonomous vehicle interfaces. This study provides empirical evidence that transparency enhances user experience and system performance.

The current study aims to investigate the effects of 3D virtual object complexity on visual working memory capacity in mobile augmented reality (MAR) interfaces. With the popularity of augmented reality technology in mobile applications, 3D virtual elements play a key role in interaction. However, prior studies ignored the correlation between virtual object presentation and visual working memory (VWM). Given that visual working memory capacity is closely related to overall cognitive ability, the current study explored the relationship between the complexity of 3D virtual objects and VWM capacity in AR interfaces. Sixty volunteers participated in a 5-point Likert scale rating to eliminate the interference factors of familiarity, concreteness and similarity in 3D virtual material objects. Then, we further conducted an MAR change detection paradigm experiment and successfully manipulated the complexity of 3D virtual objects and the set size. Thirty-one subjects completed the formal experiment. Three measurements (reaction time, proportion correct, and Cowan’s K) were analyzed for nine experimental conditions (three object complexity levels and three object set sizes). The results confirmed that the visual working memory capacity in mobile AR interfaces is modulated by the complexity of the 3D virtual objects and set size, which decreases with the increase in complexity and set size of the displayed 3D virtual objects. As the complexity of the 3D virtual objects increases, the amount of resources allocated to each item decreases, resulting in a decrease in memory capacity and memory accuracy. This study highlights the effectiveness of VWM capacity in MAR interface design research and provides valuable insights into determining the best way to present 3D virtual objects.