Shota Yamanaka

Steering operations, such as menu navigation and drawing, constitute fundamental GUI interactions. The conventional Steering Law estimates movement times by accumulating the local width of the path, but it suffers from limited predictive accuracy for paths with varying curvature. In contrast, humans tend to adjust their speed by anticipating the path ahead (e.g., when driving). This study proposes a new Preview-based Model, which integrates geometric properties with visual constraints further along the path. Our experimental results show that our model achieved a higher adjusted R² (0.983) on curve-included paths than existing models. Furthermore, a simplified formulation without additional parameter tuning also exhibited comparable accuracy (adjusted R² = 0.972), confirming its effectiveness as a practical difficulty metric.

In crowdsourced user experiments that collect performance data from graphical user interface (GUI) interactions, some participants ignore instructions or act carelessly, threatening the validity of performance models. We investigate a pre-task screening method that requires simple GUI operations analogous to the main task and uses the resulting error as a continuous quality signal. Our pre-task is a brief image-resizing task in which workers match an on-screen card to a physical card; workers whose resizing error exceeds a threshold are excluded from the main experiment. The main task is a standardized pointing experiment with well-established models of movement time and error rate. Across mouse- and smartphone-based crowdsourced experiments, we show that reducing the proportion of workers exhibiting unexpected behavior and tightening the pre-task threshold systematically improve the goodness of fit and predictive accuracy of GUI performance models, demonstrating that brief pre-task screening can enhance data quality.

As XR devices become widespread, 3D interaction has become commonplace, and UI developers are increasingly required to consider usability to deliver better user experiences. The HCI community has long studied target-pointing performance, and research on 3D environments has progressed substantially. However, for practitioners to directly leverage research findings in UI improvements, practical tools are needed. To bridge this gap between research and development in VR systems, we propose a system that estimates object selection success rates within a development tool (Unity). In this paper, we validate the underlying theory, describe the tool's functions, and report feedback from VR developers who tried the tool to assess its usefulness.