Perceptual grouping enables people to organize elements into units according to intrinsic (e.g., proximity) and extrinsic (e.g., common region) principles. However, the role of physical surfaces as extrinsic grouping cues for virtual elements in Augmented Reality (AR) remains unclear. To provide a deeper understanding, we conducted two within-subject studies. The first study (N = 24) using repetition discrimination tasks revealed that surfaces can be common-region cues in 3D, with their influence depending on their distance to target objects along the viewing direction. Building on these findings, the second study (N = 24) employed both objective and subjective measures to capture the interaction between proximity and common-region cues in AR. Results indicate that competing cues reduce group clarity. They also enable us to distill people’s strategies for improving the clarity by leveraging their physical and virtual environments. Finally, we propose design recommendations for future AR systems in assisted grouping tasks.

This paper presents an N-ary Gaussian Model for predicting endpoint distributions in pointing tasks across task scenarios. Built on the foundational principles of the Ternary Gaussian model series, our model framework allows researchers to define parameter constraints and automatically refine model combinations, eliminating the need for predefined equations based on data analysis. We utilize the Bayesian Information Criterion (BIC) for model selection, ensuring simplicity while maintaining predictive accuracy. We conducted a comparative analysis against published baselines across 7 diverse datasets, covering 1D, 2D, and 3D tasks, different input modalities, different display devices, and time-constrained scenarios, demonstrating the robustness and generalization of the N-ary Gaussian Model. The N-ary Gaussion model offers an automated solution for modeling pointing uncertainty, and also incorporates cross output device, input modality, and temporal constraint factors into spatial pointing uncertainty modeling for the first time.

Clinician skepticism toward opaque AI hinders adoption in high-stakes healthcare. We present AICare, an interactive and interpretable AI copilot for collaborative clinical decision-making. By analyzing longitudinal electronic health records, AICare grounds dynamic risk predictions in scrutable visualizations and LLM-driven diagnostic recommendations. Through a within-subjects counterbalanced study with 16 clinicians across nephrology and obstetrics, we comprehensively evaluated AICare using objective measures (task completion time and error rate), subjective assessments (NASA-TLX, SUS, and confidence ratings), and semi-structured interviews. Our findings indicate AICare’s reduced cognitive workload. Beyond performance metrics, qualitative analysis reveals that trust is actively constructed through verification, with interaction strategies diverging by expertise: junior clinicians used the system as cognitive scaffolding to structure their analysis, while experts engaged in adversarial verification to challenge the AI’s logic. This work offers design implications for creating AI systems that function as transparent partners, accommodating diverse reasoning styles to augment rather than replace clinical judgment.

Virtual reality (VR) allows users to observe and manipulate 3D geometry from multiple viewpoints. Most VR selection work, however, optimizes techniques for selecting entire objects. Selecting a single face on a polyhedron remains underexplored and is more challenging because the interaction must act on a local component while preserving the object's global structure. We introduce a design space tailored to this task with three dimensions: viewing strategy, disambiguation consistency, and interaction metaphor. Guided by this space, we design eight freehand techniques for polyhedral face selection. A within-subjects study with 16 participants evaluates these techniques across polyhedral complexity (two radii; face counts 4, 6, and 12). The results identify three complementary top techniques, reveal tradeoffs between viewing choices and geometric preservation, and yield concrete guidelines for matching techniques to target geometry and task demands. A follow-up study with complex, realistic models confirms the robustness and practical usability of the three techniques. Together, these contributions shift attention from whole object selection to precise component selection in VR and provide actionable methods for 3D modeling, assembly, and texturing.

Proximity-based feedback provides users with real-time guidance as they approach an interaction goal. This type of feedback is particularly useful for tasks that require guidance during the interaction process, such as selecting moving targets. This work explores proximity-based feedback types and modalities to improve the selection of moving targets in VR by leveraging three feedback types that combine visual, auditory, and haptic modalities. We evaluated the performance of these mechanisms through two user studies, analyzing both objective data (e.g., selection time, error rate) and subjective data (e.g., user experience, preferences) to explore the characteristics of feedback types across different modalities and to examine the roles of various modalities within multimodal combinations. Our findings suggest optimal selection mechanisms for developers and should be tailored to different goals: achieving user precision, enabling quick movement to a target, considering task duration, and enhancing entertainment value. We also discuss applications that correspond to these different perspectives.

Target selection is one of the most fundamental tasks in VR interaction systems. Prediction heuristics can provide users with a smoother interaction experience in this process. Our work aims to predict the ray landing pose for hand-based raycasting selection in Virtual Reality (VR) using a Long Short-Term Memory (LSTM)-based neural network with time-series data input of speed and distance over time from three different pose channels: hand, Head-Mounted Display (HMD), and eye. We first conducted a study to collect motion data from these three input channels and analyzed these movement behaviors. Additionally, we evaluated which combination of input modalities yields the optimal result. A second study validates raycasting across a continuous range of distances, angles, and target sizes. On average, our technique’s predictions were within 4.6° of the true landing Pose when 50% of the way through the movement. We compared our LSTM neural network model to a kinematic information model and further validated its generalizability in two ways: by training the model on one user’s data and testing on other users (cross-user) and by training on a group of users and testing on entirely new users (unseen users). Compared to the baseline and a previous kinematic method, our model increased prediction accuracy by a factor of 3.5 and 1.9, re spectively, when 40% of the way through the movement.

Recent advances in eye-tracking technology have positioned gaze as an efficient and intuitive input method for Virtual Reality (VR), offering a natural and immersive user experience. As a result, gaze input is now leveraged for fundamental interaction tasks such as selection, manipulation, crossing, and steering. Although several studies have modeled user steering performance across various path characteristics and input methods, our understanding of gaze-based steering in VR remains limited. This gap persists because the unique qualities of eye movements-involving rapid, continuous motions-and the variability in eye-tracking make findings from other input modalities nontransferable to a gaze-based context, underscoring the need for a dedicated investigation into gaze-based steering behaviors and performance. To bridge this gap, we present two user studies to explore and model gaze-based steering. In the first one, user behavior data are collected across various path characteristics and eye-tracking conditions. Based on this data, we propose four refined models that extend the classic Steering Law to predict users' movement time in gaze-based steering tasks, explicitly incorporating the impact of tracking quality. The best-performing model achieves an adjusted R2 of 0.956, corresponding to a 16% improvement in movement time prediction. This model also yields a substantial reduction in AIC (from 1550 to 1132) and BIC (from 1555 to 1142), highlighting improved model quality and better balance between goodness of fit and model complexity. Finally, data from a second study with varied settings, such as a different eye-tracking sampling rate, illustrate the strong robustness and predictability of our models. Finally, we present scenarios and applications that demonstrate how our models can be used to design enhanced gaze-based interactions in VR systems.

Piloting requires users to control and navigate the aircraft within a designated pathway, with a controller that utilizes two joysticks to control the aircraft. This task is representative of various daily and gaming scenarios, such as controlling the aircraft to capture the photo or navigating an object in a game from the start position to the end via a trajectory. In this work, we explore a model (based on the Steering Law) that predicts the piloting time required in spatial-constrained environments. Thus, two user studies are conducted to help us understand the relationship between path complexity (curvature) and spatial constraints (width and height). According to the results, we propose a model that can achieve $52.6 \%$ and $60.6 \%$ improvement in R-square and the Akaike Information Criterion (AIC), respectively. Next, an additional study was conducted to further verify the performance and efficiency of our proposed model with the change of movement direction and orientation. Our model and experimental results can benefit both game and interface designers of applications that require controlling moving objects along specific trajectories in virtual reality environments.

Object selection is essential in virtual reality (VR) head-mounted displays (HMDs). Prior work mainly focuses on enhancing and evaluating techniques for selecting a single object in VR, leaving a gap in the techniques for multi-object selection, a more complex but common selection scenario. To enable multi-object selection, the interaction technique should support group selection in addition to the default pointing selection mode for acquiring a single target. This composite interaction could be particularly challenging when using freehand gestural input. In this work, we present an empirical comparison of six freehand techniques, which are comprised of three mode-switching gestures (Finger Segment, Multi-Finger, and Wrist Orientation) and two group selection techniques (Cone-casting Selection and Crossing Selection) derived from prior work. Our results demonstrate the performance, user experience, and preference of each technique. The findings derive three design implications that can guide the design of freehand techniques for multi-object selection in VR HMDs.