Mohamad El Iskandarani

This study examined whether tactile detection reduces dual-task costs compared to visual detection when paired with a visual tracking task, and whether this advantage persists as temporal task load increases. Thirty-eight participants completed a visual tracking task and a detection task concurrently. The detection task was presented either visually or tactually, and temporal task load was manipulated by varying the stimulus presentation rate. Performance was analyzed using signal detection theory measures to determine whether differences between visual and tactile detection reflected changes in perceptual sensitivity, response bias, or both. Tactile detection produced smaller dual-task costs than visual detection across hit rate, false alarm rate, sensitivity, and response bias. Critically, tactile detection better preserved sensitivity under dual-task conditions, suggesting reduced perceptual competition with the visual tracking task. However, higher temporal load still degraded performance in the tactile condition. These findings support multimodal interface design while highlighting limits of modality diversification.

Modern XR headsets can present multiple virtual windows with flexible appearances and arrangements. In time-sensitive settings such as system monitoring, users may need to quickly locate target information among distractors across windows or decide that the target is absent. We evaluated whether gaze-responsive visual effects on virtual windows could improve multi-window visual search. In a study with 15 participants, we compared a baseline with three gaze-driven effects. Participants searched for combinations of shapes, letters, and colors across multiple virtual windows in an XR headset. Effects responded to fixation by popping the fixated window toward the user, highlighting the fixated window while dimming others, or outlining previously visited windows as reminders. Results showed that the popping effect significantly reduced search time without increasing errors. The popping and outlining effects also made users faster at determining that the target was absent and quitting the search.

Visual search is shaped by both stress-inducing and motivational factors. Time pressure, a common stressor, can impair performance by prompting hurried search strategies, whereas reward value, a motivator, may enhance performance and efficiency. Although their individual effects are well-established, their joint influence on search performance and behavior is not yet clear—specifically, whether reward value can counteract the performance costs incurred by time pressure, enabling fast yet accurate searches. Moreover, little is known on whether time pressure and reward value exert distinct effects on the two processing components of visual search: target acquisition and target verification. The present eye-tracking study examines: (1) how time pressure and reward value influence search performance and behavior, and (2) whether reward value can counteract the adverse effects of time pressure on performance. Forty participants searched for target objects in a virtual living room under various time pressure and reward value conditions. As expected, time pressure reduced accuracy. However, it incurred longer target acquisition but shorter verification times. In contrast, reward value enhanced target acquisition efficiency and accuracy. It particularly improved accuracy and reduced search time under time pressure, suggesting a mitigating role against the performance decrements incurred by time pressure. Overall, the findings show that time pressure and reward value exert distinct effects on target acquisition and verification, yet their interaction influences performance.

Optical see-through head-mounted displays (OST-HMDs) allow users to interact with both physical and virtual displays overlaid in the real world within augmented reality (AR) environments. When arranging these displays in space, spatial discontinuity—such as differences in viewing distance or lateral separation in the visual field—may occur. This study examined the effects of displays' spatial discontinuities on multitasking performance and perceived workload in AR, as well as how these effects were influenced by task load. Participants completed a multitasking simulation with four tasks distributed across physical and virtual displays under four display configurations: two configurations with spatial discontinuity, one cross-display configuration without discontinuity, and a baseline configuration where all tasks were shown on the physical display. Each configuration was tested under two task load levels. Results showed that OST-HMD-supported AR significantly impaired multitasking performance, regardless of whether spatial discontinuity between virtual and physical displays was present. Introducing display discontinuities through small depth separations or alignment offsets produced no additional significant influence on performance. The AR configurations also shown increased perceived workload than the baseline, but spatial discontinuity did not significantly affect workload ratings. Task load modulated the effect of display configurations on performance as well as physical and frustration ratings of the perceived workload.

Augmented reality (AR) may provide supplementary information to support tasks in the physical world, offering the advantage of displaying multiple windows with high flexibility in interface layout. In AR-physical world mixed scenarios, users often need to locate and retrieve target information from virtual multi-window displays. Understanding how to design effective layouts for these interfaces is critical to enhancing visual search performance, a key element of information retrieval. This study examines the effects of depth separation, information density and curvature of virtual multi-window displays on a conjunctive visual search task in AR. Results indicate that reducing information density and introducing curvature significantly reduced both search time and the time taken to decide that the target was absent (task quit time). Although depth separation did not significantly affect search time, it notably reduced quit time. The number of errors was not significantly influenced by any of the factors. Additionally, users preferred a curved display with lower information density that remained within the device's field of view, and their search time was fastest with this layout. Finally, we noticed variations in layout preference and performance changes among individuals, possibly influenced by differences in search strategies.

Objective: The present work investigates how task modality (visual, auditory), task load level (low or high), and task load type (target-distractor similarity, display rate) influence dual-task interference in virtual reality (VR). Background: Dual-task interference is influenced by various factors including perceptual modality, where tasks that share the same modality may yield larger performance decrements than tasks that do not. Task load (i.e., level and type) can also reduce performance in one or both tasks. However, the interaction between these factors in immersive environments like VR is still unclear. Method: Participants performed a: (a) visual tracking task, (b) visual/auditory detection task, or (c) both concurrently in two different experiments. In Experiment 1, visual tracking load was manipulated via increasing target-distractor similarity, while detection load was manipulated via increasing display rate. In Experiment 2, detection load was manipulated by increasing target-distractor similarity. Results: In Experiment 1, higher detection task loads induced greater dual-task costs (DTC) in the detection task regardless of task modality, whereas tracking task DTC was not influenced by higher task loads. In Experiment 2, higher detection task loads induced greater DTC in the detection task only when it was presented visually. Conclusion: The findings suggest that tasks presented in the same modality may experience greater dual-task interference in one or both tasks depending on the task load level and type. Application: These findings can inform the design of multimodal interfaces in complex multitasking environments like military operations or emergency response where minimizing dual-task interference at varying workloads is crucial.

Understanding the factors that drive dual-task interference—the performance decline when two tasks are performed concurrently—is critical for designing effective interfaces in multitasking environments such as health care and aviation. This study examines the influence of resource conflict, visual angle separation, and task priority on dual-task interference. Data was collected from 39 participants performing a visual tracking task and a digit task in virtual reality (VR). Using Multiple Resource Theory’s (MRT) computational model, an iterative approach was employed to evaluate each factor’s contribution. Incorporating resource conflict improved model fit over a demand-only baseline, and adding a visual penalty to account for angle separation further enhanced prediction accuracy. Task-specific results showed greater interference in the digit task, suggesting that task priority influences susceptibility to interference. Overall, the results highlight the importance of incorporating resource conflict and perceptual factors, such as visual angle separation, into computational models of dual-task interference.

Objective: This study investigates the effects of workload and task priority on multitasking performance and reliance on Level 1 Explainable Artificial Intelligence (XAI) systems in high-stakes decision environments. Background: Operators in critical settings manage multiple tasks under varying levels of workload and priority, potentially leading to performance degradation. XAI offers opportunities to support decision making by providing insights into AI’s reasoning, yet its adoption and effectiveness in multitasking scenarios remain underexplored. Method: Thirty participants engaged in a simulated multitasking environment, involving UAV command and control tasks, with the assistance of a Level 1 (i.e., basic perceptual information) XAI system on one of the tasks. The study utilized a within-subjects experimental design, manipulating workload (low, medium, and high) and AI-supported-task priority (low and high) across six conditions. Participants’ accuracy, use of automatic rerouting, AI miss detection, false alert identification, and use of AI explanations were measured and analyzed across the different experimental conditions. Results: Workload significantly hindered performance on the AI-assisted task and increased reliance on the AI system especially when the AI-assisted task was given low priority. The use of AI explanations was significantly affected by task priority only. Conclusion: An increase in workload led to proper offloading by relying on the AI’s alerts, but it also led to a lower rate of alert verification despite the alert feature’s high false alert rate. Application: The findings from the present work help inform AI system designers on how to design their systems for high-stakes environments such that reliance on AI is properly calibrated.

Time pressure under a time constraint is a commonly found factor in complex domains that can impair visual search. Detecting when a user is subject to a time constraint is crucial for implementing timely interventions to counteract its detrimental effect on performance. Eye tracking, a non-intrusive method for recording eye movements, offers promising potential for time pressure detection. The present study investigates whether a classifier trained exclusively on eye tracking metrics can reliably classify if a user was under time pressure. For this study, eye tracking data from 40 participants was collected as they searched for objects in a virtual living room under different timing conditions and varying reward incentives, and 13 eye tracking metrics were calculated. The results showed that the support vector machine (SVM) classifier was the best-performing model with 0.82 AUROC, 74% accuracy, and 75% f1 score. This demonstrates the potential of eye tracking in detecting time pressure. These results, while promising, underline the importance of combining eye tracking with different physiological and behavioral measures to improve time pressure detection.