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The different synesthesia effects with soundless dynamic lighting and musical dynamic lighting

Dynamic lighting has been proved to have substantial physical, psychological and sociological effects on humans. The degree of the response depends on the intensity, the light color, the flickering frequency and the duration of lighting. The study examined the physiological response to soundless lighting and musical dynamic lighting. Therefore, the physiological signals were recorded to investigate the resonance between the exposure of no sound dynamic lighting with different color (red, green, blue, 3000K, 6000K) and musical dynamic lighting. In the experiment, the heart rate, respiratory rate and electrodermal activity are recorded under dynamic lighting conditions. The results show that the musical dynamic lighting environment has a stronger impact on emotional perception than no sound dynamic lighting. Music plays a main effect role in the sound-light fusion environment. The physiological response is most sensitive under the fusion of dynamic lighting and music. This research will help us better understand synesthesia and provide theoretical support for the future practical application in meta-universe and such virtual simulation area.

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The Effect of Strategic Self-talk on Visual Attention and Volleyball Serve Performance

Introduction: The present study aimed to investigate the effect of strategic self-talk on volleyball players' performance and visual attention. Methods: This quasi-experimental research employed a pre-test/post-test design with a control group. Participants included 54 novice male volleyball players selected via convenience sampling. They were randomly assigned into five groups: instructional self-talk (n=10), motivational self-talk (n=11), instructionalmotivational self-talk (n=10), motivational-instructional self-talk (n=11), and control (n=12). The self-talk intervention was conducted over 12 weeks, with three sessions per week. In both the pre-test and post-test phases, serving scores were recorded by the researcher, and participants' gaze behavior was measured using an eye tracker while performing a simple volleyball serve task. Data were analyzed using the Analysis of Covariance (ANCOVA) and Bonferroni post-hoc tests. Results: The findings revealed that strategic self-talk had a significant effect on simple serve performance (P=0.0001) and quiet eye duration (P=0.0001). Bonferroni post-hoc tests indicated that instructional self-talk improved motor performance and increased quiet eye duration. Combined self-talk groups (instructional-motivational and motivational-instructional) also exhibited enhanced motor performance and prolonged quiet eye duration. However, motivational self-talk alone had no significant effect on motor performance and quiet eye duration. Conclusion: The results underscore the importance of instructional self-talk in enhancing performance and visual attention in novice volleyball players, supporting the attentional mechanisms underlying self-talk. … the serve task, the serve score was recorded by the researcher during the volleyball serve test, and the gaze behavior of the participants was measured by the eye tracker ERGONEERS…

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The effect of visual obstruction on targeting-reception skills and eye rest in children with developmental coordination disorder

Children with developmental coordination disorder have weaknesses in performing and tracking target-reception tasks. Research Method: In this quasi-experimental study, which was conducted with a repeated measures design, 26 children with developmental coordination disorder aged 7 to 9 years were purposefully selected from among girls and boys with developmental coordination disorder in Khorasgan Welfare and Integration Center in Isfahan. Participants threw 10 balls towards the wall and simultaneously received the returned ball under three conditions of complete vision, initial occlusion, and final occlusion. Simultaneously with throwing and receiving the ball, the participants' eye movements were recorded by a vision tracking device, as well as their performance scores. Data were analyzed using within-group analysis of variance with repeated measures and Bonferroni post hoc test. Results: The results showed that the performance of the aiming-receiving skill and the duration of the eye-rest period were significantly impaired in the initial occlusion and terminal occlusion conditions compared to the no occlusion conditions (p<0.05). Also, the results showed that the initial occlusion caused a greater impairment of the performance of the throwing and receiving skill and a shorter eye-rest period compared to terminal occlusion (p<0.05). Conclusion: In general, the results of the present study support the role of the pre-programming system in the performance of the throwing and receiving skill in children with developmental coordination disorder.

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The Effects of Cognitive Load on Pupil Dilation in Volleyball Players: An Eye-Tracking Study: Cognitive Load on Pupil Dilation in Volleyball Players

Volleyball is a sport that demands not only high levels of physical exertion but also significant cognitive engagement. Rapid decision-making, attentional control, and visuomotor coordination are essential for optimal performance. This study investigates the effects of cognitive load on pupil dilation and reaction time in competitive volleyball athletes. A within- subjects, controlled experimental design was employed, involving 26 female volleyball players (age 15.4 ± 1.2 years) who regularly train and compete at the regional level. Participants completed three cognitive load conditions in a randomized order: (P1) a motor task requiring the suppression of randomly illuminated lights as quickly as possible, (P2) a cue-based task in which the next light always appeared in the same color zone as the previous one, allowing for anticipatory responses, and (P3) a mixed cue task that introduced a more mixed cue structure, increasing task difficulty and necessitating greater motor control adaptation. Performance was assessed using the Fitlight system, which involved deactivating 25 lights under three distinct levels of cognitive load. To ensure the precise evaluation of cognitive load effects, pupil dilation was continuously measured using Tobii Pro Glasses 2. Results demonstrated that pupil dilation increased significantly under higher cognitive load conditions (p < .001) and that reaction times were faster when predictive cues were available (p < .001). These findings suggest that cognitive load plays a critical role in shaping athletes’ visual and motor responses, with direct implications for sports training and decision-making processes. Enhancing cognitive training protocols in volleyball may improve athletes’ ability to process visual information efficiently and execute rapid motor responses under high-pressure conditions.

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Toward Adaptive and User-Centered Intelligent Vehicles: AI Models with Granular Classifications for Risk Detection, Cognitive Workload, and User Preferences

As artificial intelligence (AI) increasingly integrates into our transportation systems, intelligent vehicles have emerged as research topics. Many advancements aim to enhance both the safety and comfort of drivers and the reliability of intelligent vehicles. The main focus of my research is addressing and responding to the varying states and needs of drivers, which is essential for improving driver-vehicle interactions through user-centered design. To contribute to this evolving field, this thesis explores the use of physiological signals and eye-tracking data to decode user states, perceptions, and intentions. While existing studies mostly rely on binary classification models, these approaches are limited in capturing the full spectrum of user states and needs. Addressing this gap, my research focuses on developing AI-driven models with more granular classifications for cognitive workload, risk severity levels, and user preferences for self-driving behaviours. This thesis is structured into three core domains: collision risk detection, cognitive workload estimation, and perception of user preferences for self-driving behaviours. By integrating AI techniques with multi-modal physiological data, my studies develop ML (Machine Learning) models for the domains introduced above and achieve high performance of the ML models. Feature analytical techniques are employed to enhance model interpretability for a better understanding of features and to improve the model performance. These findings pave the way for a new paradigm of intelligent vehicles that are not only more adaptive but also more aligned with user needs and preferences. This research lays the groundwork for the future development of user-centered intelligent companion systems in vehicles, where adaptive, perceptive, and interactive vehicles can better meet the complex demands of their users.

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Towards generalizable drowsiness monitoring with physiological sensors: A preliminary study

Accurately detecting drowsiness is vital to driving safety. Among different measures, physiological-signal-based drowsiness monitoring can be more privacy-preserving than a camera-based approach. However, conflicts exist regarding how physiological metrics are associated with different drowsiness labels across datasets, which might reduce the generalizability of data-driven models trained with multiple datasets. Thus, we analyzed key features from electrocardiograms (ECG), electrodermal activity (EDA), and respiratory (RESP) signals across four datasets, where different drowsiness inducers (such as fatigue and low arousal) and assessment methods (subjective vs. objective) were used. Binary logistic regression models were built to identify the physiological metrics that are associated with drowsiness. Findings indicate that distinct drowsiness inducers can lead to different physiological responses, and objective assessments were more sensitive than subjective ones in detecting drowsiness. Further, decreased heart rate stability, respiratory amplitude, and tonic EDA are robustly associated with increased drowsiness. These results enhance the understanding of drowsiness detection and can inform future generalizable monitoring designs.

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Training and dashboard design: Impact on operator performance and mental workload for flight safety

Pilot training, the design of flight instrument panels, and mental workload are essential elements for ensuring aviation safety. Prior studies on icon learning have shown that chunking techniques can improve understanding of icon-related information. The research explores the effects of different learning methods and instrument panel designs on learning. The study compares two types of panel layouts: a chunking layout and a long-scanning path layout. Thirty participants were enlisted and divided into two groups: one using the chunking method and a control group. The chunking group was trained to recognize instruments through functional grouping, whereas the control group received training in a random sequence. Both objective and subjective evaluations were used to assess the participants’ workload. Findings indicated that the chunking group was more efficient in visual search during training. However, the two groups had no notable differences in learning rates or NASA-TLX scores. The results support using chunking as a training strategy and an optimized panel layout to improve performance significantly. By integrating the proven benefits of chunking-based training and optimized panel layouts, the aviation industry could significantly enhance pilot efficiency and reduce mental workload, improving flight safety and operational effectiveness.

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Training Development and Learning Facilitation for Lower-Level Automated Driving Systems Across Age Groups

Autonomous driving holds the potential to transform the transportation industry, offering significant improvements in safety, efficiency, and convenience. However, traditional model-based planning approaches struggle to address the complexities and uncertainties of real-world driving environments. This thesis employs deep reinforcement learning (DRL) to achieve safe and efficient autonomous driving using realistic simulation settings and evaluation based on rational criteria. The proposed framework integrates five key factors—driving safety, driving efficiency, training efficiency, unselfishness, and interpretability (DDTUI) to ensure reliable and optimal decision-making across various driving scenarios. The research addresses two primary applications: highway driving and autonomous racing. In highway driving, the DRL-based framework demonstrates superior performance compared to popular baseline algorithms, improving safety and efficiency. In autonomous racing, an extreme case of autonomous driving, the framework is adapted to manage high velocities and safe control, achieving fewer collisions, faster lap times, and reduced training time in comparison to benchmark algorithms. This thesis contributes to the field by advancing RL-based planning techniques and establishing a design methodology for integrating key factors in autonomous driving. The results of this study provide evidences of the development of safer, more efficient, and interpretable autonomous driving systems. Finally, key achievements are summarized, limitations are discussed, and future research directions are proposed.

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Understanding Visual Attention to Button Design Utilizing Eye-Tracking: An Experimental Investigation

As graphical user interfaces continue to become more complex; it is becoming increasingly important for user interface (UI) and user experience (UX) designers to understand how design elements influence user attention. This study investigates the impact of button shape on user perception, focusing on shape preferences, attention distribution, and perceived pleasantness. To isolate the effect of shape, buttons with five different corner radii (completely angular to completely curved) were presented without contextual influences in a pairwise comparison. The research combined eye-tracking technology with digital questionnaires to collect both objective and subjective data. The results obtained revealed a preference for buttons with moderate corner radii, while buttons with completely angular corners received the least attention and were the least favored. Notably, discrepancies emerged between subjective preferences and objective attention rankings, particularly for wireframe buttons. This research demonstrates the effectiveness of eye-tracking in UI/UX design studies and provides valuable insights into the relationship between attention and preference for abstract design elements. The findings offer fundamental theory for creating more intuitive and effective graphical user interfaces, while also highlighting the limitation and importance of examining design elements within relevant contexts in future studies.

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Using eye tracking to study the takeover process in conditionally automated driving and piloting systems

In a conditionally automated environment, human operators are often required to resume manual control when the autonomous system reaches its operational limits — a process referred to as takeover. This takeover process can be challenging for human operators, as they must quickly perceive and comprehend critical system information and successfully resume manual control within a limited amount of time. Following a period of autonomous control, human operators’ Situation Awareness (SA) may be compromised, thus potentially impairing their takeover performance. Consequently, investigating potential approaches to enhance the safety and efficiency of the takeover process is essential. Human eyes are vital in an individual’s information gathering, and eye tracking techniques have been extensively applied in the takeover studies in previous research works. The current study aims at enhancing the takeover procedure by utilizing operators’ eye tracking data. The data analysis methods include machine learning techniques and the statistical approach, which will be applied to driving and piloting domains, respectively. Simulation experiments were conducted in two domains: a level-3 semi-autonomous vehicle in the driving domain and an autopilot-assisted aircraft landing scenario in the piloting domain. In both domains, operators ’eye tracking data and simulator-derived operational data were recorded during the experiments. The eye tracking data went through two categories of feature extractions: eye movement features linked predominantly to fixation and saccades, and Area-of-Interest (AOI) features associated with which AOI the gaze was located. Eye tracking features were analyzed using both traditional statistical techniques and machine learning models. Key eye tracking features included fixation-based metrics and AOI features, such as dwelling time, entry count, and gaze entropy. Operators’ SA and takeover performance were measured by a series of domain specific metrics, including Situation Awareness Global Assessment Technique (SAGAT) score, Hazard Perception Time (HPT), Takeover Time (TOT) and Resulting acceleration. Three research topics were discussed in the current thesis and each topic included one driving study and one piloting study. In topic 1, significant differences in eye movement patterns were found between operators with higher versus lower SA, as well as between those with better and worse takeover performance. Besides the notable differences in various Area-of-Interests (AOIs) across three pre-defined Time windows (TWs), in the driving domain, drivers with a better SA and better takeover performance showed inconsistent eye movement patterns after the Takeover Request (TOR) and before they perceived hazards. In the piloting domain, pilots with shorter TOT showed more distributed and complex eye movement pattern before the malfunction alert and after resuming control. During the intervening period, their eye movements were more focused and predictable, indicating vifast identification of necessary controls with minimal visual search. In topic 2, significant differences in eye movement patterns were observed between younger and older drivers, as well as between learner and expert pilots. As for driving domain, older drivers exhibited more extensive visual scanning, indicating difficulty in effectively prioritizing information sources under time pressure. In piloting domain, expert pilots not only allocate more attention to critical instrument areas but also dynamically adjust their scanning behavior based on the current tasks. In topic 3, machine learning models trained on eye tracking features successfully performed binary classification for both SA-related and takeover performance-related metrics. Model performance was evaluated using standard classification metrics, including accuracy, precision, recall, F1-score, and Area Under the ROC Curve (AUC). Finally, comparisons were made across Topics 1 and 2, as well as between the driving and piloting domains. The results suggest that better operators can flexibly adapt their gaze strategies to meet task demands, shifting between broad visual scanning and focused searching when appropriate. This shift in patterns underscores the importance of accounting for the specific Time window (TW) when interpreting operators’ eye movements. Overall, this thesis advances the understanding of different eye movement patterns during the takeover process by exploring a range of eye tracking features. The findings support the development of operator training programs and the design of customized interfaces to enhance the safety and efficiency of takeover performance.

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The different synesthesia effects with soundless dynamic lighting and musical dynamic lighting

The Effect of Strategic Self-talk on Visual Attention and Volleyball Serve Performance

The effect of visual obstruction on targeting-reception skills and eye rest in children with developmental coordination disorder

The Effects of Cognitive Load on Pupil Dilation in Volleyball Players: An Eye-Tracking Study: Cognitive Load on Pupil Dilation in Volleyball Players

Toward Adaptive and User-Centered Intelligent Vehicles: AI Models with Granular Classifications for Risk Detection, Cognitive Workload, and User Preferences

Towards generalizable drowsiness monitoring with physiological sensors: A preliminary study

Training and dashboard design: Impact on operator performance and mental workload for flight safety

Training Development and Learning Facilitation for Lower-Level Automated Driving Systems Across Age Groups

Understanding Visual Attention to Button Design Utilizing Eye-Tracking: An Experimental Investigation

Using eye tracking to study the takeover process in conditionally automated driving and piloting systems

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