Julian Elias Reiser

Wood is commonly used in the building sector, emitting volatile organic compounds (VOCs) contributing to indoor air quality. These VOC profiles can have a pleasant smell and positive effects e.g., induce relaxation. Contrarily, VOCs can have adverse health effects in higher concentrations. Therefore, some VOCs are regulated by guide values (GV). Potentially positive and negative effects of pinewood emissions, ranging from 0.2 mg/m ³ (German GV I for bicyclic terpenes) to 2.0 mg/m ³ (GV II) were investigated in an experimental 2 h exposure study using a within-subject design. Thirty-two healthy participants rated the perception, pleasantness, symptoms of irritation, and indicators of well-being. During a demanding working memory task (n-back) and a resting period, heart rate (HR) and HR variability (HRV) changes were measured. Before and after each session physiological markers of sensory irritation were assessed. Ratings indicated that the exposure to GV I and GV II were not perceived as more intense or pleasant. Mostly concentration-independent effects were revealed, indicating that inter-individual factors influenced the ratings rather than the VOCs. The pinewood odors during the n-back task did not cause distraction nor did it facilitate performance as previously suggested. HR/V changes indicated that pinewood odors during and after the n-back tasks did not induce relaxation. Only symptoms of nasal irritation showed some weak concentration-dependency, not supported by physiological markers or comparable ratings of sensory irritation. In conclusion, the fact that no distinct odor is detected suggests that interfering factors potentially prevent the regulation of odors at relevant indoor air concentrations. Highlights: Successful exposure to realistic indoor pinewood VOCs No effect on ratings of indoor air quality, performance or sensory irritation No induction of relaxation during or after stressful tasks Short-term exposure to concentrations between GV I and II are non-effective

This article introduces a psychologically grounded framework to describe cognitive demands in the teleoperation of highly automated agents. It builds on established models of information processing, situation awareness, and occupational stress to explain how remote operators perceive, process, and act upon task demands in dynamic environments. Three scenarios from ground-based transportation illustrate varying operational contexts and support the identification of objective task demands. The framework highlights key human factors such as attention, working memory, and situation understanding, while also accounting for individual differences in cognitive resources. It provides a theoretical foundation for future empirical studies and supports the human-centered design of adaptive teleoperation workplaces. Practical Relevance : The framework supports the design and evaluation of teleoperation workplaces by identifying cognitive demands across diverse scenarios. It provides guidance for designing adaptive interfaces and task allocation strategies in event-driven, safety-critical environments that feature frequent context switches and uncertain sensor-based information. It helps practitioners and system designers to address the specific challenges of teleoperation as a complex socio-technical system.

Developing expertise when learning a motor sequence involves optimization of bodily movement. Although there is understanding of the stages of learning development from reactive responses to accurate and well-represented sequence performance, very little is known about the contribution of movement variability for learning. Fractal scaling can quantify the complexity of variability in learning behavior, yet motor sequence learning studies have not applied it. These results could offer valuable insights for profiling learners in real-world settings. The aim of this study was to understand fractal scaling differences in movement kinematics between learners of different expertise levels performing the Dance Discrete Sequence Performance task. We recorded Centre of Mass accelerations using a lower-body seven sensor setup with the Movella Xsens MVN Awinda and extracted Hurst Exponents of the accelerations as a representation of fractal complexity, between 11 High and 11 Low expertise learners (top 50% versus bottom 50% percentile). The results of the mixed effects models showed significant differences with increased fractal scaling during preparation, but decreased scaling during movement periods for the High expertise group. Our findings show that differences in fractal scaling can quantify motor learning expertise and their contribution to learning for different phases of the task.

Contemporary work environments require humans to process audiovisual information displays during active locomotion. The attentional demands associated with using devices such as in‐ear headphones and head‐mounted displays may be significantly influenced by varying locomotor demands, yet this relationship remains poorly understood. This study investigates the interplay of information presentation modality, movement state, and cognitive task difficulty. In a virtual reality laboratory, 22 participants performed a cued task‐switch paradigm with three cognitive task difficulty levels while standing, walking, or walking with perturbations on a treadmill. We used a questionnaire, behavioral, and mobile EEG data to investigate cognitive‐motor interference. We find that locomotion interfered with cognitive task performance, and that the presentation modality of task‐switch notification modified the nature of this interference. While auditory cue presentation resulted in faster responses under low cognitive load conditions, visual information presentation was less impaired by higher cognitive and locomotor demands. A detailed analysis of the EEG response to cues addressed these differences in terms of multi‐modal attentional mechanisms. Hence, information presentation on wearable devices should be tailored to the specific task demands, particularly for cognitively demanding information in mobile work settings.

Contemporary work environments require humans to process audiovisual information displays whilst moving around the world. The attentional demands associated with using devices such as in-ear headphones and head-mounted displays may be significantly influenced by varying locomotor demands, yet this relationship remains poorly understood. This study investigates the interplay of information presentation modality, movement state, and cognitive task difficulty. In a virtual reality laboratory, 22 participants performed a cued task-switch paradigm with two difficulty levels while standing, walking, or walking with perturbations on a treadmill. We used a questionnaire, behavioral and mobile EEG data to investigate cognitive-motor interference. We find that locomotion interfered with cognitive task performance, and that the presentation modality of task-switch notification modified the nature of this interference. While auditory cue presentation resulted in faster responses under low cognitive load conditions, visual information presentation was less impaired by higher cognitive and locomotor demands. A detailed analysis of the EEG response to cues addressed these differences in terms of multi-modal attentional mechanisms. Hence, wearable information presentation should be tailored to the specific needs, particularly for cognitively demanding information in mobile work settings.

Accurately evaluating cognitive load during work-related tasks in complex real-world environments is challenging, leading researchers to investigate the use of eye blinking as a fundamental pacing mechanism for segmenting EEG data and understanding the neural mechanisms associated with cognitive workload. Yet, little is known about the temporal dynamics of eye blinks and related visual processing in relation to the representation of task-specific information. Therefore, we analyzed EEG responses from two experiments involving simulated driving (re-active and pro-active) with three levels of task load for each, as well as operating a steam engine (active vs. passive), to decode the temporal dynamics of eye blink activity and the subsequent neural activity that follows blinking. As a result, we successfully decoded the binary representation of difficulty levels for pro-active driving using multivariate pattern analysis. However, the decoding level varied for different re-active driving conditions, which could be attributed to the required level of alertness. Furthermore, our study revealed that it was possible to decode both driving types as well as steam engine operating conditions, with the most significant decoding activity observed approximately 200 ms after a blink. Additionally, our findings suggest that eye blinks have considerable potential for decoding various cognitive states that may not be discernible through neural activity, particularly near the peak of the blink. The findings demonstrate the potential of blink-related measures alongside EEG data to decode cognitive states during complex tasks, with implications for improving evaluations of cognitive and behavioral states during tasks, such as driving and operating machinery.

Evaluating and understanding the cognitive demands of natural activities has been difficult using neurocognitive approaches like mobile EEG. While task-unrelated stimuli are commonly added to a workplace simulation to estimate event-related cognitive processes, using eyeblink activity poses an alternative as it is inherent to human behavior. This study aimed to investigate the eye blink event-related EEG activity of fourteen subjects while working in a power-plant operator simulation - actively operating (active condition) or observing (passive condition) a real-world steam engine. The changes in event-related potentials, event-related spectral perturbations, and functional connectivity under both conditions were analyzed. Our results indicated several cognitive changes in relation to task manipulation. Posterior N1 and P3 amplitudes revealed alterations associated with task complexity, with increased N1 and P3 amplitudes for the active condition, indicating greater cognitive effort than the passive condition. Increased frontal theta power and suppressed parietal alpha power were observed during the active condition reflecting high cognitive engagement. Additionally, higher theta connectivity was seen in fronto-parieto-centro-temporo-occipital regions as task demands increased, showing increased communication between brain regions. All of these results suggest using eye blink-related EEG activity to acquire a comprehensive understanding of neuro-cognitive processing while working in realistic environments.