The impact of alerting designs on air traffic controller's eye movement patterns and situation awareness

Physiological measures of operators' mental state in supervisory process control tasks: a scoping review

Physiological measures are often used to assess the mental state of human operators in supervisory process control tasks. However, the diversity of research approaches creates a heterogeneous landscape of empirical evidence. To map existing evidence and provide guidance to researchers and practitioners, this paper systematically reviews 109 empirical studies that report relationships between peripheral nervous system measures and mental state dimensions (e.g. mental workload, mental fatigue, stress, and vigilance) of interest. Ocular and electrocardiac measures were the most prominent measures across application fields. Most studies sought to validate such measures for reliable assessments of cognitive task demands and time on task, with measures of pupil size receiving the most empirical support. In comparison, less research examined the utility of physiological measures in predicting human task performance. This approach is discussed as an opportunity to focus on operators' individual response to cognitive task demands and to advance the state of research.

Creating safe environments: optimal acoustic alarming of laypeople in fire prevention

Hazards like fires occur regularly and can cost people's lives. Optimal auditory alarm signals enable laypeople to recognise dangers and to protect themselves. Existing fire alarm sound research focuses on alarm sounds and voice alerts presented singularly. We explored a combination of both and aimed to identify alarm signals that work optimally in everyday life. Thus, we conducted two online experiments: In Study 1 (N = 379), we tested eight alarm sounds regarding their typicality, their familiarity, their arousal, their valence, and their dominance. Siren-like alarm sounds were the most effective. In Study 2 (N = 206), we combined the four most effective alarm sounds with a voice alert. The voice alert reinforced ambiguity reduction, action motivation, and action intention. Hence, we suggest using alarm sounds with siren-like patterns. They should be combined with a voice alert to foster a quick and specific (target task-oriented) reaction.Practitioner summary: Warning laypeople is of great importance in time-critical hazards. In two remote testing studies (NTotal = 585), auditory alarm sounds with siren-like patterns resulted in the most distinct and emotional perception. Combining the alarm sound with a voice alert adds meaning to the alarm and fosters action intention.Abbreviations: DIN: Deutsches Institut für Normung [German Institute for Standardization]; ISO: International Organization for Standardization; Mixed MANOVA: mixed measures multivariate analysis of variance; rmMANOVA: repeated measures multivariate analysis of variance.

Mining attention distribution paradigm: Discover gaze patterns and their association rules behind the visual image

BACKGROUND AND OBJECTIVE

Attention allocation reflects the way of humans filtering and organizing the information. On one hand, different task scenarios seriously affect human's rule of attention distribution, on the other hand, visual attention reflecting the cognitive and psychological process. Most of the previous studies on visual attention allocation are based on cognitive models, predicted models, or statistical analysis of eye movement data or visual images, however, these methods are inadequate to provide an inside view of gaze behavior to reveal the attention distribution pattern within scenario context. Moreover, they seldom study the association rules of these patterns. Therefore, we adopted the big data mining approach to discover the paradigm of visual attention distribution.

METHODS

We applied the data mining method to extract the gaze patterns to discover the regularities of attention distribution behavior within the scenario context. The proposed method consists of three components, tasks scenario segmented and clustered, gaze pattern mining, and association rule of frequent pattern mining.

RESULTS

The proposed approach is tested on the operation platform. The complex operation task is simultaneously segmented and clustered with the TICC-based method and evaluated by the BCI index. The operator's eye movement frequent patterns and their association rule are discovered. The results demonstrate that our method can associate the eye-tracking data with the task-oriented scene data.

DISCUSSION

The proposed method provides the benefits of being able to explicitly express and quantitatively analyze people's visual attention patterns. The proposed method can not only be applied in the field of aerospace medicine and aviation psychology, but also can likely be applied to computer-aided diagnosis and follow-up tool for neurological disease and cognitive impairment related disease, such as ADHD (Attention Deficit Hyperactivity Disorder), neglect syndrome, social attention differences in ASD (Autism spectrum disorder).

Identification of risk factors for air traffic controllers' unsafe acts based on online reviews

Online reviews may influence unsafe acts and are significant in the context of big data. This study acquired online reviews related to air traffic control from social media websites. The word frequency statistics and coding of negative comments were taken to mine risk factors. Combined with the human factors analysis and classification system (HFACS), a conceptual model of the risk factors associated with the unsafe acts of air traffic controllers (ATCers) was constructed. The results indicate that the frequency of risk factors in online reviews, ranked from high to low, is organizational influences, ATCers' adverse states, environmental factors and unsafe supervision. Organizational influences, environmental factors and unsafe supervision indirectly affect the unsafe acts through the ATCers' adverse states. It is demonstrated that the combination of HFACS and online reviews to identify risk factors enables the identification of problems in the air traffic control industry and demands of ATCers.

Correlation Evaluation of Pilots' Situation Awareness in Bridge Simulations via Eye-Tracking Technology

To maintain situation awareness (SA) when exposed to emergencies during pilotage, a pilot needs to selectively allocate attentional resources to perceive critical status information about ships and environments. Although it is important to continuously monitor a pilot's SA, its relationship with attention is still not fully understood in ship pilotage. This study performs bridge simulation experiments that include vessel departure, navigation in the fairway, encounters, poor visibility, and anchoring scenes with 13 pilots (mean = 11.3 and standard deviation = 1.4 of experience). Individuals were divided into two SA group levels based on the Situation Awareness Rating Technology (SART-2) score (mean = 20.13 and standard deviation = 5.83) after the experiments. The visual patterns using different SA groups were examined using heat maps and scan paths based on pilots' fixations and saccade data. The preliminary visual analyses of the heat maps and scan paths indicate that the pilots' attentional distribution is modulated by the SA level. That is, the most concerning areas of interest (AOIs) for pilots in the high and low SA groups are outside the window (AOI-2) and electronic charts (AOI-1), respectively. Subsequently, permutation simulations were utilized to identify statistical differences between the pilots' eye-tracking metrics and SA. The results of the statistical analyses show that the fixation and saccade metrics are affected by the SA level in different AOIs across the five scenes, which confirms the findings of previous studies. In encounter scenes, the pilots' SA level is correlated with the fixation and saccade metrics: fixation count (p = 0.034 < 0.05 in AOI-1 and p = 0.032 < 0.05 in AOI-2), fixation duration (p = 0.043 < 0.05 in AOI-1 and p = 0.014 < 0.05 in AOI-2), and saccade count (p = 0.086 < 0.1 in AOI-1 and p = 0.054 < 0.1 in AOI-2). This was determined by the fixation count (p = 0.024 < 0.05 in AOI-1 and p = 0.034 < 0.05 in AOI-2), fixation duration (p = 0.036 < 0.05 in AOI-1 and p = 0.047 < 0.05 in AOI-2), and saccade duration (p = 0.05 ≤ 0.05 in AOI-1 and p = 0.042 < 0.05 in AOI-2) in poor-visibility scenes. In the remaining scenes, the SA could not be measured using eye movements alone. This study lays a foundation for the cognitive mechanism recognition of pilots based on SA via eye-tracking technology, which provides a reference to establish cognitive competency standards in preliminary pilot screenings.

Increased functional dynamics in civil aviation pilots: Evidence from a neuroimaging study

Civil aviation is a distinctive career. Pilots need to monitor the entire system in real time. However, the psychophysiological mechanism of flying is largely unknown. The human brain is a large-scale interconnected organization, and many stable intrinsic large-scale brain networks have been identified. Among them are three core neurocognitive networks: default mode network (DMN), central executive network (CEN), and salience network (SN). These three networks play a critical role in human cognition. This study aims to examine the dynamic properties of the three large-scale brain networks in civil aviation pilots. We collected resting-state functional magnetic resonance imaging data from pilots. Independent component analysis, which is a data-driven approach, was combined with sliding window dynamic functional connectivity analysis to detect the dynamic properties of large-scale brain networks. Our results revealed that pilots exhibit an increased interaction of the CEN with the DMN and the SN along with a decreased interaction within the CEN. In addition, the temporal properties of functional dynamics (number of transitions) increased in pilots compared to healthy controls. In general, pilots exhibited increased between-network functional connectivity, decreased within-network functional connectivity, and a higher number of transitions. These findings suggest that pilots might have better functional dynamics and cognitive flexibility.

The effects of dynamic workload and experience on commercially available EEG cognitive state metrics in a high-fidelity air traffic control environment

The current study evaluated the validity of commercially available electroencephalography (EEG) cognitive state metrics of workload and engagement in differentially experienced air traffic control (ATC) students. EEG and pupil diameter recordings were collected from 47 ATC students (27 more experienced and 20 less experienced) during a high-fidelity, variable workload approach-control scenario. Scenario workload was manipulated by increasing the number of aircraft released and the presence of a divided attention task. Results showed that scenario performance significantly degraded with increased aircraft and the presence of the divided attention task. No scenario performance differences were found between experience groups. The EEG engagement metric significantly differed between experience groups, with less experienced controllers exhibiting higher engagement than more experienced controllers. The EEG workload metric and pupil diameter were sensitive to workload manipulations but did not differentiate experience groups. Commercially available EEG cognitive state metrics may be a viable tool for enhancing ATC training.