An improved understanding of how the brain allocates mental resources as a function of task difficulty is critical for enhancing human performance. Functional near infrared spectroscopy (fNIRS) is a field-deployable optical brain monitoring technology that provides a direct measure of cerebral blood flow in response to cognitive activity. We found that fNIRS was sensitive to variations in task difficulty in both real-life (flight simulator) and laboratory settings (tests measuring executive functions), showing increased concentration of oxygenated hemoglobin (HbO2) and decreased concentration of deoxygenated hemoglobin (HHb) in the prefrontal cortex as the tasks became more complex. Intensity of prefrontal activation (HbO2 concentration) was not clearly correlated to task performance. Rather, activation intensity shed insight on the level of mental effort, i.e., how hard an individual was working to accomplish a task. When combined with performance, fNIRS provided an estimate of the participants’ neural efficiency, and this efficiency was consistent across levels of difficulty of the same task. Overall, our data support the suitability of fNIRS to assess the mental effort related to human operations and represents a promising tool for the measurement of neural efficiency in other contexts such as training programs or the clinical setting.

In our anxiogenic and stressful world, the maintenance of an optimal cognitive performance is a constant challenge. It is particularly true in complex working environments (e.g. flight deck, air traffic control tower), where individuals have sometimes to cope with a high mental workload and stressful situations. Several models (i.e. processing efficiency theory, cognitive-energetical framework) have attempted to provide a conceptual basis on how human performance is modulated by high workload and stress/anxiety. These models predict that stress can reduce human cognitive efficiency, even in the absence of a visible impact on the task performance. Performance may be protected under stress thanks to compensatory effort, but only at the expense of a cognitive cost. Yet, the psychophysiological cost of this regulation remains unclear. We designed two experiments involving pupil diameter, cardiovascular and prefrontal oxygenation measurements. Participants performed the Toulouse N-back Task that intensively engaged both working memory and mental calculation processes under the threat (or not) of unpredictable aversive sounds. The results revealed that higher task difficulty (higher n level) degraded the performance and induced an increased tonic pupil diameter, heart rate and activity in the lateral prefrontal cortex, and a decreased phasic pupil response and heart rate variability. Importantly, the condition of stress did not impact the performance, but at the expense of a psychophysiological cost as demonstrated by lower phasic pupil response, and greater heart rate and prefrontal activity. Prefrontal cortex seems to be a central region for mitigating the influence of stress because it subserves crucial functions (e.g. inhibition, working memory) that can promote the engagement of coping strategies. Overall, findings confirmed the psychophysiological cost of both mental effort and stress. Stress likely triggered increased motivation and the recruitment of additional cognitive resources that minimize its aversive effects on task performance (effectiveness), but these compensatory efforts consumed resources that caused a loss of cognitive efficiency (ratio between performance effectiveness and mental effort).

Edge bundling methods reduce visual clutter of dense and occluded graphs. However, existing bundling techniques either ignore edge properties such as direction and data attributes, or are otherwise computationally not scalable, which makes them unsuitable for tasks such as exploration of large trajectory datasets. We present a new framework to generate bundled graph layouts according to any numerical edge attributes such as directions, timestamps or weights. We propose a GPU-based implementation linear in number of edges, which makes our algorithm applicable to large datasets. We demonstrate our method with applications in the analysis of aircraft trajectory datasets and eye-movement traces.

Objective: Examination of the performance and visual scanning of aircrews during final approach and an unexpected go-around maneuver. Background: Accident and incident analyses have revealed that goaround procedures are often imperfectly performed because of their complexity, their high time stress, and their rarity of occurrence that avails little time for practice. We wished to examine this experimentally and establish the frequency and nature of errors in both flight-performance and visual scanning. Method: We collected flight-performance (e.g., errors in procedures, excessive flight deviations) and eye-tracking data of 12 flight crews who performed final approach and go-around flight phases in realistic full-flight transport-category simulators. Results: The pilot performance results showed that two thirds of the crews committed errors including critical trajectory deviations during go-arounds, a precursor of accidents. Eye-tracking analyses revealed that the cross-checking process was not always efficient in detecting flight-path deviations when they occurred. Ocular data also highlighted different visual strategies between the 2 crew members during the 2 flight phases. Conclusion: This study reveals that the go-around is a challenging maneuver. It demonstrates the advantages of eye tracking and suggests that it is a valuable tool for the explicit training of attention allocation during go-arounds to enhance flight safety. In commercial aviation, the final-approach phase is one of the most dynamic flight segments, for which safety margins are minimum and time pressure is maximum. Numerous undesired external events or crew failure to stabilize the flight path or to configure the aircraft for landing could lead to the execution of a go-around. Flight crews, composed of a pilot flying (PF) and a pilot monitoring (PM), are trained to land the aircraft or to perform a go-around according to standard operating procedures (U.S. Department of Transportation, 2015). During go-around, the PF's task is to manage the missed-approach flight path while giving instructions to the PM (e.g., retracting the flaps and the landing gear). The PM carefully monitors the flight path and the status of the aircraft and executes the PF's instructions.

Pupil diameter is a widely-studied cognitive load measure, which, despite its convenience for non-intrusive operator state monitoring in complex environments, is still not available for in situ measurements because of numerous methodological limitations. The most important of these limitations is the influence of pupillary light reflex. Hence, there is the need of providing a pupil-based cognitive load measure that is independent of light conditions. In this paper, we present a promising technique of pupillary signal analysis resulting in luminance-independent measure of mental effort that could be used in real-time without a priori on luminous conditions. Twenty-two participants performed a short-term memory task under different screen luminance conditions. Our results showed that the amplitude of pupillary dilation due to load on memory was luminance-dependent with higher amplitude corresponding to lower-luminance condition. Furthermore, our experimentation showed that load on memory and luminance factors express themselves differently according to frequency. Therefore, as our statistical analysis revealed, the ratio between low (0-1.6 Hz) and high frequency (1.6-4 Hz) bands (LF/HF ratio) of power spectral densities of pupillary signal is sensitive to the cognitive load but not to luminance. Our results are promising for the measurement of load on memory in ecological settings.