Human Factors in High-Altitude Mountaineering

We describe the human performance and cognitive challenges of high altitude mountaineering. The physical (environmental) and internal (health) stresses are first described, followed by the motivational factors that lead people to climb. The statistics of mountaineering accidents in the Himalayas and Alaska are then described. We then present a detailed discussion of the role of decision-making biases in mountaineering mishaps. We conclude by discussing interpersonal factors, adaptation, and training issues

Predictive Features of a Cockpit Traffic Display: A Workload Assessment

Eighteen pilots flew a series of traffic avoidance maneuvers in an experiment designed to assess the support offered and workload imposed by different levels of traffic display information in a free flight simulation. Three display prototypes were compared which differed in traffic information provided. A BASELINE (BL) display provided current and (2nd order) predicted information regarding ownship and current information of an intruder aircraft, represented on lateral and vertical displays in a coplanar suite. An INTRUDER PREDICTOR (IP) display, augmented the baseline display by providing lateral and vertical prediction of the intruder aircraft. A THREAT VECTOR (TV) display added to the IP display a vector that indicates the direction from ownship to the intruder at the predicted point of closest contact (POCC). The length of the vector corresponds to the radius of the protected zone, and the distance of the intersection of the vector with ownship predictor, corresponds to the time available till POCC or loss of separation. Pilots time shared the traffic avoidance task with a secondary task requiring them to monitor the top of the display for faint targets. This task simulated the visual demands of out-of-cockpit scanning, and hence was used to estimate the head-down time required by the different display formats. The results revealed that both display augmentations improved performance (safety) as assessed by predicted and actual loss of separation (i.e., penetration of the protected zone). Both enhancements also reduced workload, as assessed by the NASA TLX scale. The intruder predictor display produced these benefits with no substantial impact on the qualitative nature of the avoidance maneuvers that were selected. The threat vector produced the safety benefits by inducing a greater degree of (effective) lateral maneuvering, thus partially offsetting the benefits of reduced workload. The three displays did not differ in terms of their effect on performance of the monitoring task, used to infer head-down time, nor in the extent of vertical or airspeed maneuvering. The results are discussed in terms of their implications for 19 cognitive engineering design features

Development, Testing, and Validation of a Model-Based Tool to Predict Operator Responses in Unexpected Workload Transitions

One human factors challenge is predicting operator performance in novel situations. Approaches such as drawing on relevant previous experience, and developing computational models to predict operator performance in complex situations, offer potential methods to address this challenge. A few concerns with modeling operator performance are that models need to realistic, and they need to be tested empirically and validated. In addition, many existing human performance modeling tools are complex and require that an analyst gain significant experience to be able to develop models for meaningful data collection. This paper describes an effort to address these challenges by developing an easy to use model-based tool, using models that were developed from a review of existing human performance literature and targeted experimental studies, and performing an empirical validation of key model predictions

Evaluation of Perspective and Coplanar Cockpit Displays of Traffic Information to Support Hazard Awareness in Free Flight

We examined the cockpit display representation of traffic, to support the pilot in tactical planning and conflict avoidance. Such displays may support the "free flight" concept, but can also support greater situation awareness in a non-free flight environment. Two perspective views and a coplanar display were contrasted in scenarios in which pilots needed to navigate around conflicting traffic, either in the absence (low workload) or presence (high workload) of a second intruder aircraft. All three formats were configured with predictive aiding vectors that explicitly represented the predicted point of closest pass, and predicted penetration of an alert zone around ownship. Ten pilots were assigned to each of the display conditions, and each flew a series of 60 conflict maneuvers that varied in their workload and the complexity of the conflict geometry. Results indicated a tendency to choose vertical over lateral maneuvers, a tendency which was amplified with the coplanar display. Vertical maneuvers by the intruder produced an added source of workload. Importantly, the coplanar display supported performance in all measures that was equal to or greater than either of the perspective displays (i.e., fewer predicted and actual conflicts, less extreme maneuvers). Previous studies that have indicated perspective superiority have only contrasted these with UNIplanar displays rather than the coplanar display used here

Absence of DOA Effect but No Proper Test of the Lumberjack Effect

Objective: The aim was to evaluate the relevance of the critique offered by Jamieson and Skraaning (2019) regarding the applicability of the lumberjack effect of human–automation interaction to complex real-world settings. Background: The lumberjack effect, based upon a meta-analysis, identifies the consequences of a higher degree of automation—to improve performance and reduce workload—when automation functions as intended, but to degrade performance more, as mediated by a loss of situation awareness (SA) when automation fails. Jamieson and Skraaning provide data from a process control scenario that they assert contradicts the effect. Approach: We analyzed key aspects of their simulation, measures, and results which we argue limit the strength of their conclusion that the lumberjack effect is not applicable to complex real-world systems. Results: Our analysis revealed limits in their inappropriate choice of automation, the lack of a routine performance measure, support for the lumberjack effect that was actually provided by subjective measures of the operators, an inappropriate assessment of SA, and a possible limitation of statistical power. Conclusion: We regard these limitations as reasons to temper the strong conclusions drawn by the authors, of no applicability of the lumberjack effect to complex environments. Their findings should be used as an impetus for conducting further research on human–automation interaction in these domains. Applications: The collective findings of both Jamieson and Skraaning and our study are applicable to system designers and users in deciding upon the appropriate level of automation to deploy.Peer Reviewe

Human Performance Consequences of Stages and Levels of Automation: An Integrated Meta-Analysis

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Objective: We investigated how automation-induced human performance consequences depended on the degree of automation (DOA). Background: Function allocation between human and automation can be represented in terms of the stages and levels taxonomy proposed by Parasuraman, Sheridan, and Wickens. Higher DOAs are achieved both by later stages and higher levels within stages. Method: A meta-analysis based on data of 18 experiments examines the mediating effects of DOA on routine system performance, performance when the automation fails, workload, and situation awareness (SA). The effects of DOA on these measures are summarized by level of statistical significance. Results: We found (a) a clear automation benefit for routine system performance with increasing DOA, (b) a similar but weaker pattern for workload when automation functioned properly, and (c) a negative impact of higher DOA on failure system performance and SA. Most interesting was the finding that negative consequences of automation seem to be most likely when DOA moved across a critical boundary, which was identified between automation supporting information analysis and automation supporting action selection. Conclusion: Results support the proposed cost–benefit trade-off with regard to DOA. It seems that routine performance and workload on one hand, and the potential loss of SA and manual skills on the other hand, directly trade off and that appropriate function allocation can serve only one of the two aspects. Application: Findings contribute to the body of research on adequate function allocation by providing an overall picture through quantitatively combining data from a variety of studies across varying domains

Predicting Pilot Error in Nextgen: Pilot Performance Modeling and Validation Efforts

We review 25 articles presenting 5 general classes of computational models to predict pilot error. This more targeted review is placed within the context of the broader review of computational models of pilot cognition and performance, including such aspects as models of situation awareness or pilot-automation interaction. Particular emphasis is placed on the degree of validation of such models against empirical pilot data, and the relevance of the modeling and validation efforts to Next Gen technology and procedures

Pilot Flying and Pilot Monitoring’s Aircraft State Awareness During Go-Around Execution in Aviation: A Behavioral and Eye Tracking Study

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 go-around 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

The Role of Individual Differences in Executive Attentional Networks and Switching Choices in Multi-Task Management

Individual differences in cognitive processing relate to critical performance differences in real-world environments. Task switching is required for many of them and especially for task management during overload. Research exploring individual differences related to switching behavior (both frequency, and adherence to optimal switch times) is, however, sparse. We examined these relationships here, using the attentional network task to index executive control, and an ongoing tracking task (within a larger suite of concurrent task demands) to examine switching behavior. The results failed to support a general relationship between executive control and frequency in a complex, heterogeneous multi-task environment. However, higher executive control participants more successfully exploited optimal switching times, highlighting the varying role of individual differences in task management, when choice is unconstrained

Visual Attention Allocation Between Robotic Arm and Environmental Process Control: Validating the STOM Task Switching Model

Fifty six participants time shared a spacecraft environmental control system task with a realistic space robotic arm control task in either a manual or highly automated version. The former could suffer minor failures, whose diagnosis and repair were supported by a decision aid. At the end of the experiment this decision aid unexpectedly failed. We measured visual attention allocation and switching between the two tasks, in each of the eight conditions formed by manual-automated arm X expected-unexpected failure X monitoring- failure management. We also used our multi-attribute task switching model, based on task attributes of priority interest, difficulty and salience that were self-rated by participants, to predict allocation. An un-weighted model based on attributes of difficulty, interest and salience accounted for 96 percent of the task allocation variance across the 8 different conditions. Task difficulty served as an attractor, with more difficult tasks increasing the tendency to stay on task