Pilot Task Research Articles

Scholarly Influence of the Conference and Labs of the Evaluation Forum eHealth Initiative: Review and Bibliometric Study of the 2012 to 2017 Outcomes.

BackgroundThe eHealth initiative of the Conference and Labs of the Evaluation Forum (CLEF) has aimed since 2012 to provide researchers working on health text analytics with annual workshops, shared development challenges and tasks, benchmark datasets, and software for processing and evaluation. In 2012, it ran as a scientific workshop with the aim of establishing an evaluation lab, and since 2013, this annual workshop has been supplemented with 3 or more preceding labs each year. An evaluation lab is an activity where the participating individuals or teams’ goal is to solve the same problem, typically using the same dataset in a given time frame. The overall purpose of this initiative is to support patients, their next of kin, clinical staff, health scientists, and health care policy makers in accessing, understanding, using, and authoring health information in a multilingual setting. In the CLEF eHealth 2013 to 2017 installations, the aim was to address patient-centric text processing. From 2015, the scope was also extended to aid both patients’ understanding and clinicians’ authoring of various types of medical content. CLEF eHealth 2017 introduced a new pilot task on technology-assisted reviews (TARs) in empirical medicine in order to support health scientists and health care policymakers’ information access.ObjectivesThis original research paper reports on the outcomes of the first 6 installations of CLEF eHealth from 2012 to 2017. The focus is on measuring and analyzing the scholarly influence by reviewing CLEF eHealth papers and their citations.MethodsA review and bibliometric study of the CLEF eHealth proceedings, working notes, and author-declared paper extensions were conducted. Citation content analysis was used for the publications and their citations collected from Google Scholar.ResultsAs many as 718 teams registered their interest in the tasks, leading to 130 teams submitting to the 15 tasks. A total of 184 papers using CLEF eHealth data generated 1299 citations, yielding a total scholarly citation influence of almost 963,000 citations for the 741 coauthors, and included authors from 33 countries across the world. Eight tasks produced statistically significant improvements (2, 3, and 3 times with P<.001, P=.009, and P=.04, respectively) in processing quality by at least 1 out of the top 3 methods.ConclusionsThese substantial participation numbers, large citation counts, and significant performance improvements encourage continuing to develop these technologies to address patient needs. Consequently, data and tools have been opened for future research and development, and the CLEF eHealth initiative continues to run new challenges.

Instrumental Landing Using Audio Indication

The paper proposes an audio indication method for presenting to a pilot the information regarding the relative positions of an aircraft in the tasks of precision piloting. The implementation of the method is presented, the use of such parameters of audio signal as loudness, frequency and modulation are discussed. To confirm the operability of the audio indication channel the experiments using modern aircraft simulation facility were carried out. The simulated performed the instrument landing using the proposed audio method to indicate the aircraft deviations in relation to the slide path. The results proved compatible with the simulated instrumental landings using the traditional glidescope pointers. It inspires to develop the method in order to solve other precision piloting tasks.

Evaluation of the Blood Circulation System in Pilots of Deck Aircrafts during Long-distance Missions

The goal of this study was to evaluate the state of cardiovascular system in fighter and helicopter pilots during a long-distance naval mission. The study was carried out aboard a heavy aircraft carrier in pilots of deck aircrafts, and its participants were represented by 17 fighter pilots, 9 helicopter pilots, as well as 21 flight operations controllers. The circulation indicators were studied by the method of volumetric compression oscillography, using an APKO-8 oscillometric analyzer of circulation. An AnnaFlash 2000 digital 24-h ECG accumulator was used to monitor the time course of heart rate (HR) in fighter and helicopter pilots flying off the deck of the carrier. The analysis of the central and peripheral hemodynamic indicators has shown a significant predominance of pilots with prehypertension and mild hypertension among the flight personnel of deck-based aviation. Maximal values of systolic, diastolic, and mean blood pressure prevailed in helicopter pilots, while minimal values were characteristic of fighter pilots. Differences in these parameters between helicopter and fighter pilots tended to increase gradually with the duration of the long-distance mission. Maximum HR increases (up to 157 bpm) were recorded in fighter pilots during landing on the carrier deck). The most stressful task for helicopter pilots proved to be the descent of a rescuer in a winch, which was evidenced by the HR values increasing up to 132 bpm. The indicators of adaptation to the conditions of a naval mission varied within wide ranges. Fighter pilots adapted themselves more successfully than helicopter pilots. Hence the conclusion about the necessity to upgrade the system of flight personnel selection and training for operating under the conditions of a long-distance naval mission, as well as the monitoring of the health state of pilots.

Interaction of Automation Visibility and Information Quality in Flight Deck Information Automation

An empirical study evaluated key human factors issues related to automation visibility and information quality, based on a refined definition of information automation. Next-generation air transportation system operational concepts will dramatically affect the types and amount of information available on flight decks. Information automation systems collect, process, and present information to support pilot tasks and awareness. The definition of flight deck information automation was refined to differentiate it from other types of automation. Pilots interacted with an example information automation system to investigate the premise that automation visibility will have an impact on the ability of pilots to detect problems resulting from poor information quality. Poor information quality appeared to be difficult for pilots to detect, even when presented with high automation visibility. Pilots tended to over-trust automation, so when reporting high workload and information was missing, they chose the top plan suggested by the automation even though it was not the best. Trust in automation was reduced by low information quality, but compensated for by increased automation visibility. Added information to help pilots understand information automation state and outputs, given a level of information quality, should be balanced against potential increases in pilot workload due to the time and attention needed to process the extra information.

Neural signature of inattentional deafness

Inattentional deafness is the failure to hear otherwise audible sounds (usually alarms) that may occur under high workload conditions. One potential cause for its occurrence could be an attentional bottleneck that occurs when task demands are high, resulting in lack of resources for processing of additional tasks. In this fMRI experiment, we explore the brain regions active during the occurrence of inattentional deafness using a difficult perceptual-motor task in which the participants fly through a simulated Red Bull air race course and at the same time push a button on the joystick to the presence of audio alarms. Participants were instructed to focus on the difficult piloting task and to press the button on the joystick quickly when they noticed an audio alarm. The fMRI results revealed that audio misses relative to hits had significantly greater activity in the right inferior frontal gyrus IFG and the superior medial frontal cortex. Consistent with an attentional bottleneck, activity in these regions was also present for poor flying performance (contrast of gates missed versus gates passed for the flying task). A psychophysiological interaction analysis from the IFG identified reduced effective connectivity to auditory processing regions in the right superior temporal gyrus for missed audio alarms relative to audio alarms that were heard. This study identifies a neural signature of inattentional deafness in an ecologically valid situation by directly measuring differences in brain activity and effective connectivity between audio alarms that were not heard compared to those that were heard. Hum Brain Mapp 38:5440-5455, 2017. © 2017 Wiley Periodicals, Inc.

Impact of turbulence and degraded visual environment on pilot workload

Offshore helicopter operations are frequently conducted in both turbulent and degraded visual environments (DVE). This investigation assesses the combined influence of turbulence and DVEs on pilot workload to identify first limits for safe operations. Flight tests using a simulation model of the research helicopter ACT/FHS (active control technology/flying helicopter simulator) flight mechanics model were conducted in the air vehicle simulator (AVES) at DLR Braunschweig. Tests were completed using four pilots, and results show the effects on pilot workload, task performance and control input activity. It was found that DVE and turbulence increase the workload and reduce task performance, but each in a different manner. Furthermore, the impact on control activity and pilot-induced oscillation tendencies are shown to have dependency upon the environmental conditions.

Cognitive pilot-aircraft interface for single-pilot operations

Considering the foreseen expansion of the air transportation system within the next two decades and the opportunities offered by higher levels of automation, Single-Pilot Operations (SPO) are regarded as viable alternatives to conventional two-pilot operations for commercial transport aircraft. In comparison with current operations, SPO require higher cognitive efforts, which potentially result in increased human error rates. This article proposes a novel Cognitive Pilot-Aircraft Interface (CPAI) concept, which introduces adaptive knowledge-based system functionalities to assist single pilots in the accomplishment of mission-essential and safety-critical tasks in modern commercial transport aircraft. The proposed CPAI system implementation is based on real-time detection of the pilot’s physiological and cognitive states, allowing the avoidance of pilot errors and supporting enhanced synergies between the human and the avionics systems. These synergies yield significant improvements in the overall performance and safety levels. A CPAI working process consisting of sensing, estimation and reconfiguration steps is developed to support the assessment of physiological and external conditions, a dynamic allocation of tasks and adaptive alerting. Suitable mathematical models are introduced to estimate the mental demand associated to each piloting task and to assess the pilot cognitive states. Suitably implemented decision logics allow a continuous and optimal adjustment of the automation levels as a function of the estimated cognitive states. Representative numerical simulation test cases provide a preliminary validation of the CPAI concept. In particular, the continuous adaptation of the flight deck's automation successfully maintains the pilot's task load within an optimal range, mitigating the onset of hazardous fatigue levels. It is anticipated that by including suitably designed Psychophysiological-Based Integrity Augmentation (PBIA) functionalities the CPAI system will allow to fulfil the evolving aircraft certification requirements and hence support the implementation of SPO in commercial transport aircraft.

SIMPLIFIED MATHEMATICAL MODEL FOR AIRCRAFTSRESPONSE CHARACTERISTICS

Human performance modeling provides a complementary technique to develop systems and procedures that are tailored to the pilot's tasks, capabilities, limitations and also, offers a powerful technique to examine human interactions across a range of possible operating conditions. From an initial review of past efforts in cognitive modeling, it was recognized that no single modeling architecture or framework had the scope to address the full range of interacting and competing factors driving human actions in dynamic and complex environments. The paper presents two mathematical compensatory models, based on the aircraft dynamics characteristics and frequency responses.

Topological Changes in the Brain Network Induced by the Training on a Piloting Task: An EEG-Based Functional Connectome Approach.

Training is a process to improve one's capacity or performance through the acquisition of knowledge or skills specific for the trained task. Although behavioral performance would be improved monotonically and reach a plateau as the learning progresses, neurophysiological signal shows different patterns like a U-shaped curve. One possible account for the phenomenon is that the brain first works hard to learn how to use task-relevant areas, followed by improvement in the efficiency derived from disuse of irrelevant brain areas for good task performance. Here, we hypothesize that topology of the brain network would show U-shaped changes during the training on a piloting task. To test this hypothesis, graph theoretical metrics quantifying global and local characteristics of the network were investigated. Our results demonstrated that global information transfer efficiency of the functional network in a high frequency band first decreased and then increased during the training while other measures such as local information transfer efficiency and small-worldness showed opposite patterns. Additionally, the centrality of nodes changed due to the training at frontal and temporal sites. Our results suggest network metrics can be used as biomarkers for quantifying the training progress, which can be differed depending on network efficiency of the brain.

High Working Memory Load Impairs Language Processing during a Simulated Piloting Task: An ERP and Pupillometry Study.

Given the important amount of visual and auditory linguistic information that pilots have to process, operating an aircraft generates a high working-memory load (WML). In this context, the ability to focus attention on relevant information and to remain responsive to concurrent stimuli might be altered. Consequently, understanding the effects of WML on the processing of both linguistic targets and distractors is of particular interest in the study of pilot performance. In the present work, participants performed a simplified piloting task in which they had to follow one of three colored aircraft, according to specific written instructions (i.e., the written word for the color corresponding to the color of one of the aircraft) and to ignore either congruent or incongruent concurrent auditory distractors (i.e., a spoken name of color). The WML was manipulated with an n-back sub-task. Participants were instructed to apply the current written instruction in the low WML condition, and the 2-back written instruction in the high WML condition. Electrophysiological results revealed a major effect of WML at behavioral (i.e., decline of piloting performance), electrophysiological, and autonomic levels (i.e., greater pupil diameter). Increased WML consumed resources that could not be allocated to the processing of the linguistic stimuli, as indexed by lower P300/P600 amplitudes. Also, significantly, lower P600 responses were measured in incongruent vs. congruent trials in the low WML condition, showing a higher difficulty reorienting attention toward the written instruction, but this effect was canceled in the high WML condition. This suppression of interference in the high load condition is in line with the engagement/distraction trade-off model. We propose that P300/P600 components could be reliable indicators of WML and that they allow an estimation of its impact on the processing of linguistic stimuli.

Developing a Measurement for Task Complexity in Flight.

Task complexity is regarded as an essential metric that is related to a pilot's performance and workload. Normally, pilots follow Standard Operating Procedures (SOPs) during a flight. In this study, we developed a measurement named Task Complexity in Flight (TCIF) to represent the task complexity in the SOPs. The TCIF measurement combined four complexity components into one index: actions logic complexity (ALC), actions size complexity (ASC), information control exchange complexity (ICEC), and control mode complexity (CMC).To verify the measurement, we calculated 11 tasks during the takeoff and landing phases from the SOPs, and invited 10 pilots to perform the same tasks in a flight simulator. After flight, the TCIF results were compared with two workload measurements: the Bedford scale and heart rate. The results of TCIF and the 4 components of the 11 tasks were calculated. Further, the TCIF results showed a significant correlation with the Bedford scores (R=0.851) and were also consistent with the difference in heart rate (R=0.816). Therefore, with the increased TCIF results, both the Bedford scale and the difference in heart rate increased. TCIF was proposed based on the flight operating conditions. Although additional studies of TCIF are necessary, the results of this study suggest this measurement could effectively indicate task complexity in flight, and could also be used to guide pilot training and task allocation on the flight deck.

Task sharing in rural Haiti: Qualitative assessment of a brief, structured training with and without apprenticeship supervision for community health workers.

Despite growing support for supervision after task sharing trainings in humanitarian settings, there is limited research on the experience of trainees in apprenticeship and other supervision approaches. Studying apprenticeships from trainees' perspectives is crucial to refine supervision and enhance motivation for service implementation. The authors implemented a multi-stage, transcultural adaptation for a pilot task sharing training in Haiti entailing three phases: 1) literature review and qualitative research to adapt a mental health and psychosocial support training; 2) implementation and qualitative process evaluation of a brief, structured group training; and 3) implementation and qualitative evaluation of an apprenticeship training, including a two year follow-up of trainees. Structured group training revealed limited knowledge acquisition, low motivation, time and resource constraints on mastery, and limited incorporation of skills into practice. Adding an apprenticeship component was associated with subjective clinical competency, increased confidence regarding utilising skills, and career advancement. Qualitative findings support the added value of apprenticeship according to trainees.

Pupil Diameter as a Measure of Cognitive Load during Auditory-visual Interference in a Simple Piloting Task

Pilots face the problem of attentional selectivity, when one needs to stay focused on the current task while being attentive to other inputs, whether or not they are task-relevant. In this study we evaluate the influence of cognitive load on attentional resources available to process auditory distractors, while performing a visual task. Such cross-modal auditory-visual interference is of a particular interest in the aeronautics, as focal-task is often visual (flight deck instruments, outside) while background auditory inputs are numerous (alarms, radio, cabin-crew communications etc.). In this paper we adapted an auditory-visual interference paradigm to a simple visual piloting task. Sixteen volunteers performed both low and high load conditions while their left pupil size was continuously recorded. As shown by statistical analyses, the tonic pupil diameter was higher for high load condition accompanied with performance degradation. Therefore, the pupil diameter successfully tracked the memory load level, measured objectively with targeting accuracy and subjectively with NASA-TLX questionnaire. As also revealed by statistical analyses, the pupillary reaction reflected the processing load of irrelevant auditory stimuli. In particular, as for low load condition, the incongruent auditory stimuli evoked larger pupillary response compared to the congruent distractor, showing higher effort for resolving the conflict of auditory-visual interference. No such interference effects were found under high load condition. As indicated by tonic pupil diameter and task-evoked response, for high load condition participants run out of cognitive resources to process auditory distractors. These results are in line with the hypothesis that vision and hearing share a common pool of resources, and that attentional capture of irrelevant stimuli is impaired under increased load on working memory.

Brain biomarkers based assessment of cognitive workload in pilots under various task demands.

Cognitive workload is an important element of cognitive-motor performance such as that exhibited during the piloting of an aircraft. Namely, an increase in task demands on the pilot can elevate cognitive information processing and, thus, the risk of human error. As such, there is a need to develop methods that reliably assess mental workload in pilots within operational settings. The present study contributes to this research goal by identifying physiological and brain biomarkers of cognitive workload and attentional reserve during a simulated aircraft piloting task under three progressive levels of challenge. A newly developed experimental method was employed by which electroencephalography (EEG) was acquired via a dry (i.e., gel-free sensors) system using few scalp sites. Self-reported responses to surveys and piloting performance indicators were analyzed. The findings revealed that as the challenge (task demands) increased, the perceived mental load increased, attentional reserve was attenuated, and task performance decreased. Such an increase in task demands was also reflected by changes in heart rate variability (HRV), as well as in the amplitude of the P300 component of event-related potentials to auditory probes, and in the spectral power of specific EEG frequency bands. This work provides a first step towards a long-term goal to develop a composite system of biomarkers for real-time cognitive workload assessment and state assessment of pilots in operational settings.

A sociotechnical model of the flight crew task.

The objective of this research was to advance an improved model of Flight Crew task performance. Existing task models present a "local" description of Flight Crew task performance. Process mapping workshops, interviews, and observations were conducted with both pilots and flight operations personnel from five airlines, as part of the Human Integration into the Lifecycle of Aviation Systems (HILAS) project. The functional logic of the process dictates Flight Crew task requirements and specific task workflows. The Flight Crew task involves managing different levels of operational and environmental complexity, associated with the particular flight context. In so doing, the Flight Crew act as a coordinating interface between different human agents involved in the Active Flight Operations process and other processes that interface with this process. This article presents a new sociotechnical model of the Flight Crew task. The proposed model reflects a shift from a local explanation of Flight Crew task activity to a broader process-centric explanation. In so doing, it illuminates the complex role of procedures in commercial operations. The task model suggests specific requirements for pilot task support tools, procedures design, performance evaluation and crew resource management (CRM) training. Also, this model might be used to assess future operational concepts and associated technology requirements. Lastly, this model provides the basis for the operational validation of both existing and future cockpit technologies.

Incorporating inter-relationships between different levels of genomic data into cancer clinical outcome prediction

In order to improve our understanding of cancer and develop multi-layered theoretical models for the underlying mechanism, it is essential to have enhanced understanding of the interactions between multiple levels of genomic data that contribute to tumor formation and progression. Although there exist recent approaches such as a graph-based framework that integrates multi-omics data including copy number alteration, methylation, gene expression, and miRNA data for cancer clinical outcome prediction, most of previous methods treat each genomic data as independent and the possible interplay between them is not explicitly incorporated to the model. However, cancer is dysregulated by multiple levels in the biological system through genomic, epigenomic, transcriptomic, and proteomic level. Thus, genomic features are likely to interact with other genomic features in the different genomic levels. In order to deepen our knowledge, it would be desirable to incorporate such inter-relationship information when integrating multi-omics data for cancer clinical outcome prediction. In this study, we propose a new graph-based framework that integrates not only multi-omics data but inter-relationship between them for better elucidating cancer clinical outcomes. In order to highlight the validity of the proposed framework, serous cystadenocarcinoma data from TCGA was adopted as a pilot task. The proposed model incorporating inter-relationship between different genomic features showed significantly improved performance compared to the model that does not consider inter-relationship when integrating multi-omics data. For the pair between miRNA and gene expression data, the model integrating miRNA, for example, gene expression, and inter-relationship between them with an AUC of 0.8476 (REI) outperformed the model combining miRNA and gene expression data with an AUC of 0.8404. Similar results were also obtained for other pairs between different levels of genomic data. Integration of different levels of data and inter-relationship between them can aid in extracting new biological knowledge by drawing an integrative conclusion from many pieces of information collected from diverse types of genomic data, eventually leading to more effective screening strategies and alternative therapies that may improve outcomes.

Neural signatures of team coordination are revealed by multifractal analysis

The quality of a team depends on its ability to deliver information through a hierarchy of team members and negotiate processes spanning different time scales. That structure and the behavior that results from it pose problems for researchers because multiply-nested interactions are not easily separated. We explored the behavior of a six-person team engaged in a Submarine Piloting and Navigation (SPAN) task using the tools of dynamical systems. The data were a single entropy time series that showed the distribution of activity across six team members, as recorded by nine-channel electroencephalography (EEG). A single team’s data were analyzed for the purposes of illustrating the utility of multifractal analysis and allowing for in-depth exploratory analysis of temporal characteristics. Could the meaningful events experienced by one of these teams be captured using multifractal analysis, a dynamical systems tool that is specifically designed to extract patterns across levels of analysis? Results indicate that nested patterns of team activity can be identified from neural data streams, including both routine and novel events. The novelty of this tool is the ability to identify social patterns from the brain activity of individuals in the social interaction. Implications for application and future directions of this research are discussed.

ATHENA: Identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network

BackgroundGene expression profiles have been broadly used in cancer research as a diagnostic or prognostic signature for the clinical outcome prediction such as stage, grade, metastatic status, recurrence, and patient survival, as well as to potentially improve patient management. However, emerging evidence shows that gene expression-based prediction varies between independent data sets. One possible explanation of this effect is that previous studies were focused on identifying genes with large main effects associated with clinical outcomes. Thus, non-linear interactions without large individual main effects would be missed. The other possible explanation is that gene expression as a single level of genomic data is insufficient to explain the clinical outcomes of interest since cancer can be dysregulated by multiple alterations through genome, epigenome, transcriptome, and proteome levels. In order to overcome the variability of diagnostic or prognostic predictors from gene expression alone and to increase its predictive power, we need to integrate multi-levels of genomic data and identify interactions between them associated with clinical outcomes.ResultsHere, we proposed an integrative framework for identifying interactions within/between multi-levels of genomic data associated with cancer clinical outcomes using the Grammatical Evolution Neural Networks (GENN). In order to demonstrate the validity of the proposed framework, ovarian cancer data from TCGA was used as a pilot task. We found not only interactions within a single genomic level but also interactions between multi-levels of genomic data associated with survival in ovarian cancer. Notably, the integration model from different levels of genomic data achieved 72.89% balanced accuracy and outperformed the top models with any single level of genomic data.ConclusionsUnderstanding the underlying tumorigenesis and progression in ovarian cancer through the global view of interactions within/between different levels of genomic data is expected to provide guidance for improved prognostic biomarkers and individualized therapies.

Model innovation of traditional Chinese medicine resources survey pilot work in Anhui province

In order to guarantee the smooth progress of census work, complete the traditional Chinese medicine resources survey pilot tasks better, Anhui province founded the three-combined technical team organization model and practical management model. Around the objectives, integrating the professionality with the existing distribution of traditional Chinese medicine resources and the reality of technical team of the census in Anhui province. The technical team organization model combining universities, experts, locality, expressed the strength of all parties and formed efficient working groups. The establishment of responsible management, funds management and process management ensured that the Anhui census work had a high level of quality and relatively consistent progress. The result shows that the organization and management model of Anhui census technical team were the important guarantee of finishing the Anhui census work smoothly with great quality and quantity on time.

New Genetic Technology May Help Pilots, Aviation Employees, and Color Vision Researchers

Color vision research is not new for the Federal Aviation Administration (FAA); the Civil Aerospace Medical Institute has been conducting color vision research and publishing the results since 1967 ( 3 ). The FAA originally initiated color vision research because of the emerging use of color coding in the airport environment and the FAA has continued a line of color vision research because of the increasing use of color coding resulting from changing technology inside the cockpit, on air traffic control displays, and in the airport environment. Color can be used to convey meaning without supplemental signage such as the ubiquitous traffic signal that alerts drivers to proceed with caution via a yellow flashing light or to stop via a red flashing light. However, that meaning is only conveyed if the driver can distinguish between the yellow and the red colors. Approximately 8 to 10% of the male population ( 5 ) has a congenital color vision deficiency and, depending upon the type and severity of that deficiency, that task of interpreting the meaning of color coding may be difficult or impossible. Consequently, the FAA has long maintained a color vision standard for aero-medical screening to ensure that pilots and air traffic controllers can perform safety-related tasks without adverse consequences. Throughout the past few years, the FAA has explored a variety of color vision tests, searching for a valid screening test that has high sensitivity and specificity, meaning the ability to detect the presence or absence of the deficiency, respectively. Basically, color vision tests can be categorized as diagnostic, screening, or occupational tests. Diagnostic tests are designed to specifically diagnose the type and degree of deficiency, the screening tests focus on differentiating between normal or deficient color vision, and the occupational tests seek to separate those capable versus incapable of certain tasks such as identifying colors of wires or lights (e.g., the Farnsworth Lantern test that was developed to assess the ability of potential Navy signalmen for identifying red, green, and white lights). A few tests have been developed for the purpose of precisely diagnosing and classifying color vision; however, when color vision test scores are compared to performance on occupational tasks such as identifying or discriminating colors used in signal lights, precision approach path indicator (PAPI) lights, colored navigation lights, color coded map reading tasks, color coded air traffic control displays, and cockpit displays, a specific cut-point on those selection tests has not been found that can fully separate those who can from those who cannot accurately perform the color-coded pilot or air traffic control tasks. Some tests, including new computerized instruments, have been designed to differentiate defects involving the long wavelength sensitive cones (protan-type), middle wavelength sensitive cones (deutan-type), and short wavelength sensitive cones (tritan-type). Congenital protan and deutan deficiencies are, collectively, extremely common, affecting 1 in 12 men and 1 in 230 women; however, recent evidence indicates that tritan defects are virtually never present at birth (e.g., congenital) and the inherited forms involve S cone photoreceptor degeneration that develops later in life with the exact onset depending upon the specific mutation ( 1, 4 ). Thus, the exact frequency of inherited tritan defects is uncertain; however, it is probably less than 1 in 500. In part, because the underlying pathophysiology has not been well understood, few tests have been available that are capable of detecting tritan deficiencies. In the past, those tests included the single Farnsworth F2 pseudoisochromatic plate (PIP), the Moreland anomaloscope, the Hardy, Rand, Rittler PIP test, and, most recently, the Oculus anomaloscope. Consequently, the occupational color vision tests used by most agencies only screen for the most common (protan and deutan) types of defects. The newly developed computerized color vision tests, including the Colour Assessment and Diagnostic Test, the Cambridge Colour Test, the Cone Contrast Test, and the Computerized Color Vision Test, are all designed to detect tritan defects. However, tritan weaknesses have been noted in several of the FAA ‘ s recent studies in much higher than the traditionally expected numbers and diagnostic agreement is low among those tests when tritan deficiencies are involved. In the past, the FAA and other regulatory organizations have not, or have rarely, required tritan color vision screening in their occupational screening because of the following three factors: the rarity of the congenital defect, the unknown number of individuals affected by acquired deficiencies, and the lack of effective, reliable, valid, and affordable equipment with which to diagnose the deficiency.