Dennis J. Folds

Auditory signals in military aircraft: ergonomics principles versus practice.

The complete ensembles of auditory signals in selected USAF aircraft (the F-4D, F-15, two models of the F-16, the C-5, and the C-141) are described and evaluated. Human factors research related to the design of speech and non-speech auditory signals is reviewed. Major findings are: that auditory signals are not well standardised among the aircraft, even between those with similar combat roles; that a relatively large number of non-speech auditory signals are used, which may make it difficult for the aircrew to recall the meanings of all the signals; that some non-speech signals are sufficiently similar that they may be confused, particularly in high workload and stressful conditions; and that the criticality of the warnings is not reliably indicated by any characteristic of the signals. Four problem areas requiring further research are discussed: reduction of signal loudness, enhancement of the distinctiveness and masking resistance of non-speech signals, effects of concurrent warning signals on aircrew performance, and additional uses of auditory information.

Advanced audio displays in aerospace systems: technology requirements and expected benefits

It is suggested that multiple concurrent audio signals could be used to represent the status of various onboard subsystems and processes, as well as external objects (e.g. threats). Perceptually, these sounds could remain in the background, continuously available to the operator, without causing annoyance or interference with usual voice communications. Research at Georgia Tech is examining the effectiveness of various types of complex, nonspeech sounds for conveying information in the background and its resulting impact on operator performance. The use of steady-state sounds to represent the in-bounds versus out of bounds status of one to four concurrent processes was examined. Two conditions were compared: a visual-only condition in which process states were represented solely by visual indicators, and an audiovisual condition in which the visual indicators were supplemented by continuous audio signals. Subjects in the audiovisual condition consistently responded faster and more accurately, committed fewer false alarms, and rated their workload lower than subjects in the visual-only condition.<<ETX>>

Auditory Monitoring of up to Eight Simultaneous Sources

The present research examined visual and auditory monitoring of independent, concurrent sources. Subjects monitored from one to eight concurrent visual indicators for the occurrence of a “launch” event. Five between-groups conditions were studied: a visual-only group, plus four audiovisual groups that differed in the amount of information provided over the auditory channel. Accuracy scores were very high for all groups. Response times showed an overall increase with display density (number of concurrent sources). A significant group x density interaction revealed an advantage of one of the audiovisual conditions compared to the visual-only group at moderate density levels (5 or 6 concurrent sources), but not at lower or higher density levels. This finding probably indicates the value of an auditory signal to reduce visual search time.

Engineering human capital: A system of systems modeling approach

A system of systems modeling approach can be used to plan and guide implementation of economic revitalization efforts. A key metric of success is the improved outcomes of individuals in terms of standard of living and subjective well-being. Initial results for a latent variable model based on national survey data show a moderate correlation between subjective well-being and objective measures of standard of living, and good fit for a three-factor model that relates economic, security, and social network variables to human capital. Human capital is a production system within the economy; it is equipped and maintained by the education and healthcare systems of the region. This perspective is useful in analyzing and evaluating the system of systems that comprise a regional economy.

ATMS 2000: hybrid automation or a lights out traffic management center?

Because of rapid advances in information and automation technology, traffic management center (TMC) design is in a period of rapid evolution. Larger numbers of more capable traffic and environmental sensors are being fielded. Data fusion and information processing equipment are beginning to exploit this new quantity and quality of information. Automation of routine tasks may help to moderate peaks in operator workload, and new information channels may provide more opportunities for informing the driver. Selecting the appropriate level of automation for the TMC is a major challenge. Experience with manufacturing and process control industries suggests that the lights out factory, in which there are no humans in the control loops, may become a reality; visionaries are beginning to discuss an analogous lights out TMC. The transportation system TMC, however, must control variation and deviation from the norm in many more dimensions than a factory does. In the near term, then, the TMC design philosophy should support user-centered hybrid automation.<<ETX>>

Modeling and Simulation of Skill Decay at the Organizational Team Level

When building teams, organizations select individuals capable of acquiring and maintaining skills required for team success. Often, teams undergo a period of skill non-use that can result in skill decay during the lifespan of the team. Despite this reality, researchers have failed to create a comprehensive model that can predict complex skill decay at multiple levels within an organization. McDermott and colleagues (2016) proposed an equation to address this gap. Using simulation data, the present paper applies this equation to model expected skill decay in three hypothetical contexts: simple, mid-range, and complex engineering design cases. The model predicts team-level skill decay in a holistic manner by integrating factors from task, environment, and team characteristics. Results indicate that, while novel for its integration mechanisms, the model maintains strong congruency with previous literature for individual factors. We discuss ways that organizations may reduce complex skill decay as well as future research directions.

Developing a Model of Team Skill Decay

Predicting team skill decay can support decisions intended to counter erosion of U.S. industrial base essential design skills. Despite the large extant research investigating both individual and team skill acquisition, there is no comprehensive model of skill decay involving intact teams (Day et al., 2013). We conducted a literature review and identified retention interval, initial skill level, task, environment, and team as the critical factors in determining team skill decay. Sub-factors for each critical variable were also specified. A model incorporating each factor is proposed, and in a related research effort (Trani et al., 2017), initial results of applying the model to industry-wide skill decay prediction is presented. Being able to predict and estimate team skill decay supports long-term planning and assessment in all types of industry—from military systems relevant to the industrial base to specific fields such as aviation and healthcare.

Human in the Loop Simulation

Simulations are performed for a variety of reasons, many of which involve creating experiences for people that are beneficial (such as training) or entertaining. Many of these applications require that a human be in the loop, that is, reacting to inputs from other simulation components, and generating outputs that affect the course of simulation. The presence of one or more humans in the loop dictates consideration of relevant human performance characteristics in designing the overall simulation, else the human experience will not be what is intended, and the overall outcome of the simulation may be of limited validity. Moreover, some simulations require representations of human behavior even if no live human is in the loop. These representations, if too simplistic, can restrict the validity of the simulation results. This chapter provides an overview of some important considerations related to human in the loop simulation, and of computational representations of human behavior in simulation.

Intelligent vehicle-highway systems (IVHS): the 'I' in IVHS is still mostly human intelligence

Results of two parallel studies-defining future IVHS traffic management systems agree that the most challenging traffic data interpretations and responses will remain the responsibility of human operators. The studies (1) developed a user-centered plan for implementing IVHS technology, and (2) explored examples of human factors pitfalls awaiting the designer who does so.<<ETX>>