“Dummies, Learning Modeling Made Easy”: Improving Modeling Education in Human Factors Research

This paper examines the role of human factors in the design of automobiles. A prime objective of our human factors profession is to improve the design of machines, thereby benefiting users in terms of comfort, convenience, operating speeds, accuracy and safety. Although the purpose of an automotive human factors program may be to achieve all of these objectives by improving vehicle design, the mechanisms for doing so probably cannot be discovered by focusing research attention on the vehicle element of the driver/vehicle/road system. In fact, the nonvehicle parts of this system are probably by far the most productive topics for future human factors research. The abilities of drivers, their limitations, and the tasks imposed upon them by the traffic environment should indicate how vehicles can be designed to best serve the drivers' needs. After twenty years of automotive study, the human factors research community is surprisingly unprepared to participate in vehicle design projects. The vehicle has too often ended up the subject of human factors research and researchers have been faced with the job of finding ways to improve the vehicle or a vehicle component without knowing enough about the intended user or the job the user must perform. The research community has only rudimentary and often incomplete background information about drivers and their traffic environments. The meager data base which is available suggests that traditional empirical approaches for evaluating machine design may be too cumbersome and time consuming to keep pace with other aspects of automotive technological evolution. The tradition of developing alternative versions of hardware and subjecting the alternatives to human performance tests may not be a viable methodology in the future. A look at the total automotive system shows why. Drivers in the United States accumulate about 1.6 trillion miles of travel each year. During the year, a typical driver makes over 60,000 discrete control operations not counting steering wheel movements. The immensity of the automotive system means that very small driver error rates in control usage quickly accumulate into large numbers of error events nationwide. The best information available suggests that the D. S. driving public uses their turn signals 854 billion times a year. This amounts to a nationwide rate of 27,000 times per second. If the generic human error rate in using the turn signal can assumed to be one error per 1000 operations, then turn signal errors are being made at the rate of 27 per second nationwide. Human factors research has tended to avoid error rate as a principal measure of performance in research programs. The reason becomes apparent when the number of tests which must be conducted to detect changes in rare events such as turn signal errors is computed. If two turn signal designs are to be compared and the researcher wants to be able to detect with 95 percent certainty (at the 5% level of significance) that the error rate has been cut in half by one of the two designs, then a large experiment is required. At a base human error rate of 1 per thousand, about 130,000 observations must be conducted to reliably detect the desired reduction in errors. If the base human error rate is only 1 in 100, then only 13,000 observations will be needed. Unfortunately, information on the frequency of driver control usage is sketchy, and data on driver error rates when using controls under the natural loading of the driving task is all but nonexistent. Other measures of human performance, such as speed of operation and accident involvement rates, have limited application in automotive design for reasons that are discussed in this paper. Some of the data bases which have been accumulated for human factors evaluations by Ford Motor Company are described in this paper. It is concluded that, if the human factors profession is to keep pace with automotive technological evolution, more research effort is going to have to be devoted to the study of drivers and driving-environment factors. For the sake of research efficiency, human factors principles and systems models which can be reliably generalized across vehicle designs must be developed. Several systems models that are under development at Ford are briefly described.

: Six studies are in various stages of completion in the CORE program of the Committee on Human Factors. Preliminary drafts of reports on integrated ergonomic modeling, human performance models, mental models, and multicolored displays have been prepared and are currently in the prepublication review process. In addition, reports of the workshop on distributed decision making and the seminar on expert systems, both convened in late 1986, are being drafted. The technical plans have been completed for a panel meeting on human factors for an aging population. The committee is in the process of specifying its research agenda for the next study period. Keywords: Human factors; National Research Council; Human performance models; Multicolored displays; Mental models; Integrated ergonomic models; Distributed decision making; Expert systems; Aging; Productivity.

Advances in technology are being incorporated in motor vehicles at an increasing pace. These technologies have applications that may improve driver safety, comfort, and convenience. Among the “Smart Vehicle” applications that have been proposed are navigation systems, near obstacle detection systems, drive-by-wire, and active suspensions. In order for these systems to be effective, they need to be designed in consonance with driver needs, capabilities, and limitations. One concern is that unless human factors issues are addressed, new technologies not only may fall short of their potential for improving safety, but also may confuse or overload the driver and reduce safety. Because of this concern, a major focus of the crash avoidance research program at the National Highway Safety Administration (NHTSA) is on human factors issues associated with new vehicle technologies. The panel will feature presentations of NHTSA perspectives and research programs followed by discussants from the private sector describing their views on human factors research needs for improving vehicle safety in the 90′s. The introductory paper will present background on current and near future research directions at NHTSA. The next presentation will describe the status of the NHTSA program to develop an advanced research simulator. The final presentation will discuss potential applications of the simulator to human factors research. The discussants will provide reactions to the research envisioned by NHTSA as well as their own perspectives on research from the private sector point of view. Two of the discussants are from the motor vehicle manufacturing industry and one has an academic/private consulting background. It is hoped that this panel will provide a broad perspective on the challenges of vehicle safety research and will stimulate new interest by human factors researchers to become involved in this important field.

Accident databases commonly contain factual information about the time and date of each accident, vehicle characteristics, number of persons killed and injured, and other kinds of factual data. These attributes of the environment and equipment are usually directly represented in databases. In contrast, detailed analysis of accident causes, including human factors information, are frequently not represented because they are much more difficult to obtain and code. This paper explores the suitability of transportation accident databases for use in human factors research. Given the goal of reducing the number and severity of transportation accidents, it is useful to use accident data as a tool to understand the common causes of accidents. Problems arise, however, because existing databases were typically not created explicitly for research purposes, and coding systems and file structures often omit or obscure useful information. Improved coding schemes and file structures that promote the use of databases for human factors research are discussed. Accident investigation methodologies that can improve the quality of human factors information in databases are also considered. Finally, problems associated with the use of existing databases are noted.

Naval Engineers JournalVolume 89, Issue 1 p. 69-76 INADEQUACIES IN PRESENT CARRIER INTERIOR COMMUNICATION SYSTEMS AND THEIR EFFECT ON COMMAND-AND-CONTROL GAIL J. BORDEN, GAIL J. BORDEN The Author received his BA degree in Psychology from the University of Southern California. He served on active duty in the U.S. Navy from 1956 until 1960, and since then has been employed as a Research Scientist in the area of aviation psychology by Human Factors Research, Inc. of which he is now a Vice-President. He has authored or coauthored more than 20 technical reports for the U.S. Navy in various aspects of naval aviation, and since 1967 has been conducting a program of research to redesign CV/CVN aircraft carrier work stations that are utilized in the command and control of launch-and-recovery operations. In addition, he has conducted studies to design launch-and-recovery work stations for other types of ships such as the 1052 Destroyer Class and the Sea Control Ship. Mr. Borden is a member of the Human Factors Society and the Association of Aviation Psychologists.Search for more papers by this author GAIL J. BORDEN, GAIL J. BORDEN The Author received his BA degree in Psychology from the University of Southern California. He served on active duty in the U.S. Navy from 1956 until 1960, and since then has been employed as a Research Scientist in the area of aviation psychology by Human Factors Research, Inc. of which he is now a Vice-President. He has authored or coauthored more than 20 technical reports for the U.S. Navy in various aspects of naval aviation, and since 1967 has been conducting a program of research to redesign CV/CVN aircraft carrier work stations that are utilized in the command and control of launch-and-recovery operations. In addition, he has conducted studies to design launch-and-recovery work stations for other types of ships such as the 1052 Destroyer Class and the Sea Control Ship. Mr. Borden is a member of the Human Factors Society and the Association of Aviation Psychologists.Search for more papers by this author First published: February 1977 https://doi.org/10.1111/j.1559-3584.1977.tb03851.x AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume89, Issue1February 1977Pages 69-76 RelatedInformation

The field of human factors studies the interaction between humans and technological systems in order to optimize human–system performance, research that has traditionally been focused on observation and experiment in laboratory and real-world settings. However, a multidisciplinary subfield of human factors called human performance modeling has recently emerged that involves the development and application of mathematical models, computer simulation techniques, and computational data analysis methods to the study of human performance. This chapter provides an introduction to the use of computational modeling, simulation, and analysis techniques in human factors research, with an eye toward how such methods may be used to optimize human performance in extreme settings.

Vehicles with partial automation, forerunners to those with higher levels of automation, are already being deployed by automakers. These current deployments, although incremental, have the potential to disrupt how people interact with vehicles. This chapter reports on a discussion of related issues that was held as part of the Human Factors Breakout session at the 2017 Automated Vehicle Symposium. The session, titled “Automated Vehicle Challenges: How can Human Factors Research Help Inform Designers, Road Users, and Policy Makers?”, included discussions between industry experts and human factors researchers and professionals on immediate human factors issues surrounding deployment of vehicles with Automated Driving Systems (ADS).

The success of automated vehicles ultimately hinges on how well they meet their users’ needs. The study and application of human factors throughout the automated-vehicle design cycle can yield a safe, useful, and reliable technology that does what its users want. This paper reports on a breakout session of the 2014 “Automated Vehicles Symposium” aiming to present the state of automated-vehicle human factors research and how it is being applied in the development of automated vehicles. Discussions were framed around two Transportation Research Board (TRB) Research Needs Statements that pertained to Human Factors research on automated vehicles. The two needs statements were officially balloted by TRB and covered: (1) the transfer of control between levels of automation or back to manual driving, and (2) the misuse and abuse of automated vehicles. Additionally, the group primarily considered issues associated with NHTSA’s Level 2 and Level 3 automation. The transfer of control discussions included designing for situation awareness, mental model development, and “failing gracefully.” For automation misuse, the consensus was that some drivers will unknowingly over-rely on the automation in situations that it was not designed to handle. For automation abuse, it was recognized that there will be a segment of the driving population who will knowingly improperly and unsafely use the automation for personal gain. Therefore, any design of Level 2 or Level 3 systems that require the driver to be in the loop or brought back into the loop should include feedback and possibly forcing functions that prevent unsafe vehicle operation. Ultimately, the attendees unanimously agreed that human factors methods should be employed early and iteratively in the design cycle to achieve this goal.

Making 'human factors' truly human: Cultural considerations in human factors research and practice

ABSTRACTIn recent years, concern has been raised that the trend of continuous productivity improvements of forest harvesting has stagnated and even declined in the Nordics. This selective literature review therefore examines human factors research with regard to the performance of the harvesting system, and outlines how human factors research can stimulate continued performance improvements. The focus is on implications for mechanized cut-to-length (CTL) operations and, especially, research of importance to the operation of the single grip harvester and the harvester-forwarder system. Most literature for this review was found in the Web of Knowledge and Google Scholar databases or as a consequence of the snowballing approach also applied. A conceptual framework was used to facilitate the analysis of the interrelated elements – Human, Technology, Organization and Environment (HTO-E). The right abilities, skills, techniques and training alone are not sufficient for ensuring high performance of the logging system. To address this discrepancy, education, training, (semi-) automation, shift scheduling, harvester-forwarder cooperation, inter- and intra- organizational knowledge exchange were all recognized as important areas for improvement and development. Great difficulties exist in predicting outcomes when introducing change, for example automation, into a system. However, the consequences of not trying may be detrimental to productivity as well as safety in mechanized forestry. Thus, it is important to research into methods, interfaces, forms of interaction, risk analyses and automation technology. HTO-E and a systems perspective provide a good basis for understanding the work today and making educated predictions for the future.

There are two general strategies that may be employed for "doing human factors research with nonhuman animals." First, one may use the methods of traditional human factors investigations to examine the nonhuman animal-to-machine interface. Alternatively, one might use performance by nonhuman animals as a surrogate for or model of performance by a human operator. Each of these approaches is illustrated with data in the present review. Chronic ambient noise was found to have a significant but inconsequential effect on computer-task performance by rhesus monkeys (Macaca mulatta). Additional data supported the generality of findings such as these to humans, showing that rhesus monkeys are appropriate models human psychomotor performance. It is argued that ultimately the interface between comparative psychology and technology will depend on the coordinated use of both strategies of investigation.

<div class="htmlview paragraph">The major goal of Space Human Factors program is to provide the technology which will enable safe and productive human performance throughout and after long duration space flights and planetary/lunar missions. The Human Factors element has three major sub-goals:</div> <div class="htmlview paragraph"> <ol class="list nostyle"> <li class="list-item"> <span class="li-label">1.</span> <div class="htmlview paragraph">Human Performance Models, Data and Tools - provide the basic understanding of human's activities in the physical, mental, and perceptual domains and to identify important lessons learned about human activities from prior space and lunar missions. To this end, Johnson Space Center is using a laser-based human body mapping system to measure different anthropometric sizes and shapes for incorporation into a large database. This database is employed, along with a spatial computer image graphics system, to analyze human motion in zero gravity conditions as the hypothetical, human bodysized figure performs a variety of new procedures in Space Station Freedom or in the Shuttle orbiter. At Ames Research Center, human performance models for computer-aided engineering, a program initially developed for aeronautical applications, are being modified for space applications. The value of the dynamic human performance models lies in the versatility in which various management decisions can be evaluated as to their impact on mission effectiveness, personnel skill and knowledge allocation, workload, workstation and cockpit design, and overall system design.</div> </li> <li class="list-item"> <span class="li-label">2.</span> <div class="htmlview paragraph">Crew Support and Enhancement - address the development of technology and its applications for the evaluation of the crew's living and working activities in the spacecraft and planetary/lunar habitat and on planetary/lunar surfaces. During the year, a number of projects were completed. At Johnson Space Center, new computer graphics were developed, particularly new methods for portraying procedures which reduce human error. Also, advances in information management methods for application to Shuttle maintenance operations were developed at Ames Research Center. Data from Kennedy Space Center operations is being used as a testbed for managing the information processing in maintenance.</div> </li> <li class="list-item"> <span class="li-label">3.</span> <div class="htmlview paragraph">Human-Automation-Robotic Systems - determine the means by which humans and automated or semi-autonomous systems can effectively work together. A major project at Ames Research Center is the development of the Virtual Workstation as a user interface device for exploring lunar or planetary surfaces. The effort this year has been to obtain a Mar-like data base and begin to employ it in simulated missions. At Johnson Space Center, effort was directed toward identifying the best location of visual sensors for teleoperations.</div> </li> </ol> </div>

As machine learning approaches ubiquity in industrial systems and consumer products, human factors research must attend to machine learning, specifically on how intelligent systems built on machine learning are different from early generations of automated systems, and what these differences mean for human-system interaction, design, evaluation and training. This panel invites five researchers in different domains to discuss how human factors can contribute to machine learning research and applications, as well as how machine learning presents both challenges and contributions for human factors.

Technological advancements have become widespread, and their implementation into products of everyday use is accelerating. Technology has the potential to improve the lives of older adults by increasing their safety, security, and independence in daily life. However, too often older adults' capabilities and limitations are not considered in the design of current and future technologies. In 1990, the National Research Council identified the importance of human factors in the design of technology for an aging population. The goal of this chapter is to review research on aging and technology since that report to determine the contributions of human factors research to issues of aging and technology design. In this chapter we address the extent to which older adults use new technologies, factors to consider in the adoption of technology (e.g., attitudes), the influence of technology design on older adults' performance (e.g., design of input devices), and ways to optimize training for older adults in using new technologies (e.g., age-specific instructional designs). We then review emerging areas of research that may direct the focus of human factors research in the next decade. These areas of research include ubiquitous computing (e.g., home monitoring systems), health care technologies (e.g., telehealth), robotics (e.g., Nursebot), and automated systems (e.g., cruise control). Finally, we consider opportunities and challenges to human factors research as the field continues to address the questions of optimizing technology for older adult users.

Examines the options both mainframe-based management information systems (MIS) and microcomputer applications to respond to the increasing emphasis on human factors, specifically in terms of user skills. Trends in human factors research are examined, with reference to formal development methods for information systems. The importance of training for users is considered in light of the growth of personal computing and the almost mandatory requirement for employees to use software applications in their day-to-day work. The use of computer-base training (CBT) as a solution is evaluated with reference to training systems based on embedded CBT and intelligent computer-assisted learning. A model is proposed to integrate the components of intelligent CBT (interaction, individualization, availability) with systems design methodologies and human factors issues to provide an error-free operational environment. Research applications in communication and the information sciences are discussed. >