Human Factors

This paper presents a theoretical model of situation awareness based on its role in dynamic human decision making in a variety of domains. Situation awareness is presented as a predominant concern in system operation, based on a descriptive view of decision making. The relationship between situation awareness and numerous individual and environmental factors is explored. Among these factors, attention and working memory are presented as critical factors limiting operators from acquiring and interpreting information from the environment to form situation awareness, and mental models and goal-directed behavior are hypothesized as important mechanisms for overcoming these limits. The impact of design features, workload, stress, system complexity, and automation on operator situation awareness is addressed, and a taxonomy of errors in situation awareness is introduced, based on the model presented. The model is used to generate design implications for enhancing operator situation awareness and future directions for situation awareness research.

This paper addresses theoretical, empirical, and analytical studies pertaining to human use, misuse, disuse, and abuse of automation technology. Use refers to the voluntary activation or disengagement of automation by human operators. Trust, mental workload, and risk can influence automation use, but interactions between factors and large individual differences make prediction of automation use difficult. Misuse refers to over reliance on automation, which can result in failures of monitoring or decision biases. Factors affecting the monitoring of automation include workload, automation reliability and consistency, and the saliency of automation state indicators. Disuse, or the neglect or underutilization of automation, is commonly caused by alarms that activate falsely. This often occurs because the base rate of the condition to be detected is not considered in setting the trade-off between false alarms and omissions. Automation abuse, or the automation of functions by designers and implementation by managers without due regard for the consequences for human performance, tends to define the operator's roles as by-products of the automation. Automation abuse can also promote misuse and disuse of automation by human operators. Understanding the factors associated with each of these aspects of human use of automation can lead to improved system design, effective training methods, and judicious policies and procedures involving automation use.

Objective: The objective is to lay out the rationale for multiple resource theory and the particular 4-D multiple resource model, as well as to show how the model is useful both as a design tool and as a means of predicting multitask workload overload. Background: I describe the discoveries and developments regarding multiple resource theory that have emerged over the past 50 years that contribute to performance and workload prediction. Method: The article presents a history of the multiple resource concept, a computational version of the multiple resource model applied to multitask driving simulation data, and the relation of multiple resources to workload. Results: Research revealed the importance of the four dimensions in accounting for task interference and the association of resources with brain structure. Multiple resource models yielded high correlations between model predictions and data. Lower correlations also identified the existence of additional resources. Conclusion: The model was shown to be partially relevant to the concept of mental workload, with greatest relevance to performance breakdowns related to dual-task overload. Future challenges are identified. Application: The most important application of the multiple resource model is to recommend design changes when conditions of multitask resource overload exist.

Mục tiêu: Chúng tôi đánh giá và định lượng các tác động của yếu tố con người, robot và môi trường đến niềm tin cảm nhận trong tương tác người-robot (HRI).

Bối cảnh: Cho đến nay, các tổng quan về niềm tin trong HRI thường mang tính chất định tính hoặc mô tả. Nghiên cứu tổng quan định lượng của chúng tôi cung cấp cơ sở thực nghiệm nền tảng để thúc đẩy cả lý thuyết và thực hành.

Phương pháp: Phương pháp phân tích meta được áp dụng cho các tài liệu hiện có về niềm tin và HRI. Tổng cộng có 29 nghiên cứu thực nghiệm được thu thập, trong đó 10 nghiên cứu đạt tiêu chuẩn chọn lựa cho phân tích tương quan và 11 nghiên cứu cho phân tích thực nghiệm. Các nghiên cứu này cung cấp 69 kích thước hiệu ứng tương quan và 47 kích thước hiệu ứng thực nghiệm.

Kết quả: Kích thước hiệu ứng tương quan tổng thể cho niềm tin là r̄ = +0.26, với kích thước hiệu ứng thực nghiệm là d̄ = +0.71. Các tác động của đặc điểm con người, robot và môi trường đã được xem xét với sự đánh giá đặc biệt về các khía cạnh về hiệu suất và yếu tố thuộc tính của robot. Hiệu suất và các thuộc tính của robot là những yếu tố đóng góp lớn nhất vào sự phát triển niềm tin trong HRI. Các yếu tố môi trường chỉ đóng vai trò trung bình.

Kết luận: Các yếu tố liên quan đến bản thân robot, cụ thể là hiệu suất của nó, hiện có sự liên kết mạnh nhất với niềm tin, và các yếu tố môi trường chỉ có mối liên kết ở mức độ trung bình. Có rất ít bằng chứng cho thấy tác động của các yếu tố liên quan đến con người.

Ứng dụng: Các phát hiện cung cấp ước lượng định lượng của các yếu tố con người, robot và môi trường ảnh hưởng đến niềm tin HRI. Cụ thể, tóm tắt hiện tại cung cấp ước lượng kích thước hiệu ứng hữu ích trong việc thiết lập hướng dẫn thiết kế và đào tạo liên quan đến các yếu tố robot của niềm tin HRI. Hơn nữa, kết quả cho thấy rằng việc hiệu chỉnh không đúng niềm tin có thể được giảm thiểu bằng cách điều chỉnh thiết kế robot. Tuy nhiên, nhiều nhu cầu nghiên cứu trong tương lai đã được xác định.

A model for visual recall tasks was presented in terms of visual information storage (VIS), scanning, rehearsal, and auditory information storage (AIS). It was shown first that brief visual stimuli are stored in VIS in a form similar to the sensory input. These visual “images” contain considerably more information than is transmitted later. They can be sampled by scanning for items at high rates of about 10 msec per letter. Recall is based on a verbal receding of the stimulus (rehearsal), which is remembered in AIS. The items retained in AIS are usually rehearsed again to prevent them from decaying. The human limits in immediate-memory (reproduction) tasks are inherent in the AIS-Rehearsal loop. The main implication of the model for human factors is the importance of the auditory coding in visual tasks.

This article reports on a calculational approach for combining measures of mental workload and task performance that allows one to obtain information on the relative efficiency of instructional conditions. The method is based on the standardization of raw scores for mental effort and task performance to z scores, which are displayed in a cross of axes. Relative condition efficiency is calculated as the perpendicular distance to the line that is assumed to represent an efficiency of zero. We conclude that the method for calculating and representing relative condition efficiency discussed here can be a valuable addition to research on the training and performance of complex cognitive tasks.

This paper examines the effects of stress on sustained attention. With recognition of the task itself as the major source of cognitive stress, a dynamic model is presented that addresses the effects of stress on vigilance and, potentially, a wide variety of attention-demanding performance tasks.

It is hypothesized that highly effective teams adapt to stressful situations by using effective coordination strategies. Such teams draw on shared mental models of the situation and the task environment as well as mutual mental models of interacting team members' tasks and abilities to shift to modes of implicit coordination, and thereby reduce coordination overhead. To test this hypothesis, we developed and implemented a team-training procedure designed to train teams to adapt by shifting from explicit to implicit modes of coordination and choosing strategies that are appropriate during periods of high stress and workload conditions. Results showed that the adaptation training significantly improved performance from pre- to posttraining and when compared with a control group. Results also showed that several underlying team process measures exhibited patterns indicating that adaptive training improved various team processes, including efficient use of mental models, which in turn improved performance. The implication of these findings for team adaptive training is discussed. This research spawned the adaptive architectures for a command and control project investigating adaptive models that focus on changes in the structural and process architecture of large organizations. The research also produced a cadre of integrated performance assessment tools that have been used in training and diagnostic settings, and new components for a team training package focused on effective coordination in high-performance teams.

Rear-end collisions account for almost 30% of automotive crashes. Rear-end collision avoidance systems (RECASs) may offer a promising approach to help drivers avoid these crashes. Two experiments performed using a high-fidelity motion-based driving simulator examined driver responses to evaluate the efficacy of a RECAS. The first experiment showed that early warnings helped distracted drivers react more quickly---and thereby avoid more collisions---than did late warnings or no warnings. Compared with the no-warning condition, an early RECAS warning reduced the number of collisions by 80.7%. Assuming collision severity is proportional to kinetic energy, the early warning reduced collision severity by 96.5%. In contrast, the late warning reduced collisions by 50.0 % and the corresponding severity by 87.5%. The second experiment showed that RECAS benefits even undistracted drivers. Analysis of the braking process showed that warnings provide a potential safety benefit by reducing the time required for drivers to release the accelerator. Warnings do not, however, speed application of the brake, increase maximum deceleration, or affect mean deceleration. These results provide the basis for a computational model of driver performance that was used to extrapolate the findings and identify the most promising parameter settings. Potential applications of these results include methods for evaluating collision warning systems, algorithm design guidance, and driver performance model input.