POPCORN: a Supervisory Control Simulation for Workload and Performance Research

A multi-task simulation of a semi-automatic supervisory control system was developed to provide an environment in which training, operator strategy development, failure detection and resolution, levels of automation, and operator workload can be investigated. The goal was to develop a well-defined, but realistically complex, task that would lend itself to model-based analysis. The name of the task (POPCORN) reflects the visual display that depicts different task elements milling around waiting to be released and pop out to be performed. The operator's task was to complete each of 100 task elements that ere represented by different symbols, by selecting a target task and entering the desired a command. The simulated automatic system then completed the selected function automatically. Highly significant differences in performance, strategy, and rated workload were found as a function of all experimental manipulations (except reward/penalty)

Attention and automation: New perspectives on mental underload and performance

There is considerable evidence in the ergonomics literature that automation can significantly reduce operator mental workload. Furthermore, reducing mental workload is not necessarily a good thing, particularly in cases where the level is already manageable. This raises the issue of mental underload, which can be at least as detrimental to performance as overload. However, although it is widely recognized that mental underload is detrimental to performance, there are very few attempts to explain why this may be the case. It is argued in this paper that, until the need for a human operator is completely eliminated, automation has psychological implications relevant in both theoretical and applied domains. The present paper reviews theories of attention, as well as the literature on mental workload and automation, to synthesize a new explanation for the effects of mental underload on performance. Malleable attentional resources theory proposes that attentional capacity shrinks to accommodate reductions in mental workload, and that this shrinkage is responsible for the underload effect. The theory is discussed with respect to the applied implications for ergonomics research

Response Criterion Placement Modulates the Effects of Graded Alerting Systems on Human Performance and Learning in a Target Detection Task

Human operators can perform better with the use of an automated diagnostic aid than without the use of an aid in a signal detection task. This experiment aimed to determine whether any differences existed among graded aids—automated diagnostic aids that use a scale of confidence levels reflecting a spectrum of probabilistic information or uncertainty when making a judgment—that enabled better human detection performance, and either binary or graded aid produced better learning. Participants performed a visual search framed as a medical decision making task. Stimuli were arrays of random polygons (“cells”) generated by distorting a prototype shape. The target was a shape more strongly distorted than the accompanying distracters. A target was present on half of the trials. Each participant performed the task with the assistance of either a binary aid, one of three graded aids, or no aid. The aids’ sensitivities were the same (d′ = 2); the difference between the aids lay in the placement of their decision criteria, which determines a tradeoff between the aid’s predictive value and the frequency with which it makes a diagnosis. The graded aid with 90% reliability provided a judgment on the greatest number of trials, the graded aid with 94% reliability gave a judgment on fewer trials, and the third graded aid with 96% reliability gave a judgment on the least number of trials. The binary aid with 84% reliability gave a judgment on each trial. All aids improved human detection performance, though the graded aids trended towards improving performance more than the binary aid. The binary and graded aids did not produce significantly better or worse learning than did unaided performance. The binary and graded aids did not significantly help learning, but they certainly did not worsen human detection performance when compared to the unaided condition. These results imply that the decision boundaries of a graded alert might be fixed to encourage appropriate reliance on the aid and improve human detection performance, and indicate employing either a graded or binary automated aid may be beneficial to learning in a detection task

Online Robot Introspection via Wrench-based Action Grammars

Robotic failure is all too common in unstructured robot tasks. Despite well-designed controllers, robots often fail due to unexpected events. How do robots measure unexpected events? Many do not. Most robots are driven by the sense-plan act paradigm, however more recently robots are undergoing a sense-plan-act-verify paradigm. In this work, we present a principled methodology to bootstrap online robot introspection for contact tasks. In effect, we are trying to enable the robot to answer the question: what did I do? Is my behavior as expected or not? To this end, we analyze noisy wrench data and postulate that the latter inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is generated by segmenting and encoding the data. When the wrench information represents a sequence of sub-tasks, we can think of the vocabulary forming a sentence (set of words with grammar rules) for a given sub-task; allowing the latter to be uniquely represented. The grammar, which can also include unexpected events, was classified in offline and online scenarios as well as for simulated and real robot experiments. Multiclass Support Vector Machines (SVMs) were used offline, while online probabilistic SVMs were are used to give temporal confidence to the introspection result. The contribution of our work is the presentation of a generalizable online semantic scheme that enables a robot to understand its high-level state whether nominal or abnormal. It is shown to work in offline and online scenarios for a particularly challenging contact task: snap assemblies. We perform the snap assembly in one-arm simulated and real one-arm experiments and a simulated two-arm experiment. This verification mechanism can be used by high-level planners or reasoning systems to enable intelligent failure recovery or determine the next most optima manipulation skill to be used.Comment: arXiv admin note: substantial text overlap with arXiv:1609.0494

Why Are People's Decisions Sometimes Worse with Computer Support?

In many applications of computerised decision support, a recognised source of undesired outcomes is operators' apparent over-reliance on automation. For instance, an operator may fail to react to a potentially dangerous situation because a computer fails to generate an alarm. However, the very use of terms like "over-reliance" betrays possible misunderstandings of these phenomena and their causes, which may lead to ineffective corrective action (e.g. training or procedures that do not counteract all the causes of the apparently "over-reliant" behaviour). We review relevant literature in the area of "automation bias" and describe the diverse mechanisms that may be involved in human errors when using computer support. We discuss these mechanisms, with reference to errors of omission when using "alerting systems", with the help of examples of novel counterintuitive findings we obtained from a case study in a health care application, as well as other examples from the literature

The ergonomics of command and control

Since its inception, just after the Second World War, ergonomics research has paid special attention to the issues surrounding human control of systems. Command and Control environments continue to represent a challenging domain for Ergonomics research. We take a broad view of Command and Control research, to include C2 (Command and Control), C3 (Command, Control and Communication), and C4 (Command, Control, Communication and Computers) as well as human supervisory control paradigms. This special issue of ERGONOMICS aims to present state-of-the-art research into models of team performance, evaluation of novel interaction technologies, case studies, methodologies and theoretical review papers. We are pleased to present papers that detail research on these topics in domains as diverse as the emergency services (e.g., police, fire, and ambulance), civilian applications (e.g., air traffic control, rail networks, and nuclear power) and military applications (e.g., land, sea and air) of command and control. While the domains of application are very diverse, many of the challenges they face share interesting similarities

Effects of alarms on control of robot teams

Annunciator driven supervisory control (ADSC) is a widely used technique for directing human attention to control systems otherwise beyond their capabilities. ADSC requires associating abnormal parameter values with alarms in such a way that operator attention can be directed toward the involved subsystems or conditions. This is hard to achieve in multirobot control because it is difficult to distinguish abnormal conditions for states of a robot team. For largely independent tasks such as foraging, however, self-reflection can serve as a basis for alerting the operator to abnormalities of individual robots. While the search for targets remains unalarmed the resulting system approximates ADSC. The described experiment compares a control condition in which operators perform a multirobot urban search and rescue (USAR) task without alarms with ADSC (freely annunciated) and with a decision aid that limits operator workload by showing only the top alarm. No differences were found in area searched or victims found, however, operators in the freely annunciated condition were faster in detecting both the annunciated failures and victims entering their cameras' fields of view. Copyright 2011 by Human Factors and Ergonomics Society, Inc. All rights reserved