Culture Representation in Human Reliability Analysis

Understanding human-system response is critical to being able to plan and predict mission success in the modern battlespace. Commonly, human reliability analysis has been used to predict failures of human performance in complex, critical systems. However, most human reliability methods fail to take culture into account. This paper takes an easily understood state of the art human reliability analysis method and extends that method to account for the influence of culture, including acceptance of new technology, upon performance. The cultural parameters used to modify the human reliability analysis were determined from two standard industry approaches to cultural assessment: Hofstede’s (1991) cultural factors and Davis’ (1989) technology acceptance model (TAM). The result is called the Culture Adjustment Method (CAM). An example is presented that (1) reviews human reliability assessment with and without cultural attributes for a Supervisory Control and Data Acquisition (SCADA) system attack, (2) demonstrates how country specific information can be used to increase the realism of HRA modeling, and (3) discusses the differences in human error probability estimates arising from cultural differences

Examining Decision-Making Regarding Environmental Information

Eight participants were asked to view a computer-based multimedia presentation on an environmental phenomenon. Participants were asked to play a role as a senior aide to a national legislator. In this role, they were told that the legislator had asked them to review a multimedia presentation regarding the hypoxic zone phenomenon in the Gulf of Mexico. Their task in assuming the role of a senior aide was to decide how important a problem this issue was to the United States as a whole, and the proportion of the legislator’s research budget that should be devoted to study of the problem. The presentation was divided into 7 segments, each containing some new information not contained in the previous segments. After viewing each segment, participants were asked to indicate how close they were to making a decision and how certain they were that their current opinion would be their final decision. After indicating their current state of decision-making, participants were interviewed regarding the factors affecting their decision-making. Of interest was the process by which participants moved toward a decision. This experiment revealed a number of possible directions for future research. There appeared to be two approaches to decision-making: Some decision-makers moved steadily toward a decision, and occasionally reversed decisions after viewing information, while others abruptly reached a decision after a certain time period spent reviewing the information. Although the difference in estimates of distance to decisions did not differ statistically for these two groups, that difference was reflected in the participants’ estimates of confidence that their current opinion would be their final decision. The interviews revealed that the primary difference between these two groups was in their trade-offs between willingness to spend time in information search and the acquisition of new information. Participants who were less confident about their final decision, tended to be the same group of participants who moved slowly toward a decision. These participants also tended to indicate that acquisition of information was more critical than the amount of time spent on the information search. The second group tended to form a set of specific questions for which they desired specific answers. This group was more likely to demonstrate a significant reduction in their distance to a decision much earlier than the first group. In addition, this group tended to be very confident of their final decision and indicated that time spent in information search was more critical than obtaining new information. They indicated that the value of information obtained must remain high to justify the extensive time spent in information search

I want what you've got: Cross platform portabiity and human-robot interaction assessment.

Human-robot interaction is a subtle, yet critical aspect of design that must be assessed during the development of both the human-robot interface and robot behaviors if the human-robot team is to effectively meet the complexities of the task environment. Testing not only ensures that the system can successfully achieve the tasks for which it was designed, but more importantly, usability testing allows the designers to understand how humans and robots can, will, and should work together to optimize workload distribution. A lack of human-centered robot interface design, the rigidity of sensor configuration, and the platform-specific nature of research robot development environments are a few factors preventing robotic solutions from reaching functional utility in real word environments. Often the difficult engineering challenge of implementing adroit reactive behavior, reliable communication, trustworthy autonomy that combines with system transparency and usable interfaces is overlooked in favor of other research aims. The result is that many robotic systems never reach a level of functional utility necessary even to evaluate the efficacy of the basic system, much less result in a system that can be used in a critical, real-world environment. Further, because control architectures and interfaces are often platform specific, it is difficult or even impossible to make usability comparisons between them. This paper discusses the challenges inherent to the conduct of human factors testing of variable autonomy control architectures and across platforms within a complex, real-world environment. It discusses the need to compare behaviors, architectures, and interfaces within a structured environment that contains challenging real-world tasks, and the implications for system acceptance and trust of autonomous robotic systems for how humans and robots interact in true interactive teams

Using Information from Operating Experience to Inform Human Reliability Analysis

This paper reports on efforts being sponsored by the U.S. NRC and performed by INEEL to develop a technical basis and perform work to extract information from sources for use in HRA. The objectives of this work are to: 1) develop a method for conducting risk-informed event analysis of human performance information that stems from operating experience at nuclear power plants and for compiling and documenting the results in a structured manner; 2) provide information from these analyses for use in risk-informed and performance-based regulatory activities; 3) create methods for information extraction and a repository for this information that, likewise, support HRA methods and their applications

PROOF OF CONCEPT FOR A HUMAN RELIABILITY ANALYSIS METHOD FOR HEURISTIC USABILITY EVALUATION OF SOFTWARE

An ongoing issue within human-computer interaction (HCI) is the need for simplified or “discount” methods. The current economic slowdown has necessitated innovative methods that are results driven and cost effective. The myriad methods of design and usability are currently being cost-justified, and new techniques are actively being explored that meet current budgets and needs. Recent efforts in human reliability analysis (HRA) are highlighted by the ten-year development of the Standardized Plant Analysis Risk HRA (SPAR-H) method. The SPAR-H method has been used primarily for determining humancentered risk at nuclear power plants. The SPAR-H method, however, shares task analysis underpinnings with HCI. Despite this methodological overlap, there is currently no HRA approach deployed in heuristic usability evaluation. This paper presents an extension of the existing SPAR-H method to be used as part of heuristic usability evaluation in HCI

Human Reliability Analysis in the U.S. Nuclear Power Industry: A Comparison of Atomistic and Holistic Methods

A variety of methods have been developed to generate human error probabilities for use in the US nuclear power industry. When actual operations data are not available, it is necessary for an analyst to estimate these probabilities. Most approaches, including THERP, ASEP, SLIM-MAUD, and SPAR-H, feature an atomistic approach to characterizing and estimating error. The atomistic approach is based on the notion that events and their causes can be decomposed and individually quantified. In contrast, in the holistic approach, such as found in ATHEANA, the analysis centers on the entire event, which is typically quantified as an indivisible whole. The distinction between atomistic and holistic approaches is important in understanding the nature of human reliability analysis quantification and the utility and shortcomings associated with each approach

Spatial compatibility and incidental sequence learning in the serial reaction time task

Memory for order information is studied by the presentation of a string of items, after which the subject is asked to recall the information in order. The serial learning task examines how repetition of a sequence leads to incidental sequence learning. Sequenced responses are faster and more accurate than random responses, reflecting advanced response-selection processes. Stimulus-response (S-R) compatibility also reflects response selection processes. Therefore, it is of theoretical interest to examine the interaction between the two. The Salient Features model indicates that task demands define which features will be attended in a set of stimuli or responses, which in turn determines how they will be coded. The Feature Model of memory indicates that the degree to which a cue uniquely specifies an item determines whether the correct item will be retrieved. Therefore, the task demands provide both the information that determines how the item is coded and the cues that specify the response. In serial learning this means that the task demands determine whether the subject encodes a series of responses, stimuli or stimulus-response pairs. The current experiments investigated how spatial compatibility affected sequence learning, and whether sequence learning affected the magnitude of the S-R compatibility effect, which would be expected because both reflect response-selection processes. Four experiments demonstrated that increasing the difficulty of the spatial translation decreased sequence learning. Although sequence knowledge did not eliminate the spatial compatibility effect, they did interact. The compatibility effect decreased faster in the sequenced conditions than in the random response conditions. S-R compatibility and response hand were manipulated to determine what is learned: A sequence of motor responses, stimulus positions or S-R paired associates. Subjects appeared to learn a sequence of S-R pairs and a sequence of stimulus-to-effectors relations. Under dual-task conditions, reliable evidence of sequence learning was found for the compatible S-R mapping, but not for the incompatible mapping. This supports the hypothesis that dual-task conditions suppress the expression of sequence knowledge, but do not prevent sequence learning. Time between stimulus presentations was a factor in sequence acquisition

WKU’s Technology Resource Center – A place for teaching and learning about multimedia technology

Access to higher education can come in many forms. For some, it means being able to attend a face-to-face class. For others, it means being able to attend class online. Regardless of the delivery method, multimedia content has become instrumental in facilitating information in higher education. For more than a decade, WKU’s Technology Resource Center has been offering students, faculty, and staff access to A/V equipment, training, and a knowledgeable staff to assist with multimedia content. The TRC has been a huge success in fostering the development of multimedia content while easing the frustrations of those who do not have access to, nor know how to use video/audio gear. Let us show you how to better support your faculty, staff, and students with their multimedia needs

DynamicAutonomy for Urban Search and Rescue

Abstract At the 2002 AAAI Robotics Competition and Exhibition, the Idaho National Engineering and Environmental Laboratory (INEEL) demonstrated a robot that can adjust its l evel of autonomy on the fly, leveraging its own, intrinsic intelligence to meet whatever level of control was handed down from the user. The robot had the ability to actively protect itself and the environment as it navigated through the USAR environment. In addition, the robot continuously assessed and adapted to changes in its own perceptual capabilities. The INEEL also demonstrated an interface for supporting mixed-initiative interaction between the operator and human. The interface displays an abstracted representation of the robot's experience and exploits sensor-suites and fusion algorithms that enhance capabilities for sensing, interpreting, and "understanding" environmental features. This paper reports on the current robotic system including hardware, sensor suite, control architecture, and interface system