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permanent personality characteristics of an operator or to a situational disturbance by psychophysiological
or external events. Data analysis revealed human errors that can be interpreted as a
four-dimensional error structure. Vigilance errors encompass one dimension. These are missing
or fragmentary uptake of objectively present information due to inattention, or
channellized/shifted attention. Perception errors are another dimension. These errors are
comprised of erroneous judgment, miscalculations, wrong decisions, and faulty action plans. The
third dimension is information processing errors. These are defined as false utilization of
probabilistic information. The fourth dimension is sensorimotor errors. These are deficiencies in
timing and adjustments of simple-discrete and or complex-continuous motor activities and also
perceptual-motor confusion. The study shows that there is an entanglement and interaction of
specific causal conditions.
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Gertman, D. I. (1993). Representing cognitive activities and errors in HRA trees.
Reliability Engineering and System Safety, 39, 25-34.
COGENT (cognitive event tree system) is an enriched HRA event tree method that is presented
in this paper that integrates three potential means of representing human activity. These include
an HRA event-tree approach, the skill-rule-knowledge paradigm, and the slips-lapses-mistakes
paradigm. COGENT attempts to combine the classical THERP technique with more cognitively
oriented approaches to bridge the existing gap between the modeling needs of HRA practitioners
and the classification schemes of cognitive theoreticians. The paper provides a detailed
description of the method and an application to an example scenario is performed.
Gertman, D. I., & Blackman, H. S. (1994). Human reliability and safety analysis data
handbook. New York: John Wiley and Sons.
The authors provide a comprehensive review and explanation of human reliability and safety
analysis. The background and “how to” aspects of conducting human reliability analysis are
discussed. Various methods of estimating and examining human reliability are reviewed. Some
of these include human cognitive reliability, maintenance personnel performance simulation,
techniques for human error rate prediction, and fault/event trees. It is stressed that existing data
sources and data banks are useful and important for performing human reliability and safety
analyses.
Gertman, D. I., Blackman, H. S., Haney, L. N., Seidler, K. S., & Hahn, H. A. (1992).
INTENT: A method for estimating human error probabilities for decision based errors.
Reliability Engineering and System Safety, 35, 127-136.
INTENT is a method that is used to estimate probabilities associated with decision based errors
that are not normally incorporated into probabilistic risk assessments. A hypothetical example is
created that uses a preliminary data set for 20 errors of intention that were tailored to represent
the influence of 11 commonly referenced performance shaping factors. The methodological flow
for INTENT involves six stages: Compiling errors of intention, quantifying errors of intention,
determining human error probabilities (HEP) upper and lower bounds, determining performance
shaping factors (PSF) and associated weights, determining composite PSF, and determining site
specific HEP’s for intention. The preliminary results show that the method provides an interim
mechanism to provide data which can serve to remedy a major deficiency of not accounting for
high consequence failures due to errors of intention.
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Gore, B. R., Dukelow, J. S., Mitts, T. M., & Nicholson, W. L. (1995). A limited assessment
of the ASEP human reliability analysis procedure using simulator examination results.
(NUREG/CR-6355). Pacific Northwest Laboratory.
The procedures and requirements for the ASEP analysis are explained. This volume does not
contain any of the background or theory involved in developing the approach.
Hahn, H. A., Blackman, H. S., & Gertman, D. I. (1991). Applying sneak analysis to the
identification of human errors of commission. Reliability Engineering and System Safety,
33, 289-300.
SNEAK is a method designed to identify human errors of commission. It is especially powerful
as an aid to discovering latent errors. The analysis performed in this paper is in the context of
electrical circuits, although a software SNEAK analysis has also been designed. Data acquisition
and encoding is the first major consideration of the method to determine that the data being used
adequately represents the true system. Network trees are also used to represent a simplified
version of the system. The network trees are examined for topological patterns. These patterns
　
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