Qualitative Model for Neuro-Functional Mechanism Inducing Human Error Detection to Humanlike Agents Based on Cortico-Cerebellar Function

2016 ◽  
pp. 194-200
Author(s):  
Yoshimasa Tawatsuji ◽  
Tatsunori Matsui
Keyword(s):  
Error Detection ◽  
Human Error ◽  
Qualitative Model ◽  
Cerebellar Function ◽  
Functional Mechanism

On Your Watch: Automation on the Bridge

2002 ◽  
Vol 55 (1) ◽  
pp. 83-96 ◽  
Author(s):  
M. H. Lützhöft ◽  
S. W. A. Dekker
Keyword(s):  
Error Detection ◽  
Human Error ◽  
Automated Systems ◽  
Future Directions ◽  
Research Results ◽  
Sequence Of Events ◽  
The Future ◽  
Machine Work ◽  
Increase Efficiency

In this paper, we discuss the grounding of the Royal Majesty, reconstructed from the perspective of the crew. The aim is particularly to understand the role of automation in shaping crew assessments and actions. Automation is often introduced because of quantitative promises that: it will reduce human error; reduce workload; and increase efficiency. But as demonstrated by the Royal Majesty, as well as by numerous research results, automation has qualitative consequences for human work and safety, and does not simply replace human work with machine work. Automation changes the task it was meant to support; it creates new error pathways, shifts consequences of error further into the future and delays opportunities for error detection and recovery. By going through the sequence of events that preceded the grounding of the Royal Majesty, we highlight the role that automation plays in the success and failure of navigation today. We then point to future directions on how to make automated systems into better team players.

scholarly journals Efficacy of visual surveys for monitoring populations of talus-roosting bats

2020 ◽  
Author(s):  
Paul R Moosman ◽  
David Marsh ◽  
Emily K Pody ◽  
Mitch P Dannon ◽  
Richard J Reynolds
Keyword(s):  
Error Detection ◽  
Detection Probability ◽  
Human Error ◽  
Western North America ◽  
False Negatives ◽  
Rock Outcrops ◽  
Site Characteristics ◽  
Myotis Leibii ◽  
Sources Of Variation ◽  
Talus Slopes

Winter cave and mine surveys have been the primary method to monitor status of bat populations but they are not equally effective across regions or species. Many species of bats that roost in rock outcrops during the non-hibernation period are difficult to monitor with existing methods. Visual surveys for bats roosting on talus slopes has been proposed as a means to monitor populations, but efficacy of the method is unknown. We used standardized plot-based visual surveys to quantify presence and abundance of eastern small-footed bats Myotis leibii on talus slopes in Virginia, and studied sources of variation and error. Detection probability for talus surveys was relatively high but varied based on search effort and site characteristics. Both abundance and detection probability varied more among plots within sites than among sites or years. In trials with radio-tagged bats to study the causes of false negatives, 18% of bats roosted where surveyors could not see them, and 18% of bats were visible but overlooked due to human error. Inexperienced surveyors counted slightly fewer bats than the principal investigator, perhaps as an artifact of the dual-observer approach. There also was a slight learning curve among students. Visual surveys have strong potential to aid in the study of bats that roost in talus slopes. Talus surveys, unlike longer established methods to monitor bat populations, provide ways to assess error. We recommend using talus surveys to monitor other rock roosting bat species with poorly understood populations, such as many species in western North America.

scholarly journals Human Error: The Impact of Job Insecurity on Attention-Related Cognitive Errors and Error Detection

2019 ◽  
Vol 16 (13) ◽  
pp. 2427
Author(s):  
Lara Christina Roll ◽  
Oi-ling Siu ◽  
Simon Y.W. Li ◽  
Hans De Witte
Keyword(s):  
Error Detection ◽  
Indirect Effects ◽  
Human Error ◽  
Job Insecurity ◽  
Detection Task ◽  
Self Report ◽  
Cognitive Errors ◽  
Work Related ◽  
Related Stress ◽  
The Impact

(1) Background: Work-related stress is a major contributor to human error. One significant workplace stressor is job insecurity, which has been linked to an increased likelihood of experiencing burnout. This, in turn, might affect human error, specifically attention-related cognitive errors (ARCES) and the ability to detect errors. ARCES can be costly for organizations and pose a safety risk. Equally detrimental effects can be caused by failure to detect errors before they can cause harm. (2) Methods: We gathered self-report and behavioral data from 148 employees working in educational, financial and medical sectors in China. We designed and piloted an error detection task in which employees had to compare fictitious customer orders to deliveries of an online shop. We tested for indirect effects using the PROCESS macro with bootstrapping (3) Results: Our findings confirmed indirect effects of job insecurity on both ARCES and the ability to detect errors via burnout. (4) Conclusions: The present research shows that job insecurity influences making and detecting errors through its relationship with burnout. These findings suggest that job insecurity could increase the likelihood for human error with potential implications for employees’ safety and the safety of others.

Human error detection processes

1987 ◽  
Vol 27 (5-6) ◽  
pp. 555-570 ◽  
Author(s):  
Antonio Rizzo ◽  
Sebastiano Bagnara ◽  
Michele Visciola
Keyword(s):  
Error Detection ◽  
Human Error

Evaluation of human factor engineering influence in nuclear safety using probabilistic safety assessment techniques

Kerntechnik ◽  
2021 ◽  
Vol 86 (6) ◽  
pp. 470-477
Author(s):  
M. Farcasiu ◽  
C. Constantinescu
Keyword(s):  
Reliability Analysis ◽  
Error Rate ◽  
Error Detection ◽  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Human Factor Engineering ◽  
Rate Prediction

Abstract This paper provides the empirical basis to support predictions of the Human Factor Engineering (HFE) influences in Human Reliability Analysis (HRA). A few methods were analyzed to identify HFE concepts in approaches of Performance Shaping Factors (PSFs): Technique for Human Error Rate Prediction (THERP), Human Cognitive Reliability (HCR) and Cognitive Reliability and Error Analysis Method (CREAM), Success Likelihood Index Method (SLIM) Plant Analysis Risk – Human Reliability Analysis (SPAR-H), A Technique for Human Error Rate Prediction (ATHEANA) and Man-Machine-Organization System Analysis (MMOSA). Also, in order to identify other necessary PSFs in HFE, an additional investigation process of human performance (HPIP) in event occurrences was used. Thus, the human error probability could be reduced and its evaluating can give out the information for error detection and recovery. The HFE analysis model developed using BHEP values (maximum and pessimistic) is based on the simplifying assumption that all specific circumstances of HFE characteristics are equal in importance and have the same value of influence on human performance. This model is incorporated into the PSA through the HRA methodology. Finally, a clarification of the relationships between task analysis and the HFE is performed, ie between potential human errors and design requirements.

Human Error Detection and Recovery

2017 ◽  
pp. 163-196
Author(s):  
Tom Kontogiannis ◽  
Stathis Malakis
Keyword(s):  
Error Detection ◽  
Human Error

Modeling Error Recovery in Dynamic Collaborative Domains

2002 ◽  
Vol 46 (3) ◽  
pp. 372-376
Author(s):  
Mark I. Nikolic ◽  
Nadine B. Sarter
Keyword(s):  
Error Detection ◽  
Human Error ◽  
Current Model ◽  
Error Recovery ◽  
Error Management ◽  
Negative Consequences ◽  
Collaborative Environments ◽  
Sequence Error ◽  
Recovery Strategies ◽  
Report Analysis

For many years, the focus of research in the area of human error was the prevention of erroneous actions and assessments through training and design. However, errors can never be eliminated completely. Therefore, the goal of more recent efforts is to minimize their negative consequences through support for error management, i.e., the detection, explanation, and recovery from erroneous actions. For the most part, these efforts have examined the first step in this sequence - error detection. In contrast, little is known about how operators explain and recover from errors. This is true especially for dynamic collaborative environments such as aviation. In this paper, we present findings from a survey and an incident report analysis that suggest the need for adapting the current model of error recovery. Specifically, we emphasize the importance of considering constraints imposed by specific domains in order to predict and explain the predominance and success of certain recovery strategies.

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