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Improving Performance Shaping Factors, Essay Example

Pages: 6

Words: 1752

Essay

On October 5, 1999, two commuter trains in London were traveling with passengers during the morning rush hour and one train failed to stop at a red light which provoked a collision with a high-speed train killing 31 people and injuring many more through subsequent fires and wreckage (Lilley 1).  Even though many people would have attributed this wreckage primarily to the error of the driver failing to stop at the red light, the researchers in London determined that it was an entire system-wide error related to human-machine interfacing.  This suggests that by improving the human-machine interfacing and increasing training methods to reduce human error, the efficiency and quality of machine operation and correlative human operation will greatly improve.  Through a careful analysis of performance shaping factors related to human-machine interfaces such as controls and displays, flaws in these systems will be able to be revealed and corrected to reduce material loss and injury to personnel.  It is important to note that this goal is likely to be achieved by eliminating negative performance shaping factors and improving the technologies of human-machine interfaces where ergonomic design and is inherent and efficient.

Throughout performance-based research, many statistical and theoretical-based models have been designed in order to determine flaws in human performance and human-machine interfacing.  One key methodology is described by Kim et al. as the human reliability analysis (HRA) which has been utilized to predict human error probability (HEP) in order “to predict what types of human error are possible, to assess how probable they are, and to analyze what the causes and conditions under which those errors occur are” (1261).  Similar to the London railways crash, these researchers have carefully examined HRA in order to determine its overall applicability to emergency tasks and decision making processes by employees working in nuclear power plants.  Most notably, the SPAR-H methodology for determining performance shaping factors that can hinder human performance

Research has shown that there are two HRA methodologies that are highly prevalent in determining potential control functions moving towards higher efficient human-machine interfacing.  The first HRA methodology is presented in detail by Blackman et al. entitled Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) which is used to determine as most HRAs in predicting potential sources for human error and mistakes in decision-making processes (1).  The importance of SPAR-H is that is breaks down the human achievement tasks based upon cognition, perception and response which are critical towards human behavior and task achievement.  SPAR-H was utilized throughout the research to present potential sources of human error by separation action-based and cognitive-based criteria that would otherwise have been linked together in other forms of HRA.  It is important to note that final conclusions showcased that cognitive-based performance could best be regulated or controlled through higher education and training of multiple performance scenarios; meanwhile, action-based performance could be improved through a correlative improvement in the technology and layout of technology used to assist humans in the decision-making and information retrieval processes (Blackman et al. 6).  The latter point regarding technology leads to a more detailed discussion of the importance of technological ergonomics to improve human performance in job tasks.

As previously mentioned, there are many variable performance factors that must be taken into consideration and examined when creating an optimal human-machine interface.  Most notably, the sight performance factor and the readiness of information and the speed in which the human is able to retrieve and analyze that information are factors for providing for expedient decision-making (Gould et al. S113).  Vision effectively enables an individual to acquire new information in order to engage the cognitive performance functions during emergency task achievement; inaccuracies during this process or an overabundance of information in an illogical or confusing format can negatively influence the effectiveness of human task achievement during decision making.  Aside from the biological factors that can hinder eye sight performance, technology must be designed and controlled in a manner that enables human achievement efficiency to be high as well as accurate.  For instance, research found that using multiple-monitor interfaces increases the amount of information that can be obtained by the human worker; however, using a flat panel design layout is much less effective and more difficult to navigate than using a curved design in which the turn radius and motion movements for human biological tasks are more natural (Shupp et al. 266).  By changing this simple interface format, the machine interface appeals to the natural human characteristics that will improve performance efficiency.

Another key element for multiple-monitor technology interfaces is the quality of the images that are being presented to the human workers.  Research shows that not only is the curved panel display more efficient for human motor skills and eye control, but it must also be accompanied by greater pixel depth that displays a crisp, clear picture.  Individuals experiencing work environments with increase in pixels for multi-monitor display systems were more likely to view correct images and decipher these images faster than lower pixel image presentations (Shupp et al. 259).  This presents another key component to the human-machine interface system in which technological controls can be utilized to improve the overall human performance and decision-making processes while reducing the potential for performance factors to hinder overall efficiency.

While eye sight is a major factor for human performance, multiple performance factors have been analyzed by researchers that should be described elsewhere.  Nevertheless, one key observation to note is that although researchers found that weather held no direct statistical correlative position to the railway performance of employees in accidents (O’Hare 146), the human-machine interface must be designed in such a manner that takes weather into account if it is appropriate.  For instance, a nuclear power plant where employees working inside would not be impacted by the negative effects of the weather outside would have little need to integrate a human-machine interface to adjust for weather.  However, aviation or any other field in which weather would negatively skew human or machine performance should be examined and an appropriate interface be designed in order to address potential performance risk factors.  Environmental factors can negative or positive impact human task performance and cognitive decision-making based on the previously discussed information retrieval processes.  Failure to examine environmental factors such as weather in the design, layout and controls of the technology utilized is presenting human workers in a less than desirable work situation especially considering potential emergency reactions in aviation, nuclear power plants and railway work environments.

Aside from these simplistic human traits, human judgment through cognitive functions is responsible for human performance errors in terms of decision-making and thought processes.  Research has divided performance errors into omission and commission categorical errors; however, SPAR-H suggests that these are incorrect categories and the primary sources for human error are commission (Gertman et al. 11).  Furthermore, research has shown that slips, lapses and mistakes are the growing terminologies used to describe commission errors in human judgment and performance.  This terminology changes the way in which performance-based research must analyze human behavior and performance in order to account for the “human element” which essentially proves the age-old proverb that no one is perfect.  While this may be correct, this hinders the research statistics and human-machine interfacing designs to account for these forms of human error and are unlikely to be attributed in the overall design of the interfacing system.  In order to provide a more accurate and efficient analysis for system development, design layout and system controls, these types of human errors must be acknowledged but not included in the interfacing development.  Instead, cognition, perception and response must be the key components analyzed and controlled by human-machine interface design and business operations through the framework of the SPAR-H methodology.

One final element of human-machine interfacing design is the existent of clicking-based and tapping-based technologies for human decision-making, monitor technology interaction and technological actions.  Research shows that clicking is currently the more accurate form of human action within the framework of technologies; however, the constant growth of tapping technologies is more likely to overtake clicking technology as a wave into the future (Shupp et al. 270).  The reason for this advancement is that tapping is a simple one-step process that can be fast and accurate.  Many clicking technologies have become outdated and are slower in response time than those utilized by tapping technologies (Haney & Gertman 5).  This simple change in control design and operation with regards to human-machine interfacing design is able to provide greater efficiency for human-machine interaction while also increasing response time and overall process efficiency.

In summary, through improving the quality and effectiveness of human-machine interfacing systems, human-based and machine-based operations can be improved for speed, accuracy and efficiency.  SPAR-H is an excellent resource for determining potential human errors and performance factors that may hinder response and actions.  Furthermore, through changing the technology design layout, controls and increasing pixilation for monitor-based technologies, human performance and information retrieval can be greatly improved.  The ultimate goal must be to prepare humans and machines for potential emergencies as the railway disaster in London.  By making these changes to the human-machine interface system, humans will be well-prepared and equipped to make quality decisions and improved performances to reduce the occurrence of disasters.

Works Cited

Blackman, Harold S., David I. Gertman, and Ronald L. Boring. “Human Error Quantification Using Performance Shaping Factors in the SPAR-H Method.” Proc. of 52nd Annual Meeting of the Human Factors and Ergonomics Society, Idaho Falls, ID. 2008. 1-6. Print.

Gertman, D., H. Blackman, J. Marble, J. Byers, and C. Smith. “The SPAR-H Human Reliability Analysis Method.” Proc. of U.S. Nuclear Regulatory Commission, Office of Nuclear Regulatory Research, Washington, DC. 2005. Print.

Gould, Kristian, Bjarte Knappen Røed, Vilhelm Koefoed, Robert Bridger, and Bente Moen. “Performance-Shaping Factors Associated With Navigation Accidents in the Royal Norwegian Navy.” Military Psychology 18 (2006): 111-29. Print.

Haney, Lon N., and David I. Gertman. “Framework Assessing Notorious Contributing Influences For Error (FRANCIE): Perspectives On Taxonomy Development To Support Error Reporting And Analysis.” Proc. of 12th International Symposium on Aviation Psychology, Dayton, OH. 2003. 1-6. Print.

Kim, Jae W., Wondea Jung, and Jaejoo Ha. “AGAPE-ET: A Methodology for Human Error Analysis of Emergency Tasks.” Risk Analysis 24.5 (2004): 1261-277. Print.

Lilley, Steve, ed. “Red Light.” System Failure Case Studies 3.3 (2009): 1-4. Print.

O’Hare, David. “Cognitive Functions and Performance Shaping Factors in Aviation Accidents and Incidents.” International Journal of Aviation Psychology 16.2 (2006): 145-56. Print.

Shupp, Lauren, Christopher Andrews, Margaret Dickey-Kurdziolek, Beth Yost, and Chris North. “Shaping the Display of the Future: The Effects of Display Size and Curvature on User Performance and Insights.” Human-Computer Interaction 24.1 (2009): 230-72. Print.

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