Passive and active assistance for human performance of a simulated underactuated dynamic task

2003 ◽  
Author(s):  
M.K. O'Malley ◽  
A. Gupta
Keyword(s):  
Human Performance ◽  
Dynamic Task ◽  
Active Assistance

scholarly journals Improving Human Performance in Dynamic Tasks with Debriefing-Based Interactive Learning Environments: An Empirical Investigation

2020 ◽  
Vol 19 (04) ◽  
pp. 1065-1089
Author(s):  
Hassan Qudrat-Ullah
Keyword(s):  
Decision Making ◽  
Task Performance ◽  
Human Performance ◽  
Interactive Learning ◽  
Decision Time ◽  
Decision Strategy ◽  
Interactive Learning Environment ◽  
Ongoing Research ◽  
Dynamic Task ◽  
Process Oriented

Dynamic tasks are pervasive in organizational decision making. Improving managerial performance in dynamic tasks is an ongoing research endeavor. We report a laboratory experiment in which participants managed a dynamic task by playing the roles of fishing fleet managers. The two experimental groups used a computer simulation-based interactive learning environment (ILE) with an outcome-oriented debriefing and a process-oriented debriefing. To assess the users’ learning and performance, a comprehensive five-dimensional model was used to evaluate subjects’ task performance, decision time, decision strategy, structural knowledge, and heuristics knowledge. The results showed that process-oriented debriefing improved subjects’ task performance, helped users gain task knowledge, develop heuristics, and adapt to systematic-variable consistent strategies. Contrary to our hypothesis, the process-oriented debriefing group did not use less decision time. In contrast to the cost-benefit approach to decision making, a relatively more systematic effort is needed to perform better in dynamic tasks such as fisheries management.

From Models to Methods to Models: Tools and Techniques for Using, Developing, and Analyzing Cognitive Human Performance Models

2005 ◽  
Vol 49 (12) ◽  
pp. 1139-1139
Author(s):  
Wayne D. Gray ◽  
Christopher W. Myers
Keyword(s):  
Human Performance ◽  
Statistical Technique ◽  
Cognitive Workload ◽  
Dynamic Task ◽  
And Performance ◽  
Device Use ◽  
Rigorous Model ◽  
Modeling Behavior ◽  
Tools And Techniques ◽  
Level Of Analysis

Four modelers present tools based on or for cognitive human performance modeling. Myers introduces a new statistical technique for testing the similarity of sequential behavior across conditions. This technique promises to solve what Anderson (2002) regarded as the non-determinism problem of modeling behavior at the 100–ms level of analysis. John presents a programming-by-demonstration system that creates keystroke level GOMS models in ACT-R. Her approach enables those not trained in cognitive science to build predictive models of human performance. Salvucci's work expands on John's system by applying predictive modeling techniques to in-vehicle devices. His work integrates models of device use with a rigorous model of driver behavior to predict driver distraction and performance. Finally, Gray introduces Cognitive Metrics Profiling (CMP) — a model-based approach that produces theory-based estimates of cognitive workload. CMP holds the promise of predicting transient changes in cognitive workload that occur in a dynamic task environment.

The limits of human performance

2008 ◽  
Vol 44 ◽  
pp. 11-26 ◽  
Author(s):  
Ralph Beneke ◽  
Dieter Böning
Keyword(s):  
Human Performance ◽  
Safety Margin ◽  
Afferent Input ◽  
Metabolic Energy ◽  
Human Organism ◽  
Mechanical Resistance ◽  
As Doping ◽  
Gene And Protein Expression ◽  
Underlying Mechanisms ◽  
Biochemical Research

Human performance, defined by mechanical resistance and distance per time, includes human, task and environmental factors, all interrelated. It requires metabolic energy provided by anaerobic and aerobic metabolic energy sources. These sources have specific limitations in the capacity and rate to provide re-phosphorylation energy, which determines individual ratios of aerobic and anaerobic metabolic power and their sustainability. In healthy athletes, limits to provide and utilize metabolic energy are multifactorial, carefully matched and include a safety margin imposed in order to protect the integrity of the human organism under maximal effort. Perception of afferent input associated with effort leads to conscious or unconscious decisions to modulate or terminate performance; however, the underlying mechanisms of cerebral control are not fully understood. The idea to move borders of performance with the help of biochemicals is two millennia old. Biochemical findings resulted in highly effective substances widely used to increase performance in daily life, during preparation for sport events and during competition, but many of them must be considered as doping and therefore illegal. Supplements and food have ergogenic potential; however, numerous concepts are controversially discussed with respect to legality and particularly evidence in terms of usefulness and risks. The effect of evidence-based nutritional strategies on adaptations in terms of gene and protein expression that occur in skeletal muscle during and after exercise training sessions is widely unknown. Biochemical research is essential for better understanding of the basic mechanisms causing fatigue and the regulation of the dynamic adaptation to physical and mental training.

1880: Assessment of Basic Human Performance Resources Predicts Performance of Ureterorenoscopy in Human Cadavers

2004 ◽  
Vol 171 (4S) ◽  
pp. 496-497
Author(s):  
Edward D. Matsumoto ◽  
George V. Kondraske ◽  
Lucas Jacomides ◽  
Kenneth Ogan ◽  
Margaret S. Pearle ◽  
...  
Keyword(s):  
Human Performance ◽  
Human Cadavers

Short Stress State Questionnaire

2015 ◽  
Vol 31 (1) ◽  
pp. 20-30 ◽  
Author(s):  
William S. Helton ◽  
Katharina Näswall
Keyword(s):  
Stress State ◽  
Factor Structure ◽  
Human Performance ◽  
Self Report ◽  
Confirmatory Factor ◽  
Stress States ◽  
Related Stress ◽  
Using Data ◽  
Task Conditions ◽  
Multiple Samples

Conscious appraisals of stress, or stress states, are an important aspect of human performance. This article presents evidence supporting the validity and measurement characteristics of a short multidimensional self-report measure of stress state, the Short Stress State Questionnaire (SSSQ; Helton, 2004 ). The SSSQ measures task engagement, distress, and worry. A confirmatory factor analysis of the SSSQ using data pooled from multiple samples suggests the SSSQ does have a three factor structure and post-task changes are not due to changes in factor structure, but to mean level changes (state changes). In addition, the SSSQ demonstrates sensitivity to task stressors in line with hypotheses. Different task conditions elicited unique patterns of stress state on the three factors of the SSSQ in line with prior predictions. The 24-item SSSQ is a valid measure of stress state which may be useful to researchers interested in conscious appraisals of task-related stress.

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