{'fraction_non_alphanumeric': 0.017164179104477612, 'fraction_numerical': 0.0, 'mean_word_length': 6.020942408376963, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Modeling Algorithms for Predicting the Effects of Human Performance in the Presence of Environmental Stressors

For military systems, environmental stressors (e.g. motion, temperature, noise) must be considered during decision making related to manpower requirements, workload determination, design tradeoffs, and mission effectiveness/sustainability early into and throughout the system acquisition process. Current human performance modeling techniques may have limited predictive utility and have not been fully validated against operational human in the loop (HIL) data. As a result, they may lack sufficient fidelity to support systems engineering needs to predict the individual and interactive effects that environmental stressors may have on human performance. The purpose of this paper is to describe an approach for developing performance shaping function (PSF) algorithms for environmental stressors that can be integrated into human performance modeling tools. These high fidelity plug-in algorithms are anticipated to provide an enhanced level of predictive validity when compared to current discrete event modeling tools. The algorithms will address environmentally induced limitations that are levied on human performance and enhance decision making in defense acquisition system design and cost versus performance tradeoffs.