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- Title
- MODELING AND CHARACTERIZATION OF ACUTE STRESS UNDER DYNAMIC TASK CONDITIONS.
- Creator
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Millan, Angel, Crumpton-Young, Lesia, University of Central Florida
- Abstract / Description
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Stress can be defined as the mental, physical, and emotional response of humans to stressors encountered in their personal or professional environment. Stressors are introduced in various activities, especially those found in dynamic task conditions when multiple task requirements must be performed. Stress and stressors have been described as activators and inhibitors of human performance. The ability to manage high levels of acute stress is an important determinant of successful performance...
Show moreStress can be defined as the mental, physical, and emotional response of humans to stressors encountered in their personal or professional environment. Stressors are introduced in various activities, especially those found in dynamic task conditions when multiple task requirements must be performed. Stress and stressors have been described as activators and inhibitors of human performance. The ability to manage high levels of acute stress is an important determinant of successful performance in any occupation. In situations where performance is critical, personnel must be prepared to operate successfully under hostile or extreme stress conditions; therefore training programs and engineered systems must be tailored to assist humans in fulfilling these demands. To effectively design appropriate training programs for these conditions, it is necessary to quantitatively describe stress. A series of theoretical stress models have been developed in previous research studies; however, these do not provide quantification of stress levels nor the impact on human performance. By modeling acute stress under dynamic task conditions, quantitative values for stress and its impact on performance can be assessed. Thus, this research was designed to develop a predictive model for acute stress as a function of human performance and task demand. Initially, a four factor two level experimental design [2 (Noise) x 2 (Temperature) x 2 (Time Awareness) x 2 (Workload)] was performed to identify reliable physiological, cognitive and behavioral responses to stress. Next, multivariate analysis of variance (n=108) tests were performed, which showed statistically significant differences for physiological, cognitive and behavioral responses. Finally, fuzzy set theory techniques were used to develop a comprehensive stress index model. Thus, the resulting stress index model was constructed using input on physiological, cognitive and behavioral responses to stressors as well as characteristics inherent to the type of task performed and personal factors that interact as mediators (competitiveness, motivation, coping technique and proneness to boredom). Through using this stress index model to quantify and characterize the affects of acute stress on human performance, these research findings can inform proper training protocols and help to redesign tasks and working conditions that are prone to create levels of acute stress that adversely affect human performance.
Show less - Date Issued
- 2011
- Identifier
- CFE0004056, ucf:49151
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004056
- Title
- A Psychophysical Approach to Standardizing Texture Compression for Virtual Environments.
- Creator
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Flynn, Jeremy, Szalma, James, Fidopiastis, Cali, Jentsch, Florian, Shah, Mubarak, University of Central Florida
- Abstract / Description
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Image compression is a technique to reduce overall data size, but its effects on human perception have not been clearly established. The purpose of this effort was to determine the most effective psychophysical method for subjective image quality assessment, and to apply those findings to an objective algorithm. This algorithm was used to identify the minimum level of texture compression noticeable to the human, in order to determine whether compression-induced texture distortion impacted...
Show moreImage compression is a technique to reduce overall data size, but its effects on human perception have not been clearly established. The purpose of this effort was to determine the most effective psychophysical method for subjective image quality assessment, and to apply those findings to an objective algorithm. This algorithm was used to identify the minimum level of texture compression noticeable to the human, in order to determine whether compression-induced texture distortion impacted game-play outcomes. Four experiments tested several hypotheses. The first hypothesis evaluated which of three magnitude estimation (ME) methods (absolute ME, absolute ME plus, or ME with a standard) for image quality assessment was the most reliable. The just noticeable difference (JND) point for textures compression against the Feature Similarity Index for color was determined The second hypothesis tested whether human participants perceived the same amount of distortion differently when textures were presented in three ways: when textures were displayed as flat images; when textures were wrapped around a model; and when textures were wrapped around models and in a virtual environment. The last set of hypotheses examined whether compression affected both subjective (immersion, technology acceptance, usability) and objective (performance) gameplay outcomes. The results were: the absolute magnitude estimation method was the most reliable; no difference was observed in the JND threshold between flat textures and textures placed on models, but textured embedded within the virtual environment were more noticeable than in the other two presentation formats. There were no differences in subjective gameplay outcomes when textures were compressed to below the JND thresholds; and those who played a game with uncompressed textures performed better on in-game tasks than those with the textures compressed, but only on the first in-game day. Practitioners and researchers can use these findings to guide their approaches to texture compression and experimental design.
Show less - Date Issued
- 2018
- Identifier
- CFE0007178, ucf:52250
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007178