Emergency managers make time-sensitive decisions in order to protect the public from threats including severe weather. Simulation and questionnaires were used to capture the decision-making process of emergency managers during severe weather events. These data were combined with insights from emergency manager instructors, National Weather Service (NWS) forecasters, and experienced emergency managers to develop a descriptive decision-making model of weather information usage, weather assessments, and decisions made during severe weather. This decision-making model can be used to develop better decision support tools, improve training, and to understand how innovative weather information could potentially affect emergency managers' role of protecting the public.

Displaying both the strategy that information analysis automation employs to makes its judgments and variability in the task environment may improve human judgment performance, especially in cases where this variability impacts the judgment performance of the information analysis automation. This work investigated the contribution of providing either information analysis automation strategy information, task environment information, or both, on human judgment performance in a domain where noisy sensor data are used by both the human and the information analysis automation to make judgments. In a simplified air traffic conflict prediction experiment, 32 participants made probability of horizontal conflict judgments under different display content conditions. After being exposed to the information analysis automation, judgment achievement significantly improved for all participants as compared to judgments without any of the automation's information. Participants provided with additional display content pertaining to cue variability in the task environment had significantly higher aided judgment achievement compared to those provided with only the automation's judgment of a probability of conflict. When designing information analysis automation for environments where the automation's judgment achievement is impacted by noisy environmental data, it may be beneficial to show additional task environment information to the human judge in order to improve judgment performance.

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Background-Although the digital rectal exam (DRE) is a common method of screening for prostate cancer and other abnormalities, the limits of ability to perform this hands-on exam are unknown. Perceptible limits are a function of the size, depth, and hardness of abnormalities within a given prostate stiffness.Methods-To better understand the perceptible limits of the DRE, we conducted a psychophysical study with 18 participants using a custom-built apparatus to simulate prostate tissue and abnormalities of varying size, depth, and hardness. Utilizing a modified version of the psychophysical method of constant stimuli, we uncovered thresholds of absolute detection and variance in ability between examiners.Results-Within silicone-elastomers that mimic normal prostate tissue (21 kPa), abnormalities of 4 mm diameter (20 mm 3 volume) and greater were consistently detectable (above 75% of the time) but only at a depth of 5 mm. Abnormalities located in simulated tissue of greater stiffness (82 kPa) had to be twice that volume (5 mm diameter,40 mm 3 volume) to be detectable at the same rate.Conclusions-This study finds that the size and depth of abnormalities most influence detectability, while the relative stiffness between abnormalities and substrate also affects detectability for some size/depth combinations. While limits identified here are obtained for idealized substrates, this work is useful for informing the development of training and allowing clinicians to set expectations on performance.

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