Prolonged exposure to cold air can impair reaction time and cognitive function, which can lead to serious consequences. One mitigation strategy is to develop models that can predict cognitive performance by tracking physiological metrics associated with cold stress. As females are evidenced to be more sensitive to cold exposure, this study investigated the relationship between physiological metrics and cognitive performance deterioration of female subjects under cold stress. Wearable electrodermal activity (EDA) and electrocardiogram (ECG) were collected from nineteen females who underwent five sessions of a cognitive task battery—assessing reaction time, memory, and attention—in a cold (10 °C) environment. Machine learning classifiers showed higher cognitive performance classification accuracies with heart rate variability (HRV) features than with EDA features. Particularly in detecting performance deterioration in a task associated with assessing short-term memory, our support vector machine classifier with HRV features showed an 82.4% accuracy, with a sensitivity of 84.2% and a specificity of 80.6%, whereas a 55.4% accuracy with a sensitivity of 44.7% and a specificity of 66.7% was obtained with EDA features. Our results demonstrate the feasibility of detecting performance deterioration from females who underwent cold exposure using wearable EDA and ECG, allowing for preventive measures to reduce risk in cold environments, especially for female military personnel.

Visual search performance is a critical factor in many high-stakes duties, warranting the need for strategies to enhance target detection accuracy. Research using rapid serial visual presentation (RSVP) of stimuli shows that observers can detect categorically defined, pre-specified targets even when the presentation rate is rapid, suggesting RSVP as a viable strategy. To investigate how and how well RSVP can improve target detection in complex search arrays, five experiments were conducted to compare search performance between Full-Image search conditions and various RSVP-based conditions. Stimulus presentation time/total search time was the same across conditions. Experiment 1 demonstrated the utility of RSVP to enhance target identification in simple arrays (i.e., Landolt Cs). Experiment 2 involved more complex scenes and target-present/-absent judgments. Results showed that RSVP increased target detections due to both a liberal change in criterion and an increase in sensitivity. Experiment 3 provides some evidence against the reduction in peripheral clutter as the primary contributor to RSVP performance increases. Experiments 4 and 5 prompted and limited eye movements, respectively, to distinguish the role of eye movements in RSVP-based search. These two latter experiments imply that lower target detection performance under time constraints in whole image search conditions is attributable to time-wasting, irrelevant and inefficient eye movements. These experiments suggest that RSVP advantage occurs because the method maximizes time for inspecting and processing each search image/segment. Real-world visual search tasks may benefit from segmenting the search display and presenting images in an RSVP stream.