Individuals who operate under highly stressful conditions (e.g., military personnel and first responders) are often faced with the challenge of quickly interpreting ambiguous information in uncertain and threatening environments. When faced with ambiguity, it is likely adaptive to view potentially dangerous stimuli as threatening until contextual information proves otherwise. One laboratory-based paradigm that can be used to simulate uncertain threat is known as threat of shock (TOS), in which participants are told that they might receive mild but unpredictable electric shocks while performing an unrelated task. The uncertainty associated with this potential threat induces a state of emotional arousal that is not overwhelmingly stressful, but has widespread-both adaptive and maladaptive-effects on cognitive and affective function. For example, TOS is thought to enhance aversive processing and abolish positivity bias. Importantly, in certain situations (e.g., when walking home alone at night), this anxiety can promote an adaptive state of heightened vigilance and defense mobilization. In the present study, we used TOS to examine the effects of uncertain threat on valence bias, or the tendency to interpret ambiguous social cues as positive or negative. As predicted, we found that heightened emotional arousal elicited by TOS was associated with an increased tendency to interpret ambiguous cues negatively. Such negative interpretations are likely adaptive in situations in which threat detection is critical for survival and should override an individual's tendency to interpret ambiguity positively in safe contexts. (PsycINFO Database Record

Individuals with stressful occupations, such as law enforcement and military personnel, are required to operate in high stakes environments that can be simultaneously physically and emotionally demanding. These individuals are tasked with maintaining peak performance under stressful and often unpredictable conditions, exerting high levels of cognitive control to sustain attention and suppress task-irrelevant actions. Previous research has shown that physical and emotional stressors differentially influence such cognitive control processes. For example, physical stress impairs while emotional stress facilitates the ability to inhibit a prepotent response, yet, interactions between the two remain poorly understood. Here we examined whether emotional stress induced by threat of unpredictable electric shock mitigates the effects of physical stress on response inhibition. Participants performed an auditory Go/NoGo task under safe versus threat conditions while cycling at high intensity (84% HR max ) for 50 min. In threat conditions, participants were told they would receive mild electric shocks that were unpredictable and unrelated to task performance. Self-reported anxiety increased under threat versus safe conditions, and perceived exertion increased with exercise duration. As predicted, we observed decrements in response inhibition (increased false alarms) as exertion increased under safe conditions, but improved response inhibition as exertion increased under threat conditions. These findings are consistent with previous work showing that anxiety induced by unpredictable threat promotes adaptive survival mechanisms, such as improved vigilance, threat detection, cautious behavior, and harm avoidance. Here, we suggest that emotional stress induced by unpredictable threat can also mitigate decrements in cognitive performance experienced under physically demanding conditions.

We demonstrated the potential of using domain adaptation on functional Near-Infrared Spectroscopy (fNIRS) data to detect and discriminate different levels of n-back tasks that involve working memory across different experiment sessions and subjects. Aim: To address the domain shift in fNIRS data across sessions and subjects for task label alignment, we exploited two domain adaptation approaches -Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W). Approach: We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels. We also compared with a supervised method -Convolutional Neural Network (CNN). Results: For session-by-session alignment, using G-W resulted in alignment accuracy of 70 ± 4 % (weighted mean ± standard error), whereas using CNN resulted in classification accuracy of 58 ± 5 % across five subjects. For subject-by-subject alignment, using FG-W resulted in alignment accuracy of 55 ± 3 %, whereas using CNN resulted in classification accuracy of 45 ± 1 %. Where in both cases 25 % represents chance. We also showed that removal of motion artifacts from the fNIRS data plays an important role in improving alignment performance. Conclusions: Domain adaptation is potential for session-by-session and subject-by-subject alignment using fNIRS data.

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Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing modality might allow robots to derive the best evidence of human workload. In this work, we analyzed and modeled data from a multi-modal simulated driving study specifically designed to evaluate different levels of cognitive workload induced by various secondary tasks such as dialogue interactions and braking events in addition to the primary driving task. Specifically, we performed statistical analyses of various physiological signals including eye gaze, electroencephalography, and arterial blood pressure from the healthy volunteers and utilized several machine learning methodologies including k-nearest neighbor, naive Bayes, random forest, support-vector machines, and neural network-based models to infer human cognitive workload levels. Our analyses provide evidence for eye gaze being the best physiological indicator of human cognitive workload, even when multiple signals are combined. Specifically, the highest accuracy (in %) of binary workload classification based on eye gaze signals is 80.45 ∓ 3.15 achieved by using support-vector machines, while the highest accuracy combining eye gaze and electroencephalography is only 77.08 ∓ 3.22 achieved by a neural network-based model. Our findings are important for future efforts of real-time workload estimation in the multimodal human-robot interactive systems given that eye gaze is easy to collect and process and less susceptible to noise artifacts compared to other physiological signal modalities.