Motivation moderates gender differences in navigation performance.

Gender differences in navigation performance are a recurrent and controversial topic. Previous research suggests that men outperform women in navigation tasks and that men and women exhibit different navigation strategies. Here, we investigate whether motivation to complete the task moderates the relationship between navigation performance and gender. Participants learned the locations of landmarks in a novel virtual city. During learning, participants could trigger a top-down map that depicted their current position and the locations of the landmarks. During testing, participants were divided into control and treatment groups and were not allowed to consult the map. All participants were given 16 minutes to navigate to the landmarks, but those in the treatment group were monetarily penalized for every second they spent completing the task. Results revealed a negative relationship between physiological arousal and the time required to locate the landmarks. In addition, gender differences in strategy were found during learning, with women spending more time with the map and taking 40% longer than men to locate the landmarks. Interestingly, an interaction between gender and treatment group revealed that women in the control group required more time than men and women in the treatment group to retrieve the landmarks. During testing, women in the control group also took more circuitous routes compared to men in the control group and women in the treatment group. These results suggest that a concurrent and relevant stressor can motivate women to perform similarly to men, helping to diminish pervasive gender differences found in the navigation literature.

An interpretable machine learning approach to multimodal stress detection in a simulated office environment.

BACKGROUND AND OBJECTIVE: Work-related stress affects a large part of today's workforce and is known to have detrimental effects on physical and mental health. Continuous and unobtrusive stress detection may help prevent and reduce stress by providing personalised feedback and allowing for the development of just-in-time adaptive health interventions for stress management. Previous studies on stress detection in work environments have often struggled to adequately reflect real-world conditions in controlled laboratory experiments. To close this gap, in this paper, we present a machine learning methodology for stress detection based on multimodal data collected from unobtrusive sources in an experiment simulating a realistic group office environment (N=90). METHODS: We derive mouse, keyboard and heart rate variability features to detect three levels of perceived stress, valence and arousal with support vector machines, random forests and gradient boosting models using 10-fold cross-validation. We interpret the contributions of features to the model predictions with SHapley Additive exPlanations (SHAP) value plots. RESULTS: The gradient boosting models based on mouse and keyboard features obtained the highest average F1 scores of 0.625, 0.631 and 0.775 for the multiclass prediction of perceived stress, arousal and valence, respectively. Our results indicate that the combination of mouse and keyboard features may be better suited to detect stress in office environments than heart rate variability, despite physiological signal-based stress detection being more established in theory and research. The analysis of SHAP value plots shows that specific mouse movement and typing behaviours may characterise different levels of stress. CONCLUSIONS: Our study fills different methodological gaps in the research on the automated detection of stress in office environments, such as approximating real-life conditions in a laboratory and combining physiological and behavioural data sources. Implications for field studies on personalised, interpretable ML-based systems for the real-time detection of stress in real office environments are also discussed.

The effects of acute work stress and appraisal on psychobiological stress responses in a group office environment.

BACKGROUND: The high prevalence of office stress and its detrimental health consequences are of concern to individuals, employers and society at large. Laboratory studies investigating office stress have mostly relied on data from participants that were tested individually on abstract tasks. In this study, we examined the effect of psychosocial office stress and work interruptions on the psychobiological stress response in a realistic but controlled group office environment. We also explored the role of cognitive stress appraisal as an underlying mechanism mediating the relationship between work stressors and the stress response. METHODS AND MATERIALS: Ninety participants (44 female; mean age 23.11 ± 3.80) were randomly assigned to either a control condition or one of two experimental conditions in which they were exposed to psychosocial stress with or without prior work interruptions in a realistic multi-participant laboratory setting. To induce psychosocial stress, we adapted the Trier Social Stress Test for Groups to an office environment. Throughout the experiment, we continuously monitored heart rate and heart rate variability. Participants repeatedly reported on their current mood, calmness, wakefulness and perceived stress and gave saliva samples to assess changes in salivary cortisol and salivary alpha-amylase. Additionally, cognitive appraisal of the psychosocial stress test was evaluated. RESULTS: Our analyses revealed significant group differences for most outcomes during or immediately after the stress test (i.e., mood, calmness, perceived stress, salivary cortisol, heart rate, heart rate variability) and during recovery (i.e., salivary cortisol and heart rate). Interestingly, the condition that experienced work interruptions showed a higher increase of cortisol levels but appraised the stress test as less threatening than individuals that experienced only psychosocial stress. Exploratory mediation analyses revealed a blunted response in subjective measures of stress, which was partially explained by the differences in threat appraisal. DISCUSSION: The results showed that experimentally induced work stress led to significant responses of subjective measures of stress, the hypothalamic-pituitary-adrenal axis and the autonomic nervous system. However, there appears to be a discrepancy between the psychological and biological responses to preceding work interruptions. Appraising psychosocial stress as less threatening but still as challenging could be an adaptive way of coping and reflect a state of engagement and eustress.

Neighborhood environments influence emotion and physiological reactivity.

Living in a disadvantaged neighborhood is associated with worse health and early mortality. Although many mechanisms may partially account for this effect, disadvantaged neighborhood environments are hypothesized to elicit stress and emotional responses that accumulate over time and influence physical and mental health. However, evidence for neighborhood effects on stress and emotion is limited due to methodological challenges. In order to address this question, we developed a virtual reality experimental model of neighborhood disadvantage and affluence and examined the effects of simulated neighborhoods on immediate stress and emotion. Exposure to neighborhood disadvantage resulted in greater negative emotion, less positive emotion, and more compassion, compared to exposure to affluence. However, the effect of virtual neighborhood environments on blood pressure and electrodermal reactivity depended on parental education. Participants from families with lower education exhibited greater reactivity to the disadvantaged neighborhood, while those from families with higher education exhibited greater reactivity to the affluent neighborhood. These results demonstrate that simulated neighborhood environments can elicit immediate stress reactivity and emotion, but the nature of physiological effects depends on sensitization to prior experience.

Virtual Reality Experiments with Physiological Measures.

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and laboratory experiments, respectively. VR is especially useful for geographic visualizations and investigations of spatial behavior. In spatial behavior research, VR provides a platform for studying the relationship between navigation and physiological measures (e.g., skin conductance, heart rate, blood pressure). Specifically, physiological measures allow researchers to address novel questions and constrain previous theories of spatial abilities, strategies, and performance. For example, individual differences in navigation performance may be explained by the extent to which changes in arousal mediate the effects of task difficulty. However, the complexities in the design and implementation of VR experiments can distract experimenters from their primary research goals and introduce irregularities in data collection and analysis. To address these challenges, the Experiments in Virtual Environments (EVE) framework includes standardized modules such as participant training with the control interface, data collection using questionnaires, the synchronization of physiological measurements, and data storage. EVE also provides the necessary infrastructure for data management, visualization, and evaluation. The present paper describes a protocol that employs the EVE framework to conduct navigation experiments in VR with physiological sensors. The protocol lists the steps necessary for recruiting participants, attaching the physiological sensors, administering the experiment using EVE, and assessing the collected data with EVE evaluation tools. Overall, this protocol will facilitate future research by streamlining the design and implementation of VR experiments with physiological sensors.

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants.

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and spatial cognition. With a local area network and dedicated software platform, experimenters can efficiently monitor the behavior of the participants that are simultaneously immersed in a desktop virtual environment and digitalize the collected data. These capabilities allow for experimental designs in spatial cognition and navigation research that would be difficult (if not impossible) to conduct in the real world. Possible experimental variations include stress during an evacuation, cooperative and competitive search tasks, and other contextual factors that may influence emergent crowd behavior. However, such a laboratory requires maintenance and strict protocols for data collection in a controlled setting. While the external validity of laboratory studies with human participants is sometimes questioned, a number of recent papers suggest that the correspondence between real and virtual environments may be sufficient for studying social behavior in terms of trajectories, hesitations, and spatial decisions. In this article, we describe a method for conducting experiments on decision-making and navigation with up to 36 participants in a networked desktop virtual reality setup (i.e., the Decision Science Laboratory or DeSciL). This experiment protocol can be adapted and applied by other researchers in order to set up a networked desktop virtual reality laboratory.

Mu opioid receptors on primary afferent nav1.8 neurons contribute to opiate-induced analgesia: insight from conditional knockout mice.

Opiates are powerful drugs to treat severe pain, and act via mu opioid receptors distributed throughout the nervous system. Their clinical use is hampered by centrally-mediated adverse effects, including nausea or respiratory depression. Here we used a genetic approach to investigate the potential of peripheral mu opioid receptors as targets for pain treatment. We generated conditional knockout (cKO) mice in which mu opioid receptors are deleted specifically in primary afferent Nav1.8-positive neurons. Mutant animals were compared to controls for acute nociception, inflammatory pain, opiate-induced analgesia and constipation. There was a 76% decrease of mu receptor-positive neurons and a 60% reduction of mu-receptor mRNA in dorsal root ganglia of cKO mice. Mutant mice showed normal responses to heat, mechanical, visceral and chemical stimuli, as well as unchanged morphine antinociception and tolerance to antinociception in models of acute pain. Inflammatory pain developed similarly in cKO and controls mice after Complete Freund's Adjuvant. In the inflammation model, however, opiate-induced (morphine, fentanyl and loperamide) analgesia was reduced in mutant mice as compared to controls, and abolished at low doses. Morphine-induced constipation remained intact in cKO mice. We therefore genetically demonstrate for the first time that mu opioid receptors partly mediate opiate analgesia at the level of Nav1.8-positive sensory neurons. In our study, this mechanism operates under conditions of inflammatory pain, but not nociception. Previous pharmacology suggests that peripheral opiates may be clinically useful, and our data further demonstrate that Nav1.8 neuron-associated mu opioid receptors are feasible targets to alleviate some forms of persistent pain.

Genetic ablation of delta opioid receptors in nociceptive sensory neurons increases chronic pain and abolishes opioid analgesia.

Opioid receptors are major actors in pain control and are broadly distributed throughout the nervous system. A major challenge in pain research is the identification of key opioid receptor populations within nociceptive pathways, which control physiological and pathological pain. In particular, the respective contribution of peripheral vs. central receptors remains unclear, and it has not been addressed by genetic approaches. To investigate the contribution of peripheral delta opioid receptors in pain control, we created conditional knockout mice where delta receptors are deleted specifically in peripheral Na(V)1.8-positive primary nociceptive neurons. Mutant mice showed normal pain responses to acute heat and to mechanical and formalin stimuli. In contrast, mutant animals showed a remarkable increase of mechanical allodynia under both inflammatory pain induced by complete Freund adjuvant and neuropathic pain induced by partial sciatic nerve ligation. In these 2 models, heat hyperalgesia was virtually unchanged. SNC80, a delta agonist administered either systemically (complete Freund adjuvant and sciatic nerve ligation) or into a paw (sciatic nerve ligation), reduced thermal hyperalgesia and mechanical allodynia in control mice. However, these analgesic effects were absent in conditional mutant mice. In conclusion, this study reveals the existence of delta opioid receptor-mediated mechanisms, which operate at the level of Na(V)1.8-positive nociceptive neurons. Delta receptors in these neurons tonically inhibit mechanical hypersensitivity in both inflammatory and neuropathic pain, and they are essential to mediate delta opioid analgesia under conditions of persistent pain. This delta receptor population represents a feasible therapeutic target to alleviate chronic pain while avoiding adverse central effects. The conditional knockout of delta-opioid receptor in primary afferent Na(V)1.8 neurons augmented mechanical allodynia in persistent pain models and abolished delta opioid analgesia in these models.