The Subjective Habitability & Acceptability Questionnaire (SHAQ): Development and Validation.

OBJECTIVE: Describe the development and validation of the Subjective Habitability & Acceptability Questionnaire (SHAQ). BACKGROUND: Habitat area size, layout, and design may impact individual and team behavioral health and performance (BHP) outcomes in operational environments. However, there are no standardized measures of these relationships. METHOD: SHAQ is a modular survey consisting of two 6-item scales: BHP Outcomes (Performance of Individual Activities, Performance of Group Activities, Mood, Psychological Stress, Sleep, and Social Interactions) and Habitability Moderators (Privacy, Social Density, Efficiency, Control, Comfort, and Convenience). We collected SHAQ data from NASA's Human Exploration Research Analog (HERA) crews (n = 19) in reference to multiple habitat areas (Sleep/Bedroom, Hygiene/Bathroom, Work/Office/Workshop, and Food Preparation/Kitchen/Galley) in the HERA operational environment, private hotel rooms, and individual home habitats. RESULTS: SHAQ has high construct validity (single factor solutions, mean item factor loading = 0.760, mean % variance = 60.37), internal consistency and reliability (item mean α = 0.880, mean ω=0.894, mean ICC = 0.430), concurrent validity (mean item r with System Usability Scale = 0.42), and discriminant validity (e.g., significantly higher facilitation of group activities in HERA Work/Office/Workshop and Food Preparation/Kitchen/Galley areas vs. Hygiene/Bathroom and Sleep/Bedroom areas; significantly higher ratings of privacy, comfort, and convenience in hotel vs. HERA). CONCLUSION: SHAQ is a reliable, valid, and sensitive measure of BHP impacts of habitat size and layout. APPLICATION: SHAQ can be used to inform evidence-based recommendations and thresholds for habitat area size, layout, and design options to support individual and team BHP in operational environments.

Interdisciplinary views of fNIRS: Current advancements, equity challenges, and an agenda for future needs of a diverse fNIRS research community.

Functional Near-Infrared Spectroscopy (fNIRS) is an innovative and promising neuroimaging modality for studying brain activity in real-world environments. While fNIRS has seen rapid advancements in hardware, software, and research applications since its emergence nearly 30 years ago, limitations still exist regarding all three areas, where existing practices contribute to greater bias within the neuroscience research community. We spotlight fNIRS through the lens of different end-application users, including the unique perspective of a fNIRS manufacturer, and report the challenges of using this technology across several research disciplines and populations. Through the review of different research domains where fNIRS is utilized, we identify and address the presence of bias, specifically due to the restraints of current fNIRS technology, limited diversity among sample populations, and the societal prejudice that infiltrates today's research. Finally, we provide resources for minimizing bias in neuroscience research and an application agenda for the future use of fNIRS that is equitable, diverse, and inclusive.

Using fNIRS to Identify Transparency- and Reliability-Sensitive Markers of Trust Across Multiple Timescales in Collaborative Human-Human-Agent Triads.

Intelligent agents are rapidly evolving from assistants into teammates as they perform increasingly complex tasks. Successful human-agent teams leverage the computational power and sensory capabilities of automated agents while keeping the human operator's expectation consistent with the agent's ability. This helps prevent over-reliance on and under-utilization of the agent to optimize its effectiveness. Research at the intersection of human-computer interaction, social psychology, and neuroergonomics has identified trust as a governing factor of human-agent interactions that can be modulated to maintain an appropriate expectation. To achieve this calibration, trust can be monitored continuously and unobtrusively using neurophysiological sensors. While prior studies have demonstrated the potential of functional near-infrared spectroscopy (fNIRS), a lightweight neuroimaging technology, in the prediction of social, cognitive, and affective states, few have successfully used it to measure complex social constructs like trust in artificial agents. Even fewer studies have examined the dynamics of hybrid teams of more than 1 human or 1 agent. We address this gap by developing a highly collaborative task that requires knowledge sharing within teams of 2 humans and 1 agent. Using brain data obtained with fNIRS sensors, we aim to identify brain regions sensitive to changes in agent behavior on a long- and short-term scale. We manipulated agent reliability and transparency while measuring trust, mental demand, team processes, and affect. Transparency and reliability levels are found to significantly affect trust in the agent, while transparency explanations do not impact mental demand. Reducing agent communication is shown to disrupt interpersonal trust and team cohesion, suggesting similar dynamics as human-human teams. Contrasts of General Linear Model analyses identify dorsal medial prefrontal cortex activation specific to assessing the agent's transparency explanations and characterize increases in mental demand as signaled by dorsal lateral prefrontal cortex and frontopolar activation. Short scale event-level data is analyzed to show that predicting whether an individual will trust the agent, with data from 15 s before their decision, is feasible with fNIRS data. Discussing our results, we identify targets and directions for future neuroergonomics research as a step toward building an intelligent trust-modulation system to optimize human-agent collaborations in real time.