Experiments as Code and its application to VR studies in human-building interaction.

Experiments as Code (ExaC) is a concept for reproducible, auditable, debuggable, reusable, & scalable experiments. Experiments are a crucial tool to understand Human-Building Interactions (HBI) and build a coherent theory around it. However, a common concern for experiments is their auditability and reproducibility. Experiments are usually designed, provisioned, managed, and analyzed by diverse teams of specialists (e.g., researchers, technicians, engineers) and may require many resources (e.g., cloud infrastructure, specialized equipment). Although researchers strive to document experiments accurately, this process is often lacking. Consequently, it is difficult to reproduce these experiments. Moreover, when it is necessary to create a similar experiment, the "wheel is very often reinvented". It appears easier to start from scratch than trying to reuse existing work. Thus valuable embedded best practices and previous experiences are lost. In behavioral studies, such as in HBI, this has contributed to the reproducibility crisis. To tackle these challenges, we propose the ExaC paradigm, which not only documents the whole experiment, but additionally provides the automation code to provision, deploy, manage, and analyze the experiment. To this end, we define the ExaC concept, provide a taxonomy for the components of a practical implementation, and provide a proof of concept with an HBI desktop VR experiment that demonstrates the benefits of its "as code" representation, that is, reproducibility, auditability, debuggability, reusability, & scalability.

The role of strategic visibility in shaping wayfinding behavior in multilevel buildings.

In this paper, we explore the mutual effect of prior background expectations and visibility afforded by the 3D configuration of the physical environment on wayfinding efficiency and strategy in multilevel buildings. We perform new analyses on data from 149 participants who performed six unaided and directed wayfinding tasks in virtual buildings with varying degrees of visibility. Our findings reveal that the interaction between visibility and prior background expectations significantly affects wayfinding efficiency and strategy during between-floor wayfinding tasks. We termed this interaction effect strategic visibility, which emphasizes the importance of the strategic allocation of visibility towards actionable building elements in promoting efficient wayfinding and shaping wayfinding strategy. Our study highlights the significance of strategic visibility in promoting inclusive and accessible built environments for neurodiversity. Finally, we provide an open-source dataset that can be used to develop and test new wayfinding theories and models to advance research in the emerging field of human-building interaction.

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.

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.

Evaluation of a conceptual framework for predicting navigation performance in virtual reality.

Previous research in spatial cognition has often relied on simple spatial tasks in static environments in order to draw inferences regarding navigation performance. These tasks are typically divided into categories (e.g., egocentric or allocentric) that reflect different two-systems theories. Unfortunately, this two-systems approach has been insufficient for reliably predicting navigation performance in virtual reality (VR). In the present experiment, participants were asked to learn and navigate towards goal locations in a virtual city and then perform eight simple spatial tasks in a separate environment. These eight tasks were organised along four orthogonal dimensions (static/dynamic, perceived/remembered, egocentric/allocentric, and distance/direction). We employed confirmatory and exploratory analyses in order to assess the relationship between navigation performance and performances on these simple tasks. We provide evidence that a dynamic task (i.e., intercepting a moving object) is capable of predicting navigation performance in a familiar virtual environment better than several categories of static tasks. These results have important implications for studies on navigation in VR that tend to over-emphasise the role of spatial memory. Given that our dynamic tasks required efficient interaction with the human interface device (HID), they were more closely aligned with the perceptuomotor processes associated with locomotion than wayfinding. In the future, researchers should consider training participants on HIDs using a dynamic task prior to conducting a navigation experiment. Performances on dynamic tasks should also be assessed in order to avoid confounding skill with an HID and spatial knowledge acquisition.