VR & Physiological Response Study

Introduction

Test-taking is often conducted in traditional classroom environments, yet growing evidence in cognitive psychology and human-computer interaction suggests that environmental factors—such as ambient noise, visual complexity, and perceived stress—can meaningfully impact cognitive performance.¹ These factors influence not only the emotional state of learners, but also working memory, attention, and task persistence. As education increasingly integrates digital modalities, the opportunity to redesign these testing environments becomes both technically feasible and pedagogically significant.

Virtual reality (VR) offers a promising tool for simulating a variety of test-taking environments under controlled conditions. Unlike static testing conditions, VR enables researchers to modulate visual, auditory, and spatial contexts in real time while maintaining consistency in the task itself. This allows for novel investigation into how sensory context influences performance, engagement, and stress. Prior research has shown that exposure to naturalistic, calming environments, such as those featuring greenery, open space, or soothing sounds, can reduce anxiety and enhance cognitive functioning.²^,³ Conversely, chaotic or overstimulating environments may impair attention by increasing cognitive load or distracting sensory processing.

Electrodermal activity (EDA) and heart rate (HR) are commonly used physiological markers to measure sympathetic nervous system activation, offering insight into users' real-time arousal and stress levels.⁴ By pairing these biosensory signals with self-reported fatigue, distraction, and task difficulty, researchers can gain a holistic understanding of the test-taking experience under different environmental conditions.

In this study, we use a VR headset (Meta Quest 2) and biosensors to explore how immersive environments affect participants’ physiological and subjective states during a standardized logic task. We compare a neutral classroom control condition to a more immersive and stylized environment: a quiet, natural “Moon” scene designed to deliver a peaceful feeling while maintaining novelty. This allows us to evaluate whether altering the sensory context alone (without changing task content) can shape how individuals experience and respond to a cognitive task.

We hypothesize that the calming “Moon” environment will improve engagement and comfort during testing, resulting in lower physiological stress and arousal (measured by EA and HR) and self-reported fatigue.

Understanding these relationships has broad implications for the design of digital learning and assessment environments, particularly in education and virtual learning contexts where personalization, accessibility, and cognitive support are key concerns.

Methods

Six participants will complete a standardized cognitive test (logic questions) inside four distinct virtual environments using Meta Quest 2 and Emotibit to track physiological responses. 

1. Neutral Classroom (Control): Standard VR classroom resembling traditional test settings.
2. Outer Space (Minimalist): A quiet, distraction-free space environment.

We will measure:

• Physiological Responses (EmotiBit): Electrodermal activity (EA) for arousal and heart rate (HR)
• Self-Reported Experience: Brief post-test survey on perceived difficulty, engagement, and fatigue.

Materials

• Hardware: Meta Quest 2 VR headset, Emotibit
• Software: Unity for environment setup

Results

Six participants completed a logic-question task under two different conditions: a conventional classroom (Control) and an immersive virtual reality (VR) environment (Moon setting). Physiological measures, including electrodermal activity (EA) and heart rate (HR), as well as subjective ratings of fatigue, distraction, difficulty, and presence, were analyzed to evaluate the impact of VR on cognitive and emotional engagement. Out of the six participants, one of the participants’ data was dropped due to difficulties in data collection and analysis. Thus, the following results will showcase the data collected from five participants.

Figure 1. HR Mean Across the Four Phases (Pre-Control, Control, Pre-VR, VR).

Figure 2. EA Mean Across the Four Phases (Pre-Control, Control, Pre-VR, VR).

Across the four experimental phases (Pre-Control, Control, Pre-VR, and VR), HR responses were highly variable among participants (Figure 1). Specifically, two participants (3 and 5) showed notable increases in HR from Control to VR, suggesting heightened physiological activation, while three others exhibited slight decreases, reflecting individual differences in physiological reactivity. EA patterns were comparatively more consistent, with the majority (Participants 1, 3, and 4) demonstrating clear increases in EA levels when transitioning from the Pre-Control to the Control phase, and subsequently from the Pre-VR to VR phase (Figure 2). Participant 2 showed a slight decrease in EA from Control to VR, and Participant 5 exhibited atypical EA responses, possibly due to measurement artifacts, as indicated by unusually high Control-phase values. Additionally, it is interesting to note that both Participants 2 and 5 indicated prior familiarity with VR, hinting at the possibility that prior VR experience can serve to reduce stress and EA response.

Figure 3. EA Mean vs. Fatigue (Subjective Rating). 

Subjective experiences, particularly perceived fatigue, showed varied relationships with physiological data. Increased fatigue ratings in the VR condition were generally associated with heightened EA (Figure 3) with a Spearman ρ of 0.64, which is a moderate effect size. For instance, Participant 4 showed a substantial increase in both fatigue and EA when transitioning to VR, whereas Participant 2 reported decreased fatigue despite an increase in EA, underscoring a nuanced relationship between physiological arousal and subjective workload. 

Notably, subjective ratings of distraction and qualitative participant feedback highlighted substantial individual differences. For example, one of the elements that showed a stark variability was the effect of music in the VR environment. Some participants described it positively ("the music was calming and helped me focus better"), whereas others found it disruptive ("I found the music distracting—it made me lose track of what I was supposed to do").

Discussion

The present study examined the physiological and subjective responses to conventional (Control) versus immersive virtual reality (VR) environments. Findings indicate distinct physiological responses, with electrodermal activity (EA) emerging as a more consistent measure of engagement and cognitive load compared to heart rate (HR). Specifically, EA reliably increased during VR exposure, suggesting its utility in capturing sympathetic arousal related to immersive experiences.

Individual differences in HR responses and subjective fatigue underscore the complexity of human interaction with VR environments. Qualitative data emphasized these differences, with participants reporting divergent experiences regarding specific VR elements such as music. For instance, some participants found music calming and conducive to task performance, while others perceived it as distracting, negatively impacting their cognitive focus and increasing perceived fatigue.

These observations have critical implications for designing VR-based interventions aimed at reducing stress and enhancing cognitive performance. Specifically, they highlight the necessity of individualized VR settings to accommodate personal sensory preferences and maximize beneficial outcomes. Moving forward, several steps are recommended to enhance the robustness and applicability of these findings. First, it would be recommended to carry out similar experiments with increased sample size and diversified demographics, particularly K–12 students, in order to improve generalizability and help validate the observed effects in a broader population. Additionally, conducting deeper analyses using the comprehensive data collected by Emotibit, such as photoplethysmography (PPG) and distinguishing between tonic and phasic components of the electrodermal activity data, could yield richer insights. Correlating these physiological measures with performance metrics would further clarify how physiological states translate into task outcomes.

Together, these next steps will facilitate a deeper understanding of VR’s potential for cognitive and emotional support in educational contexts, paving the way for more effective, personalized immersive experiences.

References

1. Martin, K., McLeod, E., Périard, J., Rattray, B., Keegan, R., & Pyne, D. B. (2019). Human Factors, 61(8), 1205–1246. https://doi.org/10.1177/0018720819839817
2. Mason, L., Ronconi, A., Scrimin, S., & Pazzaglia, F. (2021). Short-term exposure to nature and benefits for students’ cognitive performance: A review. Educational Psychology Review, 34(2), 609–647. https://doi.org/10.1007/s10648-021-09631-8
3. Mygind, L., Stevenson, M. P., Liebst, L. S., Konvalinka, I., & Bentsen, P. (2018). Stress response and cognitive performance modulation in classroom versus Natural Environments: A quasi-experimental pilot study with children. International Journal of Environmental Research and Public Health, 15(6), 1098. https://doi.org/10.3390/ijerph15061098
4. Yu, X., Lu, J., Liu, W., Cheng, Z., & Xiao, G. (2024). Exploring physiological stress response evoked by passive translational acceleration in healthy adults: a pilot study utilizing electrodermal activity and heart rate variability measurements. Scientific reports, 14(1), 11349. https://doi.org/10.1038/s41598-024-61656-5