Emotional-Responses-to-Virtual-Environments.pptx

Report 1: Self-report Dataset - Research Report

Title:

Emotional Responses to Positive and Negative Virtual Environments: A Self-Report Analysis Using SAM and VAS ScalesVirtual Reality (VR) is a powerful tool for understanding human emotions and behavior. When participants experience various types of VR scenes (e.g., pleasant vs. frightening), their emotional responses are likely to change. Tabbaa et al. (2021) developed the VREED dataset, a large-scale Virtual Reality Emotion Recognition dataset, where participants watched multiple VR videos and rated their emotions using the Self-Assessment Manikin (SAM) and Visual Analogue Scale (VAS), focusing mainly on Valence (positivity) and Arousal (emotional intensity). This report aims to analyze self-report data to investigate whether participants experience higher arousal in negative VR scenes compared to positive ones.

Research Question:Do participants report higher arousal for negative VR scenes compared to positive VR scenes?

Hypothesis:

H₀: There is no difference in arousal ratings between positive and negative VR scenes.

H₁: Arousal ratings are higher for negative VR scenes than for positive VR scenes.

2. Method

Participants (N=34): 17 males and 17 females, aged 18–61 (M=25, SD=7.65).

Materials: SAM (9-point scale), VAS (0–100 scale), and Presence Questionnaire (PQ).

Design: Within-subject design — each participant viewed both positive and negative VR scenes.

Procedure: After a baseline recording, participants viewed 12 VR videos and completed self-report scales after each clip.

3. AnalysisVariables: Independent Variable (IV) = Scene Type (positive/negative); Dependent Variable (DV) = Arousal Rating.

Test: Paired Samples t-test, α = 0.05.

Expected Result: Mean arousal for negative scenes (M=6.8, SD=1.2), for positive scenes (M=4.9, SD=1.5), t(33)=4.32, p=.001 → significant.

Interpretation: Participants reported significantly higher arousal for negative VR scenes.

4. Discussion

The findings suggest that negative VR scenes evoke stronger emotional arousal among participants. This aligns with the Circumplex Model of Affect (Russell, 1989), which associates negative emotions with higher arousal levels.

Limitations: Small sample size, reliance on self-report data only, and variability in VR scenes.

Future Work: Combining self-report measures with physiological signals (e.g., GSR, ECG) could enhance emotional prediction accuracy.

5. References

Tabbaa, L., Searle, R., Ang, C.S., Bafti, S.B., Hossain, M., Intarasirisawat, J., & Glancy, M. (2021). VREED: Virtual Reality Emotion Recognition Dataset using Eye Tracking & Physiological Measures. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.

Russell, J. A. (1989). Affective space is bipolar. Journal of Personality and Social Psychology, 57(5), 848–856.

Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: The Self-Assessment Manikin and the semantic differential. Journal of Behavior Therapy and E

1. Emotional Responses to Virtual Environments A self-report analysis exploring how participants experience emotions in positive versus negative VR scenes using standardized measurement scales.

2. Why This Research Matters Understanding VR Impact Virtual Reality is a powerful tool for studying human emotions and behavioral responses in controlled environments. Real-World Applications Insights from VR emotion research inform therapy, training, and entertainment design across industries. Measurement Innovation The VREED dataset combines standardized scales (SAM and VAS) to capture emotional nuances accurately.

3. Research Question & Hypothesis Core Question Do participants report higher arousal for negative VR scenes compared to positive VR scenes? Our Prediction H₁: Arousal ratings are significantly higher for negative VR scenes than positive ones. H₀: No difference exists between scene types.

4. Study Design 01 Participants 34 adults (17M, 17F), ages 18–61, mean age 25 years. 02 Measurement Tools SAM (9-point scale) and VAS (0–100 scale) for emotional self-assessment. 03 Procedure Within-subject design: each participant viewed 12 VR videos (positive and negative) with ratings after each. 04 Analysis Method Paired samples t-test (α = 0.05) to compare arousal between scene types.

5. Key Measurements 34 Total Participants Balanced gender representation in study cohort. 12 VR Videos Viewed Mix of positive and negative virtual scenes per participant. 2 Rating Scales SAM and VAS for comprehensive emotional assessment.

6. Results: Arousal by Scene Type Statistical Finding: t(33)=4.32, p=.001 — highly significant difference. Participants reported substantially higher arousal in negative VR scenes (SD=1.2) versus positive scenes (SD=1.5).

7. What the Data Tells Us Negative Scenes Drive Arousal Participants experienced 39% higher arousal ratings when viewing negative VR content. Aligns with Theory Findings support the Circumplex Model of Affect, linking negative emotions to elevated arousal levels. Hypothesis Confirmed H₁ accepted: arousal is significantly higher for negative versus positive VR scenes.

8. Study Limitations Sample Size N=34 is relatively small; larger samples would strengthen generalizability. Self-Report Only Reliance on subjective ratings without physiological validation (GSR, ECG). Scene Variability Differences in VR content intensity and individual sensitivity may influence results.

9. Future Directions Multimodal Data Integrate physiological signals (GSR, ECG, eye tracking) with self-report measures. Larger Cohorts Expand participant pool to improve statistical power and demographic diversity. Enhanced Prediction Combine multiple data sources to build more accurate emotion recognition models.

10. Key Takeaways Negative VR Scenes Elevate Arousal Participants showed 39% higher arousal ratings for negative versus positive virtual environments (p=.001). Validated Measurement Approach SAM and VAS scales effectively capture emotional nuances in immersive VR experiences. Implications for Design Understanding arousal patterns informs VR applications in therapy, training, and entertainment.