Exploring sense of spaciousness in interior settings: Screen-based assessments with eye tracking, and virtual reality evaluations
Bibliographic record
Abstract
This study investigates the perception of spaciousness in interior environments using screen-based assessments with eye tracking, and virtual reality (VR) technologies. The research explores how four key design elements -view access, view content, materiality, and ceiling geometry- influence perceived spaciousness. Thirty-five college students participated in screen-based and VR-based evaluations of 16 photorealistic interior settings. Eye tracking data were collected during screen-based assessments to analyze visual attention patterns. Statistical analyses included repeated measures ANOVAs, pairwise comparisons, and correlations between screen and VR assessments. Results showed that view access significantly affected perceived spaciousness in both screen and VR conditions, with larger windows correlating to higher spaciousness ratings. Materiality also demonstrated significant effects, with natural and textured materials perceived as more spacious than concrete surfaces. View content and ceiling geometry showed trends towards influencing spaciousness perception but did not reach statistical significance. VR presentations generally yielded higher spaciousness ratings compared to screen-based presentations, suggesting that immersive technologies may enhance spatial perception. Eye tracking analyses revealed common gaze patterns and variations in visual attention across different design conditions. This study contributes to the understanding of how design elements influence spatial perception and demonstrates the potential of integrating eye tracking and VR technologies in environmental psychology research. The findings have implications for evidence-based design practices aimed at enhancing perceived spaciousness in interior environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".