Architectural experience: clarifying its central components and their relation to core affect with a set of first-person-view videos
Why this work is in the frame
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Bibliographic record
Abstract
Abstract When studying architectural experience in the lab, it is of paramount importance to use a proxy as close to real-world experience as possible. Whilst still images visually describe real spaces, and virtual reality allows for dynamic movement, each medium lacks the alternative attribute. To merge these benefits, we created and validated a novel dataset of valenced videos of first-person-view travel through built environments. This dataset was then used to clarify the relationship of core affect (valence and arousal) and architectural experience. Specifically, we verified the relationship between valence and fascination, coherence, and hominess - three key psychological dimensions of architectural experience which have previously been shown to explain aesthetic ratings of built environments. We also found that arousal is only significantly correlated with fascination, and that both are embedded in a relationship with spatial complexity and unusualness. These results help to clarify the nature of fascination, and to distinguish it from coherence and hominess when it comes to core affect. Moreover, these results demonstrate the utility of a video dataset of affect-laden spaces for understanding architectural experience. Highlights - Developed a video database of first-person-view journeys through built environments - We explored how core affect and architectural experience relate through the videos - Previous results are supported: valence ties to fascination, coherence and hominess - Arousal correlates only with fascination, and not coherence or hominess - Arousal and fascination are tied to spatial complexity and unusualness
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it