Architectural experience: clarifying its central components and their relation to core affect with a set of first-person-view videos
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".