The influence of visual perception on responses towards real-world environments and application towards design
Bibliographic record
Abstract
Experience of the built-environment is said to be dependent on visual perception and the physical properties of space. Scene and environmental preference research suggests that particular visual features greatly influence one's response to their environment. Typically, environments which are informative and allow an individual to gain further knowledge about their surroundings are preferred. Although such findings could be applied to the design process, it is first necessary to develop a way in which to accurately and objectively describe visual properties within an environment. Recently, it has been proposed that isovist analysis could be employed to describe built-environments. In two experiments, we examine whether or not isovist analysis can capture experience of real-world environments. In experiment 1, we demonstrate that isovist analysis can be employed to describe experience within a controlled, real-world, laboratory environment. In experiment 2, we employed post-occupancy examination of a student centre to examine the robustness of isovist analysis and whether it would capture experience of a complex, real-world environment. The results of experiment 2 suggest that isovist analysis could capture certain experiences, such as spaciousness, but failed to capture other responses. Regression analysis suggests that a large number of variables predicted experience, including previous experience with the building and the presence of other individuals. This suggests that experience of real-world, complex environments cannot be captured by the visual properties alone, instead various factors influence experience. Implications towards the design process are discussed.
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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".