Optically illusive architecture (OIA): Introduction and evaluation using virtual reality
Bibliographic record
Abstract
Architects and designers communicate their ideas within a range of representational methods. No single instance of these methods, either in the form of orthographic projections or perspectival representation, can address all questions regarding the design, but as a whole, they demonstrate a comprehensive range of information about the building or object they intend to represent. This explicates an inevitable degree of deficiency in representation, regardless of its type. In addition, perspective-based optical illusions manipulate our spatial perception by deliberately misrepresenting the reality. In this regard, they are not new concepts to architectural representation. As a consequence, Optically Illusive Architecture (OIA) is proposed, not as a solution to fill the gap between the representing and represented spaces, but as a design paradigm whose concept derives from and accounts for this gap. By OIA we aim to cast light to an undeniable role of viewpoints in designing architectural spaces. The idea is to establish a methodology in a way that the deficiency of current representational techniques—manifested as specific thread of optical illusions—flourishes into thoughtful results embodied as actual architectural spaces. Within our design paradigm, we define a framework to be able to effectively analyze its precedents, generate new space, and evaluate their efficiencies. Moreover, the framework raises a hierarchical set of questions to differentiate OIA from a visual gimmick. Furthermore, we study two OIA-driven environments, by conducting empirical studies using Virtual Reality (VR). These studies bear essential information, in terms of design performance, and the public’s ability to engage and interact with an OIA space, prior to the actual fabrication of the structures.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".