Personality and Aesthetic Preferences in Architecture: A Review of the Study Approaches and Assessment Methods
Bibliographic record
Abstract
Introduction: A clear relationship exists between human personality and architectural preferences. However, reviewing the findings of previous studies reveals that this expectation is not necessarily accurate, as contradictory results have been reported. This study aims to review various research and assessment methods used in previous studies for assessing the relationship between personality and architectural preferences and identify the theoretical and practical shortcomings of each method. Methods: This is a critical review study. A search was conducted in Google Scholar and Web of Science database for published articles in English using the following keywords: "Visual aesthetics," "personality traits," "architectural preferences," "art preferences," and "aesthetic judgments." These articles were first categorized into four groups based on their methodological approaches (physiological, neurobiological, practical, and psychological) and then their degree of success and generalizability were assessed briefly. Finally, due to having lower implementation limitations and a higher theoretical background, the group using the psychological approaches was structurally analyzed from the methodological and practical aspects to develop a conceptual quadruple model. After presenting the model, neural network modeling was used to discover of hidden patterns. Results: ental stimuli, context, personality traits, and responses. The machine learning method facilitated the discovery of hidden patterns in relationship between personality and human preferences. Conclusion: This study proposes a new systematic quadruple model for evaluating aesthetic preferences.
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How this classification was reachedexpand
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".