Bibliographic record
Abstract
The purpose of the present work was to investigate whether religious monumental architecture facilitates religious feeling by inducing a sense of awe. In order to elucidate how church interiors elicit awe and otherwise shape affective and cognitive processes, we developed a rating scale for the measurement of physical properties of interior spaces in order to determine which architectural properties in an interior space can predict a sense of awe (Experiment 1). By having participants rate affective response to a set of images pre-rated on architectural properties, we were able to establish a predictive relationship between architectural properties and elicited emotion. Properties reflecting immensity and adornment significantly predicted a feeling of awe. The results from Experiment 1 guided the selection of stimuli for Experiment 2, in which we explored the effects of visually priming participants with photographs of high and low awe-inducing architectural interiors on time perception and spirituality, as well as the effects of priming participants with photographs of religious and non-religious building interiors on participant religiousness. Feeling awe led to a greater overestimation of time in a time-estimation task, and religious priming through photographs of church interiors rated low in properties of immensity and adornment led to an increase in religious feeling. This work establishes an initial understanding of cognitive processes underlying affective and social responses to the environmental cues of church interiors.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".