Absorption relates to individual differences in visual face pareidolia
Bibliographic record
Abstract
Visual face pareidolia is the experience of perceiving illusory faces in inanimate objects (e.g., rocks, buildings, appliances); however, the individual differences that relate to these pareidolia experiences remain unclear. The present set of studies assessed individual differences in face pareidolia, with a particular emphasis on personality factors previously associated with changes in perceptual experiences (openness and absorption). Study 1 measured face pareidolia in two novel ways: an implicit, speeded visual categorization task, and a self-report measure. Study 2 measured face pareidolia using more explicit categorization tasks and a slightly modified version of the self-report measure from Study 1. Across both studies, we also measured the Big Five personality dimensions, absorption, and a performance-based measure of divergent association formation, a proxy for creativity. We found that absorption was positively associated with individual differences in face pareidolia. The association between absorption and face pareidolia remained significant when controlling for factors that also positively correlated with absorption (openness, extraversion, and positive mood). Taken together, these results suggest that individual differences in face pareidolia experiences are consistently associated with absorption, which represents an especially promising construct to investigate in future pareidolia research.
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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 | 0.007 |
| 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 | 0.001 |
| 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".