Processing visual ambiguity in fractal patterns: Pareidolia as a sign of creativity
Bibliographic record
Abstract
Creativity is a highly sought after and multifaceted skill. Unfortunately, we only have a loose grasp on its cognitive underpinnings. Empirical research typically probes creativity by estimating the potential for problem solving and novel idea generation, a process known as “divergent thinking”. Here, by contrast, we examine creativity through the lens of perceptual abilities. In particular, we ask whether creative individuals are better at perceiving recognizable forms in noisy or ambiguous stimuli, a phenomenon known as pareidolia. To this end, we designed a visual perception task in which 50 participants, with various levels of creativity, were presented with ambiguous stimuli and asked to identify as many recognizable forms as possible. The stimuli consisted of cloud-like images with various levels of complexity, which we controlled by manipulating fractal dimension (FD) and contrast level. We found that pareidolic perceptions arise more often and more rapidly in individuals that are more creative. Furthermore, the emergence of pareidolia in individuals with lower creativity scores was more restricted to images with a narrow range of FD values, suggesting a wider repertoire for perceptual abilities in creative individuals. Our findings suggest that pareidolia may be used as a perceptual proxy of idea generation abilities, a key component of creative behavior. In sum, we extend the established body of work on divergent thinking, by introducing divergent perception as a complementary manifestation of the creative mind. These findings expand our understanding of the perception-creation link and open new paths in studying creative behavior in humans.
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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".