I’ve just seen a face: further search for face pareidolia in chimpanzees (Pan troglodytes)
Bibliographic record
Abstract
Introduction: Seeing faces in random patterns, such as in clouds, is known as pareidolia. Two possible mechanisms can cause pareidolia: a bottom-up mechanism that automatically detects inverted triangle or top-heavy patterns, and a top-down mechanism that actively seeks out faces. Pareidolia has been reported in nonhuman animals as well. In chimpanzees, it has been suggested that the bottom-up mechanism is involved in their pareidolic perception, but the extent of the contribution of the top-down mechanism remains unclear. This study investigated the role of topdown control in face detection in chimpanzees. Methods: After being trained on an oddity task in which they had to select a noise pattern where a face (either human or chimpanzee) or a letter (Kanji characters) was superimposed among three patterns, they were tested with noise patterns that did not contain any target stimuli. Results: When the average images of the patterns selected by the chimpanzees in these test trials were analyzed and compared with those that were not selected (i.e., difference images), a clear non-random structure was found in the difference images. In contrast, such structures were not evident in the difference images obtained by assuming that one of the three patterns was randomly selected. Discussion: These results suggest that chimpanzees may have been attempting to find "faces" or "letters"in random patterns possibly through some form of top-down processing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".