Inversion Impairs Expert Budgerigar Identity Recognition: A Face-Like Effect for a Nonface Object of Expertise
Bibliographic record
Abstract
The face-inversion effect is the finding that picture-plane inversion disproportionately impairs face recognition compared to object recognition and is now attributed to greater orientation-sensitivity of holistic processing for faces but not common objects. Yet, expert dog judges have showed similar recognition deficits for inverted dogs and inverted faces, suggesting that holistic processing is not specific to faces but to the expert recognition of perceptually similar objects. Although processing changes in expert object recognition have since been extensively documented, no other studies have observed the distinct recognition deficits for inverted objects-of-expertise that people as face experts show for faces. However, few studies have examined experts who recognize individual objects similar to how people recognize individual faces. Here we tested experts who recognize individual budgerigar birds. The effect of inversion on viewpoint-invariant budgerigar and face recognition was compared for experts and novices. Consistent with the face-inversion effect, novices showed recognition deficits for inverted faces but not for inverted budgerigars. By contrast, experts showed equal recognition deficits for inverted faces and budgerigars. The results are consistent with the hypothesis that processes underlying the face-inversion effect are specific to the expert individuation of perceptually similar objects.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".