Inversion effects in the expert classification of mammograms and faces
Bibliographic record
Abstract
A hallmark of a perceptual expert is the ability to detect and categorize stimuli in their domain of expertise after brief exposure. For example, expert radiologists can differentiate between "abnormal" and "normal" mammograms after a 250 ms exposure. It has been speculated that rapid detection depends on a global analysis referred to as holistic perception. Holistic processing in radiology seems similar to holistic perception in which a stimulus like a face is perceived as an integrated whole, not in terms of its individual features. Holistic processing is typically subject to inversion effects in which the inverted image is harder to process/recognize. Is radiological perception similarly subject to inversion effects? Eleven experienced radiologists (> 5 years of radiological experience) and ten resident radiologists (< 5 years of radiological experience) judged upright and inverted bilateral mammograms as "normal" or "abnormal". For comparison, the same participants judged whether upright and inverted faces were "happy" or "neutral". We obtained the expected inversion effect for faces. Expression discrimination was superior for upright faces. For mammograms, experienced radiologists exhibited a similar inversion effect, showing higher accuracy for upright than for inverted mammograms. Less experienced radiology residents performed more poorly than experienced radiologists and demonstrated no inversion effect with mammograms. These results suggest that the ability to discriminate normal from abnormal mammograms is a form of learned, holistic 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 | 0.002 | 0.020 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".