The Training and Transfer of Real-World Perceptual Expertise
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A hallmark of perceptual expertise is that experts classify objects at a more specific, subordinate level of abstraction than novices. To what extent does subordinate-level learning contribute to the transfer of perceptual expertise to novel exemplars and novel categories? In this study, participants learned to classify 10 varieties of wading birds and 10 varieties of owls at either the subordinate, species (e.g., "great blue crown heron,""eastern screech owl") or the family ("wading bird,""owl") level of abstraction. During training, the amount of visual exposure was equated such that participants received an equal number of learning trials for wading birds and owls. Pre- and posttraining performance was measured in a same/different discrimination task in which participants judged whether pairs of bird stimuli belonged to the same or different species. Participants trained in species-level discrimination demonstrated greater transfer to novel exemplars and novel species categories than participants trained in family-level discrimination. These findings suggest that perceptual categorization, not perceptual exposure per se, is important for the development and generalization of visual expertise.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it