Shifting Perceptions: The Effects of Subordinate Level Training on Category Restructuring
Bibliographic record
Abstract
Experts identify objects in their domain of expertise faster, more accurately, and at a more specific level of abstraction than novices (Tanaka & Taylor, 1993). Whereas a novice sees the yellow bird flitting in the bush, the expert instantly recognizes this object as a Cape May Warbler. Although substantial research has explored the behavioral and neural correlates of the expert’s downward shift in recognition, less is known about how their mental structure mediates such speeded identification. In our experiment, 75 participants were trained to identify ten images of Cape May, Magnolia, Prairie, and Townsend warblers to a criterion of 90% accuracy. Before and after training, category structure was assessed with PsiZ. PsiZ (https://psiz.readthedocs.io) is a machine learning package that generates a multi-dimensional category representation (i.e., psychological embedding) based on the participant’s judgments of image similarity. The key finding was that training produced profound changes in category structure. Specifically, warbler images belonging to different species became significantly more differentiated, while warbler images of the same species became more compact; hence, training produced between-category expansion and within-category compression. What is the relationship between category structure and category performance? Once participants completed their post-training PsiZ judgments, participants were given a recognition test where they were asked to identify the species of novel Warbler images and images used in training. Based on their recognition accuracy, the group of top 25% and bottom 25% performers were identified. The psychological embeddings were then inferred for each group and compared. The PsiZ results revealed significant differentiation between species, particularly among the lower quartile participants following training. Moreover, after training, top-performers showed denser within-species clusters than lower performers. Collectively, subordinate-level training produced significant category restructuring. Further, the quality of this reorganization appears to play a functional role in one’s expert recognition performance.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".