Category Generalization After Entrenched Versus Probabilistic Erroneous Feedback
Bibliographic record
Abstract
Abstract The present study investigated the impact of occasional erroneous feedback on category learning. Subjects were presented with exemplars from multiple prototype categories, where the exemplars were novel on each learning block (nonrepeat) or were continuously recycled through learning (repeat). In training, selected training instances were associated with a label different from other members drawn from the same prototype category. After learning, subjects received a common transfer test requiring either the classification of novel instances (Experiment 1) or the discrimination of old from new instances (Experiments 2 and 3). The major results were that as expected, learning was more accurate in the repeat condition, with subjects learning to accurately classify both the correct and incorrect feedback patterns by the terminal learning block; learning in the nonrepeat condition was worse, with subjects misclassifying the erroneous feedback patterns at an increasing rate through learning; nonetheless, classification and recognition transfer were somewhat higher after nonrepeat learning; and subjects readily discriminated old from new after repetition training but not after nonrepetition training. Overall, the enhanced classification and recognition after nonrepeat training mirrored our results reported in a similar paradigm that used only correct feedback. Formal modeling suggested that a pure prototype model could not predict learning or transfer after repetition training, whereas a prototype model was superior at capturing results after nonrepetition learning.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".