The Origin of Exemplar Effects in Rule-Driven Categorization.
Bibliographic record
Abstract
S. W. Allen and L. R. Brooks (1991) have shown that exemplar memory can affect categorization even when participants are provided with a classification rule. G. Regehr and L. R. Brooks (1993) argued that stimuli must be individuated for such effects to occur. In this study, the authors further analyze the conditions that yield exemplar effects in this rule application paradigm. The results of Experiments 1-3 show that interchangeable attributes, which are not part of the rule, influence categorization only when attention is explicitly drawn on them. Experiment 4 shows that exemplar effects can occur in an incidental learning condition, whether stimulus individuation is preserved or not. The authors conclude that the influence of exemplar learning in rule-driven categorization stems from the attributes specified in the rule or in the instructions, not from the stimulus gestalts.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".