The extralist-feature effect: Evidence against item matching in short-term recognition memory.
Why this work is in the frame
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Bibliographic record
Abstract
The authors varied the similarity between negative probes and study items in a short-term item-recognition task. Current models treat similarity as a function of the number of occurrences of the probe's features in the study set, a factor that is often confounded with the number of the probe's features occurring in the study set. Unconfounded comparisons showed that performance reflected only the latter factor, with response time a linear function of the number of probe features in the study set. The effect was obtained for both stimuli with manipulated features (colored shapes) and words. Number of presented features is a global property of the study list, but existing global models calculate familiarity by averaging across item matches and cannot readily accommodate the data. The authors proposed that the probe's features are compared with a global representation of the study set's features.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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