Recognition of categorised words: Repetition effects in rote study
Bibliographic record
Abstract
In the recognition-memory mirror effect one stimulus class exhibits both more hits and fewer false alarms than a contrasting class. This outcome is frequently detected when strong (e.g., repeated, long-duration study) and weak items have appeared in different lists but less so within lists. The mirror effect may reflect people's assignment of a more lenient recognition criterion to the weak than the strong class. The present study asked whether a paradigm that has yielded within-list mirror effects when participants make gist ratings during study (Singer, 2009, 2011) likewise obtains in rote study. In Experiments 1 and 2 people studied words from category pairs such that the stimuli from one category only were repeated three times. Both hits and false alarms were consistently higher for the repeated than the unrepeated condition, a pattern labelled "concordant" (rather than mirror). This might reflect the either a positive "distribution shift" of the repeated-category lures or a metacognitive strategy. Experiment 3 coupled the same study procedure with two-alternative forced-choice testing (2AFC) to deny the distribution shift explanation. The sorts of strategy that might favour repeated over unrepeated lures are considered.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".