The d-Prime directive: Assessing costs and benefits in recognition by dissociating mixed-list false alarm rates.
Bibliographic record
Abstract
It can be difficult to judge the effectiveness of encoding techniques in a within-subject design. Consider the production effect-the finding that words read aloud are better remembered than words read silently. In the absence of a baseline, a within-subject production effect in a mixed study list could reflect a benefit of reading aloud, a cost of reading silently, or both. To help interpret within-subject data, memory researchers have compared within-subject and between-subjects designs, with the between-subjects (i.e., pure list) conditions serving as baselines against which the within-subject (i.e., mixed-list) conditions are compared. In the present article, the authors highlight a shortcoming of using this comparison to assess costs and benefits in recognition. Unlike between-subjects experiments where separate false alarm rates are obtained for each condition, the typical within-subject experiment yields a collapsed false alarm rate, which, the authors argue, can potentially bias calculations of memory discrimination (d'). Across 3 experiments that used production as the encoding manipulation, they used a typical mixed-list versus pure-list design (Experiment 1) and then made modifications to this design (Experiments 2 and 3) that yielded separate mixed-list false alarm rates. The results of the latter 2 experiments demonstrated that words that are read aloud in a mixed list have an overall memorial benefit over words that are read aloud in a pure list-both in terms of increased hits and reduced false alarms. The authors frame these results in terms of the distinctiveness heuristic. (PsycINFO Database Record
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".