The d-Prime directive: Assessing costs and benefits in recognition by dissociating mixed-list false alarm rates.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it