Imagery-based strategies for memory for associations
Why this work is in the frame
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Bibliographic record
Abstract
Cued recall of word pairs is improved by asking participants to combine items in an interactive image. Meanwhile, interactive images facilitate serial-recall (Link Method), but even better when each item is imagined alongside a previously learned peg-word (Peg List Method). We asked if a peg system could support memory for pairs, hypothesising it would outperform interactive imagery. Tested with cued recall, five study strategies were manipulated between-subjects, across two experiments: (1) Both words linked to one peg; (2) Each word linked to a different peg; (3) Peg list method but studying as a serial list; (4) Interactive imagery (within-pairs); (5) Link Method. Participants were able to apply peg-list strategies to pairs, as anticipated by mathematical modelling. Error-patterns spoke to mathematical models; peg lists exhibited distance-based confusability, characteristic of positional-coding models, and errors tended to preserve within-pair position, even for inter-item associative strategies, suggesting models of association should incorporate position. However, the peg list strategies came with a speed-accuracy tradeoff and did not challenge the superiority of the interactive imagery strategy. Without extensive practice with peg list strategies, interactive imagery remains superior for associations. Peg strategies may excel instead in tasks that primarily test serial order or with extensive training.
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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 | 0.000 | 0.002 |
| 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 it