Backward recall and foreknowledge of recall direction: a test of the Encoding–Retrieval Matching Hypothesis
Bibliographic record
Abstract
When participants must recall a sequence of items in reverse order just after their presentation, inconsistent findings have been observed relative to when participants must recall a sequence in their presentation order. Recently, the Encoding-Retrieval Matching Hypothesis (ERM) has been developed to account for these inconsistencies. Within the ERM hypothesis, foreknowledge of recall direction plays an important role. In two experiments, we tested a key prediction of the ERM hypothesis: In backward recall with foreknowledge of recall direction, the size of the effect will vary as a function of its reliance on visuospatial representations. Participants performed an immediate serial recall task with digits. As predicted, the detrimental effect of manual-spatial tapping was larger in backward recall relative to forward recall when recall direction was predictable (Experiment 1b), but not when it was unpredictable (Experiment 1a). In Experiment 2, the word length effect, not relying on visuospatial representations, was equally large in forward and backward recall, and it was unaffected by foreknowledge of recall direction. Overall, the results support the predictions derived from the ERM hypothesis and contribute to the delineation of when and how foreknowledge can influence backward recall performance relative to forward recall performance.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".