Phonological effects in forward and backward serial recall: Qualitative and quantitative differences.
Bibliographic record
Abstract
Forward serial recall is affected by a diverse range of phonological factors that are readily replicated and relatively well understood. In contrast with backward recall, these phonological effects are not consistently replicable in that some studies show that the effects are present and some show the effects are absent or severely attenuated. Moreover at the theoretical level there is no consensus about how participants perform backward recall. The current research was aimed at understanding the differences between forward and backward recall by using meta-analytic techniques on 16 previously published experiments that examined the effects of benchmark phonological factors on both forward and backward recall. In each of the studies, recall was decomposed into 2 components, the first 2 items output and the remaining later responses. A consistent pattern emerged in the data. Each effect was present in both the early and late output positions in forward recall. The effects were present in the late output positions in backward recall, but the effects were weaker than in forward recall. The phonological variables had little impact on early output in backward recall (with the exceptions of articulatory suppression). The presence of qualitative differences between forward and backward recall and quantitative differences between studies have implications for the theoretical understanding of direction of recall in immediate memory tasks.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".