The role of working memory loads on immediate and long-term sentence recall
Bibliographic record
Abstract
It is well-established that both phonological and semantic knowledge influence verbal working memory. However, the focus has primarily been on understanding phonological effects despite evidence of semantic influences. Articulatory suppression is a well-established task for preventing phonological processing. Methods to prevent semantic processing have rarely been used in the past, highlighting a need for developing a semantic interference task. We, therefore, conceptualised two novel tasks - an animacy categorisation and semantic relatedness judgement task. This study explored the impact of phonological (articulatory suppression) and semantic loads (animacy categorisation and semantic relatedness judgement) on immediate and delayed sentence recall. Additionally, sentence concreteness (concrete vs. abstract sentences) indexed semantic knowledge in verbal working memory. Across two studies, immediate recall revealed that articulatory suppression (preventing phonological processing) increased the size of the concreteness effect, while the novel semantic tasks (preventing semantic processing) reduced it suggesting that our semantic tasks were indeed imposing a semantic load. Further, relative long-term performance showed that more new words were remembered in articulatory suppression, whereas recall was disproportionately impaired in the semantic relatedness task. Our experimental paradigm offers phonological and semantic suppression tasks that can be used in parallel to investigate the interactions between working memory and language.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".