Differences Between French and English in the Use of Suprasegmental Cues for the Short-Term Recall of Word Lists
Bibliographic record
Abstract
PURPOSE: Greater recognition of the impact of hearing loss on cognitive functions has led speech/hearing clinics to focus more on auditory memory outcomes. Typically evaluated by scoring participants' recall on a list of unrelated words after they have heard the list read out loud, this method implies pitch and timing variations across words. Here, we questioned whether these variations could impact performance differentially in one language or another. METHOD: In a series of online studies evaluating auditory short-term memory in normally hearing adults, we examined how pitch patterns (Experiment 1), timing patterns (Experiment 2), and interactions between the two (Experiment 3) affected free recall of words, cued recall of forgotten words, and mental demand. Note that visual memory was never directly tested; written words were only used after auditory encoding in the cued recall part. Studies were administered in both French and English, always conducted with native listeners. RESULT: Confirming prior work, grouping mechanisms facilitated free recall, but not cued recall (the latter being only affected by longer presentation time) or ratings of mental demand. Critically, grouping by pitch provided more benefit for French than for English listeners, while grouping by time was equally beneficial in both languages. CONCLUSION: Pitch is more useful to French- than to English-speaking listeners for encoding spoken words in short-term memory, perhaps due to the syllable-based versus stress-based rhythms inherent to each language. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.27048328.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".