Grouping by Time and Pitch Facilitates Free but Not Cued Recall for Word Lists in Normally-Hearing Listeners
Bibliographic record
Abstract
Auditory memory is an important everyday skill evaluated more and more frequently in clinical settings as there is recently a greater recognition of the cost of hearing loss to cognitive systems. Testing often involves reading a list of unrelated items aloud; but prosodic variations in pitch and timing across the list can affect the number of items remembered. Here, we ran a series of online studies on normally-hearing participants to provide normative data (with a larger and more diverse population than the typical student sample) on a novel protocol characterizing the effects of suprasegmental properties in speech, namely investigating pitch patterns, fast and slow pacing, and interactions between pitch and time grouping. In addition to free recall, and in line with our desire to work eventually with individuals exhibiting more limited cognitive capacity, we included a cued recall task to help participants recover specifically the words forgotten during the free recall part. We replicated key findings from previous research, demonstrating the benefits of slower pacing and of grouping on free recall. However, only slower pacing led to better performance on cued recall, indicating that grouping effects may decay surprisingly fast (over a matter of one minute) compared to the effect of slowed pacing. These results provide a benchmark for future comparisons of short-term recall performance in hearing-impaired listeners and users of cochlear implants.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".