From Beat to Memory: Rhythmic Priming Effects on Verbal Memory Performance
Bibliographic record
Abstract
Listening to musical rhythms has been reported to enhance performance on language tasks for people with typical and atypical language abilities. In children and adults, priming with predictable rhythms (i.e., short-term rhythmic stimulation with music) has been shown to facilitate grammaticality judgements, even among children and adults with poor rhythm processing skills. Children and adults with atypical language skills have been better able to identify incorrect grammar within sentences when they have been primed with a predictable musical beat compared to an unpredictable beat or sound scene. Until the present thesis, to our knowledge there is no research demonstrating whether these beneficial rhythmic priming effects extend to verbal memory in children and adults. In Chapter 2, I report the finding that 9–11-year-old children with and without dyslexia may be unable to better recognize novel words when first primed with a predictable musical rhythm compared to an unpredictable environmental sound scene. Furthermore, children were better at judging grammatical and ungrammatical sentences after listening to an unpredictable environmental sound scene. However, in Chapter 3, I found that listening to a predictable musical rhythm compared to an environmental sound scene before repeating a nonsense sentence may enhance immediate recall from verbal memory in both adult musicians and non-musicians. Chapter 4 examines how the tempo of the predictable musical rhythm affects the priming of nonsensical sentences during passive listening. No ideal tempo amongst slow, moderate, and fast tempi could be detected. Together, these data show that rhythmic priming does not consistently provide benefits for verbal memory or specific language tasks. As such, this work makes contributions to the understanding of the interactions between rhythm, speech, and memory, and provides future considerations for investigating rhythmic priming and its potential connections to rehabilitation research.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".