Auditory-Motor Rhythms and Speech Processing in French and German Listeners
Bibliographic record
Abstract
Moving to a speech rhythm can enhance verbal processing in the listener by increasing temporal expectancies (Falk and Dalla Bella, 2016). Here we tested whether this hypothesis holds for prosodically diverse languages such as German (a lexical stress-language) and French (a non-stress language). Moreover, we examined the relation between motor performance and the benefits for verbal processing as a function of language. Sixty-four participants, 32 German and 32 French native speakers detected subtle word changes in accented positions in metrically structured sentences to which they previously tapped with their index finger. Before each sentence, they were cued by a metronome to tap either congruently (i.e., to accented syllables) or incongruently (i.e., to non-accented parts) to the following speech stimulus. Both French and German speakers detected words better when cued to tap congruently compared to incongruent tapping. Detection performance was predicted by participants' motor performance in the non-verbal cueing phase. Moreover, tapping rate while participants tapped to speech predicted detection differently for the two language groups, in particular in the incongruent tapping condition. We discuss our findings in light of the rhythmic differences of both languages and with respect to recent theories of expectancy-driven and multisensory speech processing.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".