The iambic-trochaic law without iambs or trochees: Parsing speech for grouping and prominence
Bibliographic record
Abstract
Listeners parse the speech signal effortlessly into words and phrases, but many questions remain about how. One classic idea is that rhythm-related auditory principles play a role, in particular, that a psycho-acoustic "iambic-trochaic law" (ITL) ensures that alternating sounds varying in intensity are perceived as recurrent binary groups with initial prominence (trochees), while alternating sounds varying in duration are perceived as binary groups with final prominence (iambs). We test the hypothesis that the ITL is in fact an indirect consequence of the parsing of speech along two in-principle orthogonal dimensions: prominence and grouping. Results from several perception experiments show that the two dimensions, prominence and grouping, are each reliably cued by both intensity and duration, while foot type is not associated with consistent cues. The ITL emerges only when one manipulates either intensity or duration in an extreme way. Overall, the results suggest that foot perception is derivative of the cognitively more basic decisions of grouping and prominence, and the notions of trochee and iamb may not play any direct role in speech parsing. A task manipulation furthermore gives new insight into how these decisions mutually inform each other.
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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 | 0.008 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".