Searching High and Low: Prosodic Breaks Disambiguate Relative Clauses
Bibliographic record
Abstract
During natural speech perception, listeners rely on a wide range of cues to support comprehension, from semantic context to prosodic information. There is a general consensus that prosody plays a role in syntactic parsing, but most studies focusing on ambiguous relative clauses (RC) show that prosodic cues, alone, are insufficient to reverse the preferred interpretation of sentence. These findings suggest that universally preferred structures (e.g., Late Closure principle) matter far more than prosodic cues in such cases. This study explores an alternative hypothesis: that the weak effect of prosody might be due to the influence of various syntactic, lexical-semantic, and acoustic confounding factors, and investigate the consequences of prosodic breaks while controlling these variables. We used Spanish RC sentences in three experimental conditions where the presence and position (following the first or second noun phrase) of prosodic breaks was manipulated. The results showed that the placement of a prosodic break determined sentence interpretation by changing the preferred attachment of the RC. Listeners' natural preference for low attachment (in the absence of break) was reinforced when a prosodic break was placed after the first noun. In contrast, a prosodic break placed after the second noun reversed the preferred interpretation of the sentence, toward high attachment. We argue that, in addition to other factors, listeners indeed use prosodic breaks as robust cues to syntactic parsing during speech processing, as these cues may direct listeners toward one interpretation or another.
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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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".