Effects of Cooperating and Conflicting Prosody in Spoken English Garden Path Sentences: ERP Evidence for the Boundary Deletion Hypothesis
Bibliographic record
Abstract
In reading, a comma in the wrong place can cause more severe misunderstandings than the lack of a required comma. Here, we used ERPs to demonstrate that a similar effect holds for prosodic boundaries in spoken language. Participants judged the acceptability of temporarily ambiguous English "garden path" sentences whose prosodic boundaries were either in line or in conflict with the actual syntactic structure. Sentences with incongruent boundaries were accepted less than those with missing boundaries and elicited a stronger on-line brain response in ERPs (N400/P600 components). Our results support the notion that mentally deleting an overt prosodic boundary is more costly than postulating a new one and extend previous findings, suggesting an immediate role of prosody in sentence comprehension. Importantly, our study also provides new details on the profile and temporal dynamics of the closure positive shift (CPS), an ERP component assumed to reflect prosodic phrasing in speech and music in real time. We show that the CPS is reliably elicited at the onset of prosodic boundaries in English sentences and is preceded by negative components. Its early onset distinguishes the speech CPS in adults both from prosodic ERP correlates in infants and from the "music CPS" previously reported for trained musicians.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".