How multiple prosodic boundaries of varying sizes influence syntactic parsing: behavioral and ERP evidence
Bibliographic record
Abstract
Prosodic boundaries (cued by pitch variations, final lengthening, pause) have been consistently demonstrated to have an immediate influence on parsing in a variety of syntactic structures cross-linguistically. For example, in sentences with temporary ambiguities such as Early and Late closure (EC/LC), which contain two potential boundary positions – the first (#1) compatible with EC and the second (#2) compatible with LC (e.g., Whenever the bear was approaching #1 the people #2 (EC): …would run away; (LC): …the dogs would run away), without the benefit of prosodic information, the preferred (or default) interpretation is LC, which consequently leads to processing difficulties (garden-path effects) in EC structures. The majority of studies on spoken sentence processing has focused on the impact of a single boundary on the closure or attachment preference of a specific phrase or clause. However, more recently, several influential hypotheses have emerged that aim to account for the interplay between two boundaries in a sentence, specifically in terms of size and location; the most influential of these argue that processing is either (i) local, with large boundaries independently integrated, which serve as strategic cues to syntactic closure (Watson & Gibson, 2005), or (ii) global, with the relative difference between the magnitude of boundaries across an utterance modulating interpretation (Clifton, Carlson, & Frazier, 2002). Although differing in details, these hypotheses suggest that listeners process boundary information at the sentence level in a categorical manner. In contrast, there is some data to suggest that boundaries can differ in a gradient quantitative manner, and that listeners are sensitive to this range of boundary sizes. The aims of the current dissertation were therefore to use behavioral and event-related potential (ERP) measures: (i) to contrast the predictions of the opposing theoretical accounts using temporary syntactic ambiguities, and (ii) to test whether gradient differences in boundary size impact listeners' parsing decisions in a gradient or categorical manner.Using an innovative paradigm, I conducted two behavioral experiments (Study 1), and one ERP experiment (Study 2), where listeners were presented with highly controlled digitally-manipulated EC/LC sentences, each containing two prosodic boundaries (as in the example above), which differed only in terms of their relative sizes. The outcomes of the three experiments reveal an initial, profound bias of boundaries on syntactic preference, which was nearly impossible to override. In addition, subtle differences between prosodic boundaries are detected by the brain and affect the degree of processing difficulty. Finally, the effect of boundaries on parsing is far more intricate than previously assumed. These outcomes cannot be accommodated by a purely categorical account, and cast serious doubts on most current models of prosodic online processing. We present the extended Boundary Deletion Hypothesis (eBDH), an alternative account for prosodic phrasing, based on the results of all three experiments.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".