Canadian oats and Canadian goats: Comparing distal cues to segmentation and segments
Bibliographic record
Abstract
Distal timing cues (specifically, the speech rate of sentences temporally removed from a point of ambiguity in speech) have been shown to weakly modulate segmental perception (i.e., the perception of basic speech sounds, like "p" and "b"; Newman & Sawusch, 1996) but strongly affect the perception of word boundaries (i.e., the perception of separation between words in fluent speech; Dilley & Pitt, 2010). However, no study as of yet has directly compared the two classes of percept using identical manipulations. In this study, we will examine the role of distal timing cues to segmental perception and word segmentation using the same distal contexts (e.g., "The merchant sold Canadian oats/notes", a word boundary distinction, or "The merchant sold Canadian coats/goats", a voicing-based segmental distinction). Distal speech rate will be artificially slowed to an identical extent for both types of contrast. We predict that categorical perception leads distal context effects to be much stronger on word segmentation ("...Canadian oats/notes") than on segmental perception ("...Canadian coats/goats"). This research may help bridge speech perception theories that have been developed for each class of percept, and further clarify the role of distal cues in speech perception.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".