Combining and integrating multiple linguistic cues during spoken language comprehension: A focus on semantics and coarticulation
Bibliographic record
Abstract
This research examines how adults process and integrate a combination of higher-level semantic cues (i.e., semantic context) which are followed by lower-level acoustic cues (i.e., coarticulatory cues) during online spoken comprehension. Previous studies investigating cue integration within Martin (2016)'s framework found that listeners can flexibly use and integrate a variety of available cues across linguistic representations. The current pre-registered study used an eye-tracking paradigm and tested how listeners process coarticulation (a lower-level cue) in the presence of preceding semantic information (a higher-level cue). Adult listeners were sensitive to both semantic and coarticulatory cues; moreover, in an exploratory analysis, adults' processing of later acoustic cues varied depending on the earlier semantic context. These results demonstrate that listeners can flexibly use and weigh cues across multiple levels of linguistic representations during language comprehension. Earlier semantic information may be maintained over time and can influence the processing of later lower-level acoustic cues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".