Integration of semantic and coarticulation cues during spoken language comprehension in adults
Bibliographic record
Abstract
Recent work examining cue integration across levels of linguistic representation has found that listeners can dynamically integrate some of the lower-level and higher-level cues during spoken language comprehension. However, it is still not well understood how the mechanism of cue integration works. This study investigated how adults (n = 52) process preceding higher-level semantic cues and later low-level coarticulation cues during spoken language comprehension using an eye-tracking paradigm. Participants were tested on sentences that contained a prime (semantically related or semantically unrelated to the target) and a target which had varying coarticulation cues (matching versus mismatching splicing cues). Participants were presented with two pictures (target and competitor) on a screen. Analyses looked at the proportion of looking to the target during the prime and target time windows. Results demonstrate that adults flexibly use both the preceding semantic cues and later coarticulatory cues once they are available. Our findings also indicate that adults flexibly weighed both the preceding higher-level and later lower-level cues, such that the processing of low-level coarticulatory cue varied depending on the semantic context. We have added an unstudied level of cue (semantic context) to the set of cues that our cognitive system can integrate during language comprehension.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".