Electrophysiological responses to coarticulatory and word level miscues.
Bibliographic record
Abstract
The influence of coarticulation cues on spoken word recognition is not yet well understood. This acoustic/phonetic variation may be processed early and recognized as sensory noise to be stripped away, or it may influence processing at a later prelexical stage. The present study used event-related potentials (ERPs) in a picture/spoken word matching paradigm to examine the temporal dynamics of stimuli systematically violating expectations at three levels: entire word (lexical), initial phoneme (phonemic), or in coarticulation cues contained in the initial phoneme (subphonemic). We found that both coarticulatory and phonemic mismatches resulted in increased negativity in the N280, interpreted as indexing prelexical processing of subphonemic information. Further analyses revealed that the point of uniqueness differentially modulated subsequent early or late negativity depending on whether the first or second segment matched expectations, respectively. Finally, it was found that word-level but not coarticulatory mismatches modulated the later-going N400 component, indicating that subphonemic information does not influence word-level selection provided no lexical change has occurred. The results indicate that acoustic/phonetic variation resulting from coarticulation is preserved in and influences spoken word recognition as it becomes available, particularly during prelexical processing.
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".