Investigating the neural correlates of phonological encoding using a cluster-based analysis approach
Bibliographic record
Abstract
Phonological encoding, a subprocess in speech production, proceeds incrementally from word onset to offset. However, the incremental nature of encoding for multisyllabic words is unclear, and limited research has examined neural activity underlying the seriality of the process. In the present study, we investigated the timing of encoding between and within syllables of bisyllabic words using a data-driven cluster-based analysis of electroencephalography (EEG) data. In a phoneme-monitoring task, young adults covertly named pictures of bisyllabic words with a prespecified target phoneme present or absent. Target phonemes in target-present trials were distributed among four serial positions of the word concept: first syllable (S1) onset or offset and second syllable (S2) onset or offset. Upon covert naming, participants responded to target presence via button press or withheld responses for target absence. Neuroelectric activity during task performance was recorded using EEG and analyzed using cluster-based permutation testing. Faster response times and differences in neural activity were observed for monitoring targets at S1 onset than S2 onset, and for monitoring targets at S1 onset than S1 offset. No differences were found between monitoring targets at S2 onset and S2 offset. Our study supports the incremental nature of phonological encoding in bisyllabic words. Furthermore, the neural findings confirm that the serial time course of encoding in bisyllabic words extends to phonemes within the first, but not the subsequent syllable. Findings may have implications for current models of speech production.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".