Investigating the Time Course of Spoken Word Recognition: Electrophysiological Evidence for the Influences of Phonological Similarity
Bibliographic record
Abstract
Behavioral and modeling evidence suggests that words compete for recognition during auditory word identification, and that phonological similarity is a driving factor in this competition. The present study used event-related potentials (ERPs) to examine the temporal dynamics of different types of phonological competition (i.e., cohort and rhyme). ERPs were recorded during a novel picture-word matching task, where a target picture was followed by an auditory word that either matched the target (CONE-cone), or mismatched in one of three ways: rhyme (CONE-bone), cohort (CONE-comb), and unrelated (CONE-fox). Rhymes and cohorts differentially modulated two distinct ERP components, the phonological mismatch negativity and the N400, revealing the influences of prelexical and lexical processing components in speech recognition. Cohort mismatches resulted in late increased negativity in the N400, reflecting disambiguation of the later point of miscue and the combined influences of top-down expectations and misleading bottom-up phonological information on processing. In contrast, we observed a reduction in the N400 for rhyme mismatches, reflecting lexical activation of rhyme competitors. Moreover, the observed rhyme effects suggest that there is an interaction between phoneme-level and lexical-level information in the recognition of spoken words. The results support the theory that both levels of information are engaged in parallel during auditory word recognition in a way that permits both bottom-up and top-down competition effects.
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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 | 0.001 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".