Can You Hear What’s Coming? Failure to Replicate ERP Evidence for Phonological Prediction
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Bibliographic record
Abstract
Abstract Prediction-based theories of language comprehension assume that listeners predict both the meaning and phonological form of likely upcoming words. In alleged event-related potential (ERP) demonstrations of phonological prediction, prediction-mismatching words elicit a phonological mismatch negativity (PMN), a frontocentral negativity that precedes the centroparietal N400 component. However, classification and replicability of the PMN has proven controversial, with ongoing debate on whether the PMN is a distinct component or merely an early part of the N400. In this electroencephalography (EEG) study, we therefore attempted to replicate the PMN effect and its separability from the N400, using a participant sample size (N = 48) that was more than double that of previous studies. Participants listened to sentences containing either a predictable word or an unpredictable word with/without phonological overlap with the predictable word. Preregistered analyses revealed a widely distributed negative-going ERP in response to unpredictable words in both the early (150–250 ms) and the N400 (300–500 ms) time windows. Bayes factor analysis yielded moderate evidence against a different scalp distribution of the effects in the two time windows. Although our findings do not speak against phonological prediction during sentence comprehension, they do speak against the PMN effect specifically as a marker of phonological prediction mismatch. Instead of an PMN effect, our results demonstrate the early onset of the auditory N400 effect associated with unpredictable words. Our failure to replicate further highlights the risk associated with commonly employed data-contingent analyses (e.g., analyses involving time windows or electrodes that were selected based on visual inspection) and small sample sizes in the cognitive neuroscience of language.
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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 it