Bibliographic record
Abstract
A critical part of the Mismatch Negativity (MMN) mechanism is the construction of a memory trace encoding regularities extracted from the stimuli in an oddball paradigm. In an influential study, Phillips et al. (2000) argued that varying phonetic standards within the limits of a phoneme category prompts the auditory cortex to access representations of the “discrete phonological categories” (p. 1050), resulting in a mismatch response by comparing the deviant stimulus to an abstract, evoked category representation. The present study tested the strongest interpretation of this claim–namely, that the phoneme itself is retrieved from long-term memory and serves as the memory trace for the stimulus sequence. This interpretation has been the implicit, if not explicit, basis for a body of research employing the varying-standards paradigm to probe phonological underspecification. However, while previous research has focused on contrasts between distinct phonemes, we examined a previously untested prediction of this interpretation: that varying the standards should eliminate the MMN when the contrast is within a single phoneme category. Contrary to this prediction, we observed a mismatch negativity to a within-category deviant even when standards were varied. In two additional experiments we further examined whether the within-category MMN is from long-term memory representations of phonetic realizations of the phoneme, or from listeners constructing ad hoc statistical representations of the stimulus distribution. The weight of the evidence suggests that the within-category MMN observed with varying standards reflects sensitivity to the statistical structure of the stimuli, rather than activation of abstract phonological categories.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".