Auditory Cortex Accesses Phonological Categories: An MEG Mismatch Study
Bibliographic record
Abstract
The studies presented here use an adapted oddball paradigm to show evidence that representations of discrete phonological categories are available to the human auditory cortex. Brain activity was recorded using a 37-channel biomagnetometer while eight subjects listened passively to synthetic speech sounds. In the phonological condition, which contrasted stimuli from an acoustic /dae/-/tae/ continuum, a magnetic mismatch field (MMF) was elicited in a sequence of stimuli in which phonological categories occurred in a many-to-one ratio, but no acoustic many-to-one ratio was present. In order to isolate the contribution of phonological categories to the MMF responses, the acoustic parameter of voice onset time, which distinguished standard and deviant stimuli, was also varied within the standard and deviant categories. No MMF was elicited in the acoustic condition, in which the acoustic distribution of stimuli was identical to the first experiment, but the many-to-one distribution of phonological categories was removed. The design of these studies makes it possible to demonstrate the all-or-nothing property of phonological category membership. This approach contrasts with a number of previous studies of phonetic perception using the mismatch paradigm, which have demonstrated the graded property of enhanced acoustic discrimination at or near phonetic category boundaries.
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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 | 0.000 | 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) | 0.002 | 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".