Distributed Neural Representations of Phonological Features during Speech Perception
Bibliographic record
Abstract
A fundamental goal of the human auditory system is to map complex acoustic signals onto stable internal representations of the basic sound patterns of speech. Phonemes and the distinctive features that they comprise constitute the basic building blocks from which higher-level linguistic representations, such as words and sentences, are formed. Although the neural structures underlying phonemic representations have been well studied, there is considerable debate regarding frontal-motor cortical contributions to speech as well as the extent of lateralization of phonological representations within auditory cortex. Here we used functional magnetic resonance imaging (fMRI) and multivoxel pattern analysis to investigate the distributed patterns of activation that are associated with the categorical and perceptual similarity structure of 16 consonant exemplars in the English language used in Miller and Nicely's (1955) classic study of acoustic confusability. Participants performed an incidental task while listening to phonemes in the MRI scanner. Neural activity in bilateral anterior superior temporal gyrus and supratemporal plane was correlated with the first two components derived from a multidimensional scaling analysis of a behaviorally derived confusability matrix. We further showed that neural representations corresponding to the categorical features of voicing, manner of articulation, and place of articulation were widely distributed throughout bilateral primary, secondary, and association areas of the superior temporal cortex, but not motor cortex. Although classification of phonological features was generally bilateral, we found that multivariate pattern information was moderately stronger in the left compared with the right hemisphere for place but not for voicing or manner of articulation.
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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 | 0.001 |
| 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) | 0.001 | 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".