What Does the Right Hemisphere Know about Phoneme Categories?
Bibliographic record
Abstract
Innate auditory sensitivities and familiarity with the sounds of language give rise to clear influences of phonemic categories on adult perception of speech. With few exceptions, current models endorse highly left-hemisphere-lateralized mechanisms responsible for the influence of phonemic category on speech perception, based primarily on results from functional imaging and brain-lesion studies. Here we directly test the hypothesis that the right hemisphere does not engage in phonemic analysis. By using fMRI to identify cortical sites sensitive to phonemes in both word and pronounceable nonword contexts, we find evidence that right-hemisphere phonemic sensitivity is limited to a lexical context. We extend the interpretation of these fMRI results through the study of an individual with a left-hemisphere lesion who is right-hemisphere reliant for initial acoustic and phonetic analysis of speech. This individual's performance revealed that the right hemisphere alone was insufficient to allow for typical phonemic category effects but did support the processing of gradient phonetic information in lexical contexts. Taken together, these findings confirm previous claims that the right temporal cortex does not play a primary role in phoneme processing, but they also indicate that lexical context may modulate the involvement of a right hemisphere largely tuned for less abstract dimensions of the speech signal.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".