Reorganization of the neurobiology of language after sentence overlearning
Bibliographic record
Abstract
Abstract There is a widespread assumption that there are a static set of ‘language regions’ in the brain. Yet, people still regularly produce familiar ‘formulaic’ expressions when those regions are severely damaged. This suggests that the neurobiology of language varies with the extent of word sequence learning and might not be fixed. We test the hypothesis that perceiving sentences is mostly supported by sensorimotor regions involved in speech production and not ‘language regions’ after overlearning. Twelve participants underwent two sessions of behavioural testing and functional magnetic resonance imaging (fMRI), separated by 15 days. During this period, they repeated two sentences 30 times each, twice a day. In both fMRI sessions, participants ‘passively’ listened to those two sentences and novel sentences. Lastly, they spoke novel sentences. Behavioural results confirm that participants overlearned sentences. Correspondingly, there was an increase or recruitment of sensorimotor regions involved in sentence production and a reduction in activity or inactivity for overlearned sentences in regions involved in listening to novel sentences. The global network organization of the brain changed by ∼45%, mostly through lost connectivity. Thus, there was a profound reorganization of the neurobiology of speech perception after overlearning towards sensorimotor regions not considered in most contemporary models and away from the ‘language regions’ posited by those models. These same sensorimotor regions are generally preserved in aphasia and Alzheimer’s disease, perhaps explaining residual abilities with formulaic language. These and other results warrant reconsidering static neurobiological models of language.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".