The Effect of Disturbance on the Neural Mechanisms of Learning Word Formation Rules in a Novel Language
Bibliographic record
Abstract
Abstract Individuals learn the meaning of words mainly through feedback from others at early stages, but confusing feedback may cause disturbances in establishing lexical form-to-meaning mappings. To date, little is known about how these mappings are preciously established as language learning experiences and proficiency increase. To this end, we asked participants to perform a picture-word matching task under disturbance and non-disturbance conditions during functional magnetic resonance imaging (fMRI). Brain imaging revealed that in the non-disturbance condition, more brain network connections emerged during early (naïve) learning than later (expert) learning. However, in the disturbance condition, more connections were found during expert learning compared to naïve learning. Correspondingly, the behavioral results showed that as learning experiences increase in the disturbance condition, so do accuracy rates. Together, these findings indicate that with increased experience in mapping lexical forms to meanings, individuals appear to become less sensitive to disturbances by engaging multiple brain areas.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".