Neuroimaging of language processes: fMRI of silent and overt lexical processing and the promise of multiple process imaging in single brain studies.
Bibliographic record
Abstract
OBJECTIVE: To implement and evaluate a multiple-process functional magnetic resonance imaging (fMRI) paradigm designed to effectively and efficiently activate several language-related regions for use with neurosurgical patients. Both overt and covert response conditions were examined. METHODS: The fMRI experiments compared the traditional silent word-generation condition versus an overt one as they engage frontal language regions (Experiment 1) and silent versus overt semantic association conditions as they engage multiple language processing regions (Experiment 2). RESULTS: In Experiment 1, the overt condition yielded greater magnitude of activation, but not volume of activation, in the left inferior frontal and insular cortices than did the silent condition for most, but not all, participants. Experiment 2 demonstrated that the activation of multiple established language processing regions (ie, orthographic, phonological, and semantic) can be achieved in a significant number of participants, particularly under overt semantic association conditions and that such activation varies in predictable ways. CONCLUSION: The traditional silent response condition cannot be considered as equivalent to the overt response condition during word generation or semantic association. The multiple-process imaging method introduced here was sensitive to processing robust orthographic, phonological, and semantic regions, particularly under the overt response condition.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".