[Brain activity associated with a nouns-reading task].
Bibliographic record
Abstract
BACKGROUND: Word reading involves several steps, from the visual perception of each of its constitutent elements to its recognition as an entity with a specific meaning. Various brain structures participate in these processes, depending of the linguistic and cognitive characteristics of the stimulus. Our objective was to characterize brain activity through the use of functional magnetic resonance imaging (FMRI) associated with the process of noun reading. METHODS: Eleven healthy right-handed volunteers participated in a lexical decision task involving 58 written nouns. An equal number of letter sequences were used as control stimuli. Reaction times were also recorded. RESULTS: There was a difference (p < 0.05) in reaction time between nouns and letter sequences in the lexical decision task. FMRI contrasted between conditions revealed significant activations in several areas involved in reading. CONCLUSIONS: The brain activation may reflect the different perceptual demands associated with the initial processing of nouns, as compared to meaningless letter sequences. We attribute the difference between our results and those previously reported to the particular characteristics of the pronunciation rules of written Spanish.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".