Brain oscillations and functional connectivity involved in single-letter processing
Bibliographic record
Abstract
Abstract Understanding how the spectral-spatial-temporal dynamics of neural activity and information transfer among brain regions is an important goal in knowing how visual and orthographic information is processed throughout the human brain. This study aimed at understanding these dynamics for letter processing by investigating oscillatory and functional connectivity in the first-language (L1) adult brain. We used a multi-source beamforming method for EEG source modeling of the event-related potentials to single letter and pseudoletter stimuli in order to minimize findings of false connections. Time-frequency analyses were performed using wavelet analyses of the electrode and source waveforms. Event-related enhanced power within the theta band (4–8 Hz) was significantly different between letters and pseudoletters within the 0-300 ms, but removing the evoked response contributions mostly abolished this effect. More event-related depressed power was seen for letters than pseudoletters within the alpha band (9–15 Hz). Phase-locking value (PLV) network connectivity results showed significantly greater connectivity within theta band for pseudoletters than letters within a posteriorly distributed network. Alpha-band connectivity was significantly more desynchronized among posterior brain regions for letters than pseudoletters between 85–400 ms. Overall connectivity, revealed similar amounts of synchronized connectivity for letters and pseudoletters but predominantly more desynchronized connectivity for letters than pseudoletters. Overall network was more left-lateralized for both synchronized and desynchronized connections, regardless of stimulus condition (letters and pseudoletters). These results indicated that visual processing of letters and pseudoletters occur within a widely distributed left-dominant network with significant differences in oscillatory activity and network dynamics between letters and pseudoletters.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".