Patterns of Cortical Activity in a Silent Single‐Word Reading Task Depend on Word Frequency and Age‐Related Differences: An <scp>MEG</scp> Study
Bibliographic record
Abstract
This study investigates how word frequency and age modulate the amplitude, temporal, and spatial patterns of cortical activation during silent single-word reading, as measured by magnetoencephalography (MEG). We recorded MEG data from 30 neurotypical adults and 30 typically developing children during a silent reading task involving high-frequency words, low-frequency words, and pseudowords, with cortical activation analyzed using event-related fields (ERF) and peak latency (PL). In both adults and children, high-frequency words elicited lower ERF amplitudes and faster processing times compared to low-frequency words and pseudowords. While similar neural regions were activated across stimulus types, children demonstrated significantly higher amplitudes and longer processing times than adults. These results indicate that word frequency significantly modulates the neural dynamics of reading, with high-frequency words processed more efficiently. Furthermore, the data suggest that the reading pathways in children are still maturing, as evidenced by their increased neural activation and delayed processing. This developmental difference, particularly the demonstration of frequency-dependent processing in the superior temporal gyrus of children, offers unique evidence for the maturation of distinct lexical and sublexical reading pathways, consistent with the dual-route cascaded theory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".