Overlapping cortical regions for reading and temporal processing in developmental dyslexia
Bibliographic record
Abstract
Children with developmental dyslexia have difficulty learning to read. These children may also have deficits in temporal processing, which is the perception and integration of rapidly presented stimuli. Behavioural research indicates a link between reading and temporal processing ability; however, the cortical relationship between these two skills has not been established. This thesis examined whether tasks of reading and temporal processing activate similar cortical regions in children with average reading ability and in children with dyslexia. Using functional magnetic resonance imaging (fMRI), activity for two reading tasks (phonological and orthographic) and two temporal processing tasks (dichotic pitch and global motion perception) was assessed. Three regions of interest were established in each participant: the lateral occipital cortex (LOC) and areas engaged by dichotic pitch and global motion tasks. Results demonstrated that both groups had increased activity in bilateral LOC during reading. In average readers, left LOC was more active than right regions during the phonological task, while dyslexic readers showed equivalent activity between left and right LOC for both reading tasks. The dichotic pitch regions did not show any evidence of activation during reading in either group. However, children with dyslexia exhibited significant activity in right global motion regions during the phonological task, but only on the difficult word condition. Average readers did not illustrate activation in global motion areas during reading. The current results suggest that LOC is involved with the reading process and children with dyslexia may have a deficit in left LOC. It was hypothesized that dyslexic readers may have increased attentional processing and recruitment from additional cortical regions during difficult tasks, which may explain the similar activity between global motion and phonological reading. Since there were no similar regions between dichotic pitch and reading, this suggests that these may not be directly related through cortical activity. The current results provide novel evidence that reading and visual temporal processing may involve some of the same cortical areas, at least in children with dyslexia. Future research will investigate links between reading and temporal processing in younger children and will examine differences in white matter connectivity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".