The effects of dyslexia on the spatial and feature-based attentional modulation in the human subcortical visual nuclei.
Bibliographic record
Abstract
Introduction: Dyslexia is a prevalent reading disorder. The magnocellular hypothesis of dyslexia suggests that deficits in the magnocellular processing stream may account for some of the symptoms associated with the disorder. In the human lateral geniculate nucleus (LGN), magnocellular neurons are segregated into eye-specific layers and are disjoint from the parvocellular layers. This constitutes the only location in the human visual system where the magnocellular and parvocellular streams are spatially disjoint. Recently we found a reduction of the LGN size in dyslexics compared to IQ matched controls. Here, we functionally examine the modulation of spatial and feature-based attention in the LGN, superior colliculus and pulvinar. Methods: Thirteen dyslexics with measured behavioral deficits and 13 IQ matched controls were scanned with a Siemens Trio 3T MRI scanner at the Brain Imaging Center at the University of Missouri. In separate scanning sessions, the subjects performed detection and discrimination tasks on semi-coherent moving or static colored dot fields that required either spatial and/or feature-based attention. The LGN were anatomically isolated and traced using a series of averaged high-resolution proton density weighted images. The attentional modulation was compared between controls and dyslexics. Results: We observed differences in the spatial and feature-based modulations of attention between the subjects with dyslexia and controls in each subcortical nucleus. We examined the spatial distributions of these effects throughout the LGN and compared the regions of the LGN more activated by attention to motion than to color as well as those regions that were activated or suppressed by attentional switching. We discuss the implications of these results in terms of the magnocellular theory of dyslexia. Meeting abstract presented at VSS 2013
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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 | 0.000 |
| 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 | 0.000 |
| 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".