Examining the Neural and Cognitive Processes Underlying Typical and Atypical Reading Ability
Bibliographic record
Abstract
Although reading is an important and generative skill that is important for a child’s future academic, economic, and social success, it remains controversial how it develops and what leads to reading difficulties. Furthermore, due to the multiple and complex components involved during reading, the neuroanatomical mechanisms that support the behavioral differences of reading ability are currently not well understood. Therefore, the overarching goal of this thesis was to further the understanding of reading and reading difficulties by integrating aspects of their neuroscientific, cognitive, and educational accounts, as illustrated by functional magnetic resonance imaging (fMRI), eye tracking, and speech recording. To simplify the examination of the processes involved in reading, we focused on naming speed (NS) tasks, in which participants are required to name sets of simple stimuli as quickly and as accurately as possible, because these tasks have been shown to predict concurrent and future reading ability in both typically achieving readers and in poor readers. Using NS tasks, we examined improvements in behavioral performance associated with typical development from childhood to adulthood, and how this differed in children and adults with dyslexia. We also examined how behavioral and neural differences between typically achieving readers and readers with dyslexia were related, and how they were associated with compensatory strategies used by readers with dyslexia to account for their reading disability. Overall, the findings of the five studies presented in this thesis indicate how combining multiple techniques, such as fMRI, eye tracking, and speech recording, to study reading and reading deficits provides a better controlled, more global view of the processes that are involved and how these processes differ in individuals with reading difficulties. Future research that adopts this multidimensional approach to examining reading difficulties is important because it will not only lead to the optimization of current diagnostic criteria for dyslexia, but also has the potential to lead to the early identification of children who are biologically at risk for developing dyslexia. This in turn has the potential to lead to more effective and appropriate interventions which can positively impact and change the outcome trajectories for those with reading deficits.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".