Motor Sequence Learning and Developmental Dyslexia
Bibliographic record
Abstract
Beyond the reading-related deficits typical of developmental dyslexia (DD), recent evidence suggests that individuals afflicted with this condition also show difficulties in motor sequence learning. To date, however, little is known with respect to the characteristics of the learning impairments, nor to the neural correlates associated with this type of procedural deficit in DD patients. Here, we first summarize the results of the few behavioral and brain imaging studies that have investigated the effects of DD on motor sequence learning. To help guide research in this field, we then discuss relevant psychophysical and neuroimaging work conducted in healthy volunteers in relation to three different conceptual perspectives: when, how, and what. More specifically, we examine the cognitive boundaries that affect performance across the different stages of learning (i.e., "when"), the different cognitive processes (i.e., "how") under which learning occurs, and the mental representations (i.e., "what") that are elicited when acquiring this type of skilled behavior. It is hoped that this conceptual framework will be useful to researchers interested in further studying the nature of the motor learning impairment reported in DD.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".