Symbol relations training improves cognitive functioning in students with neurodevelopmental disorders
Bibliographic record
Abstract
Students with neurodevelopmental disorders [Specific Learning Disorders (SLD), Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD)] often experience learning challenges due to underlying weaknesses in cognitive processes. As these are some of the most common conditions to impact functioning, the development of effective treatments is a priority for neuropsychologists. However, the task of designing effective cognitive interventions has proven one of the most difficult challenges for our field. The Arrowsmith Program uses a novel approach compared to other cognitive intervention programs. We hypothesized that intensive practice of one aspect of this program would lead to improved cognitive functions in students with neurodevelopmental disorders. Twenty-seven students with neurodevelopmental disorders (ages 9.4–18.4 years) were recruited from Arrowsmith schools. Cognitive baseline and post-intervention data were gathered using components of the Woodcock–Johnson IV Tests of Cognitive Abilities. The intervention consisted of 6 weeks of intensive practice of the Symbol Relations Task. W-scores were used in a paired sample t-test analysis to determine if cognitive skill improvement occurred. Significant improvements were found in several measures of neuropsychological assessment, in particular in the Cattell–Horn–Carroll broad abilities These results provide a foundation for further work examining the utility of this novel approach to cognitive intervention.
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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 | 0.000 | 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".