Reaction time variability in children is specifically associated with attention problems and regional white matter microstructure
Bibliographic record
Abstract
Background: Increased intraindividual variability (IIV) in reaction times (RT) has been suggested as a key cognitive and behavioral marker of attention problems, but findings for other dimensions of psychopathology are less consistent. Moreover, while studies have linked IIV to brain white matter microstructure, large studies testing the robustness of these associations are needed. Methods: We utilized data from the Adolescent Brain Cognitive Development (ABCD) Study baseline assessment to test the associations between IIV and psychopathology (n=8622, age=8.9-11.1), and between IIV and white matter microstructure (n=7958, age=8.9-11.1). IIV was investigated using an ex-Gaussian distribution analysis of RTs in correct response go trials in the stop signal task. Psychopathology was measured by the Child Behavior Checklist and a bifactor structural equation model was performed to extract a general p-factor and specific factors reflecting internalizing, externalizing, and attention problems. To investigate white matter microstructure, fractional anisotropy (FA) and mean (MD), axial (AD), and radial diffusivity (RD) were examined in 23 atlas-based tracts. Results: Increased IIV in both short and long reaction times was positively associated with the specific attention problems factor (Cohen’s d=.13 and d=.15). Increased IIV in long reaction times was also positively associated with RD in the left and right corticospinal tract (d=.12 (both tracts)). Conclusions: Using a large sample and a data-driven dimensional approach to psychopathology, the results provide novel evidence for a small but specific association between IIV and attention problems in children and supports previous findings on the relevance of white matter microstructure for IIV.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".