Neuropsychological correlates of ADHD: indicators of different attentional profiles among youth with sluggish cognitive tempo
Bibliographic record
Abstract
Objective This study examined the distinctiveness of Attention Deficit Hyperactivity Disorder—Inattentive (ADHD-I) and ADHD in context of Sluggish Cognitive Tempo (ADHD + SCT) utilizing the Attention Network Test (ANT) and Continuous Performance Test (CPT) as external validators. Due to the SCT characteristics of being sluggish, spacey, and slow to arouse, we hypothesized that SCT behavioral descriptors would be uniquely related to alerting/arousal mechanisms that the ANT is uniquely designed to capture, and that ADHD symptoms would be more highly associated with cognitive control on the CPT. Method We examined associations between baseline ANT and CPT scores for N = 137 well-characterized, culturally and racially diverse youth with ADHD ( n = 107) either medication naïve or washed out prior to testing and typically developing controls ( n = 30) ages 6–17 years. Results Presence and severity of SCT were associated with ANT Alerting ( r 2 = −.291, p = .005), but not with ANT Orienting, ANT Executive Control, or any CPT measures. There was a distinct association between the presence and severity of ADHD inattention symptoms with CPT T -scores for Commission Errors ( r 2 = .282, p = .002), Omission Errors ( r 2 = .254, p = .005), Variability ( r 2 = .328, p < .001), and Hit Rate SE ( r 2 = .272, p = .002), but not with other CPT or any ANT domain measures. All associations remained significant after Bonferroni correction. Conclusions The small but enduring double dissociation, with ADHD-I symptom severity related to measures of cognitive and behavioral control measures on the CPT, and SCT symptom severity related to attentional processes underlying tonic arousal in preparation for cue detection on the ANT—provides the first objective evidence suggestive of partial neurocognitive independence of SCT from ADHD. Moreover, it points to possibly distinguishable neurobiological neurocognitive underpinnings of the two conditions.
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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 | 0.002 |
| 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 | 0.001 |
| 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".