Impaired conflict resolution and alerting in children with ADHD: evidence from the Attention Network Task (ANT)
Bibliographic record
Abstract
BACKGROUND: An important theory of attention suggests that there are three separate networks that execute discrete cognitive functions. The 'alerting' network acquires and maintains an alert state, the 'orienting' network selects information from sensory input and the 'conflict' network resolves conflict that arises between potential responses. This theory holds promise for dissociating discrete patterns of cognitive impairment in disorders where attentional deficits may often be subtle, such as in attention deficit hyperactivity disorder (ADHD). METHODS: The Attentional Network Test (ANT), a behavioural assay of the functional integrity of attention networks, was used to examine the performance of 73 children with ADHD and 73 controls. RESULTS: Performance on the ANT clearly differentiated the children with and without ADHD in terms of mean and standard deviation (SD) of reaction time (RT), the number of incorrect responses made and the number of omission errors made. The ADHD group demonstrated deficits in the conflict network in terms of slower RT and a higher number of incorrect responses. The ADHD group showed deficits in the alerting network in terms of the number of omission errors made. There was no demonstration of a deficit in the orienting network in ADHD on this task. CONCLUSIONS: The children with ADHD demonstrated deficits in the alerting and conflict attention networks but normal functioning of the orienting network.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".