Visual search in ADHD, ASD and ASD + ADHD: overlapping or dissociating disorders?
Bibliographic record
Abstract
Recent debates in the literature discuss commonalities between Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) at multiple levels of putative causal networks. This debate requires systematic comparisons between these disorders that have been studied in isolation in the past, employing potential markers of each disorder to be investigated in tandem. The present study, choose superior local processing, typical to ASD, and increased Intra-Subject Variability (ISV), typical to ADHD, for a head-to-head comparison of the two disorders, while also considering the comorbid cases. It directly examined groups of participants aged 10-13 years with ADHD, ASD with (ASD+) or without (ASD-) comorbid ADHD and a typically developing (TD) group (total N = 85). A visual search task consisting of an array of paired words was designed. The participants needed to find the specific pair of words, where the first word in the pair was the cue word. This visual search task was selected to compare these groups on overall search performance and trial-to-trial variability of search performance (i.e., ISV). Additionally, scanpath analysis was also carried out using Recurrence Quantification Analysis (RQA) and the Multi-Match Model. Results show that only the ASD- group exhibited superior search performance; whereas, only the groups with ADHD symptoms showed increased ISV. These findings point towards a double dissociation between ASD and ADHD, and argue against an overlap between ASD and ADHD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".