Neurophysiology of perceptual decision-making and its alterations in attention-deficit hyperactivity disorder (ADHD)
Bibliographic record
Abstract
Abstract Despite the prevalence of ADHD, efforts to develop a detailed understanding of the neuropsychology of this neurodevelopmental condition are complicated by the diversity of interindividual presentations and the inability of current clinical tests to distinguish between its sensory, attentional, arousal or motoric contributions. Identifying objective methods that can decompose the clinical heterogeneity of ADHD is a long-held goal that is hoped to advance our understanding of etiological processes and potentially aid the development of personalized treatment approaches. Here, we examine key neuropsychological components of ADHD within an electrophysiological (EEG) perceptual decision-making paradigm that is capable of isolating distinct neural signals of several key information processing stages necessary for sensory-guided actions from attentional selection to motor responses. We show that compared to typically developing children, children with ADHD displayed slower and less accurate performance, which was driven by the atypical dynamics of discrete electrophysiological signatures of attentional selection, the accumulation of sensory evidence, and strategic adjustments reflecting urgency of response. These findings offer an integrated account of decision-making in ADHD and establish discrete neural signals that can be used to understand the wide range of neuropsychological performance variations in individuals with ADHD. Significance Statement The efficacy of diagnostic and therapeutic pathways in ADHD is limited by our incomplete understanding of its neurological basis. One promising avenue of research is the search for basic neural mechanisms that may contribute to the variety of cognitive challenges associated with ADHD. We developed a mechanistic account of differences in a fundamental cognitive process by integrating across neurocognitive, neurophysiological (i.e., EEG), and computational levels of analysis. We detected distinct neural changes in ADHD that explained altered performance (e.g., slowed and less accurate responses). These included changes in neural patterns of attentional selection, sensory information processing, and response preparation. These findings enhance our understanding of the neurophysiological profile of ADHD and may offer potential targets for more effective, personalized interventions.
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 | 0.000 | 0.002 |
| 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".