Exploring the dynamics of design fluency in children with and without ADHD using artificial neural networks
Bibliographic record
Abstract
The neuropsychology of attention deficit/hyperactivity disorder (ADHD) has been extensively studied, with a general focus on global performance measures of executive function. In this study, we compared how global (i.e., endpoint) versus process (i.e., dynamic) measures of performance may help characterize children with and without ADHD using a design fluency task as a case study. The secondary goal was to compare the sensitivity of standard versus connectionist statistical models to group differences in cognitive data. Thirty-four children diagnosed with ADHD and 37 children without ADHD aged 8-11 years old were tested on the Five-Point Test. The continuous process measure of performance, indexed as the number of produced designs at each consecutive 1 minute interval during 5 minutes, was analyzed against the discrete process measure, that is, the number of designs between first and last intervals and the standard global performance measure of total number of produced designs. Results show that the continuous process measure distinguished the two groups better than the two other measures. The detailed observation of production patterns revealed a decreasing linear trajectory in children without ADHD that contrasts with the flat, but fluctuating productivity pattern of children with ADHD. With regards to the second goal, results show that the connectionist and standard methods are equally sensitive to group differences for the three types of measures. This illustrates the utility of quantitative process measures together with the connectionist method in neuropsychological research and suggests great potential for a dynamical approach to cognition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".