Bibliographic record
Abstract
Summary\nAttention-deficit/hyperactivity disorder (ADHD) is characterized by developmentally inappropriate levels of inattention, impulsiveness and hyperactivity. The dynamic developmental theory of ADHD (DDT) suggests that altered effects of reinforcement combined with a deficient extinction may be the main mechanisms for the development of the various symptoms observed in ADHD. Due to the combined effect of a shorter and steeper delay-of-reinforcement gradient and deficient extinction, the DDT predicts that it takes more time to build chains of predictable behavior in children with ADHD compared to other children. The primary aim of this study was to explore this prediction in a group with ADHD-C and ADHD-PI compared to a group of children with other psychiatric problems. \nThe present study is part of a collaborative study with Rosemary Tannock at the Hospital for Sick Children in Toronto, Canada.\nThe sample consisted of 45 children aged 9-12 years, 31 boys and 14 girls. These children were divided into four groups based on DSM-IV diagnoses: ADHD-C, ADHD-PI, ADHD-HI, and a control group of children with other psychiatric problems. The children completed a computerized game-like task called Feed the Animal . Mouse clicks within a specified area were associated with reinforcement. Behavior was measured as responses by the computer mouse under two reinforcement contingencies. Reinforcers were set up according to a random interval schedule (RI 15s) and a random ratio schedule (RR 5). Autocorrelations of consecutive responses was analyzed to investigate predictability of responding. Low predictability in responding would imply greater behavioral variability.\nWe found that the responding in the ADHD-combined group was significantly less predictable than the two other groups during infrequent reinforcement (RI 15s). Thus, the findings that the children with ADHD-combined have more difficulties learning long sequences of behavior than the children with ADHD-PI and psychiatric controls, support the prediction from DDT that there may be different underlying mechanisms in children with ADHD-C and ADHD-PI.\nThe testing and collection of data was conducted by Rosemary Tannock and Anne-Claude Bedard at the Hospital for Sick Children in Toronto, Canada.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".