Inhibitory Control Deficits in Children with Tic Disorders Revealed by Object-Hit-and-Avoid Task
Bibliographic record
Abstract
Background: Tic disorders may reflect impaired inhibitory control. This has been evaluated using different behavioural tasks, yielding mixed results. Our objective was to test inhibitory control in children with tics through simultaneous presentation of multiple, mobile stimuli. Methods: Sixty-four children with tics (mean age 12.4 years; 7.5-18.5) were evaluated using a validated robotic bimanual exoskeleton protocol (Kinarm) in an object-hit-and-avoid task, in which target and distractor objects moved across a screen and participants aimed to hit only the targets while avoiding distractors. Performance was compared to 146 typically developing controls (mean age 13 years; 6.1-19.9). The primary outcome was the percentage of distractors struck. Results: ANCOVA (age as covariate) showed participants struck significantly more distractors (participants without comorbid ADHD, 22.71% [SE 1.47]; participants with comorbid ADHD, 23.56% [1.47]; and controls, 15.59% [0.68]). Participants with comorbid ADHD struck significantly fewer targets (119.74 [2.77]) than controls, but no difference was found between participants without comorbid ADHD (122.66 [2.77]) and controls (127.00 [1.28]). Participants and controls did not differ significantly in movement speed and movement area. Just over 20% of participants with tics fell below the age-predicted norm in striking distractors, whereas fewer than 10% fell outside age-predicted norms in other task parameters. Conclusions: In children with tics (without comorbid ADHD), acting upon both targets and distractors suggests reduced ability to suppress responses to potential triggers for action. This may be related to increased sensorimotor noise or abnormal sensory gating.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".