EMOTIONAL SACCADE TASKS IN ATTENTION-DEFICIT HYPERACTIVITY DISORDER AND BIPOLAR DISORDER
Bibliographic record
Abstract
Objectives:We use an emotional saccade task to characterize executive functioning and emotion processing in adult Attention-deficit hyperactivity disorder (ADHD) and bipolar disorder (BD).Background:ADHD and BD share cognitive and emotion processing deficits that complicate differential diagnoses. Our ability to distinguish the symptomology of ADHD and BD, as well as to understand their underlying mechanisms, is limited by a lack of valid behavioral markers that support the diagnosis.Materials and Methods:Participants (21 control, 20 ADHD, 20 BD) performed an interleaved pro/antisaccade task (look toward vs. look away from a visual target, respectively) in which the sex of emotional face stimuli acted as the instructional cue to perform either the pro- or antisaccade.Results and Conclusions:Both patient groups made more direction errors (erroneous prosaccades on antisaccade trials) and anticipatory errors (saccades made before instructional cue processing) than controls. Control participants exhibited lower microsaccade rates during the fixation epoch preceding correct anti- vs. prosaccade initiation, but this task-related modulation was absent in both patient groups. Regarding emotion processing, the ADHD group performed worse than controls on trials with neutral faces, while the BD group performed worse than controls on trials with faces of all valence. These findings suggest that response inhibition, mediated by the fronto-striatal circuitry, is a central deficit in both ADHD and BD. This deficit is exacerbated in BD during emotion processing, presumably mediated by dysregulated limbic circuitry involving the anterior cingulate and orbitofrontal cortex. Better understanding of the interplay of these brain circuits will help identify behavioral markers in these two disorders.
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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 | 0.001 |
| 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".