Hyperactive ADHD symptoms are associated with increased variability in thought content in less constrained contexts
Bibliographic record
Abstract
The current study used two complementary methods to examine whether hyperactive and inattentive ADHD symptoms are differentially linked to thought dynamics under contexts that differ in the extent to which constraints are placed on ongoing thoughts. First, participants voiced aloud their thoughts in real-time (i.e., Think Aloud task), under two conditions varying in the levels of constraints exerted on their thoughts. Individuals with more hyperactive symptoms displayed heightened variability in thought content only in the less constrained condition. Second, participants completed seven days of ecological momentary assessment during which they received six thought probes daily asking the extent to which their thoughts were freely moving (as a proxy for thought content variability) and a question that captured different levels of constraints. Hyperactive symptoms were positively associated with freely moving thoughts only during responses that corresponded with lower levels of constraints. Across two approaches, we provide converging evidence that hyperactive, but not inattentive, ADHD symptoms are associated with increased thought content variability during lower levels of deliberate constraints on thoughts. Together, these results support the Dynamic Framework of Spontaneous Thought and highlight the importance of considering context in the study of thought dynamics in ADHD.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".