ADHD traits are not related to Multisensory Integration in a University Population
Bibliographic record
Abstract
In daily life, we are constantly bombarded with sensory information from multiple sources. Our ability to combine these cues into a single perceptual experience is known as multisensory integration. Research is starting to show that multisensory integration may be altered in individuals with attention-deficit/hyperactivity disorder (ADHD). However, most studies have focused on clinical populations, leaving little known about how multisensory integration related to ADHD traits along a dimensional spectrum, consistent with the Research Domain Criteria (RDoC) approach. The present study examined associations between ADHD traits and multisensory integration in university students using three different behavioural tasks (i.e., Sound-Induced Flash Illusion [SIFI], McGurk, and speech-in-noise). ADHD traits were assessed dimensionally, including overall ADHD traits, as well as inattentive and hyperactive-impulsive trait dimensions. Participants were also divided into High ADHD and Low ADHD trait groups for categorical comparisons. Results indicated no significant associations between overall, inattentive, or hyperactive-impulsive ADHD traits and performance on any of the multisensory tasks. Similarly, no group differences were observed between High and Low ADHD trait groups. These findings suggest that multisensory integration differences reported in previous research may emerge only when ADHD traits reach clinical severity, rather than existing across the broader continuum of traits. This study highlights the importance of considering both dimensional and categorical approaches when examining cognitive mechanisms in ADHD. Future work should explore developmental and contextual factors that may shape multisensory integration in clinically significant ADHD.
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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".