Differentiating between sensory sensitivity and sensory reactivity in relation to restricted interests and repetitive behaviours
Bibliographic record
Abstract
Recent studies have suggested that individuals who exhibit heightened sensitivity also exhibit higher rates and severity of restricted interests and repetitive behaviours. This line of research has been conducted almost exclusively through caregiver reports of sensitivity. Here, a more rigorous psychophysics paradigm was applied to assess sensory sensitivity and relate hypersensitivity to restricted interests and repetitive behaviours. In addition, commonly used questionnaire measures of sensory sensitivity were collected to determine if self-reported measures accurately reflect behavioural measures of sensory sensitivity. In all, 90 typically developing participants completed a visual detection task, a questionnaire measure of sensory processing and a measure of restricted interests and repetitive behaviours. Visual sensitivity, measured both behaviourally and with questionnaires, is positively related to restricted interests and repetitive behaviours. Surprisingly, visual sensitivity as measured behaviourally and through self-report are unrelated. Furthermore, a regression analysis suggests that while restricted interests and repetitive behaviours can be predicted based on both behavioural and self-reported sensitivity, these two predictors account for different portions of the variance in restricted interests and repetitive behaviours. Thus, while these results provide evidence supporting the contribution of sensory sensitivity to restricted interests and repetitive behaviours, these results also indicate that behavioural and questionnaire measures of sensory sensitivity are measuring two distinct constructs. We hypothesize that behavioural measures are measuring sensory sensitivity, while questionnaires measures are in fact measuring sensory reactivity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".