Social success in a noisy world: exploring the relationship between decreased sound tolerance and social profiles
Bibliographic record
Abstract
Humans are inherently social creatures, yet considerable variability exists in our social behaviours. It is unclear what factors contribute to this variability. Given the complex and abundant sensory stimuli present in our daily environments, differences in sensory processing abilities may contribute to the variation observed in social behaviours. Individual differences in sensory processing may have significant effects on an individual's capacity to navigate social settings and may influence the development and expression of social competence. Existing literature also suggests that it is common for individuals with one form of sensory processing difference, Decreased Sound Tolerance (DST), to engage in social avoidance behaviours to mitigate exposure to distressing sounds. However, limited research explores the potential relationship between DST severity and social competence. Therefore, this study investigated the relationship between DST and social competence. As such, a sample of 2095 undergraduate students completed an online survey designed to assess their DST severity and social competence. Initially, to parse the variability in social competence, scores on the multidimensional social competence scale (MSCS), underwent a k-means cluster analysis. This analysis yielded four unique social profiles based on seven social competence domains (e.g., social motivation, emotion regulation etc.). Misophonia and hyperacusis questionnaires were then used to evaluate differences in DST across the social profiles. The results indicated varying severity levels of both misophonia and hyperacusis across the four social profiles, with the individuals who reported the highest social competence exhibiting the lowest levels of DST. These findings highlight the potential relationship between sensory processing differences, such as DST, and social functioning.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".