The dimensional structure of the Camouflaging Autistic Traits Questionnaire (CAT-Q) and predictors of camouflaging in a representative general population sample
Bibliographic record
Abstract
OBJECTIVES: Some autistic people "camouflage" their differences by modeling neurotypical behaviors to survive in a neurotypical-dominant social world. It remains elusive whether camouflaging is unique to autism or if it entails similar experiences across human groups as part of ubiquitous impression management (IM). Here we examined camouflaging engagement and theoretical drivers in the general population, drawing on the transactional IM framework and contextualizing findings within both contemporary autism research and the past IM literature. METHODS: A large representative U.S. general population sample (N = 972) completed this survey study. We combined exploratory item factor analysis and graph analysis to triangulate the dimensional structure of the Camouflaging Autistic Traits Questionnaire (CAT-Q) and examined its correspondence with prior autism-enriched psychometric findings. We then employed hierarchical regression and elastic-net regression to identify the predictors of camouflaging, including demographic (e.g., age, gender), neurodivergence (i.e., autistic and ADHD traits), socio-motivational, and cognitive factors. RESULTS: We found a three-factor/dimensional structure of the CAT-Q in the general population, nearly identical to that found in previous autism-enriched samples. Significant socio-motivational predictors of camouflaging included greater social comparison, greater public self-consciousness, greater internalized social stigma, and greater social anxiety. These camouflaging drivers overlap with findings in recent autistic camouflaging studies and prior IM research. CONCLUSIONS: The novel psychometric and socio-motivational evidence demonstrates camouflaging as a shared social coping experience across the general population, including autistic people. This continuity guides a clearer understanding of camouflaging and has key implications for autism scholars, clinicians, and the broader clinical intersecting with social psychology research. Future research areas are mapped to elucidate how camouflaging/IM manifests and functions within person-environment transactions across social-identity and clinical groups.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".