Reduced Susceptibility to the Dunning–Kruger Effect in Autistic Employees
Bibliographic record
Abstract
Evidence indicates that autistic individuals are less susceptible to social influence and cognitive biases than non-autistic individuals. However, no studies have been conducted on the Dunning-Kruger effect (DKE) in autism. The DKE is a cognitive bias in which people with limited expertise in a specific domain overestimate their abilities. The purpose of this study is to compare autistic and non-autistic employees' self-assessments of their performance with their objective performance on a popular performance-based measure of analytic thinking disposition, the CRT (cognitive reflection test). After completing the task, no feedback or clues were provided regarding how well they performed. Participants were then asked to estimate how many questions they answered correctly and compare their performance to other participants by estimating the percentage of peers they outperformed. Results indicated asymmetric calibration of actual versus estimated CRT performance in autistic employees: In the low-performance group, autistic participants overestimated their abilities less than non-autistic participants. However, in the high-performance group, autistic participants underestimated their abilities more than non-autistic participants. Reduced susceptibility to the DKE highlights potential benefits of autistic employees in the workplace. Theoretical and practical implications consider the intersection of metacognitive awareness, autism, and the DKE in an organizational context.
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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 | 0.003 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".