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Record W4416133552 · doi:10.1002/aur.70139

Reduced Susceptibility to the Dunning–Kruger Effect in Autistic Employees

2025· article· en· W4416133552 on OpenAlexaff
Lorne M. Hartman, Harley Glassman, Braxton L. Hartman

Bibliographic record

VenueAutism Research · 2025
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsAutismMetacognitionCognitionAutistic traitsCognitive biasSocial cognition

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.065
GPT teacher head0.420
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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