We all fall for it: Influence of driving experience, level of cognitive control engaged and actual exposure to the driving situations on the Dunning-Kruger effect
Bibliographic record
Abstract
The Dunning-Kruger Effect (DKE), a widely recognized phenomenon, reveals that individuals with lower skills tend to overrate their abilities, while high achievers often underestimate themselves. This intriguing trend has been explored in the realm of car driving using an innovative, purpose-built methodology. This methodology integrates visual presentations of specific driving scenarios for self-assessment alongside the analysis of actual driving behaviours gathered through driving simulation. Our study recruited both inexperienced, non-drivers and seasoned, licensed drivers to delve into three key aspects: (i) explore how the acquisition of skills through extensive real-world experience influences the DKE pattern, (ii) refine the process of recalibrating self-assessed skills subsequent to a brief encounter with the tasks and (iii) determine how the DKE is affected by varying degrees of cognitive control required to execute diverse driving tasks. The data consistently confirms the DKE across driving situations, highlighting its universality. Novice and seasoned drivers both display the pattern before and after the simulated drive, regardless of the associated level of cognitive control, indicating that experienced drivers are prone to the same misestimation tendencies as novices. Novices’ self-assessments notably increased after experiencing the simulated drive, while experienced drivers’ estimations remained stable. This observation could be interpreted as the dual curse of ignorance at work. This research offers valuable insights into the dynamics of self-assessment over time, shedding light on how driving experience and task exposure impact individuals’ perceptions of their own abilities.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".