Insight into Steering Adaptation Patterns in a Driving Simulator
Bibliographic record
Abstract
The collection of data from a driving simulator before participants are fully adapted to the system can lead to erroneous and incomplete conclusions. In most studies that involve a driving simulator, researchers try to manipulate a cause and then measure the effect, that is, a driver's reaction. However, participants need time to adapt and transfer their already existing driving skills to the simulator. The process of adaptation, or skill transfer, imposes a mental load on participants that can distract them from performing the primary task, that is, driving. Therefore, before adaptation, a participant's reaction may not be representative of real-life behavior, which potentially reduces the validity of the research. In the present study, the performance of subjects doing a repetitive cornering task is traced and the different patterns of adaptation and skill transfer to the driving simulator are studied. The results show interesting characteristics of the adaptation process, including power curve fit to the learning phase and the observation of a plateau period, in which performance stops improving before it restarts. The participants were also asked to report at what point they felt adapted to the simulator while taking the test. The self-reported times were compared with the results of quantitative analysis of their performance. The results showed that self-reported values were significantly lower than the actual adaptation time, with an insignificant correlation between self-reported values and actual values occurring.
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 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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".