Drivers with Amnestic Mild Cognitive Impairment Can Benefit from a Multiple‐Session Driving Simulator Automated Training Program
Bibliographic record
Abstract
To the Editor: Amnestic mild cognitive impairment (aMCI) may affect as much as 30% of the elderly population.1 Most of these individuals will progress to Alzheimer's disease. A large proportion of older individuals with aMCI are active drivers.2 It is often mentioned that most of these individuals can drive safely but that their driving is characterized by subtle functional decrements.3 Because their driving abilities are expected to worsen with the progression of the disease,4 there is debate as to whether these individuals should continue to drive,5 but identifying at-risk drivers is problematic.6 An alternative approach could be to provide training programs. Procedural memory (implicit learning) is preserved in individuals with aMCI and those with Alzheimer' disease.7 Implicit learning, contrary to explicit learning, takes place without awareness, often by repetition, and without reference to explicit knowledge learned previously. With driving, knowledge of road safety rules is explicit knowledge, but some maneuvers involve procedural learning. For example, people explicitly know that they should brake at intersections with stop signs, but releasing the accelerator and controlling the pressure applied on the brake pedal when braking is implicit learning that takes place with practice. The current study examined whether individuals with aMCI can benefit from an automated 5-week training program in a driving simulator. Elderly individuals (six men, two women, aged 71.1 ± 7.2) with a diagnosis of aMCI were recruited from memory clinics. Healthy elderly adults without memory or cognitive problems also participated (five men, three women, aged 71.1 ± 7.2). They all drove regularly and reported similar driving habits. The ethics committee of the Institut Universitaire en santé mentale de Québec approved this study (File 362). The experiment was conducted using an instrumented fixed-based simulator (STISIM Drive 3.0, System Technology, Inc., Hawthorne, CA).8 Participants received five training sessions over a 21-day period. For each session, they first drove a 6-km practice run during which general explanations were provided. The experimenter made sure that the driver understood all recorded messages. The main scenario (27.5 km of naturalistic driving) included messages to inform the driver about requested maneuvers (e.g., instructions to safely overtake a slower-moving vehicle) and conditional recorded feedback about six driving maneuvers when an error was detected (speeding, tailgating, not indicating a lane change, absence of blind spot verification, incomplete stop at an intersection, running a yellow or red light). For instance, exceeding the speed limit by more than 10 km/h triggered the recorded feedback: “Your current speed exceeds the speed limit. You should slow down.” For all maneuvers, the driver had to respond positively to the feedback within 10 seconds to avoid another warning for the same event. No additional information was provided. No summary performance was given at the end or before any session. Both groups showed significant gradual improvements for several maneuvers (speeding, not using the turn signal, verification of the blind spot, tailgating) (Figure 1). Individuals with aMCI also showed implicit learning, with their braking requiring fewer alternate pedal responses, leading to shorter and more uniform deceleration phases with training (Figure 1). An alternation involves moving the right foot from one pedal to the other. Moving the right foot from the accelerator to the brake pedal (one alternation) is the expected behavior when driving. The learning observed suggests that proper training could help to modify unsafe behaviors and contribute to maintaining the driving performance of individuals with aMCI. Previous research has shown that individuals undergoing passive training (classroom-like training) had no improvement in their driving performance. In contrast, participants receiving specific driving feedback in a simulator allowed the learning to be transferred to safer on-road driving.8, 9 Hence, there is a strong possibility that the learning observed in the present study can transfer to safer on-road driving. In addition to conducting a large-scale study to test specifically whether this learning transfers to better on-road performance for individuals with aMCI, a follow-up longitudinal study is essential to determine the long-term persistence of training. An understanding of how driving degrades with progression of the disease would also contribute to identifying at-risk drivers.10 Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. This research received support from the Réseau Québécois de Recherche sur le Vieillissement, le Centre d'Excellence sur le Vieillissement de Québec et le Réseau de Bio-Imagerie du Québec. Author Contributions: All authors contributed to all aspects of this study. Sponsor's Role: The sponsors played no role in any aspect of this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".