Assessing the Impact of “Brain Training” on Changes in Driving Performance, Visual Behavior, and Neuropsychological Measures
DOI: 10.17077/drivingassessment.1466
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Summary
This study investigates whether "brain training" software, specifically Posit Science’s DriveSharp, improves real-world driving performance and visual behavior in older adults. While cognitive decline is a known risk for aging drivers, the transferability of benefits from computer-based cognitive interventions to actual driving tasks remains unclear. The researchers aimed to determine if improvements in neuropsychological measures induced by DriveSharp training would generalize to observable changes in driving metrics and gaze behavior. The study employed a randomized controlled design with 32 participants aged 60–75, split into intervention and control groups. The intervention group completed approximately 500 minutes of at-home DriveSharp training over two weeks, focusing on "Jewel Diver" and "Road Tour" exercises. Both groups underwent baseline and follow-up assessments consisting of neuropsychological tests (Attention Network Test and Useful Field of View) and real-world highway driving sessions. During driving, participants performed secondary cognitive tasks (clock visualization and 1-back digit recall) while vehicle telemetry (speed, acceleration, steering reversals) and eye-tracking data (gaze dispersion) were recorded. Results indicated that DriveSharp training significantly improved performance on the Useful Field of View (UFOV) divided attention task, with improvements correlating with time spent on the specific "Road Tour" exercise. However, these gains did not translate to other neuropsychological measures, such as the Attention Network Test. Crucially, the training had no significant effect on driving performance or visual behavior. Metrics including mean speed, micro-accelerations, and steering wheel reversals showed no meaningful differences between the intervention and control groups, nor did training time correlate with improved driving metrics. Gaze dispersion patterns remained unaffected by the intervention. The findings suggest that while DriveSharp training enhances performance on specific visual processing tasks similar to the training stimuli, it does not produce transferable benefits to real-world driving behavior or broader cognitive functions in high-functioning older adults. The authors conclude that the lack of transfer may be due to the specific nature of the training tasks, the high baseline functioning of the participants, or insufficient training intensity. The study highlights the limitations of assuming that cognitive improvements in controlled settings generalize to complex, real-world operational tasks like driving.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-17 |
| archive | success | canonical_url | — | — | 1 | 2026-06-25 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| enrich | success | openalex | — | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-17 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Applied Guidance: countermeasure evaluation
- Methodological Resource: tool software