Time-variant Granger causality analysis for intuitive perception collision risk in driving scenario: an EEG study
DOI: 10.3389/fnins.2025.1604751
archive: archived pipeline: cataloged verified
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Summary
This study investigates the neural mechanisms underlying intuitive collision risk perception in driving scenarios, specifically comparing experienced drivers with novice drivers. Motivated by the critical role of rapid, unconscious decision-making in driving safety and the high global mortality rate from traffic accidents, the research aims to elucidate how expertise influences brain connectivity during hazard perception. The authors posit that intuitive driving relies on distinct neural strategies that differ between experienced and novice drivers, offering insights for developing intelligent hazard perception systems and personalized training programs. To examine these mechanisms, the researchers conducted an electroencephalography (EEG) study involving 23 right-handed volunteers, divided into two groups: 12 licensed, experienced drivers and 11 unlicensed novices. Participants viewed 40 immersive, first-person driving simulation videos generated using BeamNG.drive, half of which depicted collision scenarios and half non-collision scenarios. Participants were instructed to press a spacebar when they perceived a collision as inevitable, allowing for the recording of reaction times. EEG data were recorded using a 64-channel system, pre-processed to remove artifacts via Independent Component Analysis, and analyzed using time-variant Granger causality. This method employed a sliding window approach to construct directed functional connectivity models, utilizing the Akaike Information Criterion to determine optimal model orders and assess directional influences between brain regions over time. The results revealed significant differences in neural connectivity patterns between the two groups. Experienced drivers demonstrated increased activation in intrinsic functional networks associated with visual attention and decision-making, indicating superior collision risk perception. Furthermore, experienced drivers exhibited more stable and dispersed connectivity, particularly in the beta frequency band. In contrast, novice drivers displayed more complex and less efficient connectivity patterns. These findings suggest that experienced drivers utilize more efficient neural strategies for rapid decision-making, characterized by streamlined information flow within specific functional networks, whereas novices rely on less optimized, more convoluted neural processes. The significance of this work lies in its advancement of the understanding of intuitive driving through the lens of dynamic, directed brain connectivity. By identifying specific neural markers of expertise, such as stable beta-band connectivity and targeted activation in attention and decision-making networks, the study provides a neurophysiological basis for distinguishing between novice and experienced drivers. These insights have practical implications for the development of intelligent driving hazard perception systems and the design of personalized training programs aimed at enhancing driving safety by accelerating the acquisition of efficient intuitive decision-making skills.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | pdftotext | — | — | 4 | 2026-08-10 |
| clean | success | clean | — | — | 2 | 2026-08-10 |
| chunk | success | chunk | — | — | 2 | 2026-08-10 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 2 | 2026-08-10 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 17 | 2026-08-11 |
| verify | success | — | — | — | 1 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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- Empirical Findings: physiological data
- Theoretical Contribution: computational model, theory or model