Effects of Cognitive, Simulator, and Real-World Training on Novice Driver Gaze Behaviour: A Pre–Post Study

Novice drivers demonstrate inefficient visual scanning and elevated crash risk relative to experienced drivers. Different training programmes may influence gaze behaviour and performance in distinct ways. This study compared the impact of cognitive, simulator-based, and real-world training on visual attention and driving-related outcomes in novice drivers. Thirty novice drivers (18–27 years; ≤1 year driving experience) were randomized into three training groups (n = 10 each): cognitive training (PsyToolkit, Version 3.7.0), game-based simulator training, and supervised real-world driving. Baseline and post-training assessments included visuomotor performance (Fitts’ Law), attentional cueing (valid/invalid reaction time), simulator-based driving errors, and eye-tracking measures of gaze behaviour. Eye-tracking outcomes included dwell-time percentage and first-fixation order across predefined areas of interest (AOIs). Participants completed 10 consecutive days of modality-specific training. Cognitive training improved visuomotor performance and increased forward road monitoring. Game-based simulator training yielded the largest reductions in simulator driving errors, particularly lane deviations (Z = −2.89, p = 0.004). Real-world driving altered visual scanning patterns, with significant differences in rear-view mirror prioritization (p = 0.024). Across groups, gaze shifted from dashboard view toward safety-relevant AOIs. Training modifies novice drivers’ gaze behaviour in modality-specific ways, suggesting that a multimodal training approach may enhance visual attention and driving safety

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Publication Details

Journal
Journal of Eye Movement Research
Published
2026-04-30
DOI
https://doi.org/10.3390/jemr19030045
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
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article

Effects of Cognitive, Simulator, and Real-World Training on Novice Driver Gaze Behaviour: A Pre–Post Study

Aiswaryah Radhakrishnan, Prem Sudhakar Lawrence
Journal of Eye Movement Research
Human-Automation Interaction and Safety
article

Effects of Cognitive, Simulator, and Real-World Training on Novice Driver Gaze Behaviour: A Pre–Post Study

Aiswaryah Radhakrishnan, Prem Sudhakar Lawrence
article en

Abstract

Novice drivers demonstrate inefficient visual scanning and elevated crash risk relative to experienced drivers. Different training programmes may influence gaze behaviour and performance in distinct ways. This study compared the impact of cognitive, simulator-based, and real-world training on visual attention and driving-related outcomes in novice drivers. Thirty novice drivers (18–27 years; ≤1 year driving experience) were randomized into three training groups (n = 10 each): cognitive training (PsyToolkit, Version 3.7.0), game-based simulator training, and supervised real-world driving. Baseline and post-training assessments included visuomotor performance (Fitts’ Law), attentional cueing (valid/invalid reaction time), simulator-based driving errors, and eye-tracking measures of gaze behaviour. Eye-tracking outcomes included dwell-time percentage and first-fixation order across predefined areas of interest (AOIs). Participants completed 10 consecutive days of modality-specific training. Cognitive training improved visuomotor performance and increased forward road monitoring. Game-based simulator training yielded the largest reductions in simulator driving errors, particularly lane deviations (Z = −2.89, p = 0.004). Real-world driving altered visual scanning patterns, with significant differences in rear-view mirror prioritization (p = 0.024). Across groups, gaze shifted from dashboard view toward safety-relevant AOIs. Training modifies novice drivers’ gaze behaviour in modality-specific ways, suggesting that a multimodal training approach may enhance visual attention and driving safety

Journal of Eye Movement ResearchVol. 19(3)
SRM Institute of Science and Technology (IN)
Openalex Percentile: Top 5%
Human-Automation Interaction and Safety
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