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How Sure is the Driver? Modelling Drivers' Confidence in Left-Turn Gap Acceptance Decisions
Floor Bontje1, Arkady Zgonnikov1
1Department of Cognitive Robotics, Faculty of Mechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628 CD The Netherlands.
Computational Brain & Behavior
|July 16, 2026
Summary
Driver confidence in left-turn decisions depends on gap size, mirroring basic task findings. This study links confidence judgments to naturalistic driving behavior using a dynamic drift-diffusion model.
Area of Science:
- Cognitive Psychology
- Human Factors Engineering
- Neuroscience
Background:
- Confidence judgments are subjective probability assessments accompanying decisions.
- Understanding confidence mechanisms offers insights into human behavior.
- Confidence in naturalistic dynamic tasks, like driving, remains understudied compared to laboratory settings.
Purpose of the Study:
- To investigate driver confidence in left-turn gap acceptance decisions within a naturalistic driving context.
- To connect fundamental research on confidence judgments with real-world driving behavior.
- To model confidence judgments in dynamic decision-making during driving.
Main Methods:
- A driver simulator experiment with 17 participants was conducted.
- Investigated confidence in left-turn gap acceptance decisions.
- Utilized an extended dynamic drift-diffusion model to capture confidence judgments.
Main Results:
- Driver confidence was significantly influenced by the size of the gap to oncoming vehicles.
- Confidence increased with gap size for accepted turns and decreased for rejected turns.
- Confidence judgments correlated negatively with response times and positively with action dynamics, consistent with basic tasks.
- The extended drift-diffusion model, incorporating gap size-dependent parameters and post-decision accumulation, accurately described confidence judgments.
Conclusions:
- Fundamental principles of confidence research are applicable to dynamic, naturalistic decision-making, such as driving.
- Gap size is a critical factor influencing confidence in driving decisions.
- Computational models can effectively capture and predict confidence judgments in complex, real-world scenarios.
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