Deciding When to Go: Generalisable Kinematics-Dependent Drift–Diffusion Modelling of Human Narrow-Passage Interactions

Abstract

Human drivers routinely make interaction decisions by integrating multimodal, noisy perceptual information over time to decide whether to proceed or yield. Understanding the cognitive mechanisms underlying such decisions is crucial for explaining driver behaviour and for developing models that generalise across traffic scenarios.
This study examines human driver decision-making in a narrow-passage gap-acceptance task, an interaction scenario characterised by ambiguity of priority and described by interdependent kinematic and visual information. Behavioural data from laboratory ($N=36$) and ($N=175$) online experiments were analysed to assess whether decision dynamics generalise across populations with differing variability and experimental control.
Across datasets, decision behaviour showed systematic dependencies on kinematic conditions, with increased reaction times (RTs) and choice variability in ambiguous situations. Longitudinal kinematics-based drift diffusion models (LK-DDMs) captured both decision proportions and reaction-time distributions in the narrow passage task and generalised across datasets with differing coverage of the experimental conditions, including lab and online data. The same accumulation framework transferred to a related decision-making task, indicating that the inferred dynamics are not task-specific. Incorporating visual looming in the drift function yielded selective improvements in short-distance Wait decisions, without global gains in reaction-time accuracy.
OSF link: https://osf.io/5d6em/overview?view_only=18580aa504f24d92a55cbccf53bb5deb