Knowing When to Move: Evidence Accumulation Models of Human Behavior in Traffic

2026-05-30

AI Summary

This study addresses the challenge of modeling dynamic decision-making in human continuous perception–action behaviors within complex traffic environments. Through a semi-systematic review of 28 studies, it offers the first integrative synthesis of how evidence accumulation models (EAMs) have been diversely implemented in traffic behavior research, structured along two key dimensions: modeling granularity (discrete decisions versus continuous action control) and architectural approach (standalone decision models versus embedded perception–action frameworks). The analysis highlights the critical influence of task demands on model design, clarifies distinctions among existing paradigms, and identifies methodological challenges. Building on these insights, the work proposes a new direction for continuous decision-making modeling that is grounded in real-world contexts and capable of capturing time-varying dynamics and multi-agent interactions.

Abstract

Evidence accumulation models provide a formal framework for studying decision making as a dynamic process unfolding over time. While these models have been extensively developed and reviewed in laboratory paradigms, their structured application in complex, ecologically valid domains has received comparatively little attention. Road traffic is a particularly relevant context for studying sustained, embodied perception action behavior, where decisions unfold under time pressure and involve continuous control and ongoing perception-action coupling. Examining how EAMs have been applied in this domain may therefore offer insights beyond discrete laboratory tasks toward decision making in real-world behavior. This semi-systematic review synthesizes 28 studies (2014-2026) applying EAMs to traffic-related behavior. We organize the literature along two dimensions: 1) modelling level, distinguishing models at the level of discrete decision-making and models at the level of continuous action control, and 2) model architecture, distinguishing evidence accumulation as either a stand-alone decision model or an embedded component within broader perception-action or interaction frameworks. These distinctions are associated with systematic differences in model architecture, parameterization, data usage, and validation strategies, reflecting task specific demands. By providing a structured overview of these patterns, this review clarifies how EAMs are currently instantiated in traffic contexts and highlights methodological challenges and future directions both in traffic modelling and in modelling of decision-making more broadly. Promising directions include laboratory work on evidence accumulation in sustained and time-varying tasks, interactive multi-individual decision-making, and the use of neurophysiological measures to identify the perceptual evidence underlying complex perception-action behavior.

Problem

• Evidence Accumulation Models
• Traffic Behavior
• Decision Making
• Perception-Action Coupling
• Ecological Validity

Innovation

• Evidence Accumulation Models
• traffic behavior
• continuous action control
• perception-action coupling
• ecological validity