← 研踪 PaperTrailarXiv:2606.00727 · v3
Knowing When to Move: Evidence Accumulation Models of Human Behavior in Traffic
知道何时行动：交通中人类行为的证据累积模型
首次提交 2026-05-30 21:40
版本更新 2026-11-13 01:13
雷达观测 2026-11-13 14:15
摘要
Evidence accumulation models (EAMs) describe decisions as noisy evidence accumulating toward a decision boundary. Although widely studied in laboratory tasks, their application to dynamic, real-world behavior has received less attention. Traffic offers a setting where evidence changes as situations unfold, response timing matters, and decisions occur within ongoing control and interaction. This semi-systematic review synthesizes 28 publications (2014-2026) that model traffic decisions and control actions with EAMs. We organize the literature by modeling level (discrete decisions versus continuous action control) and implementation (stand-alone accumulators versus models embedded in broader perception-action or interaction frameworks). We examine how model components are implemented and evaluated across tasks. Overall, EAMs account for a range of dynamic road-user behaviors. Several comparisons suggest that response timing is better described by accumulating evidence as situations develop than by fixed delays or thresholds on momentary evidence, although accumulation models do not consistently outperform other kinds of models. Whether decreasing boundaries improve the fit differs between studies, and the models offer testable accounts of how decision-relevant information is processed. For cognitive scientists, traffic offers a testbed for studying how evidence from changing environments shapes decision-making. For applied modelers, the findings show how EAM components can be adapted to different traffic tasks, and why the choice of components needs to be evaluated for each task. However, most publications that estimated parameters pooled across participants, and most were not tested beyond the data they were fitted to, highlighting the value of participant-level or hierarchical estimation and of testing on new data.
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v3 · 2026-11-13 01:13 · 最新观测版本
Knowing When to Move: Evidence Accumulation Models of Human Behavior in Traffic
Evidence accumulation models (EAMs) describe decisions as noisy evidence accumulating toward a decision boundary. Although widely studied in laboratory tasks, their application to dynamic, real-world behavior has received less attention. Traffic offers a setting where evidence changes as situations unfold, response timing matters, and decisions occur within ongoing control and interaction. This semi-systematic review synthesizes 28 publications (2014-2026) that model traffic decisions and control actions with EAMs. We organize the literature by modeling level (discrete decisions versus continuous action control) and implementation (stand-alone accumulators versus models embedded in broader perception-action or interaction frameworks). We examine how model components are implemented and evaluated across tasks. Overall, EAMs account for a range of dynamic road-user behaviors. Several comparisons suggest that response timing is better described by accumulating evidence as situations develop than by fixed delays or thresholds on momentary evidence, although accumulation models do not consistently outperform other kinds of models. Whether decreasing boundaries improve the fit differs between studies, and the models offer testable accounts of how decision-relevant information is processed. For cognitive scientists, traffic offers a testbed for studying how evidence from changing environments shapes decision-making. For applied modelers, the findings show how EAM components can be adapted to different traffic tasks, and why the choice of components needs to be evaluated for each task. However, most publications that estimated parameters pooled across participants, and most were not tested beyond the data they were fitted to, highlighting the value of participant-level or hierarchical estimation and of testing on new data.
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