At the Zebra Crossing: Modelling Complex Decision Processes with Variable-Drift Diffusion Models
At the Zebra Crossing: Modelling Complex Decision Processes with Variable-Drift Diffusion Models
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DOI：
10.31234/osf.io/cgj7r
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Oscar T. Giles;G. Markkula;J. Pekkanen;Naoki Yokota;Naoto Matsunaga;N. Merat;T. Daimon
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文献类型：
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作者：
Oscar T. Giles;G. Markkula;J. Pekkanen;Naoki Yokota;Naoto Matsunaga;N. Merat;T. Daimon
漂移扩散(或证据积累)模型在简单决策任务的建模中得到了广泛应用。这些模型的扩展，其中模型的瞬时漂移率不是固定的，而是允许作为感知输入流的函数随时间变化，使得这些模型能够解释更复杂的感觉运动决策任务。然而，许多现实世界的任务似乎依赖于无数甚至更复杂的底层过程。一个有趣的例子是决定是否与驶来的车辆过马路的任务。这个行动决定似乎取决于关于自己的负担能力(一个人是否能在车辆之前通过)和其他人的行动意图(车辆是否屈服于自己)的感官信息。在这里，我们比较了标准漂移扩散模型的三个扩展，关于它们在虚拟现实环境中捕捉行人过马路决策的时间的能力。我们发现，一个单变量漂移扩散模型(S-VDDM)，其中变化的漂移率由描述车辆接近和减速的视觉参数确定，并在上下限饱和，可以很好地解释大范围车辆接近场景中穿越时间的多峰分布。更复杂的模型试图将最终的交叉决策划分为组成部分的感知决策，提高了与人类数据的匹配度，但在从这一发现中得出确定的结论之前，还需要进一步的工作。
Drift diffusion (or evidence accumulation) models have found widespread use in the modelling of simple decision tasks. Extensions of these models, in which the model’s instantaneous drift rate is not fixed but instead allowed to vary over time as a function of a stream of perceptual inputs, have allowed these models to account for more complex sensorimotor decision tasks. However, many real-world tasks seemingly rely on a myriad of even more complex underlying processes. One interesting example is the task of deciding whether to cross a road with an approaching vehicle. This action decision seemingly depends on sensory information both about own affordances (whether one can make it across before the vehicle) and action intention of others (whether the vehicle is yielding to oneself). Here, we compared three extensions of a standard drift diffusion model, with regards to their ability to capture timing of pedestrian crossing decisions in a virtual reality environment. We find that a single variable-drift diffusion model (S-VDDM) in which the varying drift rate is determined by visual quantities describing vehicle approach and deceleration, saturated at an upper and lower bound, can explain multimodal distributions of crossing times well across a broad range vehicle approach scenarios. More complex models, which attempt to partition the final crossing decision into constituent perceptual decisions, improve the fit to the human data but further work is needed before firm conclusions can be drawn from this finding.