Diffusion models of evidence accumulation have successfully accounted for the distributions of response times and choice probabilities from many experimental tasks, but recently their assumption that evidence is accumulated at a constant rate to constant decision boundaries has been challenged. One model assumes that decision-makers seek to optimize their performance by using decision boundaries that collapse over time. Another model assumes that evidence does not accumulate and is represented by a stationary distribution that is gated by an urgency signal to make a response. We present explicit, integral-equation expressions for the first-passage time distributions of the urgency-gating and collapsing-bounds models and use them to identify conditions under which the models are equivalent. We combine these expressions with a dynamic model of stimulus encoding that allows the effects of perceptual and decisional integration to be distinguished. We compare the resulting models to the standard diffusion model with variability in drift rates on data from three experimental paradigms in which stimulus information was either constant or changed over time. The standard diffusion model was the best model for tasks with constant stimulus information; the models with time-varying urgency or decision bounds performed similarly to the standard diffusion model on tasks with changing stimulus information. We found little support for the claim that evidence does not accumulate and attribute the good performance of the time-varying models on changing-stimulus tasks to their increased flexibility and not to their ability to account for systematic experimental effects. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

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使用积分方程对证据积累决策过程进行建模：紧急门控和崩溃边界。


证据积累的扩散模型已经成功地解释了许多实验任务的响应时间和选择概率的分布，但最近他们关于证据以恒定速率积累到恒定决策边界的假设受到了挑战。一种模型假设决策者通过使用随着时间的推移而崩溃的决策边界来寻求优化其绩效。另一种模型假设证据不会积累，并以平稳分布表示，该分布由做出响应的紧急信号控制。我们为紧急门控模型和折叠边界模型的首次通过时间分布提出了明确的积分方程表达式，并使用它们来识别模型等效的条件。我们将这些表达式与刺激编码的动态模型相结合，从而可以区分感知和决策整合的效果。我们将所得模型与标准扩散模型进行比较，其中来自三个实验范式的数据的漂移率具有可变性，其中刺激信息要么恒定，要么随时间变化。标准扩散模型是具有恒定刺激信息的任务的最佳模型；对于具有变化的刺激信息的任务，具有随时间变化的紧迫性或决策界限的模型的表现与标准扩散模型类似。我们几乎没有发现证据支持这种说法，即证据不会积累，并将时变模型在变化刺激任务上的良好表现归因于它们增加的灵活性，而不是它们解释系统实验效应的能力。 （PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）