A common assumption in choice response time (RT) modeling is that after evidence accumulation reaches a certain decision threshold, the choice is categorically communicated to the motor system that then executes the response. However, neurophysiological findings suggest that motor preparation partly overlaps with evidence accumulation, and is not independent from stimulus difficulty level. We propose to model this entanglement by changing the nature of the decision criterion from a simple threshold to an actual process. More specifically, we propose a secondary, motor preparation related, leaky accumulation process that takes the accumulated evidence of the original decision process as a continuous input, and triggers the actual response when it reaches its own threshold. We analytically develop this Leaky Integrating Threshold (LIT), applying it to a simple constant drift diffusion model, and show how its parameters can be estimated with the D*M method. Reanalyzing 3 different data sets, the LIT extension is shown to outperform a standard drift diffusion model using multiple statistical approaches. Further, the LIT leak parameter is shown to be better at explaining the speed/accuracy trade-off manipulation than the commonly used boundary separation parameter. These improvements can also be verified using traditional diffusion model analyses, for which the LIT predicts the violation of several common selective parameter influence assumptions. These predictions are consistent with what is found in the data and with what is reported experimentally in the literature. Crucially, this work offers a new benchmark against which to compare neural data to offer neurobiological validation for the proposed processes. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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泄漏积分阈值及其对选择响应时间 (RT) 证据积累模型的影响。

选择响应时间 (RT) 建模中的一个常见假设是，在证据积累达到某个决策阈值后，将选择明确传达给执行响应的运动系统。然而，神经生理学研究结果表明，运动准备与证据积累部分重叠，并且与刺激难度水平无关。我们建议通过将决策标准的性质从简单的阈值更改为实际过程来对这种纠缠进行建模。更具体地说，我们提出了一个二级的、与运动准备相关的、泄漏的累积过程，它将原始决策过程的累积证据作为连续输入，并在达到自己的阈值时触发实际响应。我们分析开发了这个泄漏积分阈值（LIT），将其应用于简单的恒定漂移扩散模型，并展示如何使用 D*M 方法估计其参数。重新分析 3 个不同的数据集，显示 LIT 扩展优于使用多种统计方法的标准漂移扩散模型。此外，LIT 泄漏参数比常用的边界分离参数更能解释速度/精度权衡操作。这些改进也可以使用传统的扩散模型分析来验证，LIT 可以预测违反几个常见的选择参数影响假设。这些预测与数据中发现的以及文献中的实验报告一致。至关重要的是，这项工作提供了一个新的基准，可以用来比较神经数据，从而为提议的过程提供神经生物学验证。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）。