Testing the drift-diffusion model
Testing the drift-diffusion model
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DOI：
10.1073/pnas.2011446117
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发表时间：
2020-12-29
影响因子：
11.1
通讯作者：
Strzalecki, Tomasz
中科院分区：
文献类型：
--
作者：
Fudenberg, Drew;Newey, Whitney;Strzalecki, Tomasz
漂移-扩散模型（DDM）是一种带有扩散信号的顺序采样模型，在该模型中，决策者积累证据，直到过程达到上限或下限停止边界，然后停止并选择与该边界对应的替代方案。在感知任务中，过程的漂移与哪个选择客观上是正确的有关，而在消费任务中，漂移与替代方案的相对吸引力有关。DDM的最简单版本假设停止边界随时间不变。最近，一些论文使用非恒定边界来更好地拟合数据。本文提供了具有一般非常数边界的ddm的统计检验。作为一个副产品，我们证明了漂移和边界是唯一识别的。利用该条件对漂移和边界进行了非参数估计，并构造了基于有限样本的检验统计量。
The drift-diffusion model (DDM) is a model of sequential sampling with diffusion signals, where the decision maker accumulates evidence until the process hits either an upper or lower stopping boundary and then stops and chooses the alternative that corresponds to that boundary. In perceptual tasks, the drift of the process is related to which choice is objectively correct, whereas in consumption tasks, the drift is related to the relative appeal of the alternatives. The simplest version of the DDM assumes that the stopping boundaries are constant over time. More recently, a number of papers have used nonconstant boundaries to better fit the data. This paper provides a statistical test for DDMs with general, nonconstant boundaries. As a by-product, we show that the drift and the boundary are uniquely identified. We use our condition to nonparametrically estimate the drift and the boundary and construct a test statistic based on finite samples.