Department of Psychology and Neuroscience, University of Colorado Boulder.
Department of Psychological Sciences, Purdue University.
Psychol Rev. 2014 Jan;121(1):1-32. doi: 10.1037/a0034190. Epub 2013 Sep 30.
[Correction Notice: An Erratum for this article was reported in Vol 121(1) of Psychological Review (see record 2014-03591-005). The link to supplemental material was missing. All versions of this article have been corrected.] Much current research on speeded choice utilizes models in which the response is triggered by a stochastic process crossing a deterministic threshold. This article focuses on 2 such model classes, 1 based on continuous-time diffusion and the other on linear ballistic accumulation (LBA). Both models assume random variability in growth rates and in other model components across trials. We show that if the form of this variability is unconstrained, the models can exactly match any possible pattern of response probabilities and response time distributions. Thus, the explanatory or predictive content of these models is determined not by their structural assumptions but, rather, by distributional assumptions (e.g., Gaussian distributions) that are traditionally regarded as implementation details. Selective influence assumptions (i.e., which experimental manipulations affect which model parameters) are shown to have no restrictive effect, except for the theoretically questionable assumption that speed-accuracy instructions do not affect growth rates. The 2nd contribution of this article concerns translation of falsifiable models between universal modeling languages. Specifically, we translate the predictions of the diffusion and LBA models (with their parametric and selective influence assumptions intact) into the Grice modeling framework, in which accumulation processes are deterministic and thresholds are random variables. The Grice framework is also known to reproduce any possible pattern of response probabilities and times, and hence it can be used as a common language for comparing models. It is found that only a few simple properties of empirical data are necessary predictions of the diffusion and LBA models.
[勘误通知：本文在《心理评论》第 121 卷（1）（参见记录 2014-03591-005）中报告了一个勘误。缺少补充材料的链接。本文的所有版本都已更正。] 当前许多关于快速选择的研究都利用了这样的模型，其中反应是由一个随机过程跨越一个确定的阈值触发的。本文重点介绍了两个这样的模型类，一个基于连续时间扩散，另一个基于线性弹道积累（LBA）。这两个模型都假设在试验之间增长率和其他模型组成部分的随机可变性。我们表明，如果这种可变性的形式不受限制，那么这些模型可以完全匹配任何可能的反应概率和反应时间分布模式。因此，这些模型的解释或预测内容不是由其结构假设决定的，而是由传统上被视为实现细节的分布假设（例如，高斯分布）决定的。选择性影响假设（即哪些实验操作影响哪些模型参数）除了理论上有问题的假设（即速度准确性说明不影响增长率）外，没有任何限制作用。本文的第二个贡献涉及在通用建模语言之间翻译可验证的模型。具体来说，我们将扩散和 LBA 模型的预测（保留其参数和选择性影响假设）翻译成 Grice 建模框架，在该框架中，积累过程是确定性的，而阈值是随机变量。众所周知，Grice 框架也可以再现任何可能的反应概率和时间模式，因此它可以作为比较模型的通用语言。结果发现，只有几个简单的经验数据特性是扩散和 LBA 模型的必要预测。
