Independent racing evidence-accumulator models have proven fruitful in advancing understanding of rapid decisions, mainly in the case of binary choice, where they can be relatively easily estimated and are known to account for a range of benchmark phenomena. Typically, such models assume a one-to-one mapping between accumulators and responses. We explore an alternative independent-race framework where more than one accumulator can be associated with each response, and where a response is triggered when a sufficient number of accumulators associated with that response reach their thresholds. Each accumulator is primarily driven by the difference in evidence supporting one versus another response (i.e., that response's "advantage"), with secondary inputs corresponding to the total evidence for both responses and a constant term. We use Brown and Heathcote's (2008) linear ballistic accumulator (LBA) to instantiate the framework in a mathematically tractable measurement model (i.e., a model whose parameters can be successfully recovered from data). We show this "advantage LBA" model provides a detailed quantitative account of a variety of benchmark binary and multiple choice phenomena that traditional independent accumulator models struggle with; in binary choice the effects of additive versus multiplicative changes to input values, and in multiple choice the effects of manipulations of the strength of lure (i.e., nontarget) stimuli and Hick's law. We conclude that the advantage LBA provides a tractable new avenue for understanding the dynamics of decisions among multiple choices. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

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累积优势：快速多项选择的新概念。

事实证明，独立的赛车证据累加器模型可以增进对快速决策的理解，尤其是在二元选择的情况下，这种模型可以相对容易地进行估计，并且可以解释一系列基准现象。通常，此类模型假定累加器与响应之间是一对一的映射。我们探索了一种替代的独立竞赛框架，其中每个响应可以关联多个累加器，并且当与该响应关联的足够数量的累加器达到其阈值时触发响应。每个累加器主要由支持一个响应与另一个响应（即该响应的“优势”）的证据差异驱动，其次要输入对应于两个响应和一个常数项的总证据。我们使用Brown and Heathcote（2008）的线性弹道累加器（LBA）在数学上易于控制的测量模型（即可以从数据中成功恢复其参数的模型）中实例化框架。我们展示了这种“优势LBA”模型，该模型详细描述了传统独立累加器模型难以克服的各种基准二进制和多项选择现象。在二元选择中，对输入值的加性与乘性变化的影响，以及在选择中，对诱饵（即非目标）刺激强度和希克定律的操纵的影响。我们得出的结论是，LBA的优势为理解多项选择之间的决策动态提供了一条易于处理的新途径。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。