Systems factorial technology (SFT) is a theoretically derived methodology that allows for strong inferences to be made about underlying processing architectures (e.g., whether processing occurs in a pooled, coactive fashion or in serial or in parallel). Measures of mental architecture using SFT have been restricted to the use of error-free response times (RTs). In this article, through formal proofs and demonstrations, we extended the measure of architecture, the survivor interaction contrast (SIC), to RTs conditioned on whether they are correct or incorrect. We show that so long as an ordering relation (between stimulus conditions of different difficulty) is preserved, we learn that the canonical SIC predictions result when exhaustive processing is necessary and sufficient for a response. We further prove that this ordering relation holds for the popular Wiener diffusion model for both correct and error RTs but fails under some classes of a Poisson counter model, which affords a strong potential experimental test of the latter class versus the others. Our exploration also serves to point to the importance of detailed studies of how errors are made in perceptual and cognitive tasks.

中文翻译：

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将系统阶乘技术扩展到错误响应。

系统阶乘技术 (SFT) 是一种理论上派生的方法，它允许对底层处理架构进行强有力的推断（例如，处理是否以池化、协同方式或串行或并行方式发生）。使用 SFT 测量心理结构的方法仅限于使用无错误响应时间 (RT)。在本文中，通过正式的证明和演示，我们将架构的度量，即幸存者交互对比 (SIC)，扩展到以正确或不正确为条件的 RT。我们表明，只要保留排序关系（在不同难度的刺激条件之间），我们就会知道，当详尽的处理对于响应而言是必要且充分的时，就会产生规范的 SIC 预测。我们进一步证明，这种排序关系适用于流行的正确和错误 RT 的维纳扩散模型，但在泊松计数器模型的某些类别下失败，这为后者与其他类别提供了强大的潜在实验测试。我们的探索还有助于指出详细研究在感知和认知任务中如何产生错误的重要性。