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将系统因子技术扩展到错误反应。

Extending systems factorial technology to errored responses.

机构信息

Melbourne School of Psychological Sciences.

Department of Psychological and Brain Sciences.

出版信息

Psychol Rev. 2022 Apr;129(3):484-512. doi: 10.1037/rev0000232. Epub 2022 Apr 21.

Abstract

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. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

摘要

系统因素技术(SFT)是一种理论推导的方法,可用于对潜在处理架构进行强有力的推断(例如,处理是在集中、共同作用的方式下进行,还是在串行或并行方式下进行)。使用 SFT 测量心理结构的方法仅限于使用无错误的反应时(RT)。在本文中,我们通过正式的证明和演示,将架构的测量指标——幸存者交互对比(SIC)扩展到了基于 RT 是否正确的条件下。我们表明,只要保留了一种排序关系(不同难度刺激条件之间的关系),我们就可以得知,当穷尽处理对于响应是必要且充分的时,就会出现典型的 SIC 预测结果。我们进一步证明,对于流行的 Wiener 扩散模型,无论是正确的 RT 还是错误的 RT,这种排序关系都成立,但在某些 Poisson 计数器模型的类别下不成立,这为后者类别与其他类别进行强有力的潜在实验测试提供了条件。我们的探索还指出了详细研究感知和认知任务中错误产生方式的重要性。

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