Systems Factorial Technology is a powerful framework for investigating the fundamental properties of human information processing such as architecture (i.e., serial or parallel processing) and capacity (how processing efficiency is affected by increased workload). The Survivor Interaction Contrast (SIC) and the Capacity Coefficient are effective measures in determining these underlying properties, based on response-time data. Each of the different architectures, under the assumption of independent processing, predicts a specific form of the SIC along with some range of capacity. In this study, we explored SIC predictions of discrete-state (Markov process) and continuous-state (Linear Dynamic) models that allow for certain types of cross-channel interaction. The interaction can be facilitatory or inhibitory: one channel can either facilitate, or slow down processing in its counterpart. Despite the relative generality of these models, the combination of the architecture-oriented plus the capacity oriented analyses provide for precise identification of the underlying system.

中文翻译：

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好人完成得快，坏人最后完成：并行系统中的促进与抑制交互

系统因子技术是一个强大的框架，用于研究人类信息处理的基本属性，例如体系结构（即串行或并行处理）和容量（处理效率如何受到工作量增加的影响）。Survivor Interaction Contrast (SIC) 和Capacity Coefficient 是根据响应时间数据确定这些基本属性的有效措施。在独立处理的假设下，每种不同的架构都预测特定形式的 SIC 以及一定范围的容量。在这项研究中，我们探索了允许某些类型的跨渠道交互的离散状态（马尔可夫过程）和连续状态（线性动态）模型的 SIC 预测。这种相互作用可以是促进性的，也可以是抑制性的：一个通道可以促进，或减慢其对应物的处理速度。尽管这些模型具有相对普遍性，但面向架构和面向容量的分析的结合提供了对底层系统的精确识别。