Eidels Ami, Houpt Joseph W, Altieri Nicholas, Pei Lei, Townsend James T
University of Newcastle, Australia.
J Math Psychol. 2011 Apr 1;55(2):176-190. doi: 10.1016/j.jmp.2010.11.003.
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.
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特性，如架构（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）。基于反应时间数据，幸存者交互对比（SIC）和容量系数是确定这些潜在特性的有效措施。在独立处理的假设下，每种不同的架构都预测了SIC的特定形式以及一定范围的容量。在本研究中，我们探索了离散状态（马尔可夫过程）和连续状态（线性动态）模型的SIC预测，这些模型允许某些类型的跨通道交互。这种交互可以是促进性的或抑制性的：一个通道可以促进或减慢其对应通道的处理。尽管这些模型具有相对普遍性，但面向架构的分析与面向容量的分析相结合，能够精确识别底层系统。
