Cohen J D, Servan-Schreiber D, McClelland J L
Carnegie Mellon University.
Am J Psychol. 1992 Summer;105(2):239-69.
We consider how a particular set of information processing principles, developed within the parallel distributed processing (PDP) framework, can address issues concerning automaticity. These principles include graded, activation-based processing that is subject to attentional modulation; incremental, connection-based learning; and interactivity and competition in processing. We show how simulation models, based on these principles, can account for the major phenomena associated with automaticity, as well as many of those that have been troublesome for more traditional theories. In particular, we show how the PDP framework provides an alternative to the usual dichotomy between automatic and controlled processing and can explain the relative nature of automaticity as well as the fact that seemingly automatic processes can be influenced by attention. We also discuss how this framework can provide insight into the role that bidirectional influences play in processing: that is, how attention can influence processing at the same time that processing influences attention. Simulation models of the Stroop color-word task and the Eriksen response-competition task are described that help illustrate the application of the principles to performance in specific behavioral tasks.
我们思考了在并行分布式处理（PDP）框架内形成的一组特定信息处理原则如何能够解决与自动化相关的问题。这些原则包括基于激活的分级处理，这种处理会受到注意力调节的影响；基于连接的增量学习；以及处理过程中的交互性和竞争性。我们展示了基于这些原则的模拟模型如何能够解释与自动化相关的主要现象，以及许多令更为传统的理论感到棘手的现象。特别是，我们展示了PDP框架如何为自动处理和控制处理之间常见的二分法提供了一种替代方案，并且能够解释自动化的相对性，以及看似自动的过程能够受到注意力影响这一事实。我们还讨论了这个框架如何能够深入洞察双向影响在处理过程中所起的作用：也就是说，注意力如何能够在处理影响注意力的同时影响处理过程。文中描述了斯特鲁普颜色-文字任务和埃里克森反应竞争任务的模拟模型，这些模型有助于说明这些原则在特定行为任务表现中的应用。
