A Parallel Distributed Processing Approach to Automaticity

Abstract

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.