Control of Automatic Processes: A Paralled Distributed Processing Account of the Stroop Effect.

摘要

A growing body of evidence suggests that traditional views of automaticity are in need of revision. For example, automaticity has often been treated as an all-or-none phenomenon, and traditional theories have held that automatic processes are independent of attention. Yet recent empirical data suggest that automatic processes are continuous, and furthermore are subject to attentional control. In this paper we present a model of attention which addresses these issues. Using a parallel distributed processing framework we propose that the attributes of automaticity depend upon the strength of a processing pathway and that strength increases with training. Using the Stroop effect as an example, we show how automatic processes are continuous and emerge gradually with practice. Specifically, we present a computational model of the Stroop task which simulates the time course of processing as well as the effects of learning. This was accomplished by combining the cascade mechanism described by McClelland (1979) with the back propagation learning algorithm (Rumelhart, Hinton, & Williams, 1986). The model is able to simulate performance in the standard Stroop task, as well as aspects of performance in variants of this task which manipulate SOA, response set, and degree of practice. In the discussion we contrast our model with other models, and indicate how it relates to many of the central issues in the literature on attention, automaticity, and interference. (Author) (kr)