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Abstract: "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 process 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 is done by combining the cascade mechanism described by McClelland (1979) with the backpropagation 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 the number of competing words in the display. These simulations demonstrate that when two processes are in competition, the weaker process can take on several of the attributes that have previously been associated with controlled processing: susceptibility to interference and a requirement for the allocation of attention. This suggests that the traditional distinction between controlled and automatic processing is in need of reconsideration."