Traditional views of automaticity are in need of revision. For example, automaticity often has 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. A model of attention is presented to address these issues. Within a parallel distributed processing framework, it is proposed that the attributes of automaticity depend on the strength of a processing pathway and that strength increases with training. With the Stroop effect as an example, automatic processes are shown to be continuous and to emerge gradually with practice. Specifically, a computational model of the Stroop task 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 backpropagation learning algorithm (Rumelhart, Hinton, & Williams, 1986). The model can simulate performance in the standard Stroop task, as well as aspects of performance in variants of this task that manipulate stimulus-onset asynchrony, response set, and degree of practice. The model presented is contrasted against other models, and its relation to many of the central issues in the literature on attention, automaticity, and interference is discussed.