Neural networks simulation of a discrete model of continious effects of irrelevant stimuli

Presents a general simulation method based on minimal neural network representations of nonmathematical, structural models of information processes. The time-dependent behavior of each component in a given structural model is represented by a simple, noncommittal equation that does not affect the theoretical structure of the original model. The neural network simulation method then is applied to the linear stage model of S. Sternberg (1969), J. L. McClelland's cascade model, and to J. Miller's discrete model of continuous effects of irrelevant stimuli. In view of the results thus obtained it is argued that there exists a formal equivalence between discrete and continuous models of information processing in that both types of models can be represented as instances of the same neural network with variable thresholds.