Inhibition and Interference in Selective Attention: Some Tests of a Neural Network Model

Abstract

We describe a neural network system that models selective action—that is, how an organism selectively responds to an object when other objects evoke competing responses. Performance of the model during simulations of various selective action situations reveals a number of interesting patterns of data. Specifically, the model shows a complex relationship between how much a distractor interferes with response to a target and how much inhibition is associated with the distractor. Subsequent experiments with human subjects reveal that the paradoxical behaviour of the model is also observed in human behaviour. We conclude that the similar performance characteristics of the model and human subjects in a variety of situations suggest that the model has captured some of the essential properties of mammalian selective action mechanisms.