Parallel versus sequential processing of relational stimulus structures

Abstract

Organisms are often faced with sets of stimuli bearing specifiable relationships to each other. Experimental data suggest that even animals not suspected of being particularly rational can solve problems involving consistent linear relationships. We examine the information processing required to cope with these and related stimulus structures from a theoretical point of view. We show that both a parallel processing neural network model and a serially processing Turing machine model require minimal complexities to process linear hierarchical structures. When dealing with other relational stimulus structures, the models need differing, greater minimal complexities. Siemann and Delius (1994) report experimental results indicating that both pigeons and humans appear to operate according to the parallel, neural network model we propose here. Further experiments likely to be diagnostic are proposed.