The Epistemology of Learning in Artificial Neural Networks

Abstract

The aim of this paper is to validate the claim that neural networks appear to have much in common with the behavioristic view of learning. Neural networks are, according to Rumelhart & McClelland (1986), not behavioristic because of their explicit concern with the problem of internal representation and "mental" processing. The claim that neural networks are behavioristic has epistemological implications. Neural network learning theories, hereunder supervised and unsupervised learning, are compared to psychological learning theories in the two epistemological doctrines: empiricism and rationalism. The results indicate that neural networks exhibit interesting features of self-organization, implicit clustering of inner representation, and plasticity. However, the discussion also indicates that neural networks have similar features as the behavioristic learning theories of psychology. Keywords: Philosophy of Science, Learning theories, Neural Networks, Artificial Intelligence I want to ..