Application of Connectionist Models to Animal Learning

Abstract

Here the authors examine the nature of the mnemonic structures that underlie the ability of animals to learn configural discriminations that are allied to the XOR problem. It has long been recognized that simple associative networks (e.g., perceptrons) fail to provide a coherent analysis for how animals learn this type of discrimination. Indeed “The inability of single layer perceptrons to solve XOR has a significance of mythical proportions in the history of connectionism.” (McLeod, Plunkett & Rolls, 1998; p. 106). In this historic context, the authors describe the results of recent experiments with animals that are inconsistent with the theoretical solution to XOR provided by some multi-layer connectionist models. The authors suggest a modification to these models that parallels the formal structure of XOR while maintaining two principles of perceptual organization and learning: contiguity and common fate.