A hierarchical model of binary pattern learning

Abstract

A model is presented of how humans learn repeating patterns made of only two qualitatively distinct elements, for example: aabbaabab. The model proposes hierarchical coding principles or axioms that code the pattern into larger groups of elements at higher levels until perfect prediction is possible. Prior to solution the learner uses the transition probabilities associated with the coded elements present at the lower levels to make predictions. A simple rationale for weighting the contribution of the lower levels allows one to predict the proportion of errors that should occur at each pattern position, that is, to predict the error profile of a pattern, without using estimated parameters. Apparently no other such model exists to describe this basic type of human learning. Results published by Restle (1967) showing the error profile of three different binary patterns were used to test the model. Each error profile was successfully predicted, with r ranging from .91 to .94. Modifications, implications, limitations and qualifications of the model are discussed.