Reconciling Inconsistency in Encoded Morphological Distinctions in an Artificial Language

Abstract

Language learners are sometimes faced with the problem of learning from input that is inconsistent or unexpected. Unexpected patterns may be typologically rare (marked) or contrary to the pattern in the first language. Using a novel game-like experimental paradigm, we examine the interaction of these factors for a set of artificial languages differing in the consistency and naturalness of number marking. The interaction of these factors in determining the degree of regularization is highly significant, and arises from individual differences that pose challenges for formal models