Proceedings of the Annual Meeting of the Cognitive Science Society

Morphological paradigms differ widely across languages in their size and number of contrasts they mark. Recent work onmorphological complexity has argued that certain features of even very large paradigms make them easy to learn and use.Specifically, Ackerman & Malouf, 2013 propose an information-theoretic measure, i-complexity, which captures the extentto which forms in the paradigm predict each other, and show that languages which differ widely in surface complexityexhibit similar i-complexity; in other words, paradigms with many contrasts reduce the learnability challenge for learnersby having predictive relationships between inflections. We present three artificial language learning experiments testingwhether i-complexity in fact predicts learnability of nominal paradigms where nouns inflect for class and number. Ourresults reveal only weak evidence that paradigms with low i-complexity are easier to learn than paradigms with highi-complexity. We suggest that alternative aspects of complexity may have a larger impact on learning.