Familiarity, consistency, and systematizing in morphology

Language learning involves exposure to inconsistent systems - that is, systems where multiple patterns or methods exist to mark some meaning. Inconsistent systems often change to be more regular over time - they become systematized. However, some recent studies have reported that learners tend to reproduce inconsistency in the input, leading to models in which the language learning mechanism is basically preservatory. We ran an artificial language learning experiment using a novel paradigm to extend our understanding of systematizing versus preservatory mechanisms in language learning. Participants were taught two number marking systems, either completely consistently (the probability P of the system is 1.00) or inconsistently (with P = 0.875 for one system and P = 0.125 for the other, and so on for P = 0.75 and P = 0.625). One marking system was a plural-marking system. The other was a typologically rare singulative-marking system. When generalizing to novel items, participants produced more regular output patterns overall for more consistent input conditions than for less consistent ones, and more for the plural-marking conditions than for the singulative-marking conditions. For the singulative-marking conditions, the inter-participant variation was much greater than for the plural-marking ones; some individuals systematized towards the more familiar pattern, some systematized towards the less familiar pattern and some were not significantly different from probability-matching. We analyze the variation in relation to current statistical learning models, showing that preservatory learning models, as well as all models with a single free parameter, fail to capture our results. We show how a model with two free parameters in which individuals can vary in their propensity to systematize in any given situation is more successful. We also discuss implications for the theory of language change.