Modeling harmony biases in learning exceptions to vowel harmony

Abstract

Artificial language learning experiments typically show non-categorical results after training on categorical data. This is generally due to incomplete learning, but these results can also reveal biases. One example is that participants trained on a vowel harmony language with alternating and non-alternating affixes prefer the non-alternating affix in harmonic contexts (Finley 2021). In this paper, I show that (i) the preference for harmonic items in non-alternating affixes replicates for remote (online) data collection, and (ii) that this effect can be modeled with MaxEnt Harmonic Grammar. In Harmonic Grammar, the harmony score of each candidate determines its grammaticality, and the probability of surfacing. Because non-alternating affixes that satisfy vowel harmony have higher harmony scores than non-alternating affixes that violate harmony, harmonic candidates will be more likely to surface than disharmonic candidates, even when both types of items surface at levels greater than expected by chance. The theoretical and methodological implications for these results are discussed