Generalization to Novel Consonants in Artificial Grammar Learning

While theoretical phonologists rely on abstract phonetic features to account for the variety of phonological patterns that exist in the world’s languages, it is unclear whether such abstract representations bear psychological reality. Previous research has shown that learners in artificial grammar learning experiments are able to generalize a newly learned phonological pattern to novel segments, suggesting that learners are able to form abstract, feature-based representations. However, conflicting results suggest that this level of abstraction may be restricted to vowels, rather than consonants. The present experiment extends previous findings on generalization to novel segments in vowel harmony to an analogous pattern, consonant harmony. We show that learners fail to generalize to novel consonants in consonant harmony, but succeed at generalization to novel consonants in a general, consonant deletion pattern. Implications for the role of distinctive phonetic features in phonological learning are discussed.