Rapid generalization in phonotactic learning

Abstract

Speakers judge novel strings to be better potential words of their language if those strings consist of sound sequences that are attested in the language. These intuitions are often generalized to new sequences that share some properties with attested ones: Participants exposed to an artificial language where all words start with the voiced stops [b] and [d] will prefer words that start with other voiced stops (e.g., [g]) to words that start with vowels or nasals. The current study tracks the evolution of generalization across sounds during the early stages of artificial language learning. In Experiments 1 and 2, participants received varying amounts of exposure to an artificial language. Learners rapidly generalized to new sounds: In fact, following short exposure to the language, attested patterns were not distinguished from unattested patterns that were similar in their phonological properties to the attested ones. Following additional exposure, participants showed an increasing preference for attested sounds, alongside sustained generalization to unattested ones. Finally, Experiment 3 tested whether participants can rapidly generalize to new sounds based on a single type of sound. We discuss the implications of our results for computational models of phonotactic learning.