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Tier-based locality in long-distance phonotactics : learnability and typology McMullin, Kevin James


An important property of any language’s sound system is its phonotactics—the unique way in which it allows its inventory of speech sounds to combine. Interestingly, certain types of phonotactic co-occurrence restrictions found in natural languages may hold across any amount of intervening material. For example, the Samala (Chumash) language of Southern California exhibits a pattern of sibilant harmony, such that [s] and [ʃ] may not co-occur anywhere within the same word (e.g. /ha-s-xintila-waʃ/ becomes [ha-ʃ-xintila-waʃ] `his former gentile name'; Applegate, 1972). Long-distance dependencies like this, despite being relatively common cross-linguistically, are known to pose serious problems for learnability. A learner needs an enormous amount of computational power to discover an interaction in an unbounded search space defined by arbitrary distances, resulting in patterns that are not learnable in practice. Their existence in natural languages thus suggests that humans are equipped with cognitive learning biases that restrict the available hypothesis space and facilitate the learning of patterns with certain properties but not others. This dissertation presents a series of artificial language learning studies that support the hypothesis that the typology of locality relations in long-distance consonantal phonotactics is shaped, at least in part, by such biases. From a theoretical perspective, the goal is to explore and define the boundaries of the human learner's hypothesis space for phonotactic patterns. I argue that the seemingly simple constraints used in the Agreement by Correspondence framework (Rose and Walker, 2004; Hansson, 2010; Bennett 2013) generate many pathological patterns that are unattested cross-linguistically. By contrast, the properties of locality observed for patterns of long-distance consonant agreement and disagreement belong to a well-defined and relatively simple class of subregular formal languages (stringsets) called the Tier-based Strictly 2-Local languages (TSL₂; Heinz et al., 2011). I therefore argue that class of TSL₂ stringsets offers an excellent approximation of the boundaries of possible, human-learnable phonotactics. More generally, I suggest that the formal-language-theoretic approach can be used to inform phonological theory, allowing for a better understanding of the computational complexity and learnability of predicted patterns.

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