Generalization to Novel Consonants in Artificial Grammar Learning

Generalization to Novel Consonants in Artificial Grammar Learning Sara Finley (sfinley@bcs.rochester.edu) Department of Brain and Cognitive Sciences, Meliora Hall, University of Rochester Rochester, NY 14627 USA Abstract 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. Keywords: phonology. statistical learning, consonant harmony, Phonetic Features in Phonology Phonological processes are systematic patterns of sounds that govern the way words and phrases may sound in different languages. Typically, descriptive accounts of these patterns involve abstract, phonetic features to characterize the regularities of the sound patterns. Vowel and consonant harmony is characterized by agreement for a particular vowel/consonant feature value. For example, in back vowel harmony (which occurs in several languages such as Finnish, Hungarian, and Turkish (van der Hulst & van de Weijer, 1995)), if the first vowel of a word bears the feature [back] (e.g., [o, u]), all vowels of the word must also bear the [back] feature. The most concise description of vowel and consonant harmony makes use of distinctive phonetic features such as [back]. There are two ways in which phonological processes can be described without the use of distinctive features. The first is to characterize phonological patterns in terms of the individual segments involved. While this can describe the data, such an analysis misses the generalization that the vowels involved in harmony are not from arbitrary sets, but phonetically similar units. A second option is to characterize the phonological pattern in terms of an exemplar network. This exemplar network would contain a specialized model of similarity that would be used to capture the generalization about the feature involved in the phonological process without specific reference to features (Pierrehumbert, 2001). Such exemplar networks can describe the same phonological processes that make use of features, but are more complex than a feature-based representation because exemplar models require networks of association between all spoken and heard tokens, as well as an algorithm for computing similarity between exemplars. Previous attempts at providing evidence for the psychological reality of distinctive phonological features have brought mixed results. Using the artificial grammar learning paradigm, researchers trained participants on a novel phonological pattern with a limited sound inventory (e.g., four vowels of a six-vowel inventory). At test, these participants were given items that contain sounds not included in the exposure inventory (e.g., the two vowels held-out of the six-vowel inventory). If learners extend the newly learned phonological pattern to the novel segments, it suggests that they learned the pattern in terms of distinctive phonological features. If they fail to extend the pattern to the novel sounds, it suggests that the learners used some other representation to encode the novel pattern (e.g., based on segment-specific representations). When participants were exposed to a novel phonological pattern involving vowels (e.g., vowel harmony), there was significant generalization to the novel vowels (Chambers, Onishi, & Fisher, 2010; Finley & Badecker, 2009; Skoruppa & Peperkamp, 2011). However, when participants were exposed to a phonological pattern involving generalization to novel consonants, learners failed to generalize (Peperkamp & Dupoux, 2007; Peperkamp, Le Calvez, Nadal, & Dupoux, 2006). There are several important differences between the studies that did not show generalization to novel segments and the studies that did. Finley and Badecker (2009) and Skoruppa and Peperkamp (2011) used a process that involved vowels, while Peperkamp and Dupoux (2007) trained participants on a process involving intervocalic voicing in obstruent consonants (that applied either only to stops or only to fricatives). It is possible that vowel representations are more amenable to feature-based generalization than consonant representations. There are several reasons why this might be true. The acoustic space of vowels is more variable and continuous than the acoustic space of consonants, which are represented in a more discrete manner. In addition, there is evidence that consonants are processed differently than vowels. Nespor and colleagues have argued that vowels tend to have a more grammatical function, while consonants tend to have a more lexical function (Bonatti, Pena, Nespor, & Mehler, 2005; Toro, Bonatti, Nespor, & Mehler, 2007; Toro, Shukla, Nespor, & Endress, 2008). Another possibility for the mixed results on generalization to novel segments is that all three studies used relatively