Phonological generalization from distributional evidence

Phonological Generalization from Distributional Evidence Bo˙zena Paj ak ˛ & Roger Levy {bpajak, rlevy}@ucsd.edu Department of Linguistics, UC San Diego 9500 Gilman Drive #108, La Jolla, CA 92093 USA Abstract We propose a model of L2 phonological learning in which the acquisition of novel phonological category inventories pro- ceeds not by mapping L2 inputs onto existing category inven- tories available in L1 and other already known languages, but rather through general categorization processes in which L1 and other language knowledge serves as an inductive bias. This approach views linguistic knowledge as hierarchically orga- nized such that the outcome of acquisition of a language—L1 or otherwise—includes not only knowledge of the specific lan- guage in question, but also beliefs about how any language is likely to be structured. In this paper we test a set of predictions regarding how two key types of information can come together to drive L2 learning: distributional information within a single phonetic dimension and generalization bias derived from ex- isting knowledge of language. We tested these predictions by training adult monolingual English speakers in a distributional learning paradigm (Maye & Gerken, 2000; Maye, Werker, & Gerken, 2002) on a novel contrast, segmental length, and test- ing them on categorization of short and long segments for both trained and untrained items. Results show both learning and generalization from one class of segments (sonorants) to another class (obstruents), broadening the empirical range of phonetic contrasts for which distributional learning has been shown to be effective and providing evidence for our approach to L2 learning as one of inductive inference and generalization rather than of mapping. Keywords: L2 phonological acquisition; distributional learn- ing; speech perception; categorization; generalization. Introduction Language learning in adulthood has traditionally been re- garded as an inherently difficult process due to first language (L1) interference. One reason for this view is the common assumption that second language (L2) begins as parasitic on L1, and only gradually separates itself as an independent lan- guage in the course of learning (e.g., MacWhinney, 1987). We propose a model in which L2 learning (and, more specif- ically, phonological acquisition) is instead viewed as a pro- cess of inductive inference, where learners make implicit predictions about the possible underlying structures of the novel language by combining two sources of information: (1) the statistical properties of the L2 input, and (2) previous language knowledge (including both experience and any in- nate biases), which serves as an inductive bias guiding learn- ers in their inferences about novel phonological structures. The proposed model assumes that the structure of language knowledge is represented at multiple levels with one level for knowledge of specific languages, and a higher level represent- ing more abstract knowledge of the structure of languages in general. This model fits within the general approach to learn- ing as a process of rational hypothesis construction and test- ing, in which learners infer the underlying structure of their input by generalizing beyond the specific surface properties that they are exposed to (e.g., Tenenbaum & Griffiths, 2001; Xu & Tenenbaum, 2007; Gerken, 2010). At the same time, the proposed model is radically different from traditional views on L2 phonological acquisition, where perception and learn- ing of novel sounds have been assumed to rely on the pro- cess of mapping of L2 sounds onto L1 phonological cate- gories (Best, 1995; Flege, 1995; Hancin-Bhatt, 1994; Kuhl & Iverson, 1995). Under these views, L2 learners—instead of making implicit rational predictions about the L2 phonologi- cal categories—try to establish conceptual links between L2 sounds and their most similar L1 counterparts, so as to pro- cess the unfamiliar sounds directly through their L1 phono- logical system. We propose, in contrast, that learners do not directly filter the L2 speech input through their L1 phono- logical categories, but rather that they make the best possible guesses about how individual novel sounds are grouped into categories by relying on the same mechanisms that are used in general categorization processes for many types of perceptual stimuli. In order to define the details of the proposed model we fol- low the general categorization literature in that any percep- tual stimulus can be represented as a point in a multidimen- sional psychological space. People are able to categorize the stimuli by abstracting information about stimulus dimensions (e.g., color, shape, size, etc.) from single instances of the input (Posner & Keele, 1968; Kruschke, 1992). Within Kruschke’s model, learning categories occurs by computing and attach- ing weights (or attention strength) to each of the stimulus dimensions. The attention strength reflects the relevance of any given dimension for a particular categorization task. That is, high strength will be associated with dimensions hypoth- esized as the most informative in distinguishing between cat- egories. This way, people are able to perform categorization tasks by selectively attending to dimensions that are relevant, while at the same time ignoring other dimensions (Nosofsky, 1986). For instance, with stimuli varying along three dimen- sions such as color, shape, and size (Fig. 1a), people are good at categorizing by just one dimension, for example color. In this situation, the psychological space gets stretched along the color dimension—due to high attention strength assigned to this dimension (Kruschke, 1992)—and shrunk along the size and shape dimensions (Fig. 1b). This strategy is effective in categorization tasks because by attending selectively to the relevant dimension, people maximize within-category sim- ilarity and between-category discriminability, thus avoiding between-category confusion due to variation along irrelevant dimensions. We pursue a similar idea to account for phonological