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 **Abstract**
 \\
+\\
+A crucial component of language acquisition involves
+organizing words into grammatical categories and discovering
+relations between them. Many studies have argued that
+phonological or semantic cues or multiple correlated cues are
+required for learning. Here we examine how distributional
+variables will shift learners from forming a category of lexical
+items to maintaining lexical specificity. In a series of
+artificial language learning experiments, we vary a number of
+distributional variables to category structure and test how
+adult learners use this information to inform their hypotheses
+about categorization. Our results show that learners are
+sensitive to the contexts in which each word occurs, the
+overlap in contexts across words, the non-overlap of contexts
+(or systematic gaps), and the size of the data set. These
+variables taken together determine whether learners fully
+generalize or preserve lexical specificity.
+\\
+\\
+----
+**Introduction**
+\\
+\\
+Language acquisition crucially involves finding the
+grammatical categories of words in the input. The
+organization of elements into categories, and the
+generalization of patterns from some seen element
+combinations to novel ones, account for important aspects
+of the expansion of linguistic knowledge in early stages of
+language acquisition. One hypothesis of how learners
+approach the problem of categorization is that the categories
+(but not their contents) are innately specified prior to
+experiencing any linguistic input, with the assignment of
+tokens to categories accomplished with minimal exposure.
+A second possibility is that the categories are formed around
+a semantic definition. A third hypothesis, explored in the
+present research, is that the distributional information in the
+environment is sufficient (along with a set of learning
+biases) to extract the categorical structure of natural
+language. While it is likely that each of these sources of
+evidence makes important contributions to language
+acquisition, this third hypothesis regarding distributional
+learning has often been thought to be an unlikely
+contributor, given the information processing limitations of
+young children and the complexity of the computational
+processes that would be entailed.
+\\
+\\
+Furthermore, it has been difficult to test the importance of
+such a distributional learning mechanism because the cues
+to category structure in natural languages are highly
+correlated. In fact, it has been argued in many artificial
+language studies that the formation of linguistic categories
+(e.g., noun, verb) depends crucially on some perceptual
+property linking items within the category (Braine, 1987).
+This perceptual similarity relation might arise from identity
+or repetition of elements in grammatical sequences, or a
+phonological or semantic cue identifying words across
+different sentences as similar to one another (for example,
+words ending in –a are feminine, or words referring to
+concrete objects are nouns). Learners of artificial languages
+have been unable to acquire grammatical categories and to
+extend their linguistic contexts to new items correctly
+without such cues (Braine et al., 1990; Frigo & McDonald,
+; Gomez & Gerken, 2000). However, this has been
+somewhat of a puzzle: Maratsos & Chalkley (1980) argued
+that in natural languages, grammatical categories do not
+have reliable phonological or semantic cues; rather, learners
+must utilize distributional cues about the linguistic contexts
+in which words occur to acquire such categories. Mintz,
+Newport & Bever (2002), as well as several other
+researchers, have shown that computational procedures
+utilizing distributional contexts can form elementary
+linguistic categories on corpora of mothers’ speech to young
+children from the CHILDES database, and Mintz (2002) and
+Gerken et al. (2005) have shown that both adults and infants
+can learn a simple version of this paradigm in the
+laboratory, at least when there are multiple correlated
+distributional cues. In the present series of experiments we
+also begin by demonstrating that there are distributional
+properties that lead to successful learning of linguistic
+categories in artificial language paradigms. Importantly,
+however, in order to understand how this mechanism works
+in human learners and why many previous experiments have
+not found such learning, we present a series of experiments
+that manipulate various aspects of these distributional
+variables, in order to understand the computational
+requirements for successful category learning.
+\\
+\\