Many theorists have dismissed a priori the idea that distributional information could play a significant role in syntactic category acquisition. We demonstrate empirically that such information provides a powerful cue to syntactic category membership, which can be exploited by a variety of simple, psychologically plausible mechanisms. We present a range of results using a large corpus of child-directed speech and explore their psychological implications. While our results show that a considerable amount of information concerning the syntac-tic categories can be obtained from distributional information alone, we stress that many other sources of information may also be potential contributors to the identification of syntactic classes. I.

A recent report demonstrated that 8-month-olds can segment a continuous stream of speech syllables, containing no acoustic or prosodic cues to word boundaries, into wordlike units after only 2 min of listening experience (Saffran, Aslin, & Newport, 1996). Thus, a powerful learning mechanism capable of extracting statistical information from fluent speech is available early in development. The present study extends these results by documenting the particular type of statistical computation—transitional (conditional) probability—used by infants to solve this word-segmentation task. An artificial language corpus, consisting of a continuous stream of trisyllabic nonsense words, was presented to 8-month-olds for 3 min. A postfamiliarization test compared the infants’ responses to words versus part-words (trisyllabic sequences spanning word boundaries). The corpus was constructed so that test words and part-words were matched in frequency, but differed in their transitional probabilities. Infants showed reliable

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Previous research has shown that, for learners to fully acquire a miniature phrase structure language, the language must contain cues to the phrases—for example, prosodic grouping or morphological agreement of the words within a phrase (Morgan, Meier, & Newport, 1987, 1989). Research on word segmentation has shown that learners can use transitional probabilities between syllables to segment speech into word-like units (Saffran, Aslin, & Newport, 1996). In the present research, we combine and extend these two sets of findings, asking whether learners can use transitional probabilities between words (or word classes) to segment sentences into phrases, and use this phrasal information to fully acquire the syntax of a miniature language. Adult subjects were exposed to sentences from a miniature language. A pattern in the transitional probabilities between words—high within phrases, low at phrase boundaries—was created by adding syntactic properties that are widespread in natural languages: optional phrases, repeated phrases, moved phrases, different-sized form classes, or all four properties combined. All conditions outperformed controls in learning the language. The best learning occurred with all properties combined, despite the fact that this language was the most complex. These data address the important question of how language learning is successful in the face of the massive complexity of natural languages. In our experiments, learning got better, not worse, when properly structured complexity was added to a language. The results also show that the same type of statistical computation useful in word segmentation might be used as well in learning syntax, suggesting that the range of statistics needed for acquiring various types of structure in natural languages might be suitably small. Correspondence should be addressed to Susan P. Thompson, Department of Psychology, 205

This paper 1 sketches a solution to the string-to-structure problem in first language acquisition within a set of emergentist assumptions that minimizes innate linguistic knowledge, minimizes demands for linguistic analysis by the language learner, and maximizes the projection of lexical properties of words. These conceptual constraints minimize the rules that have to be developed---in number, in complexity, and in diversity. The goal of this paper is to demonstrate the compatibility of theories of grammar which describe grammars of natural languages in terms of inheritance hierarchies of constraints on linguistic object types such as word, phrase, (syntactic) category, semantic content, referential index, nominal object and the like. The constructs and distinctions that would have to be attributed to the infant learner in order to represent the knowledge of words at the one-word stage provide a foundation for developing, via incremental addition of properties and distinctions, a co...

This paper investigates the role of prosody in one child’s lexical acquisition using an ecologically valid, high-density, longitudinal corpus. The corpus consists of high fidelity recordings collected from microphones embedded throughout the home of a family with a young child. We analyze data collected continuously from ages 9 – 24 months, including the child’s first productive use of language at about 11 months and ending at the child’s active use of more than 500 words. We found significant correlations between prosody of caregivers ’ speech and age of acquisition for individual words. Index Terms: prosody, child language acquisition, word learning, corpus data

To what extent is linguistic structure learnable from statistical information in the input? One set of cues which might assist in the discovery of hierarchical phrase structure given serially presented input are the dependencies, or predictive relationships, present within phrases. In order to determine whether adult learners can use this statistical information, subjects were exposed to artificial languages which either contained or violated the kinds of dependencies which characterize natural languages. The results suggest that adults possess learning mechanisms which detect and utilize statistical cues to phrase and hierarchical structure. A second experiment contrasted the acquisition of these linguistic systems with the same grammars implemented as non-linguistic input (sequences of non-linguistic sounds or shapes). These findings suggest that constraints on the mechanisms which highlight the statistical cues which are most characteristic of human languages are not specifically tailored for language learning.

The aim of the study was to investigate the prosodic pattern of aspect markers and their homonymous counterparts in child-directed speech (CDS) and adult-directed speech (ADS) in the context of story-telling. The acoustic parameters, fundamental frequency, relative amplitude and duration, of prosody were measured. The pairs of words chosen for study were the progressive aspect marker gcm2 and its homonymous adjective, meaning tight and the perfective aspect marker jo2 and its locative adverb, meaning left. The prosodic modification in CDS was found to be consistent with previous studies. It provided further evidence to support the possibly universal prosodic features of motherese. The acoustic parameters were found to differentiate grammatical category, that is aspect markers and their homonyms. The result helped to explain the early acquisition of aspect markers by Cantonese-speaking children. The role of prosody in the acquisition of functional morphemes is discussed. 1

We propose an explicit, incremental strategy by which children could group words with similar syntactic privileges into discrete, unlabeled categories. This strategy, which can discover lexical ambiguity, is based in part on a generalization of the idea of sentential minimal pairs. As a result, it makes minimal assumptions about the availability of syntactic knowledge at the onset of categorization. Although the proposed strategy is distributional, it can make use of categorization cues from other domains, including semantics and phonology. Computer simulations show that this strategy is effective at categorizing words in both artificial-language samples and transcripts of naturally-occurring, child-directed speech. Further, the simulations show that the proposed strategy performs even better when supplied with semantic information about concrete nouns. Implications for theories of categorization are discussed.

Distributional information is a potential cue for learning syntactic categories. Recent artificial grammar studies demonstrate sophisticated distributional learning by young infants. Here we investigate the possible mechanisms and representations underlying this ability. Does prosody constrain distributional analysis? What specific distributional relations do learners track? Twelve-month-old infants were exposed to an artificial language comprised of 3-word-sentences of the form aXb and cYd, where X and Y words differed in the number of syllables. Subsequently they were tested on novel utterances that were consistent or inconsistent with the training sentences. In Experiment I, infants showed evidence for having learned the relevant relations by successfully discriminating between novel grammatical and ungrammatical sentences. In Experiment II, we asked whether prosody influences infants ’ distributional analysis. Contrary to our expectations, infants did not show a preference for relations between words that fell within a prosodic unit over those that straddled a prosodic boundary. In Experiment III, we explored whether infants ’ success in the first experiment arose from their representation of nonadjacent relations or distributional frames. Our results did not support a frames hypothesis. We discuss these results and offer hypotheses regarding the nature of infants’ distributional learning abilities.