To appear in G. Houghton (Ed.), Connectionist models in cognitive psychology. Hove, U.K.: Psychology Press.
Considerable research in language acquisition has addressed the extent to which basic aspects of linguistic structure might be identified on the basis of probabilistic cues in caregiver speech to children. In this chapter, we examine systems that have the capacity to extract and store various statistical properties of language. In particular, groups of overlapping, partially predictive cues are increasingly attested in research on language development (e.g., Morgan & Demuth, 1996). Such cues tend to be probabilistic and violable, rather than categorical or rule-governed. Importantly, these systems incorporate mechanisms for integrating different sources of information, including cues that may not be very informative when considered in isolation. We explore the idea that conjunctions of these cues provide evidence about aspects of linguistic structure that is not available from any single source of information, and that this process of integration reduces the potential for making false generalisations. Thus, we argue that there are mechanisms for efficiently combining cues of even very low validity, that such combinations of cues are the source of evidence about aspects of linguistic structure that would be opaque to a system insensitive to such combinations, and that these mechanisms are used by children acquiring languages (for a similar view, see Bates & MacWhinney, 1987). These mechanisms also play a role in skilled language comprehension and are the focus of so-called constraint-based theories of sentence processing (Cottrell, 1989; MacDonald, Pearlmutter & Seidenberg, 1994; Trueswell & Tanenhaus, 1994) that emphasise the use of probabilistic sources of information in the service of computing linguistic representations. Since the learners of a language grow up to use it, investigating these mechanisms provides a link between language learning and language processing (Seidenberg, 1997).
In the standard learnability approach, language acquisition is viewed in terms of the task of acquiring a grammar (e.g., Pinker, 1994; Gold, 1967). This type of learning mechanism presents classic learnability issues: there are aspects of language for which the input is thought to provide no evidence, and the evidence that does exist tends to be unreliable. Following Christiansen, Allen & Seidenberg (1998), we propose an alternative view in which language acquisition can be seen as involving several simultaneous tasks. The primary task —the language learner’s goal—is to comprehend the utterances to which she is exposed for the purpose of achieving specific outcomes. In the service of this goal the child attends to the linguistic input, picking up different kinds of information, subject to perceptual and attentional constraints. There is a growing body of evidence that as a result of attending to sequential stimuli, both adults and children incidentally encode statistically salient regularities of the signal (e.g., Cleeremans, 1993; Saffran, Aslin & Newport, 1996; Saffran, Newport & Aslin, 1996). The child’s immediate task, then, is to update its representation of these statistical aspects of language. Our claim is that knowledge of other, more covert aspects of language is derived as a result of how these representations are combined through multiple cue integration. Linguistically relevant units (e.g., words, phrases, and clauses) emerge from statistical computations over the regularities induced via the immediate task. On this view, the acquisition of knowledge about linguistic structures that are not explicitly marked in the speech signal—on the basis of information that is—can be seen as a third derived task. We address these issues in the specific context of learning to identify individual words in speech. In the research reported below, the immediate task is to encode statistical regularities concerning phonology, lexical stress and utterance boundaries. The derived task is to integrate these regularities in order to identify the boundaries between words in speech.
The remainder of this chapter presents on our work on the modelling of
early infant speech segmentation in connectionist networks trained to integrate
multiple probabilistic cues. We first describe past work exploring the
segmentation abilities of our model (Allen & Christiansen, 1996; Christiansen,
1998; Christiansen et al., 1998). Although we concentrate here on the relevance of
combinatorial information to this specific aspect of acquisition, our view is that
similar mechanisms are likely to be relevant to other aspects of acquisition and to
skilled performance. Next, we present results from three new sets of simulations1.
The first simulation involves a corpus analysis inspired by the Christiansen et al.
(1998) model, and which provides support for the advantage of integrating
multiple cues in language acquisition. In the second simulation, we demonstrate
the model’s robustness in terms of dealing with noisy input beyond what other
segmentation models have been shown capable of dealing with. The third
simulation extends the coverage of the model to include recent controversial data
on purported rule-learning by infants (Marcus, Vijayan, Rao & Vishton, 1999).
Finally, we discuss how multiple cue integration works and how this approach
may be extended beyond speech segmentation.
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