chapter  11
Integrating multiple cues in language acquisition: A computational study of early infant speech segmentation
ByMorten H. Christiansen, Suzanne Curtin
Pages 13

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 generalizations. 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 emphasize 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).