Learning English Metrical Phonology:When Probability Distributions Are Not Enough

Language involves knowledge of multiple complex linguistic systems, such as phonology, morphology, and syntax. For each system, the process of acquisition requires children to discover the underlying components of that system, which native speakers use to generate the observable data. Theoretical research traditionally provides a description of the knowledge to be acquired, identifying what children must know in order to have achieved the same knowledge that adults have. Experimental research often provides the time course of acquisition, identifying when children attain various pieces of this knowledge. Computational modeling research can then draw on the linguistic representations from theoretical research and the trajectory of acquisition from experimental research to examine questions about the process of acquisition. More specifically, within a model, we have complete control over the acquisition mechanism so we can precisely manipulate some part of it and see the results of that manipulation on the acquisition process. The focus of this paper is the acquisition of the complex linguistic system of metrical phonology, using a model that learns from child-directed speech input and keeps in mind restrictions on the time course of acquisition and children’s cognitive limitations. If we believe the model captures the salient aspects of acquisition, the results that come from the manipulations of this model inform us about the nature of acquisition in children. From a practical standpoint, some manipulations that are easy to do in a model are difficult to do with children, e.g. controlling the hypotheses children entertain, the data they learn from, and the way they change beliefs in competing hypotheses. Because of this, the empirical data that comes from modeling can be particularly informative about these aspects of acquisition, with respect to both what will (and what will not) work. Acquiring a complex linguistic system is not an easy task because there is often a non-transparent relationship between the observable data and the underlying system (or grammar) that speakers used to generate those data. Children must effectively reverse engineer the data, inferring the pieces of the underlying system that combined to create them. As an example, consider the metrical phonology system, which determines the stress contour associated with a word. This includes which syllables are stressed, as well as how much stress syllables receive relative to each other. Here, we will consider only the basic division into stressed and unstressed. Suppose a child encounters the word octopus, which has the stress contour [stressed unstressed unstressed]1. There are a variety of hypotheses a child might posit that are compatible with this stress contour, some of which are shown in (1). Perhaps all words ending with the sound s are stress-initial (1a), or all 3-syllable words have this stress contour (1b), or a word’s stress contour results from dividing the word into units larger than syllables according to a number of options, and then stressing the leftmost syllable in each larger unit (1c).