Evaluation, use, and refinement of knowledge representations through acquisition modeling

Generative approaches to language have long recognized the natural link between theories of knowledge representation and theories of knowledge acquisition. The basic idea is that the knowledge representations provided by Universal Grammar enable children to acquire language as reliably as they do because these representations highlight the relevant aspects of the available linguistic data. So, one reasonable evaluation of any theory of representation is how useful it is for acquisition. This means that when we have multiple theories for how knowledge is represented, we can try to evaluate these theoretical options by seeing how children might use them during acquisition. Computational models of the acquisition process are an effective tool for determining this, since they allow us to incorporate the assumptions of a representation into a cognitively plausible learning scenario and see what happens. We can then identify which representations work for acquisition, and what those representations need to work. This in turn allows us to refine both our theories of how knowledge is represented and how those representations are used by children during acquisition. I discuss two case studies of this approach for representations in metrical stress and syntax, and consider what we learn from this computational acquisition evaluation in each domain.