Privileged computations for closed-class items in language acquisition
- Heidi Getz, Center for Brain Plasticity and Recovery, Georgetown University, Washington, District of Columbia, United States
- Elissa Newport, Center for Brain Plasticity and Recovery, Georgetown University, Washington, District of Columbia, United States
AbstractIn natural languages, closed-class items predict open-class items but not the other way around. For example, in English, if there is a determiner there will be a noun, but nouns can occur with or without determiners. Here, we asked whether language learners’ computations are also asymmetrical. In three experiments we exposed adults to a miniature language with the one-way dependency “if X then Y”: if X was present, Y was also present, but X could occur without Y. We created different versions of the language in order to ask whether learning depended on which of these categories was an open or closed class. In one condition, X was a closed class and Y was an open class; in a contrasting condition, X was an open class and Y was a closed class. Learning was significantly better with closed-class X, even though learners’ exposure was otherwise identical. Additional experiments demonstrated that the perceptual distinctiveness of closed-class items drives learners to analyze them differently; and, crucially, that the primary determinant of learning is the mathematical relationship between closed- and open-class items and not their linear order. These results suggest that learners privilege computations in which closed-class items are predictive of, rather than predicted by, open-class items. We suggest that the distributional asymmetries of closed-class items in natural languages may arise in part from this learning bias.
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