March 9th: Heidi Getz

Sentence First, Arguments After: Mechanisms of Morphosyntax Acquisition

Heidi Getz, Georgetown University

Tuesday, Mar 9 2021, 16:00-17:00 GMT
Zoom Details: [Please Request]

Natural languages contain complex grammatical patterns. For example, in German, verbs are generally morphologically finite when they occur second and morphologically non-finite when they are final, as in dein Bruder möchte in den Zoo gehen (“Your brother wants to go to the zoo”). Extensive research has documented young children’s knowledge of these morphosyntactic contingencies (Poeppel & Wexler, 1993; Deprez & Pierce, 1994), but we lack a mechanistic theory of how this knowledge is actually acquired.

One approach might be to learn the position of prosodically prominent open-class items (“verbs go 2nd or last”) and then to fill in the morphological details. Alternatively, one could work in the opposite direction, first learning the position of closed-class items (“-te goes 2nd and -en goes last”) and then fitting open-class items into the resulting structure. This second approach is counter-intuitive, but I will argue that it is the one learners take. Evidence comes from a series of miniature language experiments in which adults and children analyzed closed-class items as predictive of the presence and position of open-class items, but not the reverse. This learning asymmetry was strongest when the closed-class items had several properties typical of closed class items in natural languages (e.g., high frequency, short, no coda), suggesting that the perceptual distinctiveness of closed-class items drives this effect.

Taken together, the results suggest that early attention to closed-class items (Morgan, Meier, & Newport, 1987; Shi, Werker & Morgan, 1999; Valian & Coulson, 1988) might cause these items to serve as the constant terms in learners’ computations, allowing other patterns to be learned and represented relative to them. A learning mechanism that operates in this way would ultimately represent a broad range of language patterns in terms of the distribution of a small set of closed-class items—just as patterns are represented in modern syntactic theory (Rizzi & Cinque, 2016). The results of our experiments suggest that human languages may acquire this type of structure at least in part as a consequence of perceptual biases in the human language learner.