This dissertation investigates the effect of language experience on syntactic predictions during real time language processing, and how these predictions develop. In particular, it focuses on filler-gap dependency processing. A prominent psycholinguistic theory suggests that incremental processing decisions are governed by statistics derived from the distribution of structures in the input. Children are an ideal testing ground for this theory because they are still acquiring this distributional information.
The first part of this dissertation examines children’s syntactic predictions during the real time comprehension of filler-gap dependencies. Though adults’ active association of the filler with the verb has been robustly demonstrated, visual world eye tracking data reveals that children do not actively complete the dependency at the verb. A probabilistic account of this finding would attribute it to differential experience with gap positions. A corpus analysis of the distribution of gap positions in the input to adults, child-directed speech, and children’s spontaneous utterances revealed that this was not the case; adults and children have similar experience with gap positions.
The second part of this dissertation directly manipulates adults’ recent language experience to test predictions of the probabilistic parsing model. Two eye tracking during reading experiments revealed that exposure to an improbable gap position can decrease active gap filling at the verb, but it does not increase the likelihood of predicting this alternative structure. A third experiment suggests that these effects may be due to a task-specific processing strategy.
The third part of this dissertation attempts to accelerate the development of active gap filling by manipulating the statistics in children’s input. This distribution is provided by a novel picture completion task designed to elicit wh-questions. Comprehension of concentrated filler-gap dependency input had no effect on children’s syntactic predictions, but production of a less probable gap position primed predictions for the more probable one.
Finally, this dissertation critically evaluates the probabilistic parsing model in light of the experiments reported within and finds that statistical information does not reliably predict parsing behaviors. An alternative model is proposed that accounts for these findings and appeals on the representational requirement imposed by the filler-gap dependency structure
