  Word order choices during sentence production can be primed by preceding
sentences. In this work, we test the DUAL MECHANISM hypothesis that priming is
driven by multiple different sources. Using a Hindi corpus of text productions,
we model lexical priming with an n-gram cache model and we capture more
abstract syntactic priming with an adaptive neural language model. We permute
the preverbal constituents of corpus sentences, and then use a logistic
regression model to predict which sentences actually occurred in the corpus
against artificially generated meaning-equivalent variants. Our results
indicate that lexical priming and lexically-independent syntactic priming
affect complementary sets of verb classes. By showing that different priming
influences are separable from one another, our results support the hypothesis
that multiple different cognitive mechanisms underlie priming.
