SPAWNing Structural Priming Predictions from a Cognitively Motivated
  Parser
Grusha Prasad Tal Linzen
Structural priming is a widely used psycholinguistic paradigm to study human
sentence representations. In this work we propose a framework for using
empirical priming patterns to build a theory characterizing the structural
representations humans construct when processing sentences. This framework uses
a new cognitively motivated parser, SPAWN, to generate quantitative priming
predictions from theoretical syntax and evaluate these predictions with
empirical human behavior. As a case study, we apply this framework to study
reduced relative clause representations in English. We use SPAWN to generate
priming predictions from two theoretical accounts which make different
assumptions about the structure of relative clauses. We find that the
predictions from only one of these theories (Participial-Phase) align with
empirical priming patterns, thus highlighting which assumptions about relative
clause better capture human sentence representations.