SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser
This work addresses the challenge of inferring useful assumptions from syntactic theory for characterizing human sentence representations, though it is incremental as it builds on existing psycholinguistic paradigms and theories.
The authors tackled the problem of testing syntactic theories for human sentence processing by introducing SPAWN, a cognitively motivated parser that generates priming predictions, and found that the Participial-Phase theory aligned with human behavior in relative clauses while the Whiz-Deletion theory did not.
Structural priming is a widely used psycholinguistic paradigm to study human sentence representations. In this work we introduce SPAWN, a cognitively motivated parser that can generate quantitative priming predictions from contemporary theories in syntax which assume a lexicalized grammar. By generating and testing priming predictions from competing theoretical accounts, we can infer which assumptions from syntactic theory are useful for characterizing the representations humans build when processing sentences. As a case study, we use SPAWN to generate priming predictions from two theories (Whiz-Deletion and Participial-Phase) which make different assumptions about the structure of English relative clauses. By modulating the reanalysis mechanism that the parser uses and strength of the parser's prior knowledge, we generated nine sets of predictions from each of the two theories. Then, we tested these predictions using a novel web-based comprehension-to-production priming paradigm. We found that while the some of the predictions from the Participial-Phase theory aligned with human behavior, none of the predictions from the the Whiz-Deletion theory did, thus suggesting that the Participial-Phase theory might better characterize human relative clause representations.