Putting the prime in priming: Using prime processing behavior to predict target structural processing.

Abstract

Structural priming effects are widespread and heavily relied upon to assess structural representation and processing. Whether these effects are caused by error-driven implicit learning, residual activation, a combination of these, or some other learning mechanism remains to be established. The current study used preexisting data and a novel data analysis approach that links processing at the prime to later processing at the target to better understand the nature of structural priming. This novel analytic approach was applied to total reading times from a previously published structural priming study in comprehension, which provided processing measures of the structurally critical regions of prime reduced-relative clause sentences. These were then used as predictors in a series of hierarchical linear models where analogous processing measures at the target sentence regions served as outcome variables. Separate sets of models were run for prime-target pairs that had the same structure (i.e., abstract priming) and those that had the same structure and initial verb (i.e., a lexical boost). Prime-to-target processing relationships were observed for both types of prime-target pairs, but showed very different patterns. This provides support for the claim that abstract priming effects and the lexical boost are caused by different mechanisms. Additionally, the observed effects were positive and so do not support the error-driven learning prediction that processing difficulty at the prime should lead to greater facilitation at the target. Overall, this novel method provides a new tool for investigating structural priming and processing.