We contrasted models of grammatical encoding, investigating structural priming.

Verb repetition boosted priming for thematic role order and argument structure.

Thematic role order priming and argument structure priming interacted.

We found no evidence for independent constituent structure persistence.

These results support the interactive structure-building account.

How speakers sequence words and phrases remains a central question in cognitive psychology. Here we focused on understanding the representations and processes that underlie structural priming, the speaker’s tendency to repeat sentence structures encountered earlier. Verb repetition from the prime to the target led to a stronger tendency to produce locative variants of the spray-load alternation following locative primes (e.g., load the boxes into the van) than following with primes (e.g., load the van with the boxes). These structural variants had the same constituent structure, ruling out abstract syntactic structure as the source of the verb boost effect. Furthermore, using cleft constructions (e.g., What the assistant loaded into the lift was the equipment), we found that the thematic role order (thematic role-position mappings) of the prime can persist separately from its argument structure (thematic role-syntactic function mappings). Moreover, both priming effects were enhanced by verb repetition and interacted with each other when the construction of the prime was also repeated in the target. These findings are incompatible with the traditional staged model of grammatical encoding, which postulates the independence of abstract syntax from thematic role information. We propose the interactive structure-building account, according to which speakers build a sentence structure by choosing a thematic role order and argument structure interactively based on their prior co-occurrence together with other structurally relevant information such as verbs and constructions.

Structural priming

Lexical boost

Argument structure

Sentence production

Grammatical encoding

© 2023 The Author(s). Published by Elsevier Inc.