[PS-2.69] Structural priming in artificial languages and the regularisation of unpredictable variation

Feher, O. 1 , Wonnacott, E. 2 & Smith, K. 1

1 University of Edinburgh
2 University College London

We present a novel experimental technique using artificial language learning to investigate how structural priming during communicative interaction leads to linguistic regularity. We study the learning and use of unpredictable linguistic variation, which is a well-established paradigm to explore learners? biases during acquisition, transmission and interaction. We trained participants on artificial languages exhibiting unpredictable variation in word order, and subsequently had them communicate using these artificial languages. Across two experiments, we found evidence for structural priming in two different grammatical constructions and across human-human and human-computer interaction. Priming occurred regardless of behavioural convergence: communication led to shared word order use only in human-human interaction, but priming was observed in all conditions. Furthermore, interaction resulted in the reduction of unpredictable variation, both in human-human interaction and in a condition where participants believed they were interacting with a human but were in fact interacting with a computer, suggesting that participants? beliefs about their communication partner influenced their language use. Our method offers potential benefits to both the artificial language learning and structural priming fields, and provides a useful tool not only to investigate the mental representations underlying newly-acquired linguistic knowledge, but also how communicative processes may shape linguistic structure.