Data for Feher, Wonnacott & Smith (2016), Structural priming in artificial languages and the regularisation of unpredictable variation.

This data set contains data for a study that presented a novel experimental technique using artificial language learning to investigate the relationship between structural priming during communicative interaction, and linguistic regularity. We trained participants on artificial languages exhibiting unpredictable variation in word order, and subsequently had them communicate using these artificial languages. We conducted two experiments: Experiment 1 manipulated noun phrase ordering (following the paradigm used by Culbertson et al. 2012, 2015) in which the artificial language consisted of noun phrases describing objects with either numerals or adjectives; Experiment 2 described simple actions between two actors, an agent and a patient, and the ordering of the agent and patient nouns was manipulated. We found evidence for structural priming in both grammatical constructions, and across human-human and human-computer interaction. Priming occurred regardless of behavioral 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 in all conditions, suggesting a role for communicative interaction in eliminating unpredictable variation. Regularisation was strongest 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. We suggest that participants recognise the counter-functional nature of unpredictable variation and thus act to eliminate this variability during communication. Our method offers potential benefits to both the artificial language learning and the structural priming fields, and provides a useful tool to investigate communicative processes that lead to language change and ultimately language design.