Verb bias and structural priming in non-linguistic grammar acquisition task


Domain-general statistical learning (SL) is thought to support language phenomena like verb bias and structural priming. We explored this idea by inducing these phenomena within a non-linguistic serial reaction time (SRT) task where participants learned an English-like artificial language using SL. In a series of two experiments we found error rates to be sensitive to verbs’ structural preferences and abstract structural priming. The similarities between the behaviour in this task and previous linguistic research suggests that this method may be useful for studying the nature of SL in language learning and processing.

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