Natural language cues, and the acquisition of artificial grammars. (1)
Tony Trotter, Rebecca Frost, Padraic Monaghan, presented this poster at the Fifth Implicit Learning Seminar, Lancaster, UK in June 2016.
Language is composed of complex grammatical structures that learners must make sense of in order to achieve linguistic proficiency. Key questions concern how learners come to realise that these structures are present in the language input, and how they grow to understand the purpose they serve. Much research has tested learners’ processing of language structures in laboratory studies by training participants on artificial languages, and examining their learning of the structures they contain. For example, prior work has shown that learners can extract transitional information from language input, and use it to identify word boundaries (e.g. Saffran et al., 1996) and grammatical regularities, such as non-adjacent dependencies (e.g. Frost & Monaghan, 2016). However, successful acquisition of statistically defined recursive structures has proven notoriously difficult to achieve, making measuring the way that learners process them even more difficult to accomplish. Previous studies have tended to use sequences of nonsense words to determine learning of recursive structures. Natural language, in comparison, contains multiple other sources of information to support the processing of dependencies within recursive sentences. Key structural information is provided by; prosody (learners have been found to use voice pitch directional changes to indicate phrase boundaries), rhythm (pauses occur in speech at phrase boundaries), and information about semantic relationships within dependencies (e.g., knowing that dogs chase cats, and that cats meow, helps us to determine the meaning of “the cat the dog chases meows”). In a series of experimental studies, we show that the addition of explicit phonological cues - reflecting similarity across dependent elements in recursive structures - facilitates learning. We found no evidence of prosodic pitch cues promoting learning in isolation. However, rhythmic cues did seem to support learning, and combining all three cues resulted in the best performance of all. Our results indicate that learners are able to use phonological and rhythmic information in isolation to support learning of recursive structures. In addition, we show that learners are able to combine phonological, rhythmic, and prosodic information to assist in acquisition of recursive structures.