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Statistical learning, learning biases and the nature of human languages
How do children take the language they hear and construct a mental grammar – that is, a system that allows them to produce and understand novel utterances? One approach focuses on how statistical learning mechanisms can be used to identify reoccurring patterns and form appropriate generalizations. Our research explores this question using Artificial Language Learning experiments, where participants learn and are tested on novel languages created by the experimenter. The languages can be very simple, but this provides a controlled methodology for exploring how the statistics of language input affect what is learned. Our experiments have explored how the structure of language input can affect the extent to which learners extract generalization. A new collaboration with Ben Ambridge continues this work as part of an ERC funded project. Ongoing experiments explore what types of input lead learners to avoid over-generalization of linguistic constructions (e.g. not to generalize the verb “carry” to the construction *he carried the child the parcel). There is evidence that frequently hearing utterances such as *he carried the parcel to the child plays a role, but what about the learners’ more general experience of hearing “carry” in other constructions? Other work uses similar artificial language learning methodology to looks at learners’ biases and how these might shape human languages. For example, languages exhibit variation: in English the precise way in which we pronounce the plural marker -s varies (e.g. sometimes “s” e.g. “cats”, sometimes “z” e.g. “dogs”). While it seems logically possible that this kind of variation could occur completely at random (e.g. randomly chose to produce “s” or “z”), this type of behaviour very rarely (possibly never) occurs in human languages. Instead, linguistic variation is predictable: in the case of -s, the pronunciation is predictable from the last sound in the noun. Why do languages work like this? Seminal work by Hudson Kam & Newport suggests that this is due to strong learning biases in children which lead them to regularize inconsistent input. With collaborators Kenny Smith and Olga Feher, we have been exploring the extent to which these biases for regularization are also present to a weaker extent in adult learners, how they might be exacerbated by interactions between language users, and how this might influence language structure. We have also looked at whether children and adults can learn probabilistic social linguistic conditioning (i.e. learning that certain speakers are more likely to use some forms than others).
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