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|Full Title:||Workshop on Learning Biases in Natural and Artificial Language Acquisition|
|Location:||Oxford, United Kingdom|
|Start Date:||05-Sep-2014 - 05-Sep-2014|
|Meeting Email:||click here to access email|
|Meeting Description:||This workshop, organised by Adam Albright (MIT) and Andrew Nevins (UCL), will be held in conjunction with Adam Albright's Linguistics Association Lecture on Friday 5th September at the 2014 Annual Meeting of the Linguistics Association of Great Britain in Oxford.
What expectations or biases do learners bring to the task of learning phonological grammars? Work on language typology, diachronic change, and evaluation metrics for learning algorithms has identified a number of factors that might encourage learners to favor one hypothesis over another. These include preferences based on formal properties of the grammar, such as a bias for featurally simpler or more general processes, or a bias towards certain type of interactions. They also include substantive biases
for certain types of processes, such as a preference for processes that target phonetically difficult structures, or a bias against processes that lead to perceptually salient alternations, or even limitations that make some processes completely unlearnable.
Until recently, the argument that learners favor some patterns over others has largely been based on indirect evidence: learning biases can provide an account of how grammatical preferences shape acquisition errors, language change, and typology. The past decade has seen a rapid rise of interest in studying learning directly in the lab, both among infants and adults. This work has studied the time course of acquisition of natural language (L1) patterns by children, as well as the rate or readiness with which infants and adults learn artificial grammars.
The goal of this workshop is to bring together researchers employing a variety of techniques to study this kind of phonological learning 'in the lab'. The workshop aims to foster a dialogue on questions such as: how can we relate performance in an artificial lab task to natural language acquisition? What kinds of biases have actually been supported by experimental results, to date? What kinds of biases do these techniques allow us to test, and what kinds of biases can only be observed within the context of a
full-blown linguistic system with qualitatively and quantitatively more complex training, longer timescales of learning, and learning within richer semantic contexts? What is the contribution, if any, of participants' L1 to the task of artificial grammar learning? We hope that the invited talks and the posters, selected from an open call for papers, will shed light on these and other questions through a range of theoretical and empirical contributions.
|Linguistic Subfield:||Phonology; Psycholinguistics; Language Acquisition|
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