Synthesising approaches to language learning and adaptation

Organised by Laura Bailey and Christina Kim (University of Kent)

For at least the last couple of decades, researchers working in the areas of language acquisition, learning, and learnability have been drawing from insights from both the parameter-setting and statistical learning literatures — approaches to language acquisition that at one time were seen as diametrically opposed to each other. Such ‘synthesis’ approaches (Yang 2004; Bonnati et al. 2005; Goldwater et al. 2009; Feldman et al. 2009; Frank & Tenenbaum 2010; Pearl 2011; Perfors et al. 2011; Lidz & Gagliardi 2015; among many others) have often also involved reconceptualising the problem of language acquisition (e.g. as a rational inference problem) or broadening the question to include all varieties of language learning  (e.g. second language learning, multilingualism) and attrition, adaptation in adult language, or language change. This workshop brings together researchers with a range of perspectives to comment on how the problems, methods, and answers look different today than they did a few decades ago.

We invite submissions for papers that bring together these different approaches to language learning for a special workshop at the LAGB annual meeting at the University of Kent in Canterbury.

• Talks will be 20+10 minutes long and the workshop will be held on Tuesday 5^thSeptember.
• Abstracts should be anonymous, no longer than 2 A4 pages in length (size 12, Times New Roman, 2.5cm margins, including references).
• Email abstracts to l.r.bailey AT kent.ac.uk with your name and affiliation in the body of the email.
• You may submit at most one solo-authored and one co-authored abstract.
• Deadline for submission: Sunday 28^th May 2017

Invited speakers:

Ansgar Endress (City University London)

Nina Kazanina (Bristol)

Mits Ota (Edinburgh)

Robyn Orfitelli (Sheffield)

References:

Bonatti, Peña, Nespor & Mehler 2005. Linguistic Constraints on Statistical Computations

The Role of Consonants and Vowels in Continuous Speech Processing. Psychological Science 16(6).

Feldman, Griffiths & Morgan 2009. The influence of categories on perception: Explaining the perceptual magnet effect as optimal statistical inference. Psychological Review, 116(4), 752-782.

Frank & Tenenbaum 2010. Three ideal observer models for rule learning in simple languages. Cognition 120:360–71.

Goldwater, Griffiths & Johnson 2009. A Bayesian framework for word segmentation:

Exploring the effects of context. Cognition, 112(1), 21–54.

Lidz & Gagliardi 2015. How Nature Meets Nurture: Universal Grammar and Statistical Learning. Annual Review of Linguistics, 1(1):333–352.

Pearl 2011. When unbiased probabilistic learning is not enough: Acquiring a parametric system of metrical phonology. Language Acquisition, 18(2):87–120.

Perfors, Tenenbaum & Regier 2011. The learnability of abstract syntactic principles.

Cognition, 118(3), 306–338.

Yang 2004. Universal Grammar, statistics or both? TRENDS in Cognitive Sciences 8(10).