Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing
We would like to thank four anonymous reviewers, whose comments and suggestions have been extremely helpful in revising the manuscript. We are especially grateful to Lourdes Ortega, who went far beyond her duty in providing invaluable insights and assisting us with the revisions. This research was supported by an NIH postdoctoral fellowship to BP (NIH Training Grant T32‐DC000035 awarded to the Center for Language Sciences at University of Rochester), an NSF Graduate Research Fellowship to DK, an NIH postdoctoral fellowship to AF (NIH Training Grant T32‐HD055272), and by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number R01HD075797 to TFJ. The content is solely our responsibility and does not necessarily represent the official views of the National Institutes of Health.
Abstract
We present a framework of second and additional language (L2/Ln) acquisition motivated by recent work on socio‐indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio‐indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/Ln learning uses the same type of socio‐indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/Ln acquisition as probabilistic inference under uncertainty over socio‐indexical structure. It also offers a new perspective on crosslinguistic influences during L2/Ln learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa.
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