Introduction: Statistical learning and language acquisition
Patrick Rebuschat, John N. Williams · 2011
Reading the papers in Rebuschat and Williams' volume, ''Statistical learning and language acquisition,'' brings me both back in time and looking ahead to the future.I suppose that is appropriate, given that the kind of learning at issue is precisely the kind that avails itself of prior experience to predict future events.The construct of statistical learning is both intuitively appealing and frustratingly vague.The appeal of statistical learning, I believe, derives from its apparent simplicity: it would be sensible for learners to exploit distributions of events in their environments to predict future events.Unfortunately, the flip side of this apparent simplicity is that the construct is so easily applied that it is di‰cult to decide where statistical learning rightly begins and ends.Appropriately, then, the chapters in the current volume bring out both the pleasures and the pitfalls of accounts that invoke statistical learning mechanisms.When Dick Aslin, Elissa Newport, and I began to work on our collaborative studies on infant and adult statistical language learning in the early 1990s, we were keenly aware of the history surrounding these ideas.Their roots lie in the structural linguistics of Leonard Bloomfield (1933) & Zellig Harris (1955), and in prior experimental and theoretical work by Hayes & Clark (1970), Goodsitt, Morgan, & Kuhl (1993), Braine (1966), Reber (1967), Morgan & Newport (1981), Maratsos & Chalkley (1981), and many others.Despite the long history of research and debate surrounding these ideas, I did not anticipate the field's reaction to our initial infant studies.There were two interesting and surprising dimensions to those reactions.The first dimension spanned responses ranging from ''Duh!'' to ''Impossible!''Some colleagues, particularly those in the visual sciences, responded to our initial studies by saying: ''Of course learners track statistics in environmental input; how could they not?''At the other extreme, some readers questioned the idea that statistical information could have any e‰cacy whatsoever given the complexities of natural language: ''How could a learning ability that allows you to remember wallpaper patterns possibly have anything to do with real linguistic input?''While the current incarnations of these perspectives are markedly less extreme, they continue to provide necessary counterpoints as we work to expand and refine our theories.The second dimension is also still quite current.After we published our first paper on infant statistical language learning, some readers responded