Friday, September 30, 2011

Some thoughts on Dunbar et al. (2010)

This is probably one of the more linguistically technical articles we've read in the group to date, but I think that even if the linguistic details weren't as accessible to someone without a linguistic background, there was still a very good, basic point made about the simplicity of abstract structures, given principles of Bayesian reasoning. On the one hand, this might seem surprising since adding another layer of representation might seem de facto more complex; on the other hand, there's something clearly simpler about having three basic units of representation instead of six (for instance).

Some more targeted thoughts:

p.7: The particular example they discuss involving phonemes (specifically, three with derivational rules vs. six with no need for derivational rules) - this reminds me of Perfors et al. (2010), where they were looking at recursion in language, also from a Bayesian perspective. In that case, the decision was between a non-recursive grammar, a partially-recursive grammar, and a fully recursive grammar. The outcome turned out to be that for different structures (subject embedding vs. object embedding), different grammars fit the data best, with one of the winners being the partially-recursive grammar. In essence, this is a "direct store + some computation" approach. For the phoneme example in Dunbar et al., it seems like the choices are between "direct store of six" vs. "store three + some computation", and the "some computation" option ends up being the best. (Related note on p.30: I agree that it would be nice to have formal theoretical debates take place at this level when discussing learnability, rather than relying on intuitions of whether computation or direct storage is more complex/costly.)

p.9: Just a quick note about their justification of looking for a theoretically optimal solution (using the ideal learner paradigm, essentially) - I do agree that this has a place in acquisition studies. Basically, if you formulate a problem (and accompanying hypothesis space), and then find that this problem is unsolvable by an ideal learner, this is a clue that some thing is not right - maybe it's the hypothesis space, maybe it's a missing learning bias on how to use the data, etc.

p.14: Another main message of the authors: "Probability simply a way...of formalizing reasoning under uncertainty." I get the impression that this is to persuade readers who aren't normally very fond of probability.

Monday, September 26, 2011

Welcome back!

The CoLa Reading Group will be holding its meetings on Mondays at 3pm in SBSG 2221 this quarter. We'll meet four times during the quarter, approximately every other week (schedule available here). Our first meeting will be held this coming Monday October 3rd, when we'll be looking at Dunbar, Dillon, & Idsardi (2010), who examine the utility of abstract linguistic representations viewed from a Bayesian perspective:

Dunbar, E., Dillon, B., & Idsardi, W. (2010 ms) A Bayesian Evaluation of the Cost of Abstractness. University of Maryland, College Park and University of Massachusetts, Amherst.

And remember: Even if you aren't able to come to the meeting in person, you're always welcome (and encouraged) to post on the reading group discussion board here!

See you next Monday!