PERSPECTIVES Evaluating learning-strategy components: Being fair (Commentary on Ambridge, Pine, and Lieven)
Lisa Pearl · 2014
I completely agree with Ambridge, Pine, and Lieven (APL AP &L) highlight with regard to proposed learning strategies seems exactly right: What will ac - tually work, and what exactly makes it work? They note that 'nothing is gained by positing components of innate knowledge that do not simplify the problem faced by language learners ' (p. e82), and this is absolutely true. To examine how well several current learning -strategy proposals that involve innate linguistic knowledge work, AP&L present evidence from a commendable range of linguistic phenomena, from what might be considered fairly fundamental knowledge (e.g. grammatical categories) to fairly sophisticated knowledge (e.g. subjacency and binding). In each case, AP &L identify the shortcomings of some existing universal grammar (UG) proposals and observe that these proposals do not seem to fare very well in realistic scenarios. The challenge at the very end underscores this : AP&L contend (and I completely agree) that a learning -strategy proposal involving innate knowledge needs to show 'precisely how a particular type of innate knowledge would help children to acquire X ' (p. e82 ). More importantly , I believe this should be a metric that any component of a learning strategy is measured by. That is, for any component (whether innate or derived, whether language-specific or domain-general), we need to not only propose that this component could help children learn some piece of linguistic knowledge but also demonstrate at least 'one way that a child could do so ' (p. e82 ). To this end, I first want to highlight how computational modeling is well suited for doing precisely this: for any proposed component embedded in a learning strategy , modeling allows us to empirically test that strategy in a realistic learning scenario. It is my view that we should test all potential learning strategies, including the ones AP&L themselves propose as alternatives to the UG-based ones they find lacking. An additional and highly useful benefit of the com - putational modeling methodology is that it forces us to recognize hidden assumptions within our proposed learning strategies, a problem with many existing proposals that AP&L rightly recognize. This leads me to suggest certain criteria that any learning strategy should satisfy, re - lating to its utility in principle and practice, as well as its usability by children. Once we have a promising learning strategy that satisfies these criteria, we can then concern our - selves with the components comprising that strategy. With respect to this, I briefly dis -