There are never enough good papers illustrating the poverty of the stimulus. Here’s a recent one that I read by Jennifer Culbertson and David Adger (yes, that David Adger!) (C&A) that uses artificial language learning tasks as probes into the kinds of generalizations that learners naturally (i.e. uninstructed) make. Remember that generalization is the name of the game. Everyone agrees that no generalizing beyond the input, no learning. The debate is is not about whether this exists, but what the relevant dimensions are that guide the generalization process. One standard view is that it’s just frequency of some kind, often bigram and trigram frequencies. Another is that the dimension along which a learner generalizes is more abstract, e.g. along some dimension of linguistic structure. C&A provide an interesting example of the latter in the context of artificial language learning, a technique, I believe, that is still new to most linguists.
Let me say a word about this technique. Typological investigation provides a standard method for finding UG universals. The method is to survey diverse grammars (or more often, and more superficially, languages) and see what properties they all share. Greenberg was a past master of this methodology, though from the current perspective, his methods look rather “shallow,” (though the same cannot be said of modern cartographers like Cinque). And, looking for common features of diverse grammars seems like a plausible way to search for invariances. The current typological literature is well developed in this regard and C&A note that Greenberg’s U20, which their experiment explores, is based on an analysis of 341 languages (p.2/6). So, these kinds of typological investigations are clearly suggestive. Nonetheless, I think that C&A are correct in thinking that supplementing this kind of typological evidence with experimental evidence is a very good idea for it allows one to investigate directly what typological surveys can only do indirectly: to what degree the gaps in the record are principled. We know for a fact that the extant languages/grammars are not the only possible ones. Moreover, we know (or at least I believe) that the sample of grammars we have at our disposal are a small subset of the possible ones. As the artificial language learning experiments promise to allow us to directly probe what typological comparison only allows us to indirectly infer, better to use the direct method if it is workable. C&A’s paper offers a nice paradigm for how to do this that those interested in exploring UG should look at this method with interest.
So what do C&A do? They expose learners to an artificial version of English wherein pre-nominal order of determiner, numeral and adjective are flipped from the English case. So, in “real” English (RE), the order and structure is [Dem [ num [ adj [ N ] ] ] (as in: these three yellow mice). C&A expose learners to nominal bits of artificial English (AE) where the dem, num, and adj are postnominal. In particular, they present learners with data like mice these, mice three, mice yellow etc. and see how they generalize to examples with more than one postnominal element, e.g. do learners prefer phrases in AE like mice yellow these or mice these yellow? If learners treat AE as just like RE but for the postnominal order then they might be expected to preserve the word order they invariably see pre-nominally in RE postnominally in AE (thus to prefer mice these yellow). However, if they prefer to retain the scope structure of the expressionsin RE and port that over to AE, then they will prefer to preserve the bracketing noted above but flip the word order, i.e. [ [ [ N ] adj ] num ] dem]. On the first hypothesis, learners prefer to orders they’ve encountered repeatedly in RE before, while on the second they prefer to preserve RE’s more abstract scope relations when projecting to the new structures in AE.
So what happens? Well you already know, right? They go for door number 2 and preserve the scope order of RE thus reliably generalizing to an order ‘N-adj-num-det.’ C&A conclude, reasonably enough, that “learner’s overwhelmingly favor structural similarity over preservation of superficial order” (abstract, p.1/6) and that this means that “when they are pitted against one another, structural rather than distributional knowledge is brought to bear most strongly in learning a new language” (p.5/6). The relevant cognitive constraint, C&A conclude, is that learners adopt a constraint “enforcing an isomorphism in the mapping between semantics and surface word order via hierarchical syntax.”
This actually coincides with similar biases young kids exhibit in acquiring their first language. Lidz and Musolino (2006) (L&M) show a similar kind of preference in relating quantificational scope and surface word order. Together, C&A and L&M show a strong preference for preserving a direct mapping between overt linear order and hierarchical structure, at least in “early” learning, and, as C&A’s results show, that this preference is not a simple L-R preference but a real structural one.
One further point struck me. We must couple the observed preference for scope preserving order with a dispreference for treating surface forms as a derived structure, i.e. a product of movement. C&A note that ‘N-dem-num-adj’ order is typologically rare. However, this order is easy enough to derive from a structure like (1) via head movement given some plausible functional structure. Given (1), N to F0 movement suffices.
(1) F0 [Dem [ num [ adj [ N ] ] ] à [N+F0 [Dem [ num [ adj [
] ] ] ]
We know that there are languages where N moves to above determiners (so one gets the order N-det rather than Det-N) and though the N-dem-num-adj is “rare” it is, apparently, not unattested. So, there must be more going on. This, it goes without saying I hope, does not detract from C&A’s conclusions, but it raises other interesting questions that we might be able to use this technique to explore.
So, C&A have written a fun paper with an interesting conclusion that deploys a useful method that those interested in FL might find productive to incorporate into their bag of investigative tricks. Enjoy!