I really enjoyed L&L2016’s take on poverty of the stimulus and how it relates to the argument for Universal Grammar (UG) — so much so that I’m definitely using this chapter as a reference when we talk about poverty of the stimulus in my upper-division language acquisition course.
One thing that surprised me, though, is that there seems to be some legitimate confusion in the research community about how to define UG (more on this below), which leads to one of two situations: (i) everyone who believes there are induction problems in language by definition believes in Universal Grammar, or (ii) everyone who believes there are induction problems in language that are only solvable by language-specific components believes in Universal Grammar. I feel like the linguistics, cognitive science, and psychology communities need to have a heart-to-heart about what we’re all talking about when we argue for or against Universal Grammar. (To be fair, I’ve felt this way when responding to some folks in the developmental psych community — see Pearl 2014 for an example.)
Pearl, L. (2014). Evaluating learning-strategy components: Being fair (Commentary on Ambridge, Pine, and Lieven). Language, 90(3), e107-e114.
Specific thoughts:
(1) Universal Grammar:
Chomsky (1971)’s quote in section 10.6 about structure-dependence concludes with “This is a very simple example of an invariant principle of language, what might be called a formal linguistic universal or a principle of universal grammar.” — Here, the term Universal Grammar seems to apply to anything that occurs in all human languages (not unreasonable, give the adjective “universal”). But it doesn’t specify whether that thing is innate vs. derived, or language-specific vs. domain-general. I begin to see where the confusion in the research community may have come from.
Right now, some people seem to get really upset at the term Universal Grammar, taking it to mean things that are both innate and language-specific (and this is the certainly working definition Jon Sprouse and I use). But Chomsky’s use of Universal Grammar above can clearly be interpreted quite differently. And for that interpretation of Universal Grammar, it’s really just a question of whether the thing is in fact something that occurs in all human languages, period. It doesn’t matter what kind of thing it is.
Related: In the conclusion section 10.9, L&L2016 zero in on the innate part of UG: “…there must be something inside the learner which leads to that particular way of organizing experience…organizing structure is what we typically refer to as Universal Grammar…”. This notably leaves it open about whether the innate components are language-specific or domain-general. But the part that immediately follows zeros in on the language-specific part by saying Universal Grammar is “the innate knowledge of language” that underlies human language structure and makes acquisition possible. On the other hand….maybe “innate knowledge of language” could mean innate knowledge that follows from domain-general components and which happens to apply to language too? If so, that would back us off to innate stuff period, and then, by that definition, everyone believes in Universal Grammar as long as they believe in innate stuff applying to language.
(2) Induction problems: I really appreciate how the intro in 10.1 highlights that the existence of induction problems doesn’t require language-specific innate components (just innate components). The additional step of asserting that the innate components are also language-specific (for other reasons) is just that — an additional step. Sometimes, I think these steps get conflated when induction problems and poverty of the stimulus are discussed, and it’s really great to see it so explicitly laid out here. I think the general line of argument in this opening section also makes it clear why the pure empiricist view just doesn’t fly anymore in cognitive development — everyone’s some kind of nativist. But where people really split is whether they believe at least some innate component is also language-specific (or not). This is highlighted by a Chomsky (1971) quote in section 10.3, which notes that the language-specific part is an “empirical hypothes[i]s”, and the components might in fact be “special cases of more general principles of mind”.
(3) The data issue for induction problems:
Where I think a lot of interest has been focused is the issue of how much data are actually available for different aspects of language acquisition. Chomsky’s quote in 10.1 about the A-over-A example closes with “…there is little data available to the language learner to show that they apply”. Two points:
(a) "Little" is different than "none", and how much data is actually available is a very important question. (Obviously, it’s far more impressive if the input contains none or effectively none of the data that a person is able to judge as grammatical or ungrammatical.) This is picked up in the Chomsky (1971) quote in section 10.6, which claims that someone “might go through much or all of his life without ever having been exposed to relevant evidence”. This is something we can actually check out in child-directed speech corpora — once we decide what the “relevant evidence” is (no small feat, and often a core contribution of an acquisition theory). This also comes back in the discussion of English anaphoric one in section 10.8, where the idea that of what counts as informative data is talked about in some detail (unambiguous vs. ambiguous data of different kinds).
(b) How much data is "enough" to support successful acquisition is also a really excellent question. Basically, an induction problem happens when the data are too scarce to support correct generalization as fast as kids do it. So, it really matters what “too scarce” means. (Legate & Yang (2002) and Hsu & Chater (2010) have two interesting ideas for how to assess this quantitatively.) L&L2016 bring this up explicitly in the closing bit of 10.5 on Principle C acquisition, which is really great.
Legate, J. A., & Yang, C. D. (2002). Empirical re-assessment of stimulus poverty arguments. Linguistic Review, 19(1/2), 151-162.
Hsu, A. S., & Chater, N. (2010). The logical problem of language acquisition: A probabilistic perspective. Cognitive science, 34(6), 972-1016.
(4) Section 10.4 has a nice explanation of Principle C empirical data in children, but we didn’t quite get to the indirect negative evidence part for it (which I was quite interested to see!). My guess: Something about structure-dependent representations, and then tracking what positions certain pronouns allow reference to (a la Orita et al. 2013), though section 10.5 also talks about a more idealized account that’s based on the simple consideration of data likelihood.
Orita, N., McKeown, R., Feldman, N., Lidz, J., & Boyd-Graber, J. L. (2013). Discovering Pronoun Categories using Discourse Information. In CogSci.
(5) A very minor quibble in section 10.5, about the explanation given for likelihood. I think the intuition is more about how the learner views data compatibility with the hypothesis. P(D | H ) = something like “how probable the observed data are under this hypothesis”, which is exactly why the preference falls out for a smaller hypothesis space that generates fewer data points. (How the learner’s input affects the beliefs is the whole calculation of likelihood * prior, which is transformed into the posterior.)
Related: I love the direct connection of Bayesian reasoning to the Subset Principle. It seems to be exactly what Chomsky was talking about as something that’s a special case of a more general principle of mind.
(6) Structure-dependence, from section 10.6: “Unfortunately, other scholars were frequently misled by this into taking one particular aspect of the aux-fronting paradigm as the principle structure dependence claim, or, worse still, as the principle poverty of the stimulus claim.” — Too darned true, alas! Hopefully, this paper and others like it will help rectify that misunderstanding. I think it also highlights that our job, as people who believe there are all these complex induction problems out there, should be to accessibly demonstrate what these induction problems are. A lot.
(7) Artificial language learning experiments in 10.7: I’ve always thought the artificial language learning work of Takahashi and Lidz was a really beautiful demonstration of statistical learning abilities applied to learning structure-dependent rules that operate over constituents (= what I’ll call a “constituent bias”). But, as with all artificial language learning experiments, I’m less clear about how to relate this to native language acquisition, where the learners don’t already have a set of language biases about using constituents from their prior experience. It could indeed be that such biases are innate, but it could also be that such biases (however learned) are already present in the adult and 18-month-old learners, and these biases are deployed for learning the novel artificial language. So, it’s not clear what this tells us about the origin of the constituent bias. (Note: I think it’s impressive as heck to do this with 18-month-olds. But 18-month-olds do already have quite a lot of experience with their native language.)
(8) Section 10.8 & anaphoric one (minor clarification): This example is of course near and dear to my heart, since I worked on it with Jeff Lidz. And indeed, based on our corpus analyses, unambiguous data for one’s syntactic category (and referent) in context is pretty darned rare. The thing that’s glossed over somewhat is that the experiment with 18-month-olds involves not just identifying one’s antecedent as an N’, but specifically as the N’ “red bottle” (because “bottle” is also an N’ on its own, as example 20 shows). This is an important distinction, because it means the acquisition task is actually a bit more complicated. The syntactic category of N’ is linked to 18-month-olds preferring the antecedent “red bottle” — if they behaved as if they thought it was “bottle”, we wouldn’t know if they thought it was N’ “bottle” or plain old N0 “bottle”.
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