Some material to point to when the uninformed say that GG is dead, which it isn't

Here are several pieces by our own estimable Jeff Lidz that fight the good fight against the forces of darkness and ignorance.  We need much more of this. We need to get stuff into popular venues defending the work that we have done.[1]

The most important is this piece in Scientific American rebutting the profoundly ignorant and pernicious piece by Ibbotson and Tomasello (I&T). (see here and here and here for longer discussion). Jeff does an excellent job of pointing out the issues and debunking the “arguments” that I&T advance. It is amazing, IMO, that T’s views on these issues still garner any attention. They no doubt arise from the fact that he has done good work on non-linguistic topics. However, his criticisms of GG are both of long-standing and very low quality and have been so for as long as they have been standing.  So, it is good to see the Sci Am has finally opened up its pages to those willing to call junk junk. Read it and pass it around widely in your intellectual community.

Here are two other pieces (here and here). The latter is a response to this. This all appears in PNAS. The articles are co-atuhored with Chung-hye Han and Julien Musolino. The discussion is an accessible entry into the big issues GG broaches for the scientifically literate non GGer. As such, excellent for publicity purposes.

So, read and disseminate widely. It is important to call out the idiocy out there. It is even fun.

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[1] I have a piece in Current Affairs with Nathan Robinson that I will link to when it is available on the web.

POS and PLD

What do linguists study? The obvious answer is language. GGers beg to differ. The object of study, at least if you buy into the Chomsky program in GG (which all right thinking linguists do), is not language but the faculty of language (FL). GGers treat FL realistically. It is part of the mind/brain, a chunk of psychology/biology.  IMO, perhaps Chomsky’s biggest achievement lies in having found a way of probing the fine structure of FL by studying its outputs and thinking backwards to the properties that a mind/brain with such outputs would have to have. This mode of reasoning has a name: the Poverty of Stimulus argument (POS).

The POS licenses inferences about the structure of FL from the structure of the Gs that native speakers of a natural language (NL) acquire.  As I’ve before illustrated how the POS can be pressed into service for this end, I will refrain from doing so again here. Instead, I’d like to recommend a good paper by Jeff Lidz and Annie Gagliardi (L&G) (here) that goes over these issues again with novel illustrations. Here’s some of what’s in it.

First, the paper illustrates something that’s often obscured: how POS reasoning focuses attention onto the properties of the Primary Linguistic Data (PLD). As usually structured, the argument relies on there being a gap between what can be gleaned from the data the LAD can exploit and the structure of the knowledge attained. It’s through this gap that structure of FL can be discerned. L&G nicely walk us through the logic of the gap and the inferential procedure underlying the POS.  They observe, correctly in my view, that there is nothing antithetical between GGers and a commitment to UG like principles and statistical learning procedures (a point Charles has made in his recent posts as well).  The question is not whether stats are relevant, but how they are. Here’s what L&G says.

L&G notes that there are effectively two views of how stats can enter into acquisition discussions. The first view, what L&G dubs the “input driven view,” (IDV) conceives of acquisition as a process of “track[ing] patterns of co-occurrence in the environment and stor[ing] them in a summarized format” (3). Generalizing beyond actual experience is licensed by this “summary representation of experience” (4). IDVs tend to take this learning procedure to be domain general, rather than linguistically specific.

There is a second approach, the “knowledge-drive view” (KDV). For KDV, experience functions to “select” a knowledge representation from a pre-specified (aka: innate) domain of options.

There is a long tradition distinguishing “instructive” versus “selective” theories of “learning.” This has even played a role in studies of the immune system where several people got Nobels (Jerne, Edelman) for showing that earlier instructive theories of antibody formation were wrong and that antibody formation is actually selection against a given set of possible options.

Nowadays, given the rise of Bayes, selection theories of learning are all the rage. However, there is a sense in which any statistical theory must be selective for probabilities presuppose an antecedent demarcation of possibilities over which the probabilities are assigned. In other words, we need a given hypothesis space/algebra in order to assign, e.g. probability densities. No space, no probabilities.[1] The probable selects from the possible.

One of the nice features of the L&G discussion is that it notes that the instruction/selection question is orthogonal to the empiricism/rationalism issue. The relevant question for the latter is the nature of the relation between the input and what is “selected.” If selection requires matching inputs (i.e. if acquisition is essentially a bottom up affair in which the generalizations are simply distillations of regularities found in the input) then the relevant mechanism is Empiricist as, using traditional terminology, the generalization “resembles” the input. If one allows for a “distance” between the attained generalization and the input, then one is approaching the issue from a Rationalist perspective. Rationalists are necessarily selectionists, but Empiricists need not be instructionists. The deep question lies not on this dimension, but on how close the input must “resemble” the generalized output. As L&D explains, the important question is whether the statistically massaged PLD “shapes” or “triggers” the attained generalization.[2] For the interested, this is further discussed (here).

Here’s how L&G contrasts IDV and KDV:

1.     For IDV the learning process goes from “specific to general” as the main operation is a process that “generalizes over specific cases” (4). For KDV, learners do not need to “arrive at abstract representations via a process of generalization” as these are built in. Rather input is sampled to “identify the realization of those abstract representations” in the PLD (5).

2.     For IDV the end product of learning is “a recapitulation of the inputs to the learner, [t]he acquired representation [being] a compressed memory representation of the regularities found in the input” (5). For KDV, the attained representations (or many features thereof) are innately provided so the “representation may bear no obvious relation to the input that triggered it” (5). There need be, in other words, no similarity between the attained generalizations and the specific structure of the PLD. Or, another way of putting this (which L&G does) is that whereas for IDVs the input shapes the attained representation, for KDVs it merely triggers it. 

Thus, though both IDVs and KDVs recognize that experience is critical in G attainment (i.e. to learn English you need English PLD), how this PLD functions is different in the two cases. Forming the Gish generalizations in the case of IDVs and triggering them in the case of KDVs. As such, both approaches require inferential mechanisms that take one from PLD to Gs. 

Indeed, because KDVs cannot rely on “similarity” to bridge the gap from data to generalizations as IDVs do,[3] KDVs need learning theories that show how FL/UG supplies “predictions about what the learner should expect to find in the environment” to guide acquisition which, for KVDs consists in “compar[ing] those [FL/UG provided (NH)] predicted features against the perceptual intake…[which] drives an inference about the grammatical features responsible for the sentence under consideration” (10-11).  L&G provide a nice little schema for this interactive process in their table 1.

Given these distinctions one can now begin to investigate to what degree language acquisition resembles IDVs vs KDVs.  L&G provide several interesting case studies. Let me mention two.

The first is based on Gagliardi’s thesis work on noun class acquisition in Tsez. Here’s a surprise: there are lots of them and it’s complicated.  L&G notes that classification (at least in part) depends on a noun’s semantic and phonological features. What L&G notes is that despite being sensitive to both kinds of info, LADs used them “out of proportion with their statistical reliability, even ignoring highly predictive semantic features” (24-5). L&G asks why. The discussion relies on distinguishing the input to the LAD from its intake; the former being the information available in the ambient linguistic environment, the latter being the PLD that the child actually uses.

This is an important distinction and it has been put to use elsewhere to great effect.  One of my favorites is the explanation of how Old English, which was a good Germanic OV NL, changed to VO.  David Lightfoot (here, here) asked a very interesting question about this transition: how could the change have happened? Here’s the problem: while the data in main clauses concerning the OV status of Old English (OE) was obscure and not particularly conclusive, the data that OE was OV was very clear in embedded clauses. He reasoned that if LADs had (robust) access to embedded clause PLD then there would have been no change from OV to VO in English as there was plenty of very reliable data showing that the language was OV in embedded clauses. Conclusion: the LAD did not have use embedded clause information. Given the reasonable conclusion that OE kids had the same FLs as anyone else, this means that in language acquisition kids do not “intake” embedded clause data (i.e. the PLD is degree 0+).

Lisa Pearl in her thesis work and later publications (here, here) addresses this question in terms analogous to L&G (viz. intake vs input of PLD to the OE kids). Pearl asks how much embedded data would have been enough to prevent the shift to VO order. She does this to pin down whether the LAD actively ignores embedded data (an intake restriction) or whether embedded data info is absent because complex clauses with embedding are just rare in the PLD (an input restriction). Lisa was able to “quantify” the question and the data she harvested from CHILDES (current English data not OE, of course) suggested that kids must actively ignore available data if we are to account for the English shift from OV to VO. Note, there could have been another answer to Lightfoot’s question: degree 1 sentences are too rare in the input for their information to be effectively usable, i.e. the restricted intake is due to restricted input. However, both Lisa and L&G show apparent cases where this answer is insufficient. It appears that the LAD might be deliberately myopic. And this raises another interesting question: WHY?

L&G discuss a second case that I would like to bring to your attention; acquisition of Double Object Constructions (DOC) in Kannada. The facts are once again subtle, though recognizably similar to what we find in languages like English and Spanish. At any rate, it is very complex, with binding allowed between pronouns in the accusative and dative DPs in some conditions but not in others. L&G notes that the data present a standard POS puzzle and show how it might be addressed. What was most interesting to me is the discussion of possible triggers and how exactly FL/UG might focus attention on some kinds of available information (spoiler alert: it has to do with animacy in possession constructions and their relation to DOCs), which would function as triggers. L&G notes that this kind of info is available with some statistical regularity if you are primed to look for it. In other words, the abstract POS considerations immediately generate a search for triggers that FL/UG might make salient and thus be in-takeable (i.e perceptually prominent data that would permit the triggering).  This is a very nice illustration of the fecundity of POS considerations in considering how real time acquisition works. It also nicely illustrates how the gaps that POS arguments identify can be bridged via data that is in no way “similar” to the representations attained.

Let me end. L&G is a fun paper. And there’s lots of other good stuff (I really liked the discussion of Korean and how POS allows a kind of data free parameter setting). It gives a good overview of the intuitions behind the competing acquisition traditions, makes some nice distinctions concerning how to think of PLD in the wild, and provides a budget of nice novel illustrations of POS arguments. A colleague (Colin Phillips) keeps insisting that he is tired of the focus on Y/N questions in English as the “hard” case of POS reasoning. I am not nearly as tired as he is, but we agree on one thing: POS arguments are thick on the ground once one looks at any even slightly complex bit of grammatical competence. There is nothing special about Chomsky’s original illustration, except perhaps its extreme accessibility. L&G provides some good new grist for those interested in this kind of milling and shows how useful such reasoning is in attacking the question of how real kids (not just ideal speaker-hearer LADs) manage to acquire Gs.

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[1] Perfors makes essentially this point (here).

[2] For the interested, this is further discussed (here).

[3] This is not to agree that ‘similarity’ is a serviceable notion. It’s not, as philosopher’s (e.g. Nelson Goodman) have repeatedly shown.

Lifted from the comments on 'Mother knows best.'

There has been an intense discussion of the APL paper in the comments to my very negative review of this paper (here). I would like to highlight one interchange that I think points to ways that UG like approaches have combined with (distributional) learners to provide explicit analyses of child language data. In other words, there exists concrete proposals dealing with specific cases that fruitfully combine UG with explicit learning models. APL deals with none of this kind of material, despite its obvious relevance to its central claims and despite citing papers that deal with such work in its bibliography. It makes one (e.g. me) think that maybe the authors didn't read (or understand) the papers APL cites.

One more point: Jeff observes that "it is right to bring to the fore the question of how UG makes contact with data to drive learning," and that APL are right to raise this question. I would agree that the question is worth raising and worth investigating. What I would deny is that APL contributes to advancing this question in any way whatsoever. There is a tendency in academia to think that all work should be treated with respect and politesse. I disagree. All researchers should be so treated, not their work. Junk exists (APL is my existence proof were one needed) and identifying junk as junk is an important part of the critical/evaluative process.  Trying to find the grain of trivial truth in a morass of bad argument and incoherent thinking retards progress. Perhaps the only positive end that APL might serve is to be a useful compendium of junk work that neophytes can go to to practice their critical skills. I plan to use it  with my students for just this purpose in the future.

At any rate, here is a remark by Alex C that generated this informative reply (i.e. citations for relevant work) by Jeff Lidz.

Alex Clark:

So consider this quote: (not from the paper under discussion)

"It is standardly held that having a highly restricted hypothesis space makes
it possible for such a learning mechanism to successfully acquire a grammar that is compatible with the learner’s experience and that without such restrictions, learning would be impossible (Chomsky 1975, Pinker 1984, Jackendoff 2002). In many respects, however, it has remained a promissory note to show how having a well-defined initial hypothesis space makes grammar induction possible in a way that not having an initial hypothesis space does not (see Wexler 1990 and Hyams 1994 for highly relevant discussion).
The failure to cash in this promissory note has led, in my view, to broad
skepticism outside of generative linguistics of the benefit of a constrained initial hypothesis space."

This seems a reasonable point to me, and more or less the same one that is made in this paper: namely that the proposed UG don't actually solve the learnability problem.

Jeff Lidz:

Alex C (Oct 25, 3am ([i.e. above] NH])) gives a quote from a different paper to say that APL have identified a real problem and that UG doesn't solve learnability problems.

The odd thing, however, is that this quote comes from a paper that attempts to cash in on that promissory note, showing in specific cases what the benefit of UG would be. Here are some relevant examples.

Sneed's 2007 dissertation examines the acquisition of bare plurals in English. Bare plural subjects in English are ambiguous between a generic and an existential interpretation. However, in speech to children they are uniformly generic. Nonetheless, Sneed shows that by age 4, English learners can access both interpretations. She argues that if something like Diesing's analysis of how these interpretation arise is both true and innate, then the learner's task is simply to identify which DPs are Heim-style indefinites and the rest will follow. She then provides a distributional analysis of speech to children does just that. The critical thing is that the link between the distributional evidence that a DP is indefinite and the availability of existential interpretations in subject position can be established only if there is an innate link between these two facts. The data themselves simply do not provide that link. Hence, this work successfully combines a UG theory with distributional analysis to show how learners acquire properties of their language that are not evident in their environment.

Viau and Lidz (2011, which appeared in Language and oddly enough is cited by APL for something else) argues that UG provides two types of ditransitive construction, but that the surface evidence for which is which is highly variable cross-linguistically. Consequently, there is no simple surface trigger which can tell the learner which strings go with which structures. Moreover, they show that 4-year-olds have knowledge of complex binding facts which follow from this analysis, despite the relevant sentences never occurring in their input. However, they also show what kind of distributional analysis would allow learners to assign strings to the appropriate category, from which the binding facts would follow. Here again, there is a UG account of children's knowledge paired with an analysis of how UG makes the input informative.

Takahashi's 2008 UMd dissertation shows that 18month old infants can use surface distributional cues to phrase structure to acquire basic constituent structure in an artificial language. She shows also that having learned this constituent structure, the infants also know that constituents can move but nonconstituents cannot move, even if there was no movement in the familiarization language. Hence, if one consequence of UG is that only constituents can move, these facts are explained. Distributional analysis by itself can't do this.

Misha Becker has a series of papers on the acquisition of raising/control, showing that a distributional analysis over the kinds of subjects that can occur with verbs taking infinitival complements could successfully partition the verbs into two classes. However, the full range of facts that distinguish raising/control do not follow from the existence of two classes. For this, you need UG to provide a distinction. 

In all of these cases, UG makes the input informative by allowing the learner to know what evidence to look for in trying to identify abstract structure. In all of the cases mentioned here, the distributional evidence is informative only insofar as it is paired with a theory of what that evidence is informative about. Without that, the evidence could not license the complex knowledge that children have.

It is true that APL is a piece of shoddy scholarship and shoddly linguistics. But, it is right to bring to the fore the question of how UG makes contact with data to drive learning. And you don't have to hate UG to think that this is valuable question to ask.