Three pieces to look at

I am waiting for links to David Poeppel’s three lectures and when I get them I will put some stuff up discussing them. As preview: THEY WERE GREAT!!! However, technical issues stand in the way of making them available right now and to give you something to do while you wait I have three pieces that you might want to peak at.

The first is a short article by Stephen Anderson (SA) (here). It’s on “language” behavior in non-humans. Much of it reviews the standard reasons for not assimilating what we do with what other “communicative” animals do. Many things communicate (indeed, perhaps everything does as SA states in the very first sentence) but only we do so using a system that of semantically arbitrary structured symbols (roughly words) that it combines to generate a discrete infinity of meanings (roughly syntax). SA calls this, following Hockett, the “Duality of Patterning” (5):

This refers to the fact that human languages are built on two essentially independent combinatory systems: phonology, and syntax. On the one hand, phonology describes the ways in which individually meaningless sounds are combined into meaningful units — words. And on the other, the quite distinct system of syntax specifies the ways in which words are combined to form phrases, clauses, and sentences.

Given Chomsky’s 60 year insistence on the centrality of hierarchical recursion and discrete infinity as the central characteristic of human linguistic capacity, the syntax side of this uniqueness is (or should be) well known. SA usefully highlights the importance of combinatoric phonology, something that Minimalists with their focus on the syntax to CI mapping may be tempted to slight. Chomsky, interestingly, has focused quite a lot on the mystery behind words, but he too has been impressed with their open textured “semantics” rather than their systematic AP combinatorics.[1] However, as SA notes, the latter is really quite important.

It is tempting to see the presence of phonology as simply an ornament, an inessential elaboration of the way basic meaningful units are formed. This would be a mistake, however: it is phonology that makes it possible for speakers of a language to expand its vocabulary at will and without effective limit. If every new word had to be constructed in such a way as to make it holistically distinct from all others, our capacity to remember, deploy and recognize an inventory of such signs would be severely limited, to something like a few hundred. As it is, however, a new word is constructed as simply a new combination of the inventory of familiar basic sound types, built up according to the regularities of the language’s phonology. This is what enables us to extend the language’s lexicon as new concepts and conditions require. (5)

So our linguistic atoms are peculiar not only semantically but phonetically as well. This is worth keeping in mind in Evolang speculations.

So, SA reviews some of basic ways that we differ from them when we communicate. It also ends with a critique of the tendency to semanticize (romanticize the semantics of) animal vocalizations. SA argues that this is a big mistake and that there is really no reason to think that animal calls have any interesting semantic features, at least if we mean by this that they are “proto” words. I agree with SA here. However, whether I do or not, if SA is correct, then it is important for there is a strong temptation (and tendency) to latch onto things like monkey calls as the first steps towards “language.” In other words, it is the first refuge of those enthralled by the “continuity” thesis (see here). It is thus nice to have a considered take down of the first part of this slippery slope.

There’s more in this nice compact little paper. It would even make a nice piece for a course that touches on these topics. So take a look.

The second paper is on theory refutation in science (here). It addresses the question of how ideas that we take to be wrong are scientifically weeded out. The standard account is that experiments are the disposal mechanism. This essay, based on the longer book that the author, Thomas Levenson has written (see here), argues that this is a bad oversimplification. The book is a great read, but the main point is well expressed here. It explains how long it took to loose the idea that Vulcan (you know Mr Spock’s birthplace) exists. Apparently, it took Einstein to kill the idea. Why did it take so long? Because, that Vulcan existed was a good idea that fit well with Newton’s ideas and that it experiment had a hard time disproving. Why? Because small modification of good theories are almost always able meet experimental challenges, and when there is nothing better on offer, such small modifications of the familiar are reasonable alternatives to dumping successful accounts. So, naive falsificationism (the favorite methodological stance of the hard headed, non nonsense scientist) rails to describe actual practice, at least in serious area of inquiry.

The last paper is by David Deutsch (here). The piece is a critical assessment of “artificial general intelligence” (AGI). The argument is that we are very far from understanding how thought works and that the contrary optimism that we hear from the CS community (the current leaders being the Bayesians) is based on an inductivist fallacy. Here’s the main critical point:

[I]t is simply not true that knowledge comes from extrapolating repeated observations. Nor is it true that ‘the future is like the past’, in any sense that one could detect in advance without already knowing the explanation. The future is actually unlike the past in most ways. Of course, given changes’ in the earlier pattern of 19s are straightforwardly understood as being due to an invariant underlying pattern or law. But the explanation always comes first. Without that, any continuation of any sequence constitutes ‘the same thing happening again’ under some explanation.

Note, the last sentence is the old observation about the vacuity of citing “similarity” as an inductive mechanism. Any two things are similar in some way. And that is the problem. That this has been repeatedly noted seems to have had little effect. Again and again the idea that induction based on similarity is the engine that gets us to generalizations we want keeps cropping up.  Deutsch notes that is still true with our most modern thinkers on the topic.

Currently one of the most influential versions of the ‘induction’ approach to AGI (and to the philosophy of science) is Bayesianism, unfairly named after the 18th-century mathematician Thomas Bayes, who was quite innocent of the mistake. The doctrine assumes that minds work by assigning probabilities to their ideas and modifying those probabilities in the light of experience as a way of choosing how to act. … As I argued above, that behaviourist, input-output model is appropriate for most computer programming other than AGI, but hopeless for AGI. It is ironic that mainstream psychology has largely renounced behaviourism, which has been recognised as both inadequate and inhuman, while computer science, thanks to philosophical misconceptions such as inductivism, still intends to manufacture human-type cognition on essentially behaviourist lines.

The only thing that Deutsch gets wrong in the above is the idea that main stream psych has gotten rid of its inductive bias. If only!

The piece is a challenge. I am not really fond of the way it is written. However, the basic point it makes is on the mark. There are serious limits to inductivism and the assumption that we are on the cusp of “solving” the problem is deserving of serious criticism.

So three easy pieces to keep you busy. Have fun.

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[1] I put ‘semantics’ in scare quotes because of Chomsky does not think much of the idea that meaning has much to do with reference. See here and here for some discussion.

Why, How and When We Need Cowbell

The comments, especially by always level headed David Pesetsky, have provoked the following restatement of my beef with falsificationism.  Its main defect is that it presents a lopsided and hence counter-productive image of scientific practice. It thus encourages the wrong methodological ideals and this has a baleful effect on the research environment.  How so?

First, falsificationism tacitly assumes that the primary defect a proposal/story/theory can have is failure to cover the data.  In other words, in tacitly presents the view that the primary virtue a theory has is data coverage.  In sophisticated versions, other benchmarks are recognized, however, even non-naïve versions of falsificationism, in virtue of being falsificationist, place primary stress on getting the data points well organized. Everything else is second to this. I don’t buy this. In the sciences in general, and linguistics in particular, the enterprise is animated by larger ‘why’ questions and the goal of a proposal is to answer (or at least address) these.  This means that there are at least two dimensions in proposal evaluation (PE)[1]: (i) how they cover the “facts,” and  (ii) how they advance explanation. Moreover, neither is intrinsically more important than the other, though in some contexts one is weighted higher than the other.  A good project is to try to identify these contexts, at least roughly. However, the null position should be to recognize that both are equally important dimensions of evaluation ceteris paribus.

Second point: in practice, I believe, the two virtues are not genrally equally weighted.  For lots of linguistics (and many outside critics of the field), the second is only weakly attended to.  Many think it meet to devalue a proposal if it does not cover the “relevant” facts. The number of times this criticism has been levied with approval are numerous.  Far rarer are the times when suspicion is cast on a proposal because it entirely misses the explanatory boat.  However, and here is my main point, failure to advance the explanatory agenda is just as problematic as failure to cover some data points.  In fact, in practice, it is often easier to evaluate if a particular story has any Explanatory Oomph (EO) than to determine whether the data points not covered are actually relevant. We all agree (or should) that only data points worth covering are the relevant ones and that determining what’s relevant is no simple matter. However, we often act as if it is clear what the relevant data is whereas what the explanatory goal is we take to be irremediably obscure.  I don’t agree.

The goal of early syntactic theory was to explain why grammatically competent humans were able to use and understand sentences never before encountered. Answer: they had internalized generative grammars composed of recursive rules. The fine structure of possible grammars were investigated and some consensus was reached on what kinds of rules they deployed and constraints they obeyed.  This set up the next question (Plato’s Problems): what allows humans to develop generative grammars? And we answered this by attributing to human minds an FL and the project was to describe it.  We concluded that an FL with a principles and parameters architecture in which parameter values are set by PLD would explain why we are linguistically capable, i.e. a description of how this is done, answers the ‘why’ question.[2] The current Minimalist project, I have argued, rests on a similar ‘why’ question: why do we have the FL we have and not some other conceivable kind?  And we are looking for an answer here too. Roughly we are betting on the following kind of story being right: FL is a congery of domain general powers (aka operations and principles) with a small dollop of linguistic specificity thrown in.  If we can theoretically actualize this sort of picture we will have answered Darwin’s Problem, yet another ‘why’ question.  My modest proposal: part of proposal evaluation should involve seeing how a particular story helps us answer these questions.

Let me go further. There are times to emphasize one of the two criteria over the other.  A good time to have high regard for (ii) is when a program is starting out. The goal of the early stages of inquiry into a new question is to develop a body of doctrine (BOD) and this in practice requires putting some recalcitrant facts to the side. One develops a BOD by showing what a proposal buys you, in particular how, if correct, it can address an animating ‘why’ question. When there is some BOD with some EO in place, empirical coverage becomes crucial, as this is how we refine and choose between the many basic approaches all of which are of the right kind to answer the motivating ‘why’ question.  Of course, both activities go on at the same time. It’s not like for the first 10 years we value (ii) and ignore (i) and vice versa for the second ten.  Research is not so discrete.  However, there are times when answers to (ii) are hard to come by and at these times valuing PEs that potentially meet these kinds of demands is, I would argue, very very advisable. Again, the trouble with falsificationsim is that it encourages a set of attitudes that devalue the virtue of (ii).

Where does this leave us/me?  I agree with David Pesetsky that there are different levels of ‘why’ questions, and that they can be pursued in parallel. Addressing one does not preclude addressing another. I also agree that we pursue these higher-level questions by making proposals of how things work.  I also endorse the view that how and why are intimately intertwined. However, I suspect that there might be a disagreement of emphasis: I think that whereas we both value how “data” allows us to develop and judge our proposals, we don’t equally weight the impact of EO.  David has no problem relegating the big ‘why’ questions to “the grand scheme of things” making it sound like some far off fairyland (like Keyne’s long run, it’s where we are all dead?).  True, David mitigates this by adding the qualifier that we should not try and live there “all the time,” suggesting that occasional daydreaming is fine.  However, my point is that even in the “more humble scheme of things” when we work on detailed analyses of specific phenomena, indeed when we muddle along finding “semi-organized piles of semi-analyzed, often accidental discoveries,” even then we should try to keep our eyes on the explanatory prize and ask how what we are doing bears on these animating questions.  Why? Because they are important in evaluating what you are doing no less than seeing if the story covers some set of forms/sentences in some paradigm.  Both are critical, though to my eyes only one (i.e. (i) above) is uncontroversially valued and considered part of everyone’s every day research basket of values. This is what I want my rejection of falsificationsim to call into question as it is something that even sophisticated versions (which of course are correct if sophisticated in the right ways), tend to still operationally relegate to a secondary position.

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[1] I would normally use ‘theory’ in place of ‘proposal’ but it sounds too grand. I think that non encompassing self-perceived smaller scale projects are subject to the same dual evaluation streams.

[2] Let me add before I am inundated by misplaced comments that I do not believe that we have “solved” Plato’s Problem.  I have written about this elsewhere.

Formalization and Falsification in Generative Grammar

There's been a very vigorous discussion in the comment sections to this post which expatiates on the falsifiability of proposals within generative grammar of the Chomskyan variety.  It interweaves with a second point: the value of formalization.  The protagonists are Alex Clark and David Pesetsky. It should come as no surprise to readers that I agree with David here. However, the interchange is worth reading and I recommend it to your attention precisely because the argument is a "canonical" one in the sense that they represent two views that about the generative enterprise that we will surely hear again (though I hope that very soon David's views prevail, as they have with me).

Though David has said what I would have said (though much better) let me add three points.

First, nobody can be against formalization. There is nothing wrong with it (though there is nothing inherently right about it either).  However, in my experience its value lies not in making otherwise vague theories testable. Indeed, as we generally evaluate a particular formalization in terms of whether it respects the theoretical and empirical generalizations of the account it is formalizing, it is hard to see how formalization per se can be the feature that makes an account empirically evaluable.  This comes out very clearly, for example, in the recent formalization of minimalism by Collins and Stabler. At virtually every point they assure the reader that a central feature of the minimalist program is being coded in such and such a way. And, in doing this, they make formal decisions with serious empirical and theoretical consequences and that could call into question the utility of the particular formalization. For example, the system does not tolerate sidewards movement and whether this formalization is empirically and theoretically useful may rest on whether UG allows sidewards movement or not. But the theoretical and empirical adequacy of sidewards movement and formalizations that encode it or not is not a question that any given proposed formalization addresses nor can address (as Collins and Stabler know). So, whatever, the utility of formalization, to date with some exceptions (I will return to two), it does not primarily lie in making theories that would otherwise be untestable, testable.

So what is the utility? I think that when done well, formalization allows us to clarify the import of our basic concepts. It can lay bear what the conceptual dependencies between our basic concepts are. Tim Hunter's thesis is a good example of this, I think.  His formalization of some basic minimalist concepts allows us to reconceptualize them and consequently extend them empirically (at least in principle).  So too with Alex Drummond's formal work on sidewards movement and Merge over Move. I mention these two because this work was done here at UMD and I was exposed to the thinking as it developed. It is not my intention to suggest that there is not other equally good work out there.

Second, any theory comes to be tested only with the help of very many ancillary hypotheses.  I confess to feeling that lots of critics of Generative Grammar would benefit by reading the work criticizing naive falsificationsim (Lakatos, Cartwright, Hacking, and a favorite of mine, Laymon). As David emphasizes, and I could not agree more, it is not that hard to find problems with virtually every proposal. Given this, the trick is to evaluate proposals despite their evident shortcomings.  The true/false dichotomy might be a useful idealization within formal theory, but it badly distorts actual scientific practice where the aim is to find better theories.  We start from the reasonable assumption that our best theories are nonetheless probably false. We all agree that the problems are hard and that we don't know as much as we would like. Active research consists in trying to find ways of evaluating these acknowledged false accounts so that we can develop better ones. And where the improving ideas will come from is often quite unclear.  Let me give a couple of example of how vague the most progressive ideas can be.

Consider the germ theory of disease. What is it? It entered as roughly the claim that some germs cause some diseases sometimes. Not one of those strongly refutable claims. Important. You bet. It started people thinking in entirely new ways and we are all the beneficiaries of this.

The Atomic Hypothesis is in the same ball park. Big things are made up of smaller things. This was an incredibly important idea (Feynman, I think, thought this was the most important scientific idea ever).  Progress comes from many sources, formalization being but one. And even pretty labile theories can be tested, as, e.g. the germ theory was.

Third: Alex Clark suggests that only formal theories can address learnability concerns. I disagree. One can provide decent evidence that something is not learnable without this (think of Crain's stuff or the conceptual arguments against the learnability of island conditions). This is not to dispute that formal accounts can and have helped illuminate important matters (I am thinking of Yang's stuff in particular, but a lot of stuff done by Berwick and his students are, IMO, terrific). However, I confess that I would be very suspicious of formal learnability results that "proved" that Binding Theory was learnable, or that Movement locality theory (aka Subjacency) was or that ECP or structure dependence was. The reasons for taking these phenomena as indictions of deep grammatical structural principles is so convincing (to me) that they currently form boundary conditions on admissible formal results.

As I said, the discussion is worth reading. I suspect that minds will not be changed, but this does not make going through this (at least once anyhow) worthwhile.