Another three things to read: pedagogy, connectors and Marr

Here are three pieces (and one youtube clip) that you might find interesting and provocative. In the last one, Chomsky discusses Marr.

The first is a piece on teaching. It responds to a piece by Brain Leiter on teaching philosophy in mixed gender environments and whether or not males create environments which make it harder for females to participate and learn. Leiter and the blogger Harry Brighouse (HB) are philosophers so their concern is with philo pedagogy. But I believe that ling classes and philo classes have very similar dynamics (less lecture and more discussion, “discovery,” give and take, argument) and so the observations HB makes on Leiter’s original post (link included in above piece) seems relevant to what we do.  Take a look and let me know what you think.

FWIW, I personally found some of the suggestions useful, and not only as applied to women. In my experience some very smart people can be quite reluctant to participate in class discussion. This is unfortunate for I know for a fact that the class (and me too) would benefit from their participation (as, I suspect, would they). IMO, learning takes place in classes less because information is imparted and more because a certain style of exploration of ideas is promoted. If lucky, the process is fun and develops a dynamic of its own, which leads to new ideas which leads to more discussion which promotes more amusement which… A really good class shows how to ride this kind of enthusiasm and think more clearly and originally. The problem that Leiter and HB identify would impede this. So, is it a problem for linguistics? My guess is absolutely. If so, what to do? Comments welcome.

The second paper (here) is on a new IARPA funded project to get machines to “think” more like brains. I don’t really care about the technology concerns (though I don’t think that they are uninteresting or trivial either though the ends to which they will be put are no doubt sinister), but it is interesting to hear how leaders in cog-neuro see the problem. The aim is to get machines to think like brains and so what do they fund? Projects aimed at complete wiring diagrams of the brain. So, for example, Christof Koch and his team at the very well endowed Allen Institute are going to do a “complete wiring diagram of a small cube of brain – a million cubic microns, totaling one five-hundredth of cortext.” The idea is that once we have complete wiring diagrams we will know how brains do what they do. Here’s Andreas Tolias being quoted: “without knowing all of the component parts, he said, “maybe we’re missing the beauty of the structure.” Maybe. Then again, maybe not. Who knows? Well, I think I do and that’s because of observations that Koch has made in the past.

It is simply false that we do not have complete wiring diagrams. We do. We have the complete wiring diagram and genome of the nematode c-elegans. Despite this we know very little about what the little bugger does (actually we do know a lot about how it defecates David Poeppel informed me recently). So, having the complete diagram and genome has not helped crack the critter’s cognitive issues. Once you see this, you understand that the whole project discussed here is based on the assumption that the relation of human cognition/behavior to brain diagrams is simpler than that of the behavior/cognition of a very simple worm to its wiring diagram and genome. A bold conjecture, you might say. Yup, very bold. Foolhardy anyone? But see below.

It is hard to avoid the suspicion that this is another case of research following the money. Koch knows that there is little reason to think that this will work. But big deal, there’s money there so work it will. And if it fails, then it means we have not gotten to the right level of wiring detail. We need yet more fine grained maps, or maps of other things, or maps between maps of other things and the connectome or.... There really is no end to this and so it is the perfect project.

The little piece is also worth reading for it reports many of the assumptions that our leaders in neuroscience make about brains. Here’s one I liked: Some brain types really believe the neural networks of the 1980s vintage “mimic the basic structure of brains.” So now we know why neural nets they were so popular: they looked “brainy”! I used to secretly think that this kind of belief was too silly to attribute to anyone. But, nope, it seems that some really take arguments from pictorial resemblance to be dispositive.

We also know that they have no idea what “feedback loops” are doing, especially from higher order to lower order layers. Despite the mystery surrounding what top down loops do, the assumption still seems to be that, largely, “information flows from input to output through a series of layers, each layer is trained to recognize certain features…with each successive layer performing complex computations on the data.” In other words, the standard learning model is a “discovery procedure,” and the standard view of the learning involved is standard Empiricism/Associationsim, the only tweak being that maybe we can do inductions over inductions over inductions as well as inductions over just the initial input. This is the old discredited idea central to American Structuralist Linguistics. Early GG showed that this could not be true and that the relations between levels is much more complex than this picture envisaged. However, the idea that levels might be autonomous is not even on the neuroscience agenda, or so it appears.

In truth, none of this should be surprising. If the report in Quanta accurately relays the standard wisdom, neuroscience is completely unencumbered by any serious theories of cognition. The idea seems to be that we will reverse engineer cognition from wiring diagrams. This is nuts. Imagine reverse engineering the details of a word processing program from a PC’s wiring diagram. It would be a monumental task, though a piece of cake compared to the envisioned project of reverse engineering brains from connectomes.

At any rate, read the piece (and weep).

As a relevant addendum to the above piece take a look at the following. Ellen Lau send me a link to a debate about the utility of studying the connectome moderated by David Poeppel at the last CNS meeting in NYC. It is quite amusing. The protagonists are Moritz Helmstaeder (MH) and Tony Movshon (TM). The former holds the pro connectome position (don’t let his first remarks fool you, they are intended to be funny), while the latter embraces a more skeptical Marr like view.

Here’s one remarkable bit: MH presents an original argument regarding the recognized failure of c-elegans connectomics to get much function out of structure. He claims that simple systems are more complex than more complex ones. As TM notes, this is more guess than argument (and there is no argument given). I am pretty sure that were the c-elegans case “successful” this would be generally advertised. David P questions him on this with, IMO, little satisfactory reply. Let’s just say that the position he holds is, ahem, possible but a stretch.

The one things about the debate that I found interesting is that MH seems to be defending a position that nobody could object to while TM is addressing a question that is very hard to be dispositive about. MH is arguing that connectomics has been and can be useful. TM is arguing that there are other better ways to proceed right now. Or, more accurately, that the Marr three prong attack is the way to go and that we will not get cognition from wiring diagrams, no matter how carefully drawn they are.

IMO, TM has the better of this discussion because he notes that the cases that MH points to as success stories for connectomics are areas where we have had excellent functional stories (Barlow results are the basis of MH’s results) for a while. And in this context, looking at the physiology is likely to be very useful and likely successful. To put this crudely, TM (who cited Marr) seems to appreciate that questions of CN interest can be pursued at different levels, which are somewhat independent. And of course, we want them to be related to each other. MH seems to think in a more reductive manner, that level 3 is basic and that we will be able to deduce/infer level 2 and level 1 stories once we understand the connectomic details. Thus, we can get cognition from wiring diagrams (hence the relevance of the failure of c-elegans).

You know where I stand on this. But the discussion is interesting and worth the 90 minutes. There is a lot of mother and apple pie here (as questioners point out). Nobody argues (reasonably enough) against doing any connectomics work. The argument should be (but often isn’t) about research strategy; about whether connectomics can bypass the C part of CNS? As David P puts it: can one reverse engineer the other two levels given level 3 (see discussion from about 1:15 ff)? Connectomics (MH) leans towards a ‘yes,’ the critics TM think ‘no.’ Given the money at stake, this is no small debate. Those who want to see the relevance of Marrian methodological reasoning, need look no further than here.

The last piece is something that I probably already posted once before but might be of interest to those following the Marr discussion in recent posts. It’s Chomsky talking about AI and its prospects (here). It’s a fun interview and a good antidote to the second piece I linked to. It also has the longest extended discussion of Marr as it relates to linguistics that I know of.

Chomsky makes two points. First, the point that David Adger made that there is “no real algorithmic level” when it comes GG because “it’s just a system of knowledge” and “there is no process” a system of knowledge not reducible to how it gets used. (24)

He also makes a second point. Chomsky allows that “[m]aybe information about how it’s used [can] tell you something about the mechanisms.” So ontologically speaking, Gs are not things that do anything, but it might be possible for us (Chomsky notes that some higher (Martian?) intelligence might not require this) to learn something about the knowledge by inspecting how it is used: “Maybe looking at process by which it’s used gives you helpful information” about the structure of the knowledge. (26)

The upshot: there is an ontological/conceptual difference between the knowledge structures that GG describes and how this knowledge is put to use algorithmically but looking at how the system of knowledge is used may be helpful in figuring out what the structure of that knowledge is.

I agree with the ontological point, but I think that Marr might too. Level 2 theories, as I read him, are not less abstruse descriptions of level 1 theories. Rather, level 1 theories specify computational problems that level 2 theories must solve if they are to explain how humans see or speak or hear or…. In other words, level 2 theories must solve level 1 problems to do what they do. So, for example, in the domain of language, to (at least in part) explain linguistic creativity (humans can produce and understand sentences never before encountered) we must show how information Gs describe (i.e. rules relating sound with meaning) is extracted by parsers in real time. So, the Marr schema does not deny the knowledge/use distinction that Chomsky emphasizes here, and that is a good thing as the two are not the same thing.

However, putting things in this way, misidentifies the value of the Marr schema. It is less a metaphysical doctrine than a methodological manual. It notes that it is very useful in vision to parse a problem into three parts and ask how they talk to one another. Why is it helpful? Because it seems that the parts do often enough talk to one another. In other words, asking how the knowledge is put to use can be very helpful in figuring out what the structure of that knowledge is. I think that this is especially true in linguistics where there is really nothing like physical optics or arithmetic to ground level 1 speculations. Rather we discover the content of level 1 theories by inspecting a particular kind of use (i.e. judgments in reflective equilibrium). It seems very reasonable (at least to me) to think that insight we get into the structures using this kind of data will carry over to our study of processing and real time acquisition. Thus, the structures that the processor or LAD is looking for very close to those that our best theories of linguistic knowledge say that they are. Another way of saying this is that we assume that there is a high level of transparency between what we know and those things we parse. There may even be a pretty close relation between derivations that represent knowledge and variables that measure occurrent psychological processes (think the DTC). This need not be the case, for Chomsky and Adger are right that there is an ontological distinction between knowledge and how knowledge is put to use, but it might be the case. Moreover, if it is, that would offer a terrifically useful probe into the structure of linguistic knowledge. And this is precisely what a methodological reading of Marr’s schema suggests, which is shy I would like to emphasize that reading.

Let me add one more point once I am beating a hobbyhorse that I have lately ridden silly: not only is this a possibility, but we have seen recent efforts that suggest its fecundity. Transparency plays an important conceptual role in Pietroski et al’s argument for its proposed semantic structure of most and it also plays an important role in Yang’s understanding of the Elsewhere Principle. I found these arguments very compelling. They use a strong version of transparency to motivate the conclusions. This provides a reason for valuing transparency as a regulative ideal. And this is what, IMO, a Marr schema encourages.

Ok, I’ll stable the pony now with the following closing comments: Chomsky and Adger are right about the ontology. However, there is an interesting reading of Marr where the methodology urged is very relevant to linguistic practice. And Marr is very worthwhile under that reading for it urges a practice where competence and performance issues are more tightly aligned, to the benefit of each.

Oh yes: there is lot’s more interesting stuff in the Chomsky interview. He takes shots at big data, the distinction between engineering and science, and the difference between reduction and unification. You’ve no doubt seen/heard/read him make these points before, but the interview is compact and easy to read.