The Gallistel-King conjecture; an update

As time goes by, bets against the veracity of the Gallistel-King conjecture (see here and here) are becoming longer and longer. Don’t get me wrong. The cog-neuro world is not about to give up on its love affair with connectionism. It’s just that as the months pass, the problems with this (sadly, hyper Empiricist) view of things becomes ever more evident and this readies people for a change. Moreover, as you can’t beat something with nothing but a promise of something (you actually need a concrete something), it is heartening to see that the idea of classical computation within the neuron/cell is becoming ever more conventional. Here is a recent report that shows how far things have come.

It shows how living cells can classically compute, in the sense of programmable circuits (“predictable and programmable RNA-RNA interactions”), which “resemble” conventional electronic circuits” with the added feature that they “self-assemble” within cells “sense incoming messages and respond to them by producing a particular computational output.” Furthermore, “these switches can be combined…to produce more complex logic gates capable of evaluating and responding to multiple outputs, just like a computer may take several variables and perform sequential operations like addition and subtraction in order to reach a final result.” Recall, that as Gallistel has long argued, being able to compute a number and store it and use it for further computation is precisely the kind of neural computation we need to be cognitively adequate. We now know that cells have the chemical wherewithal to accomplish this using little RNA circuits, and that this is actually quite easy for the cell to do (“The RNA-only approach to producing cellular nanodevices is a significant advance, as earlier efforts required the use of complex intermediaries, like proteins”) reliably.

So, the idea that cells can classically compute is true. It would be surprising if evolution developed an entirely novel computational procedure instead of exploiting the computational potential of ready available ones to get our cognitive capacities off the ground. This is possible (of course) but seems like a weird way to proceed if the ingredients for a standard kind of computation (symbolic) are already there for the taking. This is the point of the Gallistel-King conjecture, and to me, it seems like a very good one.

Ideas that break the mold

I am currently re-reading a terrific book on the history of modern molecular biology called The Eight Day of Creation (here, henceforth 8-day). The book reviews some of the seminal scientific events in modern biology, starting with Watson and Crick’s discovery of the double helix structure for DNA. The book is really fun to read given that it intersperses serious science with lots of titillating gossip about the relevant personalities.

The fun aside, the book (confession: I’ve read the first 200 pages so far and this deals exclusively with DNA) raises two interesting questions for someone like me. 

First, it seems to point to two kinds of “revolutions” in the sciences. The first kind is one that everyone is waiting to happen and that had the work that fomented it not been done, analogous work would soon have been produced making an analogous intellectual contribution. The second kind of work is the opposite: had the people who did it not been around, then nobody else would have done it (or at least not soon). Rather, the idea’s birth would have been long (maybe perpetually) delayed. Both kinds of work are groundbreaking and deserving of the kudos and prizes heaped upon it. The difference is that the discoverers of the first kind are distinguished by breaking the tape a bit ahead of others, while the latter is distinguished by having only one person running the race at all.

The second question, of course, is whether any work currently being pursued in my extended neck of the woods smells like either one of these. What is the next big idea? Needless to say, the first kind will be easier to sniff out than the second given that the second seems to come out of nowhere. But, I suspect that nothing really comes completely out of nowhere and I will suggest that one idea that we have been tracking in FoL that has been treated as scientifically dubious until now is gaining traction so that it is beginning to look like an idea whose time has come. In other words, if we take the progression from Ridiculous! to Obvious! via Sorta/Maybe! as an early indicator of an intellectual revolution, then I think the Gallistel-King conjecture (GKC) is about to enjoy some quality time in the intellectual sun.

It should go without saying (but I will say it nonetheless) that everything I say in what follows is entirely half-assed and speculative. This partially comes with the subject matter. But as I like these sorts of issues, and cannot resist, and have nothing better to talk about at the moment, I will indulge myself. You need not follow.

Let’s start with the two kinds of revolutions. 8-day makes the case (not deliberately, I should add) that the helix was waiting to be discovered and though Watson and Crick got their first, someone else would have grocked the structure very soon if they had not. Likely candidates include Wilkins, Franklin and, almost certainly Pauling. There were probably others around that could have figured out the basic ideas as well (or so 8-day leads me to believe).  In fact, Crick seems to agree with this assessment (see 8-day:155). I do not intend this observation to denigrate the achievement (more exactly: who the hell am I to be able to denigrate it?), just to note that it seems to be an idea whose time had arrived. Many researchers thought that DNA was the important big molecule to chemically understand. They thought this because they knew that it was the repository of hereditary information. Many thought that it was some sort of helix and many thought that X-ray pictures were the right kind of evidence to probe their structure. There were several mathematical accounts available (albeit imperfect) to argue from pictures to structure and it seems from the story 8-day tells that sooner or later the story would be cracked (maybe in dribs and drabs as Crick notes that Medawar suggested in the quoted note on 8-day p. 155).

One could say something similar for other great discoveries. Einstein’s theory of special relativity was very similar to other theories that cropped up at the time (Lorentz, Poincare), Darwin’s theory of natural selection was simultaneously discovered by Wallace. The same appears to be true of the work on QED in more modern times. Again, all of this stuff is great, but it was stuff that seems to have been “in the air” and was something that someone would have discovered pretty soon after whoever is credited with the work did it.[1]

This contrasts with other kinds of discoveries. I am told that Einstein’s General Theory is something that really arrived unexpectedly and that nobody was working along the same lines. Ditto with Mendelian genetics (which was so far ahead of its time that it lay undiscovered for about 35-50 years till it was rediscovered by others (Morgan)). McClintock’s theory of jumping genes might fit in here too from what I know of it as would Marshall and Warren’s theory of the bacterial origins of ulcers (which the rest of the scientific community scoffed at until it received the Nobel). 

To this list, I would add Chomsky’s discovery that humans have an FL built to acquire and use recursive Gs with distinctive computational properties. This is an idea, which though obviously correct is still resisted in many quarters. From my read of the history, it seems clear that had Chomsky not made the case for Generative Grammar nobody would have made it for many years to come (if ever, if current resistance is any indication).

There is one more idea that is coming into its own that I would add to the list, and that brings us to the second question (i.e. anything like this on the horizon now?): Gallistel’s conjecture that human cognitive computation is intra-neuronal and and a species of chemical computation rather than inter-neuronal and “connectionist.” This idea has been roundly resisted (and dismissed) by most of the cog-neuro community. The idea that brain computations are not “like” classical computing at all (no registers, variables, write-to and read-from memory etc.) is a virtual dogma in the neurosciences (and has been for well over 30 years). Neo-connectionism is the name of the cog-neuro game and Gallistel’s critiques have been largely ignored and his more positive proposals barely attended to.  Until recently.

I have noted several recentish studies that have argued that there is (at least) some intra-neuronal calculations that cells do (type “Gallistel-King conjecture” into the find box on the top left corner for posts on the topic). Another one has just appeared in Science(here). The authors  are Tagkopoulos, Liu, and Tavazoie (TLT). They show how the e-coli are capable of “forming internal representations that allow prediction of environmental change.” They do this using “intracellular networks” of “biochemical reactions.” Using these networks, these single cell microbes “form internal representations of their dynamic environments that enable predictive behavior.” Further, consistent with the Gallistel-King conjecture (GKC), it appears that these biochemical representations consist of “genome wide transcriptional responses” based on the DNA-RNA-Protein system characteristic of modern cellular bio-chemistry. 

I am no expert in these matters, but it sure looks like what TLT is finding comports quite nicely with the most straightforward version of the GKC in which cognitive computation is based in the same kinds of processes and networks used to convey hereditary information. First, both take place withinsingle cells. Second the information processing has a pretty classical look and embodies a computational architecture (as discussed in detail in The Gallistel & King book) exploiting DNA/RNA/Proteins in the way GKC initially proposed. Not bad for armchair theorizing. Not bad at all.

As I mentioned, there is more and more stuff coming out that provides empirical support for this big idea. And as I have also mentioned elsewhere, this is roughly what we should expect. The GKC is the conservativehypothesis concerning cognitive computation, despite its also being iconoclastic. It claims that cognition supervenes on an information processing network that we know that cells have and that is used for another purpose (passing traits onto future generations). This system is computationally very rich (it embodies a classical (Turing/von Neumann) computational architecture) as Gallsitel and King show. GKC makes the intellectually conservative proposal that an in placeinformation processing network (aan extant system that passes genetic information across generational time) is also used (or repurposed) for other kinds of info processing tasks (i.e. cognitive information processing). This is standard Darwinian thinking. 

In contrast connectionism is quite radical as it proposes a novel computational apparatus to do the heavy cognitive lifting, bypassing a perfectly respectable extant in place and up and running system. Of course, this might be what happened, but it is still a very radical proposal and should only be accepted if there is very significant evidence in its favor. And as Gallistel has argued, there is really no good evidence to support it and lots of problems with it. I will not rehearse these here (but see here), except to say that it is quite amazing how a bad idea gains staying power if it leverages another really bad idea. The marriage of connectionism and associationism is one such stable couple as Gallistel has shown and the fact the neither is convincing on its own seems not to have convinced the neuro-cognoscenti to dumb the pair.

It is fun to speculate just how game changing a world that accepts GKC would be. Cogneuro could really start stealing liberally from our biological friends. Learning would be to cognition what development is to biology (the building of forms based on genetic information plus environmental inputs). All that inter-neuronal chatter might be re-analyzed as sharing computational results rather than executing actual computations. One could imagine a kind of neuronal wisdom of crowds kind of system where individual neurons compute and then “vote” with the popular favorite output carrying the day. But all of this is realfancifulspeculation, completely unmoored from any knowledge (my specialty!). The important point is that it’s looking more and more like GKC is onto something and if it turns out to be even roughly correct, the consequences for what we do in the cog-neuro sciences will be profound. Why do I think this? Because it’s what happened in biology. Indeed, it’s  the big moral from 8-day. Let me explain.

As I said, 8-day is a terrific read and spurs endless fun speculation. It also carries a moral for linguists (and psychologists) with a cognitive bent. To wit: The intellectual challenge facing people in the mid 50s as regards finding the structure of DNA is quite analogous to the central problems in cog-neuro today. The problem then was to find a way of physically grounding the gene. The problem was usefully bounded by the fact that it had to be a structure that comported with the insights of Mendelian genetics (in particular the fact that reproduction leads to half of the genetic traits of the parents being passed onto the offspring). The intellectual challenge was to find a physical structure that would make clear how this was possible. The Watson-Crick structure for DNA did this in a beautiful way. It showed how Mendel’s genetics could be incarnated. Thus, Mendel’s insights formed a boundary condition on the structure of whatever it was that served to transmit hereditary information. The helical structure of DNA did this almost perfectly and Watson and Crick noted as much in their original paper. Here’s the money quote (8-day:154):

It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material. 

We find ourselves in a similar situation today. We know a great deal about parts of cognition. We know a lot about some of the computational properties of cognition. We know that this requires representations with complex properties that demand something very like a classical computational architecture. We know that something like innate cognitive knowledge exists that allows for the kinds of cognitive computations biological systems perform. The goal of neuroscience should be to figure out how this is incarnated in biological material: e.g. What’s an address? How do you read-from and write-to the system, what’s a variable? What’s a pointer to an address? How do you store a number in memory? How is “innate” knowledge genetically coded. These are all things we understand how to execute in silicon. The cognitive theories we have tell us that our embodied computational system musthave these kinds of structures and operations as well. The neuro question is how this is embodied in biological material (as opposed to silicon)? The GKC builds on the fact that though we know how this could be done using intra-neuronal chemistry and have no idea how this could be done using inter-neuronal connections. The obvious conclusion is that cognitive computation is physically grounded in intra-neuronal chemistry. Amazingly, we are starting to get some details about how this might done. And nobody would have thunk it when the GKC was first mooted. 

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[1]That said, the various “versions” had different virtues. So Einstein’s theory of special relativity was substantially different from Lorentz’s and the ways it was different mattered. So too the various versions of QED. Feynman’s formulation spread quickly to the community because of its intuitive appeal. Schwinger’s (I am told) was no less adequate but it was far more technically challenging and harder to conceptualize. These are not small differences, but the general point stands: the basic analyses were very similar and had Einstein not come up with his theory or Feynman with his someone would have come up with a working version that the community would have embraced.

The Gallistel-King Conjecture

Ever since Randy Gallistel came to UMD to give a series of lectures that eventually became his book (with Adam King) Memory and the Computational Brain Bill Idsardi and I have been discussing his deliberately provocative thesis that current neuroscience has fundamentally misidentified basic brain architecture. The book argues that the current conception of the brain in which learning is understood as rewiring of a plastic brain via changes of synaptic conductance (what fires together wires together) cannot be correct (connectionism is an expression of this neuronal worldview). The argument is elaborate and I will leave a more detailed discussion of its main argument for another post. However, what I want to very briefly mention here is a Gallistel and King (G&K) conjecture and some recent work that appears to relate to it.  I say “appears” because, believe me, I am no expert (in fact I am not even knowledgeable enough to be a novice) and so readers should take what follows as essentially an impressionistic riff based on chapter 16 of G&K’s book and a recent broadcast on Science Friday; What Your Genes Can Tell You About YourMemory. So with this caveat lector firmly before you, my conscience is clear and my riff begins.

What is G&K’s conjecture?  They argue in the book that brains must have the architecture of classical computers. Concretely this means that brains must be able to put things in memory, retrieve things from memory and compute over those things stored in memory.  As computation requires applying functions to arguments, we need a way of coding variables and operations that bind and value them.  These are all operations characteristic of a classical machine with a Turing-von Neumann (TvN) architecture.  As most of cognition consists of operations that redeem symbols from memory, computations over these symbols and subsequent storage, the brain, the organ that secretes cognition, must have a TvN architecture.  This is the conclusion. The argument is detailed, very pedagogical and a must read. 

G&K contrast the TvM conception with the currently common view of brains. In this view, brains do not have a TvM structure but are more like neural nets, which, G&K argue is an inadequate physical basis for cognitive computations and so must be wrong. This is very much a minority view. Why? Because brains don’t “look like” computers and they do look like neural nets. Ok, it’s probably more complicated than that, but certainly part of it as anyone who has had the misfortune to hear a connectionist talk knows. So if G&K are right that the common view is wrong and the brain has TvM structure then how does the brain do this? The G&K conjecture is that the requisite structure already exists within neurons, in its molecular structure (169):

…the genome contains complex data structures, just as does the memory of a computer, and they are encoded in both cases through the use of the same architecture employed in the same way: an addressable memory in which many of the memories addressed themselves generate probes for addresses. [There is a] close parallel between the functional structure of computer memory and the functional structure of the molecular machinery that carries inherited information forward in time for use in the construction and maintenance of organic structure…

Their conjecture is that the physical platform for the kinds of computational mechanisms we need to understand cognition exploits this same molecular structure.  DNA, RNA and proteins constitute (part of) the cognitive code in addition tos being the chemical realization of the genetic code.

G&K are very careful to moot this suggestion with all the caution that it deserves. In fact to call it a ‘suggestion’ is already too grandiose, let’s say a hunch or a guess.  This is where the Science Friday segment comes in. It appears that research is discovering that memories are in fact molecularly coded.  There are epigenetic mechanisms that code specific memories in various brain regions by laying down the proteins of the right kind using basically the same DNA mechanisms that code for genetic inheritance and development.  Combined with the G&K conjecture, this discovery might be the tip of a pretty exciting cognitive-neuroscience iceberg and has the potential of overturning a good deal of conventional wisdom, as the scientists interviewed hint at.

This has serious implications for linguists, if true (and recall this is all an impressionistic riff). There exists a very bad argument that the representations that linguists know and love cannot be “psychologically real” because they are not implementable in brain architecture, i.e. neural nets.  In my view, this has always been a weak argument, but one that seems to have quite a bit of suasive power to non-generative grammarians.  If the G&K conjecture is on the right track, however, there is no reason to think that brains cannot code for the kinds of representations we regularly use to account for grammatical competence.  The question will shift from whether they do to how they do.

Let’s end with a little parable based on some G&K remarks (p.281). They make an interesting observation about the history of modern biochemistry and consider its implications for current neuroscience. Watson and Crick’s (W&C) great accomplishment was to find a way to chemically incarnate the classical gene. Until they did their work, the gene was considered a nice computing device, but many biologists believed that it was not “biologically real.”  W&C proved otherwise and this entirely changed biochemistry. The field after W&C was entirely different from the field before W&C.  But what of classical genetics, how much did it change? In contrast to biochemistry, the basics remained essentially as they were before W&C.  Biochemistry had to “catch up” to genetics, not the other way around.  This story has a moral: substitute neuroscience for biochemistry and cognition/linguistics for genetics.  There is no reason a priori to think that “hard” (and expensive) neuroscience occupies the intellectual high ground to which “soft” (and cheap) cognition/linguistics must accommodate itself. Matters might well be the reverse, as they were once before when “hard” biochemistry ended up conforming to “soft” genetics.  The discoveries discussed on Science Friday make the G&K conjecture a little less farfetched and tentatively suggests that the analogy with biochemistry/genetics is prophetic. We may be getting ready to say bye-bye to all those inane connectionist models. Yeah!!!

The Gallistel-King Conjecture; part deux

A while ago I wrote about an idea that I dubbed the Gallistel-King conjecture (here).  The nub of their conjecture is that (i) cognition requires something like a Turing-von Neumann architecture to support it (i.e. connectionist style systems won’t serve) and (ii) the physical platform for the kinds of computational mechanisms needed exploits the same molecular structure used to pass information across generations. In other words, DNA, RNA and proteins constitute (part of) the cognitive code in addition to being the chemical realization of the genetic code.  Today, Science Daily reports (here) that the European bioinformatics Institute “have created a way to store data in the form of DNA.” And not just a little, but tons. And not just for an hour but for decades and centuries if not longer.  As Nick Goldman, the lead on the project, says:

We already know that DNA is a robust way to store information because we can extract it from bones of wooly mammoths, which date back tens of thousands of years, and make sense of it. It is also incredibly small and does not need any power for storage, so shipping and keeping it is easy.

G&K observe that these same virtues (viz. stability, energy efficiency and longevity) would be very useful for storing information in brains.  Add to this that DNA has the kind of discrete/digital structure making information thus stored easy to retrieve and appropriate for computation (c.f. G&K 167ff. for a computational demo), and it would seem that so storing information is just what we would expect from a reasonably well designed biological thinking machine.

Let me shout from the rooftops so that it is clear: I AM NO EXPERT IN THESE MATTERS. However, if G&K are right then we need a system with read-write memory to support the kinds of cognition we find in animals.  As the Science Daily article reports: “Reading DNA is fairly straightforward” the problem is that “writing it has until now been a major hurdle to making DNA storage a reality.” There are two problems that Goldman and his colleague Ewan Birney had to solve: (i) “using current methods, it is only possible to manufacture DNA in short strings” and (ii) “both writing and reading DNA are prone to errors, particularly when the same DNA letter is repeated.” What Goldman and Birney did was devise a code “using only short strings of DNA, and do it in such a way that creating a run of the same letter would be impossible.”

Before proceeding, note the similarity of this and the kinds of considerations about optimal coding that we were talking about in earlier posts (here).  This is a good example of the kind of thing I was thinking of concerning efficient coding and computation.  Note how sensitive the relevant considerations are to the problem required to be solved and to the physical context within it which it needs solving.  This is a good example, I would argue, of the kind of efficiency concerns minimalists should be interested in as well.  Ok, so what did they do?

Birney describes it as follows:

So we figured, let’s break up the code into lots of overlapping fragments going in both directions, with indexing information showing where each of the fragments belongs in the overall code, and make a coding scheme that doesn’t allow repeats. That way, you would have to have the same error on four different fragments for it to fail- and that would be very rare.

The upshot, Goldman says is “a code that is error tolerant using a molecular from we know will last in the right conditions for 10,000 years, or possibly longer...As long as someone knows what the code is, you will be able to read it back if you have a machine that can read DNA.”

Talk about long-term memory! And we certainly all embody machines that can read DNA!

Goldman and Birney see this as a great technological breakthrough; good-by hard drives, hello DNA.  However, with a little mental squinting it is not that hard to imagine how this technological breakthrough would be just what G&K would have hoped for.

Science often follows the leading technology of the day, especially in the neuro/physio world. In Descartes day the brain was imagined as a series of interconnected pipes inspired by the intricate fountains on display (c.f. Vaucanson’s duck). Earlier it was clockwork brains. In our day, it was computers of various kinds. Now, biotech may be pointing to a new paradigm.  If humans can code to DNA and retrieve info from it, why shouldn’t brains?  Moreover, wouldn’t it be odd if brains had all this computational power at its disposal but nature never figured out how to use it?  A little like birds having wings but never learning to fly? As I said, I’m no expert, but you gotta wonder…

More on the Gallistel-King Conjecture

Recall the idea: mental computation involves DNA/RNA manipulations. The idea is that these complex molecules have the structure to support classical computations, as opposed to neural nets, and are ideal candidates for memory storage, again as opposed to nets. The Gallsitel-King conjecture is that brain computations exploit this structure (e.g. here).  One place quite ripe for this kind of process is long term memory. There is progressively more evidence that the genome plays an important role in this area (see here). Here are two more reports (here and here) on recent memory research highlighting  the role of genes in fixing and extinguishing memories.

The Gallsitel-King conjecture; another brick in the wall

Several people sent me this piece discussing some recent work showing how to store and retrieve information in "live" (vs synthetic) DNA.  It's pretty cool. Recall the Gallistel-King conjecture (GKC) is that a locus of cognitive computing will be intra-cellular and that large molecules like DNA will be the repository of memories. The advantage is that we know how to"write to" and "read from" such chemical computers and that this is what we need if we are to biologically model the kinds of computations that behavioral studies have shown to be what is going on in animal cognition. The proof of concept that this is realistic invites being able to do this in "live" systems. This report shows that it has been done.

The images and videos the researchers pasted inside E. Coli are composed of black-and-white pixels. First, the scientists encoded the pixels into DNA. Then, they put their DNA into the E. coli cells using electricity. Running an electrical current across cells opens small channels in the cell wall, and then the DNA can flow inside. From here, the E. Coli’s CRISPR system grabbed the DNA and incorporated it into its own genome. “We found that if we made the sequences we supplied look like what the system usually grabs from viruses, it would take what we give,” Shipman says.
Once the information was inside, the next step was to retrieve it. So, the team sequenced the E. coli DNA and ran the sequence through a computer program, which successfully reproduced the original images. So the running horse you see at the top of the page is really just the computer's representation of the sequenced DNA, since we can’t see DNA with the naked eye.

Now we need to find more plausible mechanisms by which this kind of process might take place. But, this is a cool first step and makes the GKC a little less conjectural.

Slime molds and plants

A while ago I mentioned work done suggesting that Gallistel's conjecture that cognitive computation does not require neural nets is correct. The work discussed "learning" in single cell slime molds and plants. At any rate, this stuff i going mainstream in that Quanta brings this research together in this review (republished in Wired).

The piece focuses on the controversy of whether this can actually be "primitive cognition" noting that for many cognition is only something that brains can do (by brains kogneuro types mean ensembles of neurons). The fear is that this kind of research amounts to "'devaluing' of the specialness of the brain" (12). Others comfort these kogneuro fears by claiming that the "debate is arguably not a war about science, but about words" (13).

Both claims are wrongheaded. These studies are direct challenges to the standard cogneuro paradigm that brain computation is fundamentally inter-neuronal. This is what the Gallistel-King conjecture challenges. The work on slime molds and plants indicates that what fits the behavioral definitions of learning exist in organisms without the requisite neural nets. The conclusion is that neural nets are not necessary for learning. This surely points to the possibility that the standard picture in cog-neuro concerning the centrality of neural nets to cognition needs a fundamental rethink. In fact, it would be biologically amazing if intra-neuronal/cellular cognitive computation was possible and extant in lower organisms but higher organisms didn't use this computational power at all.

Read the review. The content is not news to FoLers. But the reactions to the work and the weird attempts to either discredit, downplay or reinterpret it is fun to look at. The significant thing, IMO, is that this stuff is becoming more and more mainstream. I think we might be on the edge of a big change of mind.

Yet more things to read

Here are some papers I’ve come across lately that you might also find interesting.

1. “Is evolvability evolvable” (here) is by Massimo Pigliucci. Pigliucci has a background in biology (he was a practicing professional evolutionary biologist for many years) but is currently in the phil dept at CUNY (see here). He currently also manages a web blog called Scientia Salon that is often quite amusing (I subscribe) that deals with larger philo questions of interest to the general public. At any rate, the paper above is an interesting run down of the current state of the Modern Synthesis (MS) (the standard theory in evolution) and, in particular, how biologists are trying to supplement it with what is effectively a theory of variation; where does variation come from? Is it actually random? How’s it link to development? The discussion makes all sorts of nice distinctions, like the one between variation and variability. The former depends on “the standing genetic variation,” the latter the potential variation that is as yet unrealized in the population (a distinction that those familiar with the G vs UG distinction should find congenial). This extension of MS is all the rage now, and it will transform how we conceive of evolution, if Pigliucci is right. As he puts it

The heritability end of the spectrum sits squarely within the Modern Synthesis. However, the end of the continuum that deals with major transitions is squarely in the territory that should be covered by the EES (Extended Evolutionary Synthesis-NH). This is not because the new ideas are incompatible with the Modern Synthesis (arguably, nothing in the EES is), but because they introduce new processes that enlarge the scope of the original synthesis and cannot be reasonably subsumed by it without resorting to anachronistic post facto reinterpretations of what that effort was historically about.

In other words, the EES provides new mechanisms to account for evolutionary change. The MS’s main mechanism was natural selection. EES wants to expand the range of explanatory processes. How? Well by constricting the range of possible variation. In other words, natural selection operates over a restricted space of options that the theory of variation aims to explicate. This should all sound very familiar.

Let me add one point: I suspect that to the degree that the theory of variability constrains evolutionary options, to that degree natural selection qua mechanism will seem less and less important. It won’t go away as the options are never unique. But, as in the debate over acquisition, the interesting action may begin to shift from Natural Selection to Variability (learning in a UG context). These are points that people like Chomsky and Fodor have been making for a long time (and have been ridiculed for making it).[1] It is interesting to see that sometimes logic suffices to see which way things ought to go.  

2. It seems that the Gallistel-King conjecture is getting traction in the popular science press. We discussed this paper before (here). Chris Dyer sent me this link to a SciAm piece on the topic. It seems that intra-neuron computations are capturing the popular science imagination (see here). The discussion in this article is not that informative, but I think it indicates that the Gallistel conjecture has legs. If so, we might be witnessing a truly magnificent intellectual event: an understanding of how things are from considerations of how they must be. This is theoretical speculation at its best. Very exciting.

3. Here is more on my hobby-rodent: mouse songs. Bill Idsardi sent me this little article (along with sound files). It seems that male mice are real crooners with at least two types of songs at their paw-tips. Interestingly, it seems that the female doesn’t do much singing, though it appears that she can. She just doesn’t. Were female mice Piraha we would conclude form their reluctance to sing that they couldn’t. In other words, we could conclude that mice can’t sing qua mice. We would be wrong, but hey, no reason to ever go beyond the data, right? At least these mice biologists have not confused capacity and behavior.

4. This paper is relevant to our discussion of the receptivity of the general (scientific) public to our kinds of results. This work got big play, including on NPR apparently. What it shows is that kids know a ton and that knowing a ton is what makes it possible for them to know anything else. I don’t recall any papers getting big play where it is argued that kids know nothing and learn it all. That is the default assumption, perhaps, which is why it is not “news.” So these kinds of results are not only news, they are eagerly taken up. I am told that it even got featured on NPR. I would add that child development is not, so far as I know, a general high school subject. Yet this doesn’t prevent the GP from lapping this stuff up. So, though I agree that getting linguistics into high schools would be a fine thing (and a pretty cheap and efficient way to teach the scientific method as Wayne O’Neil has argued for quite a while) there is plenty of room for improvement publicity wise in getting our views out there in the public domain.

5. As many may know, Dennis Ott and Angel Gallego are editing a 50^th anniversary of Aspects volume. I’ve just read Paul Pietroski’s contribution and I cannot recommend it highly enough (here). It concentrates on elucidating Chomsky’s related conceptions of descriptive and explanatory adequacy and outlines how these notions are related to questions of language acquisition. The position he comes down on is quite closely related to the one discussed in an early paper by Fodor (discussed here).  

To me the most interesting feature of the paper was the way Paul relates these discussions to Goodman’s problem of induction. He notes that Chomsky noted that the central issue is finding the right “vocabulary that makes it possible to construct a certain range of grammars.” Note, the focus is not on the weak or even the strong generative capacity of Gs, but the vocabulary that they are written in. The conclusion: “linguists who want a descriptively adequate theory presumably need to aim for the “higher goal” of characterizing the vocabulary that children use to formulate grammars” (p. 7). Why the emphasis on basic vocabulary? Because the basic vocabulary determines the natural projectable predicates. This is what Goodman showed in 1954 and it is what makes the basic vocabulary the main event. Indeed, absent a specification of the main predicates induction just cannot work as we think it should. Paul rehearses these points in a very accessible manner and shows how central they are to the explanatory enterprise. As he puts it “kids project gruesomely” (read the paper to understand this Goodmanism) and the linguist’s problem is to find the predicates that support such projections. Terrific little paper. Bodes well for the whole volume.

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[1] Indeed, Pigliucci (here) wrote a very critical review of Fodor and Piatelli-Palmarini’s book indicating that their main criticism, which to my mind amounted to observing that the MS needed a theory of variation. It is not clear whether he disagreed with their point or whether he thought that the field had already internalized it. If the latter, then whether or not this is “news” or not seems less relevant than whether or not this is true. In this piece Pigliucci seems to agree that it would be a very important addition to MS.