Cogneuro cross training; the Gallistel method

I am getting ready to fly to the Netherlands where I am going to defend Generative Grammar’s (GG) neuro-cognitive relevance. The venue? David Poeppel has been invited to give three lectures on brain and language (see here (neat poster btw)) and I have been invited to comment on the third, Peter Hagoort being the other discussant. The lectures are actually billed as “neurobiological provocations” and David thought that I fell snugly within the extension of the nominal predicate. Given Peter Hagoort’s published views (see here for link and discussion) about GG and his opinion that it has lost its scientific mojo, I suspect (and hope) that the exchange will be lively. The position that I will argue for is pretty simple. Here are the main points:

1.     The following two central claims of GG are, conceptually, near truisms (though, sadly, not recognized as such):

a.     Grammars (G) are real mental objects

b.     FL exists and has some linguistically proprietary structure

2.     At least one defining feature of FL/UG is that it licenses the construction of Gs which generate objects with unbounded hierarchical complexity (aka: recursion).

3.     Most versions of GG identify (more or less) the same kinds of G objects and invoke the same kinds of G principles and operations.

4.     Contrary to received wisdom, GG does not change its theoretical character every 15 minutes. In fact, the history of theory change in GG has been very conservative with later theoretical innovations retaining most of the insights and generalizations of the prior stage.

5.     It’s a big mistake to confuse Greenberg vs Chomsky universals.

6.     Linguistic data is, methodologically speaking, almost as pure as the driven snow (Yay for Sprouse, Almeida, Schutze, Phillips and a host of others). There is nothing wrong with more careful vetting of the data except that most of the time it’s a pointless expenditure of effort (i.e. little marginal return in insight for the extra time and money) whose main objective seems to be to make things look “sciency” (as in “truthy”).

7.     The autonomy of syntax thesis does not mean that GG eschews semantics. It is in fact, another, almost certainly, truistic claim about the structure of human Gs (viz. that syntactic structure is not reducible to phonetic or semantic or informational or communicative structure).

8.     GG recognizes that there is G variation and has things to say about it.

9.     Studying linguistic communication is certain to be much harder than studying G competence precisely because the former presupposes some conception of the latter. Gs have a hope of being natural kinds whereas communication is certainly a massive interaction effect and hence will be very hard to study. Disentangling interaction effects is a real pain, and not only in the cog-neuro of language!

That’s what I will be saying, and given Hagoort’s diametrically opposite views on many of these matters, the discussion should be (ahem) lively. However, I have promised to be on my best behavior and given that I believe it to be very important for linguistics for cog-neuro to appreciate how much GG has to offer I am going to play as nicely as I know how, all the while defending a Rationalist conception of FL/UG and the Gs that FL generates.

I admit that I have been training a bit for this event. My preparation has mainly consisted of re-reading a bunch of papers, and aside from getting me re-focused on some relevant issues, this has also allowed me to re-engage with some really terrific stuff. I want to especially mention a terrific paper by Randy Gallistel and Louis Matzel (G&M) (here). I have noted it in other posts, but I don’t think that I ever discussed it in any detail. I want to somewhat rectify that oversight here.

IMO, the paper is indispensible for anyone interested in why neuroscience and cognition have mixed about as well as oil and water. What makes G&M so good? It argues that the problems stems from the deep-seated Empiricism of contemporary neuroscience. This Empiricist bias has largely prevented neuroscience from even asking the right kinds of questions, let alone providing real insights into how brains embody cognition. A commitment to an Empiricist Associationist psychology has blinded neuroscience from interesting questions. Moreover, and this is what is most interesting in G&M, Empiricist blinders have prevented neuroscience from noticing that there is little cognitive evidence in favor of its pet theory of the brain and no neuro evidence for it either. This, G&M argues, has been obscured by a kind of unfortunate intellectual two step: psychologists believe that some of the best evidence for Associationsim comes from neuroscience and neuroscience thinks that some of the best evidence for it comes from psychology. In other words, there is a reinforcing delusion in which associationist learning and synaptic plasticity take in one another’s dirty laundry and without doing any vigorous washing or even mild rinsing conclude that the two dirty piles are together crisp and clean. G&M argues that this is fundamentally wrong-headed. Here are the basics of the argument.

G&M identifies two broad approaches to learning and memory. The first is the complex of associationism plus neural nets with Hebbian synapses (ANN) (“what fires together wires together”):

In the associative conceptual framework, the mechanism of learn-

ing cannot be separated from the mechanism of memory expression. At the psychological level of analysis, learning is the formation of associations, and memory is the translation of that association into a behavioral change. At the neuroscientific level of analysis, learning is the rewiring of a plastic nervous system by experience, and memory resides in the changed wiring. (170)

This contrasts with an information processing (IP) approach:

[In] the information-processing perspective, learning and memory are distinct mechanisms with different functions: Learning mechanisms extract potentially useful information from experience, while memory carries the acquired

information forward in time in a computationally accessible form that is acted upon by the animal at the time of retrieval (Gallistel & King 2009). (170)

G&M notes another critical difference between the two approaches:

The distinction between the associative and information-processing frameworks is of critical importance: By the first view, what is learned is a mapping from inputs to outputs. Thus, the learned behavior (of the animal or the network, as the case may be) is always recapitulative of the input-output conditions during learning:

An input that is part of the training input, or similar to it, evokes the trained output, or an output similar to it. By the second view, what is learned is a representation of important aspects of the experienced world. This representation

supports input-output mappings that are in no way recapitulations of the mappings (if any) that occurred during the learning. (170)

It is not, then, an accident that so many Associationists have a problem distinguishing a model of the data from a model of the system that generates the data. For an ANN modeling the data is modeling the system, as the latter is just a way of modeling the I/O relations in the data. The brain for ANNers captures the generalizations in the data and more or less faithfully encodes these. Not so for an IPer.

And this leads to a host of other important differences. Here’s two that G&M makes much of:

1.     ANN approaches eschew “representations” and, as such, are domain general

2.     IP theories are closely tied to the computational theory of mind and this “leads to the postulation of domain-specific learning mechanisms because no general-purpose computation could serve the demands of all types of learning” (175).

Thus representations, computation and domain specificity are a natural triad and forsaking one leads naturally to rejecting all. There really is little middle ground, which is precisely why the Empiricism/ Rationalism divide is so deep and consequential.

However, interesting though this discussion is, it is not what I wanted to focus on. For me, the most interesting feature of G&M is its critique of Hebbianism (i.e. the fire-wire pairing of synapses), the purported neural mechanism behind ANN views of learning.

The main process behind the Hebbian synapse is a process known as “long term potentiation” (LTP). This is the process wherein inputs modify transmission between synapses (e.g. increase amplitude and/or shorten latencies) and this modification is taken to causally subvene associative learning. In other words, association is the psychology of choice because synapses reorganize LTPishly thereby closely tracking the laws of association (i.e. “properties of LTP aligned closely with those of the associative learning process as revealed by behavioral experimentation” (171)).

This putative relation between the association and LTP biology has been one of the main arguments for ANN. Thus connectionist neural net models not only look “brainy”, they actually work like brains do! Except, as G&M shows, they don’t really, as “the alignment” between LTP mechanisms and association measured behaviorally “is poor” (171).

How poor is the fit? Well G&M argues that the details of the LTP process lines up very badly the associationist ones over a host of dimensions. For example, associationist and LTP time scales are vastly different, a few milliseconds for LTP versus (up to) hours for associations. Moreover, whereas LTP formation cares about inter-stimulus intervals (how close the relevant effects are in time to one another) associations don’t. They care about ratios of conditioned and unconditioned stimuli pairs (i.e. the CS-US ratio being smaller than the US-US ratio). In sum, as regards “timing,” LTP growth and behavioral association formation are very different.

Moreover, as G&M notes, this is widely recognized to be the case (172) but despite this the conclusion that association laws supervene on LTP biology is not called into question. So, the disconnect is acknowledged but no consequences for ANN are drawn (see p 172-3 for discussion). Defenders of the link rightly observe that the fact that LTP and association formation don’t track one another does not imply that they are not intimately linked. True, but irrelevant. The issue is not whether the two might be related but whether they indeed are and the fact that they don’t track one another means that the virtues of either cannot rebound to the benefit of the other. In fact, as I note anon, things are worse than this.

But first, here are some other important differences G&M discusses:

·      “Behaviorally measured associations can last indefinitely, whereas LTP always decays and usually does so rapidly” (172).

·      “Both forgotten and extinguished conditioned responses exhibit facilitated reacquisition; that is, they are relearned more efficiently than when they were initially acquired” whereas “following its decay to baseline LTP is neither more easily induced nor more persistent than it was after previous inductions” (172).

G&M provide a handy table (174) enumerating the ways that LTP and associations fail to track one another. Suffice it to say, that the two mechanisms seem to be very different and how LTP biology is supposed to support associations is a mystery. And I mean this literally.

Rather than draw the problematic conclusion that there is little evidence that Hebbian synapses can causally undergird associative learning, ANNers appeal to emergent properties of the network rather than LTP operations at the synapses to provide the neural glue for associationsim (see p.173). As G&M note, this is mysterianism, not science.  And though I sympathize with the view that we may never understand how the brain does what it does, I don’t consider this a breakthrough in neuroscience. The following is a funny kind of scientific argument to make in favor of ANN: though the main causal mechanisms for learning are association via Hebbian synapses we cannot understand this at the micro level of associations and synapses but only at the macro level of whole brains. Brains are where the action is, in virtue of the structures of their synapses but how the synapses do this will be forever shrouded in mystery. So much for the virtues of analysis. I love it when hard-headed Empiricism saves itself by flights of mystical fancy.

There is a second line of argument. G&M shows that classical associationist effects require the calculation of intervals (numbers coding for duration) and that Hebbian based neural nets can’t code this kind of info in a usable form (172). As G&M puts it:

“[T]he mechanism that mediates associative learning and memory must be able to encode the intervals between events in a computationally accessible form. There is no hypothesis as to how this could be accomplished through the modification of synaptic transmission” (172).

So, the temporal properties don’t fit together and the basic facts about classical conditioning invoke information that cannot be coded in a set of synapses in terms of LTP. It appears that there really is no there there. What we are left with are arguments from pictograms: ANN stories make for nice pictures (i.e. neural nets look so brainy and synapsy and connectionist nets “learn” so well!) but as the putative fit between neural mechanism and behavioral pattern is very poor (as G&M shows and, it seems, is largely conceded) there is no good biological reason for holding onto associations and no good psychological reason for embracing neural nets. Time to move on.

The rest of G&M outlines what kinds of stories we should expect from an IP cog-neuroscience. Surprise surprise: we are waist deep in representations from the get-go. We hare awash with domain specific computations and mechanisms. In other words, we get what looks to be a Rationalist conception of the mind/brain, or as G&M puts it, “a materialist form of Kantian rationalism” (193). A place, I would add, where GG of the Chomsky variety should feel very much at home. In other words, the problems that neuroscience claims to have with GG is more indicative of problems with the current state of the brain sciences than problems with GG. GG is not perfect (ok, I am kidding, it is) but there is little reason to believe that what we know about the brain (which is quite limited) precludes accounts of the GG variety, contrary to ANN doctrine.

Conclusion? Time to assign ANN to the trash bin of ideas that looked nice but were entirely off track. The sooner we do this, the sooner we can start addressing the serious problems of relating minds to brains G&M list a bunch on p. 175. We have a long way to go. And maybe I can make this point in Nijmegen too, but nicely, very nicely.

Domain Specificity

One of the things that seems to bug many about FL/UG is the supposition that it is a domain specific module dedicated to ferreting out specifically linguistic information. Even those that have been reconciled to the possibility that minds/brains come chock full of pre-packaging, seem loath to assume that these natively provided innards are linguistically dedicated.  This antipathy has even come to afflict generativists of the MP stripe for there is believed to be an inconsistency between domain specificity and the minimalist ambition to simplify UG by removing large parts of its linguistically idiosyncratic structure. I have suggested elsewhere (here) that this tension is only apparent, and that it is entirely possible to pursue the MP cognitive leveling strategy without abandoning the idea that there is a dedicated FL/UG as part of human biological endowment. In a new paper, Gallistel and Matzel (G&M) (here) argue that domain specificity is the biological default once one rejects associationism and adopts an information processing model of cognition. Put more crudely, allergy to domain specificity is just another symptom of latent empiricism (i.e. a sad legacy of associationism).

I, of course, endorse G&M’s position that there is nothing inconsistent between accepting functionally differentiated modules and the assumption that these are largely constructed using common basic operations. And, I of course love G&M’s position that once one drops any associationist sympathies (and I urge you all to immediately do this for your own intellectual well being!), then the hunt for general learning mechanisms looks, at least in biological domains, ill-advised.  Or put more positively: once one adopts an information processing perspective then domain specificity seems obviously correct. Let’s consider G&M’s points in a little detail.

G&M contrasts associationist (A) and information processing (IP) models of learning and memory. The G&M paper is divided into two parts, more or less. The first several pages comprise a concise critique of associationist/neural net models in which learning is “the rewiring of a plastic nervous system by experience, and memory resides in the changed wiring (170).” The second part develops the evidence for an IP perspective on neural computations. The IP models contrast with A-models in distinguishing the mechanisms for learning (whose function is to “extract potentially useful information from experience”) and those for memory (whose function is to “carr[y] the acquired information forward in time in a computationally accessible form that is acted upon by the animal at the time of retrieval”) (170).  Here are some of their central points.

A-models are “recapitulative.” What G&M (170) intend here is that learning consists in finding the pattern in the data (see here): “An input that is part of the training input, or similar to it, evokes the trained output, or an output similar to it.”  IP models are “in no way recapitulations of the mappings (if any) that occurred during the learning.” This is the classical difference between rationalist vs empiricist conceptions of learning. A- models conceive of environmental input as adapting “behavior” to environmental circumstances. IP-models conceive of learning as building a “representation of important aspects of the experienced world.”

A-models gain a lot of their purchase within neuro-science (and psychology) by appearing to link so directly to a possible neural mechanism; long-term potentiation (LTP). However, G&M argue vigorously that LTP support for A-models entirely evaporates when the evidence linking LTP to A-models is carefully evaluated.  G&M walk us slowly through the various disconnects between standard A-processes of learning and LTP function; their time scales are completely different (“…temporal properties of LTP do not explain the temporal properties of behaviorally measured association formation (172).”), their persistence (i.e. how long the changes in LTP vs associations last) is entirely different and so “LTP does not explain the persistence of associative learning (172),” their reactivation schedules are entirely different (i.e. If L is learned and then extinguished, L is reacquired more quickly, but “LTP is neither more easily nor more persistent than it was after previous inductions.”), nor do LTP models provide any mechanism for solving the encoding problem (viz. A-learning is mediated by the comparison of different kinds of temporal intervals and there is no obvious way for LTP nets to do this) except by noting that what gets encoded is emergent (and this amounts to punting on the encoding problem, rather than addressing it).

In short, there is no support for A-models from neural LTP models. Indeed, the latter seem entirely out of synch with what’s needed to explain memory and learning. As G&M put it: “…if synaptic LTP is the mechanism of associative learning- and more generally, of memory- then it is disappointing that its properties explain neither the basic properties of associative learning nor the essential properties of a memory mechanism (173).” So much for the oft insinuated claim that connectionist models are preferable because they are neurally plausible (indeed, obvious!).

General conclusion: A-models have no obvious support from standard LTP models and these standard LTP models are inadequate for handling the simplest behavioral data. In effect, A- and LTP- accounts are the wrong kinds of theories (not wrong in detail, but in conception and hence without much (if any) redeeming scientific value) if one is interested in understanding the neural bases of cognition.

So what’s the right approach? IP-models. In the last parts of the paper G&M go over some examples.  They note that biologically plausible IP models will all share some important features:

1.     They will involve domain specific computations. Why? “Because no general purpose computation could serve the demands of all types of learning (175),” i.e. domain specificity is the natural expectation for IP models of neuro-cognition.

2.     The different computations will apply the same “primitive operations” in achieving functionally different results (175).[1]

3.     The IP approach to learning mechanisms “requires an understanding of the rudiments of the different domains in which the different learning mechanisms operate” (175). So, for example, figuring out if A is cause of B, or A is the edge of B will involve different computations from each other and from those that mediate the pairing of meanings and/with sounds.

4.     Though the neuro-science is at a primitive stage right now, “…if learning is the result of domain-specific computations, then studying the mechanism of learning is indistinguishable from studying the neural mechanisms that implement computations (175).”

Note that this will hold as much in the domain of language as in navigation and spatial representation.  In other words, once one dumps Associationism (as one must as it is empirically completely inadequate and intellectually toxic) then domain specificity is virtually ineluctable. There exist no interesting general purpose learning systems (just as there it no general sensing mechanism, as Gallistel has been wont to observe). That’s the G&M message. Cognitive computation, if it’s to be neurally based, will be quite specifically tailored to the cognitive tasks at hand, even if built from common primitive circuits.

The most interesting part of G&M, at least to me, was the review of the specific neural cells implicated in animal capacities for locating oneself in space and moving around within it. It seems that neuro-scientists are finding “functionally specialized neurons [that] signal abstract properties of the animal’s relation to its spatial environment (185).” These are genetically controlled and, as G&M note, their functional specialization provide “compelling evidence for problem-specific mechanisms.”

Note that the points G&M make above fit very snugly with standard assumptions within the Chomsky version of the generative tradition. In other words, the assumptions that generative linguists make concerning domain specific computations and mechanisms (though not necessarily primitive operations) simply reflects what is, or at least should be, the standard assumption in the study of cognition once Associationism is dumped (may it’s baneful influence soon disappear). If G&M are right, then there are no good reasons from neuro-biology for thinking that the standard assumptions concerning native domain specific structures for language are exotic or untoward.  They are neither. The problem is not with these assumptions, but with the unholy alliance between some parts of contemporary neuroscience and the A-models of learning and cognition that neuro types have uncritically accepted.

If you read the whole G&M paper (some parts involve some heavy lifting) and translate it into a linguistics framework, it is very hard to avoid the conclusion that if G&M are correct, (and, in case you’ve missed it, IMO they are) then the Chomskyan conception of language, mind, and brain is both anodyne and the only plausible game in cog-neuro town.

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[1] A similar point wrt MP and UG is made here.