Faculty of Language

There are many (e.g. Erich Jarvis) who think that the basic hierarchical properties of language are direct reflections of its vocalic expression. This is what makes ASL and other signed languages so useful. The fact that they exist and that modulo their manual articulation they look, so far as we can tell, just like any other language (see here for discussion) puts paid (and paid in full!) to any simple minded idea that linguistic structure is “just” a reflection of their oral articulation.

Why do I mention this? Because I have just been reading some popular pieces about a potentially analogous case in neuroscience. Let me explain.

Several years ago I read a piece on plant “neurobiology” in the New Yorker penned by Michael Pollan (MP) (here). The source of the quotes around ‘neurobiology’ is a central concern of the article in that it explores whether or not it is appropriateto allow that plants may have a cognitive life (‘bullshit’ is one of the terms tossed around) or whether this is just another case of metaphors run amok. One large influential group of critics thought the idea obscurantist bordering on the incoherent (see here). Here’s a quote from the MP piece quoting one of the critics Lincoln Taiz:

Taiz says that the writings of plant neurobiologists suffer from “over-interpretation of the data, teleology, anthropomorphizing, philosophizing and wild speculations. (6)

Wow, this is really bad, as you can tell when the charge is “philosophizing”!!! Can’t have any of that. At any rate, the main reason for Taiz’s conclusion, it appears, is that plants do not have brains (an uncontroversial point) or neurons (ditto). And if one further assumes that brains with neurons are required for cognition of any kind then the idea that plants might have memory, might use representations, learn and make context sensitive judgments is simply a category mistake. Hence the heat in the above quote.

In a recent Aeon essay, Laura Ruggles (LR) reprises the issues surrounding thinking plants (here). It appears that things are still contentious. This does not really surprise me. After all, the idea that plants cognize really is a weird and wonderful suggestion. So, that it could be false or, at the least, ill supported, strikes me as very plausible. However, as the quote above indicates, this is not the nature of the criticism. The objection is not that the evidence is weak but that the very idea is incoherent. It is not false. It is BS. It is not even high class BS, but a simple category mistake due to bad anthropomorphic philosophical speculation generated by teleologically addled minds. Needless to say, I got interested.

Why the heat? Because it directly challenges the reductive neurocentric conception of cognition that animates most of contemporary cog-neuro (just as ASL challenges the vocalic conception of grammar). And it does so in two ways: (i) It reflects the strong commitment of the methodology of “neuron doctrine” in cog-neuro and (ii) It reflects the strong commitment to the idea that biological memory and computation supervenes on a connectionist architecture (i.e. the relevant computations are inter-neural rather than intra-neural). Let me say a word or two about each point.

The “neuron doctrine” is the idea “that cognitive activity can be accounted for exclusively by basic neuroscience. Neuronal structure and function, as identified by neurophysioplogy, neuroanatomy and neurochemistry, furnish us with all we need to appraise the animal mind/brain complex” (see here: 208). This idea should sound familiar because it is the same one that we discussed in the previous post (here). The position endorses reductionist methodology to the study of the brain (more accurately, a methodological monism), one that sees no fruitful contribution from the mental sciences to cog-neuro. KGG-MMP rehearses the arguments against this rather silly view, but it clearly has staying power. Marr fought it in the 1980s and his proposal that problems in cog-neuro need to be tackled at (at least) three different levels (computational, algorithmic/representational, implementational), which interact but are distinct and provide different kinds of explanatory power is a response to precisely this view. This view, to repeat, was prevalent in his day (well over 30 years ago!) and is still going strong, as witnessed by the fact that KGG-MMP felt the need to reiterate his basic arguments yet again.

The plant “neuroscience” debate is a manifestation of the same methodological dogmatism, the position that takes it for granted that we once we understand neurons, we will understand thought. But it is even more of a challenge to this neurocentric view. If plants can be said to cognize (have representations, memories, process information, learn) then not only is the methodological thesis inappropriate, but the idea that cognition reduces to (exclusively lives on) neuronal structure, is wrong as well (again, think ASL and vocalization wrt grammar). If the plant people are onto something then the having memories is independent of having brains, as is learning and representation and who knows what else. So if the plant people are right, then not only is the neuron doctrine bad methodology, it is also ontologically inadequate.

None of this should be surprising if you have any functionalist sympathies. As Marr noted, the materials that make up a chess board/pieces can vary arbitrarily (wood, marble, bread, papier mache) and the game remains the same (the rules/game of chess is readically independent of the physical make-up of the board and pieces). Whether the relation of cognition to brains is more like the chess case or less is an open question. One view (Searle comes to mind) is that no brains, no cognition. On this view, the connection between brain structure and cognition is particularly tight in that the former is a necessary feature of the latter (thinking can only live in brains (though, IMO, there is more than a touch of mystical vitalism in Searle’s position)). If the plant cognitivists are right, then this is simply incorrect.[1]

In sum, though metaphysical reduction does not lend credence to methodological reduction, if even the former is untenable, then it is quite implausible that the former can stand. Why import neuronal methodological dicta in the study of cognition if cognitive machinery need not live in neuronal wetware?

The neuron doctrine has a more specific expression in todays cog-neuro. It’s the claim that the brain is basically a complex neural net and that memory, learning, cognition are products of such neural nets. In other words, the prevalent view in contemporary cog-neuro is that cognition is an inter-neuronal phenomenon not an intra-neuronal one. Brains are the locus of cognition because it brains have inter-neuronal connections. Memories, for example, are expressed in connection weights and learning amounts to adjusting these inter-neuronal weights. The plant stuff challenges this view as well. How so? Because if plants to cognize they seem to do it without anything analogous to a brain (more exactly, this assumption is common ground in the discussion). So, if plants have memories then it looks like they encode these within cells. LR mentions epigenetic memory as a possible memory substrate. These involve “chromatin marks,” which “are proteins and small chemical groups that attach to DNA within cells and influence gene activity” (LR: 3). This mechanism within cells suffices to physically implement “memory.” And if this is so, then it would provide evidence for the Gallistel-King conjecture that memories can be stored biochemically within cells. Or to state this more carefully: if plants can code memories in this way, why not us too and maybe neuronal connectionism is just a wrong-headed assumption, as Gallistel has been arguing for a while. Here is MP making this point:

How plants do without a brain…raises questions about how our brains do what they do. When I asked Mancuso about the function and location of memory in plants, he…reminded me that mystery still surrounds where and how our memories are stored: “it could be the same kind of machinery, and figuring it out in plants may help us figure it out in humans.” (MP:19)

Indeed.

Ok, there is lots of fun stuff in these essays. It is fun to see how plant people go about arguing for mental capacities in plants. There are nice discussions of experiments that appear to show that plants can “habituate” to stimuli (they pretend-drop plants and see how they react), can “learn” new associations (use wind as conditioned stimulus for light) among stimuli, and can to anticipate what will happen (where sun will be tomorrow) in the absence of the thing being anticipated (in the absence of input from the sun), which suggest that plants can represent the trajectory of the sun. Is this “true” and do plants cognize? I have no idea. But an a priori denial that it is possible is based on conceptions of what proper cog-neuro is that we have every reason to reject.

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Posted by Norbert at 5:55 AM No comments:

Labels: Laura Ruggles, Michael Pollan, neuron doctrine

Posted by Norbert at 2:13 PM 21 comments:

There is currently a fight going on in cog-neuro whose outcome GGers should care about. It is illuminatingly discussed in a recent paper by Krakauer, Ghazanfar, Gomez-Marin, MacIver and Poeppel (KGG-MMP) (here). The fight is about how to investigate the mind/brain connection. There are two positions. One, which I will call the “Wrong View” (WV) just to have a useful mnemonic, takes a thoroughly reductionist approach to the problem. The idea is that a full understanding of brain function will follow from a detailed understanding of “their component parts and molecular machinery” (480). The contrary view, which I dub the “Right View” (RV) (again, just to have a name),[1] thinks that reductionism will not get nearly as far as we need to go and that the only way to get a full understanding of how brains contribute to thinking/feeling/etc. requires neural implementations in tandem with (and more likely subsequent to) “careful theoretical and experimental decomposition of behavior.” More specifically, “the detailed analysis of tasks and of the behavior they elicit is best suited for discovering component processes and their underlying algorithms. In most cases,…the study of the neural implementation of behavior is best investigated after such behavioral work” (480). In other words, WV and RV differ not over the end game (an understanding of how the brain subvenes the brain mechanisms relevant to behavior) but the best route to that end. WV thinks that if you take care of the neuronal pennies, the cognitive dollars will take care of themselves. The RV thinks that doing so will inevitably miss the cognitive forest for the neural trees and might in fact even obscure the function of the neural trees in the cognitive forest. (God I love to mix metaphors!!). Of course, RV is right and WV is wrong. I would like to review some of the points KGG-MMP makes arguing this. However, take a look for yourself. The paper is very accessible and worth thinking about more carefully.

Here are some points that I found illuminating (along with some points of picky disagreement (or, how I would have put things differently)).

First, framing the issue as one of “reductionism” confuses matters. The issue is less reduction than it is a neurocentric myopia. The problem KGG-MMP identifies revolves around the narrow methods standard practice deploys not the ultimate metaphysics that it endorses. In other words, even if there is, ontologically speaking, nothing more than “neurons” and their interactions,[2] discovering what these interactions are and how they combine to yield the observed mental life will require well developed theories of this mental life expressed in mentalistic non-neural terms. The problem then with standard practice is not its reduction but its methodological myopia. And KGG-MMP recognizes this. The paper ends with an appeal for a more “pluralistic” neuroscience, not an anti-reductionist one.

Second, KGG-MMP gives a nice sketch of how WV has become so prevalent. It provides a couple of reasons. First, has been the tremendous success of “technique driven neuroscience” (481). There can be no doubt that there has been an impressive improvement in the technology available to study the brain at the neuronal level. New and better machines, new and better computing systems, new and better maps of where things are happening. Put these all together and it is almost irresistible to grab for the low hanging fruit that such techniques bring into focus. Nor, indeed should this urge be resisted. What needs resisting is the conclusion that because these sorts of data can be productively gathered and analyzed that these data suffice to answer the fundamental questions.

KGG-MMP traces the problem to a dictum of Monod’s: “what is true of the bacterium is true of the elephant.” KGG-MMP claims that this has been understood within cog-neuro as claiming that “what is true for the circuit is true for the behavior” and thus that “molecular biology and its techniques should serve as the model of understanding in neuroscience” (481).

This really is a pretty poor form of argument. It effectively denies the possibility of emergence. Here’s Martin Reese (here) making the obvious point:

Macroscopic systems that contain huge numbers of particles manifest ‘emergent’ properties that are best understood in terms of new, irreducible concepts appropriate to the level of the system. Valency, gastrulation (when cells begin to differentiate in embryonic development), imprinting, and natural selection are all examples. Even a phenomenon as unmysterious as the flow of water in pipes or rivers is better understood in terms of viscosity and turbulence, rather than atom-by-atom interactions. Specialists in fluid mechanics don’t care that water is made up of H₂O molecules; they can understand how waves break and what makes a stream turn choppy only because they envisage liquid as a continuum.

Single molecules of H₂O do not flow. If one is interested in fluid mechanics then understanding will come only by going beyond the level of the single molecule or atom. Similary if one is interested in the brain mechanisms underlying cognition or behavior then it is very likely that we will need to know a lot about how groups of fundamental neural elements interact, not just how one does what it does. So just as a single bird doesn’t flock, nor a single water molecule flow, nor a single gastric cell digest, so neither does a single brain particle (e.g. neuron) think. We will need more.

Before I get to what more, I should add here that I don’t actually think that Mondo meant what KGG-MMP take him to have meant. What Monod meant was that the principles of biology that one finds in the bacterium are the same as those that we find in the elephant. There is little reason to suppose, he suggested, that what makes elephants different from bacteria lies in their smallest parts respecting different physical laws. It’s not as if we expect the biochemistry to change. What KGG-MMP and Reese observe is that this does not mean that all is explained by just understanding how the fundamental parts work. This is correct, even if Monod’s claim is also correct.

Let me put this another way: what we want are explanations. And explanations of macro phenomena (e.g. flight, cognition) seldom reduce to properties of the basic parts. We can completely understand how these work without having the slightest insight into why the macro system has the features it does. Here is Reese again on reduction in physics:

So reductionism is true in a sense [roughly Monod’ sense, NH]. But it’s seldom true in a useful sense. Only about 1 per cent of scientists are particle physicists or cosmologists. The other 99 per cent work on ‘higher’ levels of the hierarchy. They’re held up by the complexity of their subject, not by any deficiencies in our understanding of subnuclear physics.

So, even given the utility of understanding the brain at the molecular level (and nobody denies that this is useful), we need more than WV allows for. We need a way of mapping two different levels of description onto one another. In other words, we need to solve what Embick and Poeppel have called the “granularity mismatch problem” (see here ). And for this we need to find a way of matching up behavioral descriptions with neural ones. And this requires “fine grained” behavioral theories that limn mental mechanisms (“component parts and sub-routinges”) as finely as neural accounts describe brain mechanisms. Sadly, as KGG-MMP notes, behavioral investigation “has increasingly been marginalized or at best postponed” (481-2), and this has made moving beyond the WV difficult. Rectifying this requires treating behavior “as a foundational phenomenon in its own right” (482).[3]

Here is one more quibble before going forward. I am not really fond of the term ‘behavioral.’ What we want is a way of matching up cognitive mechanisms with neural ones. We are not really interested in explaining actual behavior but in explaining the causal springs and mechanisms that produce behavior. Focusing on behavior leads to competence/performance confusions that are always best avoided. That said, the term seems embedded in the cog-neuro literature (no doubt a legacy of psychology’s earlier disreputable behaviorist past) and cannot be easily dislodged. What KGG-MMP intends is that we should look for mental models and use these to explore neural models that realize these mental systems. Of course, we assume that mental systems yield behaviors in specific circumstances, but like all good scientific theories, the goal is to expose the mental causes behind the specific behavior and it is these mental causal factors whose brain realization we are interested in understanding.The examples KGG-MMP gives show that this is the intended point.

Third, KGG-MMP nicely isolates why neuroscience needs mental models. Or as KGG-MMP puts is: “Why is it the case that explanations of experiments at the neural level are dependent on higher level vocabulary and concepts?” Because “this dependency is intrinsic to the very concept of a “mechanism”.” The crucial observation is that “the components of a mechanism do different things than the mechanism organized as a whole” (485). As Marr noted, feathers are part of the bird flight mechanism, but feathers don’t fly. To understand how birds fly requires more than a careful description of their feathers. So too with neurons.

Put another way, as mental life (and so behavior) is an emergent property of neurons how neurons subvene mental processes will not be readily apparent by only studying neural properties singularly or collectively.

Fourth, KGG-MMP gives several nice concrete examples of fruitful interactions between mental and neural accounts. I do not review them here save to say that sound localization in barn owls makes its usual grand appearance. However, KGG-MMP provides several other examples as well and it is always useful to have a bunch of these available on hand.

Last, KGG-MMP got me thinking about how GGish work intersects with the neuro concerns the paper raises, in particular minimalism and its potential impact for neuroscience. I have suggested elsewhere (e.g. here) that MP finally offers a way of bridging the granularity gap that Embick and Poeppel. The problem as they saw it, was that the primitives GGers were comfortable with (binding, movement, c-command) did not map well to primitives neuro types were comfortable with. If, as KGG-MMP suggests, we take the notion of the “circuit” as the key bridging notion, the problem with GG was that it did not identify anything simple enough to be a plausible correlate to a neural circuit. Another way of saying this is that theories like GB (though very useful) did not “dissect [linguistic, NH] behavior into its component parts or subroutines” (481). It did not carve linguistic capacity at its joints. What minimalism offers is a way of breaking GB parts down into simpler subcomponents. Reducing macro GB properties to products of simple operations likeMerge or Agree or Check Feature promises to provide mental parts simple enough to be neurally interpretable. As KGG-MMP makes clear finding the right behavioral/mental models matters and breaking complex mental phenomena down into its simpler parts will be part of finding the most useful models for neural realization.

Ok, that’s it. The paper is accessible and readable and useful. Take a look.

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Posted by Norbert at 7:00 AM 4 comments:

Labels: Ghazanfar, Gomez-Marin, Krakauer, MacIver and Poeppel, methodological pluralism, reduction

The content of the post that follows was first published in roughly this form in Biolinguistics. I put it up here for obvious reasons. Jerry Fodor was a great philosopher. I knew him personally but not as well as many of my friends did. I was charmed the few times I socially interacted with him. He was so full of life, so iconoclastic, so funny and so generous (most of his insights he graciously attributed to his grandmother!). I looked up how often I talked about Jerry's stuff on FoL and re-reading these made me appreciate how much my own thinking largely followed his (though less funny and less incisive). So, he will be missed.

So, without further ado, here is a reprise of what I take to have been the Jerry/Massimo argument against Natural Selection accounts of evolution.

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Posted by Norbert at 11:48 AM 4 comments:

Labels: Darwinian natural selection