How to study brains and minds

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 like  Merge 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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[1] As we all know, the meaning of the name is just what it denotes so there is no semantic contribution that ‘wrong’ and ‘right’ make to WV and RV above.

[2] The quotes are to signal the possibility that Gallistel is right that much neuronal/cognitive computation takes place sub neuronally.

[3] Again, IMO, though I agree with the thrust of this position, it is very badly put. It is not behavior that is foundational but mentalistic accounts of behavior, the mechanisms that underlie it, that should be treated as foundational. In all cases, what we are interested in are the basic mechanisms not their products. The latter are interesting exactly to the degree that they illuminate the basic etiology.