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The Causal Topography of Cognition*
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The causal structure of cognition can be simulated but not implemented computationally, just as the causal structure of a furnace can be simulated but not implemented computationally. Heating is a dynamical property, not a computational one. A computational simulation of a furnace cannot heat a real house (only a simulated house). It lacks the essential causal property of a furnace. This is obvious with computational furnaces. The only thing that allows us even to imagine that it is otherwise in the case of computational cognition is the fact that cognizing, unlike heating, is invisible (to everyone except the cognizer). Chalmers’s “Dancing Qualia” Argument is hence invalid: Even if there could be a computational model of cognition that was behaviorally indistinguishable from a real, feeling cognizer, it would still be true that if, like heat, feeling is a dynamical property of the brain, a flip-flop from the presence to the absence of feeling would be undetectable anywhere along Chalmers’s hypothetical component-swapping continuum from a human cognizer to a computational cognizer―undetectable to everyone except the cognizer. But that would only be because the cognizer was locked into being incapable of doing anything to settle the matter simply because of Chalmers’s premise of input/output indistinguishability. That is not a demonstration that cognition is computation; it is just the demonstation that you get out of a premise what you put into it. But even if the causal topography of feeling, hence of cognizing, is dynamic rather than just computational, the problem of explaining the causal role played by feeling itself―how and why we feel―in the generation of our behavioral capacity―how and why we can do what we can do―will remain a “hard” (and perhaps insoluble) problem.

causation , cognition , computation , consciousness , dancing qualia dynamics , feeling
  • 1. Computational Explanation

    The thesis that is true is that (just about) any causal system―whether it’s a galaxy, a gall-bladder or a grain of sand ―can be modeled computationally, thereby (if successful) fully “capturing” (and hence explaining) its (relevant) causal mechanism―in other words, explaining, causally, how it works.

    This explanatory power is certainly something that psychology, cognitive science, neuroscience and artificial intelligence would want, for the kinds of things they study and hope to explain causally: organisms, their brains, and their behavior, as well as artificial devices we build that are capable of similar kinds of behavior. But it’s an explanatory power that physics, chemistry, biology and engineering already have for the kinds of things that they study, without the need for “A Computational Foundation for the Study of Planetary Motion” (or of Helium, or of Hemoglobin, or of Convection Heaters). It’s simply the ubiquitous observation that―alongside language and mathematics―computers and computational algorithms are useful tools in explaining the things there are in the world and how they work. (This is what Searle 1980 called “Weak AI.”)

    2. Computational Sufficiency

    Chalmers’s second thesis is that―unlike, say, flying, or digesting, which can likewise be modeled and explained computationally (but are not, as Chalmers agrees, themselves instances of computation)―cognition is (just) computation:

    I will try to flesh out both of Chalmers’s theses without getting bogged down in technical details that are interesting but not pertinent to this fundamental distinction.

    3. Algorithms

    Computation is symbol manipulation: symbols are objects of arbitrary shape (e.g., 0 and 1) and they are manipulated on the basis of rules (“algorithms”) that operate on the shapes of the symbols, not their meanings. In other words, computation is syntactic, not semantic. However, most computations can be interpreted as meaning something (otherwise we would not bother designing and doing them): We are interested in algorithms that can compute something useful, whether it’s planetary motion or payroll checks.

    How do computations do useful things? There are many ways. Numerical algorithms compute quantitative results we are interested in knowing. Desk calculators implement numerical algorithms. Boolean (and/or/not) search in Google’s database can retrieve documents or data we are interested in. Virtual Reality can fool, entertain, or train our senses and movements. NASA’s flight simulations anticipate problems that might arise in actual space flights. If Copernicus and Galileo had had digital computers, they might (just might!) have reached their conclusions faster, or more convincingly. Appel & Haken proved the four-color theorem with the help of computation in 1976. And if Mozart had had a computer to convert keyboard improvisation into metered notation, ready to print or edit and revise online, humankind might have been left a much larger legacy of immortal masterpieces from his tragically short 35 years of life.

    4. Causal Structure

    A word about causal structure―a tricky notion that goes to the heart of the matter. Consider gravitation. As currently understood, gravitation is a fundamental causal force of attraction between bodies, proportional to their respective masses. If ever there was a prototypical instance of causal structure–causing―gravitational attraction is such an instance.

    Now gravitational attraction can be modeled exactly, by differential equations, or computationally, with discrete approximations. Our solar system’s planetary bodies and sun, including the causal structure of their gravitational interactions, can be “mirrored” formally in a computer simulation to as close an approximation as we like. But no one would imagine that that computer simulation actually embodied planetary motion: It would be evident that there was nothing actually moving in a planetary simulation, nor anything in it that was actually exerting gravitational attraction. (If the planetary simulation was being used to generate a Virtual Reality, just take your goggles off.)

    The computer implementation of the algorithm would indeed have causal structure―a piece of computer hardware, computing, is, after all, a physical, dynamical system too, hence, like the solar system itself, governed by differential equations. But the causal structure of the implementation, as a dynamical system in its own right, would be the wrong causal structure (and would obey the wrong differential equations), insofar as planetary motion was concerned. It would not be the causal structure of planets, moving, nor gravity, attracting: it would be the causal structure of computer hardware, executing a certain algorithm, thereby formally “mirroring” the causal structure of planetary motion, as encoded in the algorithm. Two different dynamical systems, with different dynamical properties: those of the hardware, implementing the algorithm, and those of the planets, orbiting. (This is exactly the same as the two examples Chalmers concedes to be noncomputational: flying and digestion.)

    So in what sense does the causal structure of the computational model “mirror” the causal structure of the thing that it is modeling? The reply is that it mirrors it formally. That means that the symbols and symbol manipulations in the model can be systematically interpreted as having counterparts in the thing being modeled. (Virtual Reality can even make it into a visual interpretation, for our senses.) We don’t even have to resort to computational simulations of planetary motion, nor even to the exact differential equations of physics in order to see this: We can see it in geometry, in the way x2 + y2 = r2 “mirrors” the shape of a circle: Yes, x2 + y2 = r2 “captures” the invariant structure of the circle, but it is not a circle, it is not shaped like a circle. (Reminder: a circle is the kind of thing you see on the Japanese flag!) No one would think otherwise, despite the accurate―indeed isomorphic―mirroring. (And again, if the algorithm is dynamically generating something that looks round, like a circle, via VR, just take your goggles off.)

    5. Formally Mirroring Versus Physically Instantiating Causality

    But this is all obvious. Everyone knows that the mathematical (or verbal) description of a thing is not the same kind of thing as the thing itself, despite the formal invariance they share. Why would one even be tempted to think otherwise? We inescapably see by observation that a computational solar system lacks the essential feature of a real solar system despite the formally mirrored “causal structure,” namely, there are no bodies there, moving, any more than there is anything round in the formal equation for a circle. The model―whether static or dynamic―is just an explanatory device, a formal oracle, not a reincarnation of the thing it is modelling.

    So it is evident by inspection in the case of physics, chemistry, and biology (e.g., when we note that synthetic hearts pump blood, but computational hearts do not), and even in mathematics, that the “causal structure” of the model (whether computational or analytic, symbolic or numeric, discrete or continuous, approximate or exact) may be the right one for a full causal explanation of the thing being modeled (explanation being a formal exercise), but not for a causal instantiation―unless it actually embodies the relevant causal properties of the thing being modeled (the way a synthetic heart does) rather than just formally “mirroring” its properties by physically implementing their computation (the way a computational heart would do). Why is this not so evident in the case of cognition?

    6. Cognition Is Visible Only To the Cognizer

    How could Chalmers (and so many others) have fallen into the error of confusing these two senses of causality, one formal, the other physical? The reason is clear and simple (indeed Cartesian), even though it has been systematically overlooked. This very error is always made in the special case of cognition. What on earth is cognition? Unlike, say, movement, cognition is invisible to all but the cognizer! (1) We all know what cognizing organisms can do. (2) We all know what brain activity is. (3) And we all know what it feels like to cognize. Both behavioral capacity (1) and brain activity (2) are perfectly visible: they give rise to ordinary empirical data (observable, detectable, measurable). But we do not yet know what it is about the brain activity that generates (causes) the behavioral capacity, let alone the cognition. And the only one to whom cognition itself is “visible” is the cognizer (3). So when we theorize about what causes cognition (rather than just behavioral capacity), we are theorizing about something that is invisible to everyone except the cognizer, namely, cognizing itself: We are theorizing about what brain activity (or synthetic device activity, if cognizing is possible in synthetic devices) generates not only our observable behavior (and behavioral capacity) but also our unobservable cognizing.

    7. Cognizing Versus Moving

    Let’s contrast the case of the brain and its invisible cognizing with the case of planets, moving, as well as with the case of a bodily organ other than the brain, one for which the problem of invisible properties does not arise: the heart, pumping. The reason we would never dream of saying that planetary motion was just computational―or of saying that planets in a computational model were actually moving because the model “mirrors” their “causal structure”―is simply the fact that planetary motion is visible (or observable by instruments). Hence it is inescapably obvious that the computational model, even if it shares the formal causal properties of planetary motion, does not move. The same is true of the computational heart: Unlike the synthetic heart, which really can pump blood (or some other liquid), a (purely) computational heart cannot pump a thing. (And note that I am talking about a physically implemented computational heart that faithfully mirrors the causal structure of a real heart―formally, algorithmically, “isomorphically.” It is merely symbolically pumping symbolic blood.)

    8. Bodily Activity, Brain Activity, and Cognizing

    Now imagine the same thing for the brain. It’s a bit more complicated, because, unlike the heart, the brain is actually “doing” not one, nor two but three different things. One thing is generating (1) behavioral capacity: The brain is generating just about everything our bodies do, and are able to do, in the external world. The second thing is (2) the internal activity of the brain itself (the action potentials and secretions that are going on inside it). And finally, the brain is (3) cognizing (whatever that turns out to be―we will return to this).

    So, unlike the planets and the heart, which are doing just one kind of thing, all of it fully observable to us (moving and pumping, respectively), the brain is doing three kinds of things, two of them visible (behavior and brain activity), one of them not (cognition).

    9. Turing Testing

    Now we are in a position to pinpoint exactly where the error keeps creeping in: No one would call a cognitive model a success if it could not be demonstrated to generate our behavioral capacity―if it could not do what we can do. So the question becomes: what kind of model can generate our behavioral capacity? That’s where the Turing Test (TT) comes in (Harnad 2008): A model can generate our behavioral capacity if it can pass TT―the full robotic version of TT, not just the verbal version (i.e., the ability to do everything a human can do in the world, not just to talk about it): A sensory-motor system that could pass the robotic TT would have to be able to perform indistinguishably from any of us, for a lifetime.

    10. “Neuralism”

    Let’s set aside the second kind of thing that brains do― internal brain activity―because it is controversial how many (and which) of the specific properties of brain activity are necessary either to generate our behavioral capacity or to generate cognition. It could conceivably turn out to be true that the only way to successfully generate cognition is one that preserves some of the dynamic (noncomputational) features of brain activity (electrochemical activity, secretions, chemistry etc.). In that case the fact that those observable (neural) features were missing from the computational model of cognition would be just as visible as the fact that motion was missing from the computational model of planetary motion (or that a computational plane was not flying, or a computational stomach not digesting). Let’s call the hypothesis that, say, biochemical properties are essential ―either to generate our behavioral capacity or to generate cognition― “neuralism.”

    It’s important to understand that my critique of the thesis that cognition is computation does not rest on the assumption that neuralism is true. We will discuss the issue of the implementation-independence of computation in a moment, but I don’t want to insist that generating cognition depends on a requirement of neurosimilitude (necessarily preserving some of the dynamic properties of the brain). Neurosimilitude would definitely be needed in order to explain how the brain works, but not necessarily in order to explain either how to generate the brain’s behavioral capacity or to explain how to generate cognition. Synthetic, non-neural means might be able to generate them too.

    The basis of my critique of cognitive computationalism is, however, analogous to neuralism, and could perhaps be dubbed “dynamism.” It is not that the brain’s specific dynamic properties may be essential for cognizing, but that some dynamic properties may be essential for cognizing. We will return to this when we consider Chalmers’s “Dancing Qualia” argument, according to which there could be a seamless transition from a real cognizing body and brain to a purely computational cognizer with all the same causal powers, replacing each internal component, one by one, by a purely computational simulation of it, the endpoint being the reconfiguration of a computer by its software so as to give it all the causal powers of a cognizing brain.

    11. Sensing and Doing

    Consider behavioral capacity first: Let us agree at once that whatever model we build that succeeds in generating our actual behavioral capacity―i.e., the power to pass the full robotic version of TT, for a lifetime―would definitely have explained our behavioral capacity (1), fully and causally, regardless of whether it did it via secretions or computations. If it were doing it computationally, by implementing an algorithm, it’s clear that it would also need a robotic body, with sensors and moving parts, and that those could not be just the implementations of algorithms any more than flying or digestion could be.

    Sensing, like moving (and flying, and digesting), is not implementationindependent symbol-manipulation. Consider the question of whether there could be a successful TT-passing robot that consisted of nothing other than (i) sensors and movable peripheral parts plus (ii) a functional “core” within which all the work (other than the I/O [sensory input and motor output] itself) was being done by an independent computational module that mirrored the causal structure of the brain (or of any other system capable of passing the TT). This really boils down to the question of whether the causal link-up between our sensory and motor systems, on the one hand, and the rest of our nervous system, on the other, can in fact be split into two autonomous modules―a peripheral sensorimotor one that is necessarily noncomputational, plus a central one that is purely computational.

    12. Computational Core?

    makes sense. To me it seems just as unlikely as the possibility that we could divide heart function (or flying, or digestion) into a noncomputational I/O module feeding into and out of a computational core. I think sensorimotor function is mostly what the brain does through and through, and that the intuition of a brain-in-a-vat receiving its I/O from the world―the intuition from which the computational-core-in-a-vat intuition derives―is both unrealistic and homuncular.

    But let us agree that the possibility of this functional partition is an empirical question, depending on whatever will turn out to be the causal power it takes to generate our behavioral capacity. If TT could not be passed by a computational core plus I/O peripherals then cognitive computationalism fails. But if TT could be successfully passed by a computational core plus I/O peripherals, would that entail that cognitive computationalism was correct after all?

    13. It Feels Like Something To Cognize

    This is the point to remind ourselves that we’ve left out the third burden of cognitive theory, in addition to (1) behavioral capacity and (2) brain function (which we’ve agreed to ignore): Even if we define cognition as whatever it takes to generate TT capacity, there is something the TT leaves out, something invisible to everyone except the cognizer, namely, consciousness: it feels like something to cognize (3). But that third property, unlike movement or secretions, cannot be perceived by anyone other than the cognizer himself. And I think it is this invisibility of cognition that is the real reason for Chalmers’s error of confusing the computational implementation of the “causal structure” of cognition with the causal instantiation of cognizing itself: It looks from the outside as if there is no difference between what is going on inside a computational model of cognition and what is going on inside the brain of a cognizer. And it is for that reason that computation alone looks like a viable candidate for actually instantiating, rather than merely explaining cognition.

    It is not that Chalmers is unaware of this distinction. He writes:

    14. The “Dancing Qualia” Argument

    But Chalmers thinks his “dancing qualia” argument shows that feeling must, like computation itself, be an implementation-independent property, present in every implementation of the algorithm that successfully captures the right causal structure, no matter how radically the implementations differ:

    What Chalmers is saying here is that if we hypothesize that there could be two physically different but “organizationally invariant” implementations of the same causal structure, one feeling one way and the other feeling another way (or not feeling at all), both variants implemented within the same hardware so that we could throw a switch to flip from one implementation variant to the other, the fact that the causal structure was the same for both variants would prevent the hypothetical difference in feeling from being felt. So the causal invariance would guarantee the feeling invariance.

    But the trouble with this argument is that it fails to take into account the fact that feeling, unlike movement, is invisible (to all but the feeler), yet, like movement, real.

    15. Implementation-Dependent Properties

    The reason the flip/flop thought experiment could not guarantee that all implementations of the causal structure of the solar system or the heart would move and beat, respectively, is that moving and pumping (and flying and digestion) are not computational properties: We can see that. (It is empirically “observable.”)

    In the case of feeling, the reason this very same distinct possibility (that feeling is not a computational property) is not equally evident (as it ought to be) is that the only one feeling the feeling (or not-feeling the non-feeling, as the case may be) is the cognizer (or non-cognizer, not-feeling, in case there’s no feeling going on, hence no cognition). But unless we are prepared to declare feeling to be identical with observable behavioral capacity by definition―which would be tantamount to declaring that cognitive computationalism is true by definition―we have to allow for the possibility that feeling, like moving or flying, is not an implementation-independent computational property but a dynamical, implementation-dependent one.

    For if feeling is (invisibly) like moving, then flipping from one causally invariant implementation to another could be like flipping between two causally invariant implementations of planetary motions, one that moves (because it really consists of planets, moving) and one that does not (because it’s just a computer algorithm, encoding and impementing the same “organizational invariance”): In the case of feeling, however, unlike in the case of moving, the only one who would “see” this difference would be the cognizer, as the “movements” (behavior) would (ex hypothesi) have to be identical.

    16. Argument By Assumption

    Chalmers’s “Dancing Qualia” argument simply does not take this distinct possibility into account at all. It won’t do to say that there might be a felt difference, but it would have to be a “thin” one (because it could have no behavioral consquences). Chalmers gives no reason at all why the state difference could not be as “thick” as thick can be, as mighty as the difference between mental day and nonmental night, if the flip/flop were between a feeling and a non-feeling state, rather than just between two slightly different feeling states with a “thin” difference between them. But because Chalmers has imposed his premise of “functional” (i.e., empirical, observational) indistinguishability (with feeling unable to contradict that premise, because feeling is not externally observable), this would entail that his premise itself (not reality: Chalmers’s premise) locks us into the consequence that even a flip from an insentient “Zombie” state to a behaviorally indistinguishable conscious state could make no empirically detectable difference. The feeling state says, truly, “I am feeling,” and it is; the nonfeeling state says (as obliged by the premise, but falsely) “I am feeling,” but it’s not.

    I am not saying that I believe there could be a behaviorally indistinguishable Zombie state (Harnad 1995)―just that it cannot be ruled out simply on the grounds of having assumed a premise! (Or, better, one must not assume premises that could allow empirically indistinguishable Zombies.) Nor is it necessarily true that “noticing” has to be “functional”―if functional means computational: it feels like something to notice; and what is on trial here is whether computationalism alone can implement feeling at all, as opposed to merely simulating its externally observable correlates, formally. Chalmers’s argument trades on the fact that behavioral indistinguishability entails that we cannot see any difference between noticing and not-noticing―feeling and not-feeling―except if it has some external behavioral (or internal computational) expression. But who promised that the system could feel anything at all, if it was merely computational? There remains the distinct possibility that what would turn the mental lights on or off would be a dynamical property―as in digesting, heating or flying―rather than a computational one. (This is the “dynamism” mentioned earlier.)

    “Demoting” feeling to a dynamical property, its presence or absence dependent on conformity with the right differential equations rather than the right computer program releases feeling from having to be an implementation- independent computational property.

    17. The Easy Problem

    But does cognition have to be felt cognition? Elsewhere, Chalmers (1995) has given a name to a longstanding distinction that ―unlike the distinction between sensorimotor peripherals and computational core―marks a solid empirical difference: The problem of explaining behavioral capacity (doing)―i.e., how and why we can do what we can do ―is “easy” to solve. Cognitive science is nowhere near solving it, but there seem to be no principled obstacles. The problem of explaining how and why we feel (the “mind/ body problem”), in contrast, is hard to solve, perhaps even impossible (Harnad 1995, 2000).

    So there’s something to be said for concentrating on solving the “easy” problem of explaining how to pass the TT first (Harnad & Scherzer 2008). I think it is unlikely (for reasons that go beyond the scope of this discussion) that the solution to even that “easy” problem will be a purely computational one (Harnad 1994). It is more likely that the causal mechanism that succeeds in passing TT will be hybrid dynamic/computational―and that the dynamics will not be those of the hardware implementation of the computation (those really are irrelevant to cognition). The dynamics will be those of the sensory-motor surface―plus all the internal topographic (spatial shape-preserving) dynamics in between (Silver & Kastner 2009), possibly including molecular shape dynamics as well. That―and not computation alone―will be the “causal topography” of cognition.

    18. The Hard Problem

    Once that (“easy”) problem is solved, however, only the TT-passing system itself will know whether it really does cognize― i.e., whether it really feels like something to be that TT-passer. (Being able to determine that through empirical observation would require solving the (insoluble) other-minds problem.)

    But even if we had a guarantee from the gods that the TT-passer really cognizes, that still would not explain the causal role of the fact that the TT-passer feels. The causal role of feeling in the causal topography of cognition will continue to defy explanation even if feeling really is going on in there―probably because there is no more explanatory “room” left in a causal explanation (not just “thin” room, but no room), once all the relevant dynamics are taken into account (Harnad 2011).

    So not only is it unlikely that implementing the right computations will generate cognition, but whatever it is that does generate cognition― whether its causal topography is computational, dynamical, or a hybrid combination of both―will not explain the causal role of consciousness itself (i.e., feeling), in cognition. And that problem may not just be “hard,” but insoluble.1

    1If it were true, neuralism—the hypothesis that certain dynamical properties of the brain are essential in order to generate the brain’s full behavioral capacity —would already refute computationalism, because it would make the premise that computation alone could generate the brain’s full causal power false. The truth of neuralism would also refute Chalmers’s “Dancing Qualia,” argument, a fortiori. Even as a possibility, neuralism invalidates the Dancing Qualia argument. Dynamism provides a more general refutation than neuralism; it does not depend on the brain’s specific dynamics having to be the only possible way to generate either the brain’s behavioral capacity or feeling. It is based on the fact that feeling, like moving, is a dynamical property, but, unlike moving, observable only to the feeler. Hence both computationalism and the Dancing Qualia argument could fail, with no one able to detect their failure except the feeler—while the computationalist premise pre-emptively defines the feeler a priori as unable to signal the failure of the premise in any way. This is not just a symptom of the circularity of Chalmers’s premise about the causal power of computation, however. It is also a symptom of the “hard problem” of explaining the causal role of feeling: Except if (contrary to all empirical evidence to date) dualism were true—with feeling being an autonomous psychokinetic causal force, on a par with gravitation and electromagnetism— feeling seems to be causally superfluous in any explanation of cognitive capacity and function, regardless of whether the causality is computational, dynamic or hybrid.

  • 1. Chalmers D. 1995 Facing Up to the Problem of Consciousness. [Journal of Consciousness Studies] Vol.2 P.200-219 google
  • 2. Chalmers D. 2012 A Computational Foundation for the Study of Cognition. [Journal of Cognitive Science.] google
  • 3. Harnad S. 1994 Computation Is Just Interpretable Symbol Manipulation: Cognition Isn’t. Special Issue on “What Is Computation” [Minds and Machines] Vol.4 P.379-390 google doi
  • 4. Harnad S. 1995 Why and How We Are Not Zombies. [Journal of Consciousness Studies] Vol.1 P.164-167 google
  • 5. Harnad S. 2000 Correlation vs. Causality: How/Why the Mind/Body Problem Is Hard. [Journal of Consciousness Studies] Vol.7 P.54-61 google
  • 6. Harnad S. 2008 The Annotation Game: On Turing (1950) on Computing, Machinery and Intelligence. In: Epstein, Robert & Peters, Grace (Eds.) Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer. google
  • 7. Harnad S., Scherzer P. 2008 First, Scale Up to the Robotic Turing Test, Then Worry About Feeling. [Artificial Intelligence in Medicine] Vol.44 P.83-89 google doi
  • 8. Harnad S. 2011 Doing, Feeling, Meaning And Explaining. In: On the Human. google
  • 9. Searle John R. 1980 Minds, brains, and programs. [Behavioral and Brain Sciences] Vol.3 P.417-57 google doi
  • 10. Silver Michael A., Kastner Sabine. 2009 Topographic maps in human frontal and parietal cortex. [Trends in Cognitive Sciences] Vol.13 P.488-495 google doi
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