Primitive Concepts and Innateness

March 29th, 2008

Fodor (1998, p.15), presenting the (his) RTM view of concepts, says, “I can’t … afford to agree that the content of the concept H2O is different from the content of the concept WATER.”  At least in part, this is a consequence of his assertion that “Concepts are public; they’re the sorts of things that lots of people can, and do, share.” (p.28, italics in original) 


If the content of concepts is public (I, for one have no problem with this view), then nobody and everybody is responsible for them and their denoters have to be learned.  It’s easy enough to argue, following Eric Baum (2004, What Is Thought?), that our genome builds us in such a way that we all acquire categories in pretty much the same way.  I’m not sure why I insisted on “categories” in the previous sentence rather than sticking with “concepts.”  I guess it’s because I have already done a lot of thinking about concepts and I’m not sure whether I’m willing to grant concepthood to categories.     


A priori, there must be a set of parameterizable functions that are built-in by the genome.  When I talk about parameterization here, I’m talking about learning processes; when I talk about parameterizing models, I’m talking about the inputs to a particular content model at a moment in time.  The former takes place during concept development; the latter during concept utilization.  Taking such a set of parameterizable functions as a basis, content models can (only) be constructed from these components.  The genome thus ensures that ceteris paribus (over a reasonable range of normal human ontogenetic experience) the structure of the content model(s) epigenetically constructed will tend to converge (dare I say they will be the same up to some threshold of difference?). 


The convergence we expect to find looks like this: If things that are modeled by a particular content model a in creature A are pretty much the same things that are modeled by a particular content model b in creature B, and if that is true also for particular content models c, d, e, …, etc. in C, D, E, …, etc., then those content models are the content model of a concept whose satisfaction conditions include (pretty much) those things.  Moreover, the human genome is sufficiently restrictive to ensure that in the vast majority of cases (enough to ensure the functioning of language, anyway) we can take these models to implement (represent?) by definition the same concept.  That is, sameness of concepts across individuals arises from the identity of the (shared) facilities available to construct them and the identity of the (shared, lower level) processes that construct them out of (things that turn out to be) invariants these processes extract from the real world. 


DOG means dog because the (already strongly constrained) models the human brain automatically constructs when presented with dogs are such that across individuals the models will use identical processes in identical ways (process identity is obviously level-sensitive—I can’t possibly argue that the neural circuits are isomorphic across individuals, but I can argue that the brain is sufficiently limited in the ways it can operate that there is at some level of explanation only one way a dog model can be implemented).


This is similar to the poverty of the stimulus argument that argues for much of language to be innate.


I think we’re almost there now, but it occurs to me that I have built this on the identity of things, which may itself be tendentious.  There’s no problem with saying a particdular thing is identical to itself.  But that’s not where the problem arises.  How do we know what a thing is?  A thing is presumably something that satisfies the concept THING.  But careful examination of the reasoning above shows that I have assumed some kind of standardized figure-ground system that reliably identifies the things in an environment.  Now where are we?  Suppose the things are dogs.  Do we have to suppose that we know what dogs are?


Let’s try to save this by saying by substituting environments for things and then talking about world models.  That is, if the environment that is modeled by a particular world model a in creature A is pretty much the same environment that is modeled by a particular world model b in creature B, and if that is true also for particular world models c, d, e, …, etc. in C, D, E, …, etc., then those world models are the world model of a world whose satisfaction conditions include (pretty much) those environments.  Moreover, the human genome is sufficiently restrictive to ensure that in the vast majority of cases (enough to ensure the identification of things, anyway) we can take these models to be (implement, represent?) by definition the same world model.


As a practical matter, this does not seem to be a problem for human beings.  We learn early how to parse the environment into stable categories that we share with others in the same environment.  Somewhere in this process, we acquire thingness.  Thingness is necessary for reference, for intentionality, for aboutness.  I don’t know, and I don’t think it makes much of a difference, whether thingness is innate or (as I suspect) the acquisition of thingness requires postnatal interaction with the environment as part of the brain’s boot process.


Fodor (1998, p.27) and the Relational Theory of Mind (RTM) crowd have a rather similar way around this.  “[A]ll versions of RTM hold that if a concept belongs to the primitive basis from which complex mental representations are constructed, it must ipso facto be unlearned.”  This is actually several assertions.  The most important one from my point of view is:


There are innate (unlearned) concepts. 


I take it that my use of the word innate here will seem comfortably untendentious when I tell you I am explicitly ruling out the possibility that unlearned concepts are injected into us by invisible aliens when we are small children.  The only worry I have about innate concepts is that like Baum I suspect that in reality the members of the set of such innate concepts are far removed from the concepts traditionally paraded as examples of concepts, that is, I don’t think COW is innate any more than KODOMO-DRAGON.  (Baum doesn’t talk much about concepts per se, but his central position is that everything that’s innate is in our DNA and our DNA has neither room nor reason to encode any but the most primitive and productive concepts.)  Fodor is coy about COW and HORSE, but he counterdistinguishes the status of COW from the status of BROWN COW, which “could be learned by being assembled from the previously mastered concepts BROWN and COW.”


I don’t think Fodor really needs COW to be innate.  I think the problem is that he doesn’t want it to have constituents.  I sympathize.  I don’t want it to have constituents.  But making COW innate is not the only alternative.  All that is needed is a mechanism that allows for cows in the world to have the ability to create a new primitive COW that is (by my argument above) the same primitive COW that Little Boy Blue has and indeed the same primitive as most everybody else familiar with cows has.  In other words, what I have proposed is a mechanism that enables concepts to be public, shareable, primitive, and learnable.  I haven’t got a good story about how one could be familiar with cows and not have the same concept COW as most everybody else.  Maybe if one’s familiarity with cows was always in the context of partially obscuring bushes one might come to acquire a concept COW that meant bushes partially obscuring a cowlike animal.  But if that were the case, I’d expect that same COW concept to be created in others familiar with cows in the same context.


The rest of the story is that this way of making COW primitive but not innate requires reexamination of the assertion that there are innate concepts.  It looks like the things I am postulating to be innate are not properly concepts, but rather concept-building processes.  So the correct statement is:


There are innate (unlearned) concept-building processes that create primitive concepts.  I’d be willing to buy the so-called “universals” of language as a special case of this.


It will work, I think, because the putative processes exist at prior to concepts.  So, we still have primitive concepts and non-primitive concepts in such a way as to keep RTM in business for a while longer.  And we can build a robust notion of concept identity on identity of primitive concepts without requiring all primitive concepts to be innate.  This does not, of course, rule out the possibility (offered by the ethology of other species, as Fodor points out) that we also possess some innate primitive concepts.



How language processing might go awry

March 29th, 2008

Re: April Benasich’s ongoing studies of Specific Language Impairment in children ( If we believe Maass, et al. (2004, “On the computational power of circuits of spiking neurons”)  with respect to the ability of a (plausible, cortical) recurrent neural network to retain in its state a usable record of (order of) the last three seconds of its inputs, we may get some insight into possible underlying mechanisms for what Benasich hypothesizes to be  “the primary deficit in … SLI … the inability to integrate and discriminate two or more sensory events which enter the nervous system in rapid succession.” 


Maass’s work suggests that in normal processing, asynchronous discriminations can be replaced by synchronous discriminations.  I interpret this to mean that as soon as the neural circuitry has enough of the stimulus to recognize, it can be pulsed out synchronously to the next stage of processing.  Looking at psychophysical results like those of Richard Warren (1970, “Perceptual Restoration of Missing Speech Sounds.”  Science 167: 392-393) could be interpreted as indicating that a later pulse can refine or override (confirm or disconfirm) the data delivered in an earlier pulse. 


So here’s what I think happens.  Suppose Stimulus A is followed in quick succession by Stimulus B.  Sometime during or after Stimulus A (because the neuro-glial circuitry doesn’t have to wait until what we think is the “end” of a stimulus before reacting) the circuitry approaches or settles into a provisional identification of an Event Pattern of, call it, Type X.  Event Pattern Type X is the pattern of activation (state) created by A in the context of mutter-mutter (unknown or irrelevant—whatever the state was when A arrived).  This provisional identification state somehow gets sent on as a synchronous pulse.  That pulse “runs out of oomph” somewhere and leaves neuro-glial elements in its trail primed with EPSPs and IPSPs.  (All I intend by that is to indicate that the pulse isn’t sufficient in and of itself to cause recognition of A tout court (as Jerry Fodor would say).


In normal processing, sometime during or after Stimulus B (which follows Stimulus A in rapid succession) the circuitry will  approach or settle into a provisional identification of an Event Pattern of Type Y (which is the state created by B in the context of immediately preceding A and whatever came before).  That information gets sent on in a pulse.  In between, there may be (there probably are) confirming or disconfirming pulses (possibly at regular intervals).  The net result is (insert hand waving here) recognition of A followed by B.


So what could be happening in SLI?  Possibilities are:


1)       Stimulus history traces decay so rapidly that at the time Stimulus B arrives, it has insufficient context information and gets sent on as an Event Pattern of Type Z (B in the context of mutter-mutter).  In later processing, this acts as disconfirmation of Pattern Type X (A in the context of mutter-mutter) rather than confirmation of the temporal evolution from A to B.  So information about A is lost.  I suppose it’s also possible that the apparent disconfirmation could be treated as spurious, so information about B could be lost.  Or the conflict could lead to no decision and loss of the distinctness of A and B.  Checkmate in all cases.


2)       State information isn’t being read out on a rapid enough schedule, so what comes through is only Event Pattern Type V (in the context of mutter-mutter, A followed by dim intimations of B) or Event Pattern Type W (dim intimations of A followed by B followed by whatever).  In either case, one of the stimuli is represented only by dim intimations that don’t reach above whatever threshold is necessary to affect subsequent stages, so information about A or B is lost.


3)       There is a timing mismatch in later processing so that differential decay rates of EPSPs and IPSPs cause information from what should have been distinct pulses to get muddled and the pattern looks like Pattern Type U (A overlaid with B in the context of mutter-mutter overlaid with A).  So the distinctness of A and B is lost.


4)       The state attractor that has developed in the neural circuit that gets first crack at A followed by B classifies them both the same way (like /p/ and /b/, I suppose Merzenich might say).

What might ‘wanting’ be?

March 28th, 2008

I have long wondered what ‘wanting’ is from a physiological standpoint.  Antonio Damasio (1999, The Feeling of What Happens: Body and Emotion in the Making of Consciousness) has given me an idea that, I think, accounts for the human experience of wanting.  Homeostasis.  The argument goes like this.  In unicellular organisms, homeostasis doesn’t have a lot of ways to operate.  When an organism becomes mobile, homeostatic processes can trigger behaviors that with better than chance probability (from an evolutionary standpoint) result in internal state changes that serve to maintain homeostasis.  In effect, evolution favors behaviors that can be triggered to achieve homeostatic goals. 

In complex organisms, there are homeostatic mechanisms that work on the internal environment directly, but there are some internal environment changes for which it is not possible to compensate adequately by modifying the internal environment directly.  Thence, hunger.  Hunger is how we experience the process that is initiated when homeostatic mechanisms detect an insufficiency of fuel.  (Actually, it’s probably more sophisticated than that—more like detection of a condition in which the reserve of fuel drops below a particular threshold—and maybe there are multiple thresholds, but the broad outline is clear.) 

All organisms have phylogenetically established (built-in) processes for incorporating food.  In mammals, there is rooting reflex and a suckle reflex.  Chewing (which starts out as gumming, but who’s worrying?) and swallowing are built-ins as well.  But those only help when food is presented.  Problem: how to get food to be presented?  Well, if food is presented before hunger sets in, it’s not a homeostatic problem.  If not, homeostatic mechanisms switch the organism into “need-fuel mode”.  In “need-fuel” mode, organisms do things that tend to increase the likelihood that fuel will become available.  Babies fuss, and even cry, sometimes lots and loudly. 

Pain is another place where internal homeostatic processes intersect with the external universe.  Pain is how we experience the process that is initiated when homeostatic sensors detect deviations from internal stability that arise from a physical process (heat, cold, puncture, etc.).  Again, evolution has sophisticated the process somewhat.  The pain process arises when a threshold condition is passed.  Pain does not wait for serious damage to take place, pain is triggered when it’s time to take action to prevent serious damage.   

Pain actually has to be a bit subtle, too.  Some pain may and should be ignored.  If fight is an alternative to flight, then fight arguably ups the threshold for debilitating pain. 

There are other obvious situations in which homeostatic considerations require some action with respect to the outside world.  Urination and defecation are two.  Similarly, vomiting (with its warning homeostatic signal, nausea). 

Our wanting, then, has its origin as the experience of a process that responds to some (serious or prospectively serious) homeostatic imbalance. 

As an aside, I want to propose that one of the characteristics that distinguishes reptiles from mammals is that when a reptile is in reasonable homeostatic equilibrium, it does nothing.  When a mammal is in the same state, it does something—explores its environment, plays, writes poetry, etc.  In the most general terms, it sets out to learn something.  This characteristic arguably confers at least a marginal advantage to animals that possess it, viz. it is possible that something learned in the absence (at the time) of any pressing need will turn out to be valuable in dealing with future situations in which there will be no opportunity to learn it.  So, the concept of homeostasis has to be broadly construed. 

My central point, however, is that ultimately our wants, wishes, desires, dislikes, disgusts, and delights all refer to internal homeostatic processes.  The fact that there are so many distinguishable variants of wanting suggests to me that the many shades of our experience reflect the many kinds of homeostatic processes that have been phylogenetically established in our brains and bodies, each presumably for the most part having proved advantageous over evolutionary time.

Free Will, Searle, and Determinism

February 28th, 2008

A propos determinism: I recently looked into John Searle’s latest (2007) book, Freedom & Neurobiology. As usual, he gets his knickers into the traditional twist that comes from being a physical determinist and an unacknowledged romantic dualist. In this connection, the following line of reasoning occurred to me.

Searle says (p.64) that the conscious, voluntary decision-making aspects of the brain are not deterministic, in effect for our purposes asserting the following. If there is an algorithm that describes conscious, voluntary decision-making processes, it must be (at least perceived as) non-deterministic. Although it would be possible to extend the definition of an algorithm to include non-deterministic processes, the prospect is distasteful at best. How can we respond to this challenge? Searle reasons (p.57) that

We have the first-person conscious experience of acting on reasons. We state these reasons in the form of explanations. [T]hey are not of the form A caused B. They are of the form, a rational self S performed act A, and in performing A, S acted on reason R.

He further remarks (p.42) that an essential feature of voluntary decision-making is the readily-perceivable presence of a gap:

In typical cases of deliberating and acting, there is a gap, or a series of gaps between the causes of each stage in the processes of deliberating, deciding and acting, and the subsequent stages.

Searle feels the need to interpret this phenomenological gap as the point at which non-determinism is required in order for free will to assert itself.

Searle’s non-determinist position in respect of free will is his response to the proposition that in theory absolutely everything is and always has been determined at the level of physical laws. “If the total state of Paris’s brain at t1 is causally sufficient to determine the total state of his brain at t2, in this and in other relevantly similar cases, then he has no free will.” (p. 61) By way of mitigation, however, note that quantum mechanical effects render the literal total determinism position formally untenable and a serious discussion requires assessing how much determinism there actually is. As Mitchell Lazarus pointed out to me, in neuro-glial systems, whether an active element fires (depolarizes) or not may be determined by precisely when a particular calcium ion arrives, a fact that ultimately depends on quantum mechanical effects. On the other hand, Edelman and Gally 2001 have observed that real world neuro-glial systems exhibit degeneracy, which is to say that algorithmically (at some level of detail) equivalent consequences may result from a range of stimulation patterns. This would tend to iron out at a macro level the effects of micro level quantum variability. Even so, macro catastrophes (in the mathematical sense) ultimately depend on micro rather than macro variations, again leaving us with not quite total determinism.

To my way of thinking, the presence of a gap is better explained if we make two assumptions that I do not think to be tendentious: 1) that the outcome of the decision-making process is not known in advance because the decision really hasn’t been made yet and 2) that details of the processes that perform the actual function of reaching a decision are not consciously accessible beyond the distinctive feeling (perception?) that one is thinking about the decision. When those processes converge on, arrive at, a decision, the gap is perceived to end and a high-level summary or abstract of the process becomes available, which we perceive as the reason(s) for, but not cause(s) of, the decision taken.

Presumably, based on what we know of the brain, the underlying process is complex, highly detailed and involves many simultaneous (parallel) deterministic (or as close to deterministic as modern physics allows) evaluations and comparisons. Consciousness, on the other hand, is as Searle describes it a unified field, which I take to mean that it is not well-suited to comprehend, deal with, simultaneous awareness of everything that determined the ultimate decision. There is a limit to the number of things (chunks, see Miller 1956) we can keep in mind at one time. Presumably, serious decision-making involves weighing too many chunkable elements for consciousness to deal with. This seems like a pretty good way for evolution to have integrated complex and sophisticated decision-making into our brains.

Where that leaves us is that we make decisions 1) precisely when we think (perceive) we are making them, 2) on the basis of the reasons and principles we think we act on when making them. That the processes underlying our decision-making are as deterministic as physics will allow is, I think, reassuring. It seems to me that this is as good a description of free will as one could ask for. When we have to decide something, we do not just suddenly go into mindless zombie slave mode during the gap and receive arbitrary instructions from some unknown free-will agency with which we have no causal physical connection. Nor is it the case that it is desirable that the process be non-deterministic. To hold non-determinism to be a virtue would be to argue for randomness rather than consistency in decision-making. Rather, we simply do not have direct perceptual access to the details of its functioning.

Concept Identity vs. Concept Similarity

February 28th, 2008

In the 2007 Pufendorf lectures, Patricia Churchland said a few things that made me stop and think. One relates to concepts and concept membership. Churchland proposed, following Rosch, that concepts are built around prototypes, that they have a “radial structure”; that concepts have “fuzzy borders (boundaries)” and that concept membership is a “similarity” relationship. I can arrive at a set of similar, but not identical (to use the two hot terms in Fodor’s polemics on concepts) conclusions; but I think the differences are worth elaborating.

By way of intellectual history (mine) background, I have long been troubled by an aporia in what I believe about concepts and concept membership:

A. Concepts have (as Churchland’s slide said) fuzzy borders, and that fuzziness certainly seems to be essential.

On the other hand,

B. I find Fodor’s argument for identity and against similarity to be compelling.

The problem, of course, is that A argues for similarity as the touchstone of concept membership and implies that identity is much too strict to be a useful criterion; whereas B argues that similarity is a meaningless criterion unless there is a preexisting underlying criterion of identity: if similarity requires identity, identity is the fundamental criterion.

It seems odd, however, to argue for a robust notion of identity in the context of the complex non-linear processes of the brain; and just saying “Well, that’s the way it has to be, learn to live with it” is hardly compelling. So, the first issue we have to deal with is where does identity come from? Here’s what I currently think.

It all goes back to a central fact of neuro-epistemology, to wit: the brain has no direct access to the outside world; all access is via transducers–receptors and effectors. I think you mentioned this in one of the lectures. Thence, via Marr, Choe, Maturana & Varela, and von Foerster, I arrive at the following. In the general case, the only thing the brain can reliably identify in the brain-environment system of which it is a component is invariances, that is, invariant states. For a state to be invariant, it must be invariant under some set of operations. The particular set of operations under which a state remains unchanged is, in a real sense, the meaning of the state insofar as the brain is concerned. Nothing else can be known with certainty. Von Foerster, writing at a higher level of generality, uses the terms “eigen states” to describe these meta-stable (stable over at least some period of time) states.

Von Foerster’s terminology derives from a result of matrix algebra. An arbitrary square matrix has the characteristic that there are families of “eigenvectors” such that if E is an eigenvector of matrix M, then multiplying E by M yields a vector of the form k times E. In other words, multiplication by M takes certain vectors (its eigenvectors) into themselves up to a multiplicative constant. Von Foerster notes that the mathematics of a dynamic system is such that it has eigen states that the system maps into themselves (they are invariants of a sort); he characterizes eigen states as the way the system is able to “classify” its environment. A key result of von Foerster’s is that the eigen states of such systems are discrete and meta-stable. In the terminology of neural networks, these states are like attractor points (I am eliding some caveats, but the assertion is correct enough for the argument to stand). Like attractor points, they give the system the ability to do pattern completion.

Self-modifying systems have the diachronic ability to adaptively create (learn) new eigen states. But synchronically eigen states always have the discreteness property. Two eigen states are either identical or different. Similarity is not a characteristic of eigen states. Remind you of Fodor?

Let’s identify a concept with an eigen state. (In certain details, I think this is an oversimplification to the point of incorrectness, but I’ll hold that polemic for another time because it’s not central to this argument.) So, here we are:

Thesis: Concept similarity is at the core of concept membership; there’s no need for concept identity.

Antithesis: Concept identity is at the core of concept membership; similarity is a nonsensical thing to hang concept membership on.

Synthesis: Concepts are eigen states (states defined by sets of operations that preserve an invariant) and as such are unique and have identity conditions. The processes that work to arrive at a particular eigen state may (and probably in the brain generally do) involve completion effects that are undeniably “similarity” effects. So, at one and the same time,

1) Concepts cannot be fuzzy because eigen states are discrete


2) Concepts are essentially fuzzy because completion effects are always involved in arriving at them.

If you have some large enough portion of the eigen state associated with a concept, completion effects will fill in the rest and arrive at the unique eigen state (and thus the concept) itself. To the extent that completion effects vary in response to other things going on in the brain, there can be no precise specification of which patterns will or will not complete to a particular concept. This is why the merest ripple on the surface of cat-infested waters is sufficient to cause CAT thoughts and why during an invasion of robot cats from outer space, a host of cat-like creatures emerging from a flying saucer does not cause CAT thoughts.

So much for concept similarity versus concept identity.

Consciousness: Seeing yourself in the third person

February 28th, 2008

Re: Patricial Churchland’s presentation on December 1, 2005 at the Inaugural Symposium of the Picower Institute at MIT. Two things Churchland said (at least according to my notes) lead me to an interesting take on the phenomenology of the self. She noted that the brain, without access to anything but its inputs and its outputs builds a model of the external world that includes a model of itself in the external world. She also noted (or was it Christoph Koch) in the Q&A period that there may be some advantage to a neural structure or system that “believes” it is the author of certain actions and behaviors; and there may be some advantage to an organism that includes such a neural structure or system.

Here’s where that takes me. Churchland pointed out that, ceteris paribus, selection favors organisms with better predictive ability. So, the ability to predict and / or reliably affect (relevant aspects of) the behavior of the outside world arises over the course of evolution. In particular, the need to predict (model) the behavior of conspecifics, and the development of the ability to do so has significant favorable consequences. The ability to predict and / or reliably affect (relevant aspects) of the behavior of conspecifics includes the ability to predict interactions among conspecifics (from a third-party perspective).

Once there is a model that predicts the behavior of conspecifics, there is a model that could be applied to predict ones own behavior from a third-party perspective as if one were an external conspecific.

One may suppose that the model of conspecific behavior that arises phylogenetically in the brain consists in the activity of different processes from the phylogenetically established brain processes that internally propose and select among courses of action. That being the case, the model of conspecific behavior constitutes an additional (at least in some ways independent) source of information about ones own behavior, information that could be used to improve ones ability to predict and reliably affect the behavior of the world (thus improving one’s fitness).

I take it as given that independently evolved and epigenetically refined processes that internally propose and select among alternative courses of action take as inputs information about the internal state of the organism and information about the external (black box) world. I further take it that ones own behavior has effects that can and ought to be predicted. Thus, ones own behavior should be an input to the system(s) that internally propose and select courses of action.

Now, information about ones own behavior can be made available within the brain via (at least) two possible routes:

(1) Make available (feed back) in some form an advance or contemporaneous statement of the behavior the brain intends to (is about to, may decide to) perform (close the loop internally).

(2) Observe ones own behavior and process it via the system whose (primary, original) purpose is to predict the behavior of others (close the loop externally).

Assuming, as proposed above, that the total information available from both routes together is greater than the information available from either one alone, selection favors an organism that is able to use information from both sources. However, there is little point to developing (i.e., evolving) a separate system to model (predict) ones own behavior, within an organism that has already a system to predict a conspecifics behavior on the basis of observables. It is better to adapt (exapt?) the existing system.

But, note: certain information that must be inferred from external inputs (abduced) about conspecifics and is thus inherently subject to uncertainty is available more reliably from within the brain. It is thus advantageous to add a facility to translate internally available information into a form usable within the model and provide it as additional input to the conspecifics model.

To the extent that the model preserves its significance as a model of external behavior as extracted from the external (black box) world, internally provided information will be processed as if it came from outside. But, such internally provided information is different in that it actually originated inside. Thus, it needs to be distinguished (distinguishable) from the information that really does come from outside.

The significant consequence of the preceding is that the introduction, as a matter of evolutionary expediency, of internally originating information into a system originally evolved to model the external behavior of conspecifics results in a model that treats the organism itself as if its own agency originated externally, literally outside the brain. This formulation is remarkably similar to some characterizations of the phenomenology of self-consciousness.

Once such a system is in place, evolutionary advances in the sophistication of the (externally shaped) model of (in particular) conspecifics can take advantage of and support the further development of the ability to literally re-present internal information as if it originated externally.

There is nothing in the preceding that requires uniquely human abilities. Accordingly, one may or may not wish to call this “self consciousness”; although I might be willing to do so and keep a straight face.

030909 – Dennett’s competing drafts

September 9th, 2003


Well, I think I finally begin to understand Dennett’s idea of multiple competing drafts.  What he’s getting at is very much along the lines of my flow of patterns concept.

What characterizes processes in the brain?  Lateral inhibition seems to be a fundamental process that has been adopted in the course of evolution because it has the effect of sharpening boundaries.  Hebbian learning seems also to be a fundamental process that has been adopted in the course of evolution because it has the effect of collecting similar patterns of activation together.  Taking the simple Hebbian learning paradigm as a starting point, evolution has selected a number of variants for preservation and refinement: populations of neurons vary in terms of their “plasticity per unit time” and their plasticity as a function of neurochemical modulators.

On the outputs (efferents) side, it may be that lateral inhibition is what helps resolve race conditions.  There is clearly some sort of winner take all process on the efferents side, although its scope is clearly not global because we can in fact walk and chew gum at the same time.

Suppose each neuron in the brain is connected to about 10,000 other neurons, and suppose arbitrarily that on the order in half of those connections are afferent and the other half are efferent.  Then if there are about 20 billion neurons in the brain and each receives input from 5000 other neurons, there must be about 100 trillion synapses in the brain[1] and who knows how to factor in the 200 billion glial cells that cluster around certain synapses.  This calculation makes me wonder about the distribution of glial cells.  There clearly are many fewer glial cells and there are synapses.  Something I read makes me think that the glial cells are associated with axonal synapses, but even that, at least if my estimation of 5000 axonal synapses per neuron is correct, still leaves many fewer glial cells than synapses.  About the only additional assumption I might make would be that the glia are associated with axonal synapses on cell bodies.  That might make the numbers come out right, but I don’t think so.  So I guess I’m still left puzzling over the distribution of glial cells.

Nonetheless, 100 trillion synapses is a lot of synapses.  Now go back and think about the so-called Chinese room puzzle.  The hapless person in this room is busily simulating with pencil and paper the activity of 100 trillion synapses.  It will take an awfully long time to simulate even a few seconds of brain activity.  Suppose the simulation interval (granularity) is one millisecond.  To simulate a second will require evaluating 100,000 trillion synapses.  Suppose the person is very fast and can update the state of a synapse in a second.  A year is about 30 million seconds. 100,000 trillion seconds is roughly 3 billion years.

=============== Notes =================

[1] Jeff Hawkins 2004 (p.210) estimates 32 trillion, but he doesn’t say how.  Hawkins, Jeff with Blakeslee, Sandra.  2004.  On Intelligence.  New York: Times Books, Henry Holt and Company.

030907 – Rationality and communication

September 7th, 2003

030907 – Rationality and communication

Following up on Watzlawick, et al., Pragmatics of Human Communication I find that in later discussions by the “communications” community, there is an unspoken assumption that communication has rational motivation.  For example, quoted from Dirk Schouten, “Real Communication with Audiovisual Means”


Habermas divides speech acts (what someone says) into two principal categories.  There are Strategic actions (speech acts which make people do things) and Communicative Actions (speech acts which are designed to arrive at a common understanding of a situation).


Speech acts, according to Habermas contain a propositional and a performative part (like Watzlawick and Austin, he believes that when we say something we also do something.) The propositional part indicates a state of affairs in reality. For example: “The average income of farmers in South America is just 87 dollars per annum”. The performative part implies or indicates how the propositional part needs to be understood (in this case “The speaker thinks this is disgraceful”). In that way one can categorize or question something. An audience can respond: “I think that is disgraceful, too.” Or: “Why do you think it disgraceful?” Or: “I see what you mean, but…”

In fact a speaker, by saying something, not only says something that is true to her, but also says: “I claim the communicative right towards you to have an opinion and to say it to you in this defined situation”. The performative part defines the boundaries of the communicative action. It marks out the (communicative) context of the (propositional) content. It makes clear which relation the speaker wants to make to their audience. As long as the participants are aimed at reaching mutual agreement, a communicative situation is shaped, because the speaker makes three “validity claims” with their speech act:

1. They claim that they are speaking the truth in the propositional part of the speech act;
2. They claim normative legitimacy concerning the communicative act in a smaller sense (the performative part); and
3. They claim truthfulness/authenticity concerning the intentions and emotions they express.

These validity claims the speaker makes can, in principle, be criticized, although in practice this possibility is often blocked. In communicative action the hearers can (if they wish) demand reasons from the speakers to justify their validity claims.

The problem with this analysis is that the process of originating a communication is one of shaping and selection of behaviors based on internal models and internal states.  The “intention” of behavior is to add a pattern that will move the shape of the current pattern towards a projected pattern which is created by feeding the current pattern and the “intended” behavior into the optimal projection pattern.  Huh?

Let’s try this again.  There is a current pattern of activation.  It is a combination of

existing patterns


patterns created by external receptors, enteroceptors, and proprioceptors


patterns created for and by effectors (motor patterns, behaviors)


modulating influences (generally neurochemicals)

030828 – Are human beings rational?

August 28th, 2003

030828 – Are human beings rational?

My wife asked an interesting question: Do I think that human beings are inherently rational.  I think the answer is emphatically no.  Human beings have the ability to learn procedures.  One of the procedures that human beings have discovered, found useful, and passed along culturally is the procedure of logical analysis or logical thinking.  The fact that in many cases logic enables us to find good solutions to certain classes of significant problems ensures that logical analysis will be one of the procedures activated as a candidate for execution in a broad range of external circumstances and internal states.

What strikes me is that the end result of evolution selecting organisms with greater and greater ability to learn and apply procedural patterns has resulted in an organism that is capable of learning to simulate serial computations, at least on a limited scale.  Certainly it was Dennett who put this idea into my mind, but I do not believe that he arrived at this conclusion by the same path that I did.

This raises an interesting question: what kind of pattern and procedural learning capabilities are required in order to be able to simulate serial computations or, more precisely, to be able to learn and execute a logical thinking pattern?  Human beings certainly aren’t much in the way of serial computers.  We’re not fast.  We’re not computationally adept.  We don’t have a lot of dynamic memory.  Our push down stack for recursion seems to be limited to one level.  (The fact that we must use the logical thinking pattern to analyze pathological sentences like, “The pearl the squirrel the girl hit bit split,” rather than the (unconscious) language understanding pattern simply underlines this limitation on our capability for recursion.)

So, is human language ability the result of the evolution of ever more sophisticated procedural pattern learning capabilities?  Is the driving force behind the evolution of such enhanced procedural pattern learning the advantage obtained by the organisms who best understand their conspecifics?  Is this evolution’s de facto recognition that brawn being equal, better brains confer a reproductive advantage?  Now if better understanding of one’s conspecifics is the goal, language ability may just fall out automatically, because if one has a mechanism that can build a model of others, it makes it a lot easier to figure out what the other intends or is responding to.

Clearly, since the ability to take the viewpoint of another person does not manifest itself in children until some time after they have acquired at least the rudiments of language, the manifestation of the ability to take the viewpoint of another person is not a requirement for the acquisition of at least the rudiments of the language.  There seems to be a subtle distinction to be made here: when daddy says “hudie” (the Chinese equivalent of “butterfly”) and looks at, or taps, or points to a butterfly or a representation of a butterfly, something has to help the child attend to both the butterfly instance and the sound.  That something may be the emerging model of the other.  Or maybe it’s the other way around as I suggested earlier: the trick is for the parent to take advantage of his or her own model of the child in order to intuitively construct or take advantage of the situation in which both the butterfly and the sound of the word will be salient to the child.

Still, I keep coming back to the idea that the internal model of the other is somehow crucial and the even more crucial is the idea that the internal model of the other contains the other’s model of others.  As I think about it though, it seems to me that creating an internal pattern, that is to say learning a pattern, based on experience and observation of the behavior of another organism is not a capability that is uniquely human.  It would seem to be a valuable ability to have.  What seems to be special about the patterns we humans develop of other people is that we attribute to the other a self.  An or to animal can get a long way without attributing a self (whatever that means) to other creatures with which it interacts.

030826 – Parallel processing in the mind

August 26th, 2003

030826 – Parallel processing in the mind

I don’t know if it originated with Grossberg, but I like the concept of complementary processing streams.  Actually, he talks about it as if it always involves a dichotomy.  Could it not also be multiple (any number) parallel streams?  Certainly, the convergence of inputs from a large number of brain areas on the amygdala indicates that it’s not just dichotomous streams.

Grossberg writes as if he is describing exactly what happens—especially with his neural circuit diagrams, but the more I read, the more they seem fanciful.  Certainly there’s something missing when the diagrams only show neurons in layers 2/3, 4, and 6.

In also seems that there’s something missing from the analysis of the visual “what” pathway.  Edge and Surface processing seem very closely tied throughout.  In visual area V1, the “blob” neurons are surrounded by “interblob” neurons and in visual area V2, the “thin stripe” the neurons alternate with the “interstripe” neurons.  Surely there is some crosstalk between (among) the channels.

Grossberg uses the term “catastrophic forgetting”.  He also talks about complementary channels of processing in the brain.  And he, among others, and talks about a “where” channel to the parietal lobe and a “what” channel to the temporal lobe.  Things then get a little confused.  Part of the point of “catastrophic forgetting” is, in effect that certain memories need to get overwritten, e.g., memories of where a particular movable object is located.  In contrast other memories should not be easily forgotten.

It is not clear that the categories “easily over writable” and “not easily over writable” (or should it be “things that change often” and “things that don’t often change”?) are the same as “where” and “what”.  It’s certainly possible from an evolutionary standpoint that what and where are sufficiently essential aspects of the environment that they should be per se ensconced in genetically determined neural structures.  Nonetheless, is reasonable to ask whether what evolution has provided is also being used in ways unrelated to its evolutionarily determined functionality.

Or alternatively, given that evolution has cobbled together mechanisms capable of recording information with differing degrees of environmental permanence, it seems reasonable to suppose that the same mechanism could show up in different places; although, I am well aware that the essentially opportunistic functioning of evolution leaves open the possibility that the same function is performed in many different ways.  Still, in our environment and the environment of our animal ancestors some things change rapidly and some things don’t.