Andy Clark. 8.6 of Being There, “Continuous Reciprocal Causation”, summary

summary by Corry Shores
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[My own commentary is in brackets. All boldface and underlining is my own.]

Andy Clark

Being There:

Putting Brain, Body, and World Together Again

Ch.8

Being, Computing, Representing

Part 8.6
Continuous Reciprocal Causation

Brief Summary:

Separate parts of a system can be in a state of continuous reciprocal causation, meaning that the behavior of each part simultaneously affects the behavior of the other parts. In such cases, it is not best to explain the whole system’s by analyzing the system into insulated parts. And also, representational accounts might not best explain how one part can be found internally affecting another part.

Summary

[Recall that a position is “representationalist if it depicts whole systems of identifiable inner states (local or distributed) or processes (temporal sequences of such states) as having the function of bearing specific types of information about external or bodily states of affairs”. (147a)] Clark will offer one last way to make a strong anti-representationalist argument. He will appeal to “the presence of continuous, mutually modulatory influences linking brain, body, and world.” (163b) Clark previously described the neuronal processes involved in vision, which had “hints of such mutually modulatory complexity in the interior workings of the brain itself.” (163b) Clark now wonders what if “something like this level of interactive complexity characterized some of the link among neural circuitry, physical bodies, and aspects of the local environment?” (163b)

 

[Consider if a radio and a transmitter were near one another, and the transmitter is broadcasting music from a turntable, also nearby. This means that low frequencies playing on the radio will disrupt the needle on the record, but the disruption of the needle on the record will change what the radio is playing.] Clark gives this example.

Consider a radio receiver, the input signal to which is best treated as a continuous modulator of the radio’s “behavior” (its sound output). Now imagine (here is where I adapt the analogy to press the point) that the radio’s output is also a continuous modulator of the external device (the transmitter) delivering the input signal. In such a case, we observe a truly complex and temporally dense interplay between the two system components – one which could lead to different overall dynamics (e.g. of positive feedback or stable equalibria) depending on the precise details of the interplay. The key fact is that, given the continuous nature of the mutual modulations, a common analytic strategy yields scant rewards. The common strategy is, of course, componential analysis, as described in chapter 6. To be sure, we can and should identify different components here. But the strategy breaks down if we then try to understand the behavior unfolding of one favored component (say, the receiver) by treating it as a unity insulated from its local environment by the traditional boundaries of transduction and action, for such boundaries, in view of the facts of continuous mutual modulation, look arbitrary with respect to this specific behavioral unfolding. They would not be arbitrary if, for example, the receiver unit displayed discrete time-stepped behaviors of signal | receiving and subsequent broadcast. Were that the case, we could reconceptualize the surrounding events as the world’s giving inputs to a device which then gives outputs (“actions”) which affect the world and hence help mold the next input down the line – for example, we could develop an interactive “catch and toss” version of the componential analysis, as predicted in chapter 6. (163-164)

[So if we were to analyze for example the component of the radio as if insulated from its environment, we would not know where to begin, assuming that the process had already begun. But if each causal event happened in temporal steps with gaps between, then we could analyze the components of the causal relation.]

Clark offers a second example (from Randy Beer). [First consider this description of oscillating or reverberating circuits in Marieb and Hoehn’s Human Anatomy & Physiology (quoting):

In reverberating, or oscillating, circuits, the incoming signal travels through a chain of neurons, each of which makes collateral synapses with neurons in a previous part of the pathway.

As a result of the positive feedback, the impulses reverberate (are sent through the circuit again and again), giving a continuous output signal until one neuron in the circuit fails to fire. Reverberating circuits are involved in control of rhythmic activities, such as the sleep-wake cycle, breathing, and certain motor activities (such as arm swinging when walking). Some researchers believe that such circuits underlie short-term memory. Depending on the specific circuit, reverberating circuits may continue to oscillate for seconds, hours, or (in the case of the circuit controlling the rhythm of breathing) a lifetime. (Marieb and Hoehn, 422d)

Andy Clark’s second example involves such oscillating neurons,] he writes:

Consider a simple two-neuron system. Suppose that neither neuron, in isolation, exhibits any tendency toward rhythmic oscillation. Nonetheless, it is sometimes the case that two such neurons, when linked by some process of continuous signaling, will modulate each other's behavior so as to yield oscillatory dynamics. Call neuron 1 "the brain" and neuron 2 "the environment." What concrete value would such a division have for understanding the oscillatory behavior? (164a.b)

[So the neurons mutually modify one another, because they have both inputs from and outputs to one another.]

When we are interested in the behavior of the two insofar as they are mutually affecting one another, it would not make sense to analyze the workings into insulated components, even though indeed the system is made of discrete parts.

in the case of biological brains and local environments it would indeed be perverse—as Butler (to appear) rightly insists—to pretend that we do not confront distinct components. The question, however, must be whether certain target phenomena are best explained by granting a kind of special status to one component (the brain) and treating the other as merely a source of inputs and a space for outputs. In cases where the target behavior involves continuous reciprocal causation between the components, such a strategy seems ill motivated. In such cases, we do not, I concede, confront a single undifferentiated system. But the target phenomenon is an emergent property of the coupling of the two (perfectly real) components, and should not be "assigned" to either alone. (164c.d)

Such continuous reciprocal causation is common in our everyday lives.

Nor, it seems to me, is continuous reciprocal causation a rare or exceptional case in human problem solving. The players in a jazz trio, when improvising, are immersed in just such a web of causal complexity. Each member's playing is continually responsive to the others' and at the same time exerts its own modulatory force. Dancing, playing interactive sports, and even having a group conversation all sometimes exhibit the kind of mutually modulatory dynamics which look to reward a wider perspective than one that focuses on one component and treats all the rest as mere inputs and outputs. Of course, these are all cases in which what counts is something like the social environment. But dense reciprocal interactions can equally well characterize our dealings with complex machinery (such as cars and airplanes) or even the ongoing interplay between musician and instrument. What matters is not whether the other component is itself a cognitive system but the nature of the causal coupling between components. Where that coupling provides for continuous and mutually modularity exchange, it will often be fruitful to consider the emergent dynamics of the overarching system. (165a.b)

[This is like Deleuze’s notion of rhythm in Spinoza’s affection, see the end of section 6 of my paper “Body and World in Merleau-Ponty and Deleuze”:

Our active self-affection and adaptive interaction with the world around us is what Deleuze here calls "rhythm." He also offers the example of swimming through a powerful wave. When we collide with the wave, its affection begins to decompose our body. Yet, by self-affectively altering the arrangements of our own body's parts, we may swim in conjunction with the wave and together form a larger composite body. Deleuze suggests another illustration to explain more clearly how affective rhythm involves couplings of continuous affective variations. He has us consider a dual improvisation of a violin and a piano. On the one hand, each one needs to improvisationally choose its own development. Yet, the musicians' decisions will influence how the other plays in concord with it. So, in order for both instruments to maintain their differential co-composition, they must make self-modifications that are differentially compatible with those of the other player. (Shores 203)

]

 

So when there is continuous reciprocal causation, there is little use for an analysis that looks at the parts of such systems as if they were separate.

Thus, to the extent that brain, body, and world can at times be joint participants in episodes of dense reciprocal causal influence, we will confront behavioral unfoldings that resist explanation in terms of inputs to and outputs from a supposedly insulated individual cognitive engine. (165c)

Clark thinks that there are then only two possibilities for the use of internal representation for cognitive scientific explanations. (165c)

To understand the first possibility, we consider a complex neural network, called ‘A’. It is coupled with its environment, and part of its dynamics is an ability to sense whether it the environmental processes it is coupled to are present. “Imagine a complex neural network, A, whose environmentally coupled dynamics include a specific spiking (firing) frequency which is used by other onboard networks as a source of information concerning the presence or absence of certain external environmental processes—the ones with which A is so closely coupled.” (165d) So internally we might say the system has patterns for when it is coupled to external processes. Now we are to consider those signals normally coming from outside to be produced from the inside, causing the system to ‘imagine’ being engaged with the environment rather than physically being so. This would be like internal representation.

The downstream networks thus use the response profiles of A as a stand-in for these environmental states of affairs. Imagine also that the coupled response profiles of A can sometimes be induced, in the absence of the environmental inputs, by top-down neural influences, and that when this happens the agent finds herself imagining engaging in the complex interaction in question (e.g., playing | in a jazz trio). In such circumstances, it seems natural and informative to treat A as a locus of internal representations, despite its involvement, at times, in episodes of dense reciprocal interaction with external events and processes.” (163-164)

The other possibility is that even such inner processes cannot operate unless they are coupled, and thus there are nonrepresentational dynamics at play.

A second possibility, however, is that the system simply never exhibits the kind of potentially decoupled inner evolution just described. This will be the case if, for example, certain inner resources participate only in densely coupled, continuous reciprocal environmental exchanges, and there seem to be no identifiable inner states or processes whose role in those interactions is to carry specific items of information about the outer events. Instead, the inner and the outer interact in adaptively valuable ways which simply fail to succumb to our attempts to fix determinate information processing roles to specific purely internal, components, states, or processes. In such a case the system displays what might be called nonrepresentational adaptive equilibrium. (A homely example is a tug of war: neither team is usefully thought of as a representation of the force being exerted by the other side, yet until the final collapse the two sets of forces influence and maintain each other in a very finely balanced way.) (166b.c)

Thus,

Where the inner and the outer exhibit this kind of continuous, mutually modulatory, non-decouplable coevolution, the tools of information processing decomposition are, I believe, at their weakest. What matters in such cases are the real, temporally rich properties of the ongoing exchange between organism and environment. (166c)

Such instances do not challenge the representational model, because they do not fall under the class of cases best suited for representational explanations. Clark will explain this in the next section. (166d)

 

 

Clark, Andy. Being There: Putting Brain, Body, and World Together Again. Cambridge, Massachusetts/London: MIT, 1997.

 

Marieb, Elaine N., & Katja Hoehn. Human Anatomy & Physiology. London: Pearson, 2007.

 

Shores, Corry. “Body and World in Merleau-Ponty and Deleuze” in Sudia Phaenomenologica, vol.12, 2012, pp.181-209.

https://cdn.anonfiles.com/1360747598945.pdf

Andy Clark. 7.3 of Being There, “Primate Vision: From Feature Detection to Tuned Filters,” summary

summary by Corry Shores
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Andy Clark

Being There:

Putting Brain, Body, and World Together Again

Ch.7

The Neuroscientific Image

Part 7.3
Primate Vision: From Feature Detection to Tuned Filters

Brief Summary:

The neuronal visual systems in the brain have parts that are maximally tuned to process data for one visual parameter or another, meaning that rather than having cells responsible for dealing only with certain complex forms [like a spiral], many various cells in the system cooperatively play a role in understanding all the visual properties of something being seen [like something’s spirality, breadth, etc, perhaps].

Summary

Clark will discuss neuroscientific research into primate vision, especially work by David Van Essen. (133c.d)

Cognitive neuroscience examines neuronal responses.

Anatomically, the macaque monkey possesses at least 32 visual brain areas and over 300 connecting | pathways. Major areas include early cortical processing sites such as V1 and V2, intermediate sites such as V4 and MT, and higher sites such as IT (inferotemporal cortex) and PP (posterior parietal cortex) (plate 1). The connecting pathways tend to go both ways—e.g. from V1 to V2 and back again. In addition, there is some "sideways" connectivity—e.g. between subareas within VI. (133-134, boldface mine)

 

(From Clark p.170)

There are ten levels of cortical processing in the system, and we will look at some of the more important ones. There are three populations of sub-cortical cells from which the system receives input. One population is the magnocellular (M) and another is the Parvocellular (P). And there is a processing pathway for M, and another one for P. Each population specializes in a different type of low-level information. P cells “have high spatial and low temporal resolution”, while M cells have “high temporal resolution.” (134b) This means that M cells deal with rapid motion perception, while P cells deal with color discrimination (among other things). So when we selectively destroy a monkey’s P cells, it can no longer distinguish colors although it still recognizes motion. (134b)

So the magno M cells discern motion, and there is a magno-denominated (MD) stream of processing. This stream includes neuron populations that are sensitive to the direction of some motion, especially in area MT, which we said above was an intermediate cortical processing site. When we electrically stimulate a part of MT, the monkey might “perceive” left motion even if the target object is really moving to the right. There is a higher stage in the processing hierarchy, MSDT, where there are cells sensitive to spiral motion.

The MD stream is ultimately connected to the posterior parietal cortex, which appears to use spatial information to control such high level functions as deciding where objects are and planning eye movements. (134d)

There is also the task of object recognition, which is determining what things are. This is handled by a stream rooted in P inputs, moving through V1, V4, and posterior inferotemporal areas (PIT), and it leads into central and anterior inferotemporal areas. (134d) This pathway specializes in form and color. As we go up the hierarchy, we find sites capable of processing increasingly complex forms. At a high level, there are even cells that respond maximally to such complex geometrical visual stimuli as hands and faces. (135a). But although one cell responds maximally to one kind of form, like a spiral, it will also to a lesser extent respond to other sorts of patterns.

[This means that cells are not like yes-no sensors that detect the presence of one form or its absence, but rather each participate in contributing information about some property of what is being seen, with all working together cooperatively.]

Although a cell may respond maximally to (e.g.) a spiral pattern, the same cell will respond to some degree to multiple other patterns also. It is often the tuning of a cell to a whole set of stimuli that is most revealing. This overall tuning enables one cell to participate in a large number of distributed patterns of encoding, contributing information both by its being active and by its degree of activity. Such considerations lead Van Essen and others to treat cells not as simple feature detectors signaling the presence or absence of some fixed parameter but rather as filters tuned along several stimulus dimensions, so that differences in firing rate allow one cell to encode multiple types of information. There is also strong evidence that the responses of cells in the middle and upper levels of the processing hierarchy are dependent on attention and other shifting parameters (Motter 1994), and that even cells in VI have their response characteristics modulated by the effects of local context (Knierim and Van Essen 1992). Treating neurons as tunable and modulable filters provides a powerful framework in which to formulate and understand such complex profiles. (135a.b, boldface mine)

But even though visual systems are complex, they can still be analyzed. (135d)

Andy Clark. Being There: Putting Brain, Body, and World Together Again. Cambridge, Massachusetts/London: MIT, 1997.

Andy Clark. Being There: Putting Brain, Body, and World Together Again, entry directory

 

summary by Corry Shores
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Entry Directory for

Andy Clark

Being There:

Putting Brain, Body, and World Together Again

Ch.2

The Situated Infant

Part 2.4
Soft Assembly and Decentralized Solutions

Ch.7

The Neuroscientific Image

Part 7.3
Primate Vision: From Feature Detection to Tuned Filters

 

Ch.8

Being, Computing, Representing

Part 8.2
What is this Thing Called Representation?

 

Part 8.6
Continuous Reciprocal Causation

Part 8.8
Roots

[this summary focuses on the Merleau-Ponty parts]

 

 

 

 

Posthumanism and Pixels, Condensed Version

by Corry Shores
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[The following is a condensed version of a previous text. There you will find proper citation. I thank Professor Ulrich Melle, Professor Aziz Zambak, and my Father Ebbie V. Shores for their contributions to this research. And may I thank kvond at Frames/ sing for opening new directions for the philosophy of Analog & Digital. Later we will examine his developments and ways to integrate these ideas with Spinoza's thinking.]

Corry Shores

Do Posthumanists Dream of Pixilated Sheep?

Bostrom and Sandberg's Brain Emulation,

Examined and Critiqued

Enhancement technologies may someday give us capacities far beyond what we dream humanly possible. We could become post-human. Nick Bostrom & Anders Sandberg suggest that we might survive our body's death by living as a computer simulation. They issued a report from a conference where experts in all relevant fields collaborated to determine the path to "whole brain emulation." This technology will in the very least be an effective research tool for the neurosciences. It could even aid philosophical research too. Their "roadmap" defends certain philosophical assumptions required for this technology's success. So by determining the reasons why it succeeds or fails, we can obtain empirical data for philosophical debates regarding our mind and selfhood. I have chosen four issues to discuss: emergentism, analog vs. digital, chance, and personal identity.

Brain emulation succeeds if a computer program replicates human neural functioning. Yet for the authors, its success increases when it perfectly replicates one specific person’s brain. She might then survive her body’s death by living as the simulation.

This prospect has posthumanist proponents. Their view presupposes certain traits of human consciousness and selfhood. Hans Moravec for example thinks our personal identities exist independently to our bodies. According to his pattern-identity theory of selfhood, we are no more than the patterns and the processes found in our brains and bodies. William Bainbridge explains that we are neither man nor machine. We are just the dynamic patterns of information that can be realized in a wide variety of materials. Hence our personal patterns might be found in this body or in that computer. Either way we are the same person.

To emulate someone’s neural patterns, we first scan a particular brain to obtain precise detail of its structures and their interactions. Using this data, we program a simulation that will behave essentially the same as the original brain. Now first consider a gnat’s wild flight pattern. It seems irrational and random. But the motion of a whole swarm is smooth, controlled, and intelligent, as though the whole group of gnats has a mind of its own. To simulate the swarm, perhaps we will not need to understand how the whole swarm thinks. We instead just learn the way one gnat behaves and interacts with other ones. When we combine thousands of these simulated gnats, the swarm’s collective intelligence should thereby appear. Whole brain emulation presupposes this principle. The simulation will mimic the human brain’s functioning on the cellular level. Then automatically, higher-and-higher orders of organization should spontaneously arise. Finally human consciousness might emerge at the highest level of organization.

Early in this technology's development, we should only expect simpler brain states, like wakefulness and sleep. But in its ultimate form, whole brain emulation would enable us to make back-up copies of our minds. Then we might somehow survive our body’s death.

According to Bostrom & Sandberg, whole brain emulation should replicate all the original brain’s relevant properties so to produce a 1-to-1 model of the brain’s functioning. In this sense, the brain and its emulation are black boxes. We feed each one on its own the same sequence of stimuli. If they both respond with the same sequence of reactions, then the two separate machines are functionally equivalent. In this way, the same mind could be realized in two physically different systems. Hilary Putnam claims that electronic computers can be functionally equivalent to mechanical ones and even to humans using pencil and paper. Their insides may differ drastically, but their outward behaviors are identical.

There are various levels of emulation success. The highest ones are the most philosophically interesting.

When the technology achieves individual brain emulation, Bostrom & Sandberg write, it produces emergent activity characteristic of one particular brain. With further success, we would emulate someone’s personal identity. Perhaps somehow it would be numerically the same person. But at least it would continue-on as that person even after her body dies. We achieve such a simulation when it becomes rationally self-concerned for the brain it emulates.

Minds emerge from the brain’s pattern of physical dynamics. If you replicate this pattern-dynamic in some other physical medium, the same mental phenomena should likewise emerge. One mind would then be realized in a multiplicity of different physical embodiments. So whole brain emulation’s success would provide evidence for the theory of multiple realizability.

According to emergentist theories, all reality is made-up of a single kind of stuff. But its parts aggregate and assemble into dynamic organizational patterns. The higher levels exhibit properties not found in the lower ones. But, there can be no higher order without lower ones underlying it.

Consider the H2O molecule. It does not itself bear the properties of liquidity, wetness, and transparency. However, a large enough aggregate of water molecules will exhibit these properties.

In our brains, no one single neuron is conscious. Yet, your minds emerge from the complex dynamic pattern of all our neurons communicating and computing in parallel. Roger Sperry offers compelling evidence. There are "split brain" patients whose right and left brain hemispheres are disconnected from one another. Nonetheless, they maintain unified consciousness.

William Hasker offers the analogy of magnetic fields, which are distinct from the magnets producing them. The iron atoms themselves need to be organized in alignment in order for a magnetic field to emerge on a higher scale. In a similar way, the particular organization of the brain’s neurons generates a field of ‘consciousness.’ This emergent consciousness-field permeates and haloes our brain-matter, occupying its space and traveling along with it.

Not everyone agrees that the mind emerges from the brain. Todd Feinberg is one example. Now in fact, he does think that consciousness results from the complex interaction of many layers of neural organization. However, he argues that consciousness does not get squirted-out from neural activity and thereby obtain a life of its own. Instead, the layers of neural activity are all mutually interdependent and simultaneously cooperative. Consider for example when we recognize our grandmother. One layer of neurons transmits information about the whole visual field. Another layer picks-out lines. Another one, shapes. Finally the information arrives at the grandmother cell, which only fires when it is she that we see. But this does not make the grandmother cell emergently higher. Rather, all the neural layers of organization must work together simultaneously to achieve this recognition. The brain is a vast network of interconnected circuits. So we cannot say that any layer of organization emerges over and above the others.

Feinberg’s objection may prove problematic for whole brain emulation. Bostrom & Sandberg explicitly state that we only need to simulate the lower levels of activity.

But if Feinberg’s holistic theory is correct, we cannot only emulate the lower levels and expect the rest to spontaneously emerge. For, we need already to understand the higher-levels in order to program the lower ones. So, whole brain emulation’s emergentist assumptions might not express the actual way that consciousness appears.

Notice how in the recent past, many digital technologies have replaced analog ones. It would seem that these two sorts of quantity-representation reside in contrary worlds: the continuous versus the discrete. An abacus is digital. It computes one discrete value or another, but it is blind to the values between its lowest digit-places. When we count to two on our fingers, meaningless empty space spans between our digits. So spreading our fingers further apart does not change their numerical value. Slide-rules, however, are analog. One ruler slides against another continuously. So it may calculate any possible real number along the continuum. It could potentially compute and display irrational numbers like pi or the golden ratio. However, a digital computer would never cease calculating into lower digit places. For, there can be no final figure when rendering irrational numbers into digits. But if you move the slide rule from three to four, you will for one instant display pi. Analog is dense. Between any two values is already a third one, and lying between those are yet even more, and so on infinitely. On account of this infinite divisibility, analog can compute and display an infinity of different values found within a finite range. But like the gaps between our fingers, digital at some point will be blind to a middle value, no matter how precise it is. So, because analog computers can deal with an infinity of values, they have more computing potential for certain applications.

Our emulated brain will receive simulated sense-signals. Does it matter if they are digital signals rather than analog? Many audiophiles swear by the unsurpassable superiority of analog. It might be less precise, but it always flows like natural sound waves. Digital, even as it becomes more accurate, still sounds to them artificial or cartoon-like. In other words, there might be a qualitative difference to how we experience analog and digital stimuli, even though it might take a person with extra sensitivities to bring this difference to our explicit awareness.

And if the continuous and discrete are so fundamentally different, then maybe a brain computing in analog would experience a qualitatively different feel of consciousness than if the brain were instead computing in digital. Perhaps digital emulations might even produce a mental awareness quite foreign to what humans normally experience.

Bostrom’s & Sandberg’s brain emulation exclusively uses digital computation. But, they acknowledge the argument that analog and digital are qualitatively different. And, they admit that implementing analog in brain emulation could present profound difficulties. Yet, there is no need to worry, they say.

They pose what is called “the argument from noise.” Analog devices always take some physical form. It is unavoidable that interferences and irregularities, called noise, will make the analog device imprecise. So analog might be capable of taking-on an infinite range of variations. However, it will never be absolutely accurate, because noise always causes it to veer-off slightly from where it should be. Yet digital has its own inaccuracies. It is always missing variables between its discrete values. Nonetheless, digital is improving. Little-by-little it is coming to handle more variables. It is filling in the gaps. Digital will never be completely dense like analog. Values will always slip through its fingers. And analog will always miss its mark. But soon the distance between digital’s smallest fingers will equal the distance that analog veers-away from its proper course. Digital’s blindness would then match analog’s sloppiness. So, we only need to wait for digital technology to improve enough that it can compute the same values with equivalent precision. Both will be equally inaccurate, but for fundamentally different reasons.

But perhaps the argument from noise reduces the analog/digital distinction to a quantitative difference rather than a qualitative one. And analog is so prevalent in neural functioning that we should not so quickly brush it off.

Note first that our nervous system’s electrical signals are discrete pulses, like Morse code. In that sense they are digital. However, the frequency of the pulses can vary continuously. As well, there are many other neural quantities that are analog in this way.

Fred Dretske argues that our memories store information in analog. We might consider an event that occurred somewhere within a 15 minute time-span. But later, we also might search our memory for finer details to indicate more specifically when that event occurred. Because we can always further refine our remembered determinations, it could very well be that our brains record data in analog form.

But suppose anyway that the argument from noise is correct, and that we can dismiss analog’s computational superiority. Would there still be some reason to implement analog technology?

Recent research on neural-network learning supplies an answer. Analog noise interference is significantly more effective than digital at aiding adaptation. Being "wrong" allows neurons to explore new possibilities for computational values and connections. This enables us to learn and adapt to a chaotically changing environment. Using digitally-simulated neural noise might be inadequate. Analog is better. For, it affords our neurons an infinite array of alternate configurations. Hence, in response to Bostrom’s & Sandberg’s argument from noise, I propose this argument for noise. Analog’s inaccuracies take the form of continuous variation. In my view, this is precisely what makes it necessary for whole brain emulation.

Neural noise can result from external interferences like magnetic fields. Or internal random fluctuations might make the signals unpredictable. In both cases, chance & chaos reign our brains. And in fact, these random, indeterminate, and probabilistic events assist our brain’s computations. It implements noise to keep us adjusted to the world’s changes and uncertainties.

Some also theorize that noise is essential to the human brain’s creativity. Johnson-Laird claims that creative mental processes are never predictable. On this basis, he suggests a way to make computers think creatively. We make them alter their own functioning by submitting their programs to artificially-generated random variations. According to Daniel Dennett, such indeterminism is precisely what endows us with what we call free will. Likewise, Bostrom & Sandberg suggest we introduce random noise into our simulation by using pseudo-random number generators. They are not truly random, because eventually the pattern will repeat. But if it takes a very long time before the repetitions appear, then probably it would be sufficiently close to real randomness. It would be a major obstacle, Bostrom & Sandberg write, if artificial noise is not random enough for whole brain emulation.

Research suggests that we may characterize our neural irregularities as pink noise, or what is called 1/f noise. Benoit Mandelbrot classifies 1/f noise as what he terms “wild randomness.” This sort of random might not be so easily simulated. The stock market for example is wildly random. In such natural systems, astronomically improbable fluctuations occur frequently. There is no way to predict when they will appear or how drastic they will be.

For this reason, he considers wild variation to be a state of indeterminism that is qualitatively different than the usual mild variations we encounter at the casino. For, there is infinite variance in the distributions of wild randomness. Anything can happen at any time. He says, “the fluctuation from one value to the next is limitless and frightening.” And this is the wildness of our brains.

Paul Shepard considers our minds to be wild in an even more literal sense: we are wild animals. He distinguishes tameness from domestication. Cows are domesticated. They have been bred to suit our needs. And now their genes would probably not prepare them to live in the wild without human protections. But the human species has merely been tamed by culture and not domesticated like cows. Genetically, we are still the same wild creatures who hunted the Pleistocene savannas. So to emulate the human brain is to simulate the workings not of a rational machine, but of a wild animal. He writes, “The savage mind is ours! ... as a species we have in us the call of the wild.”

But let’s suppose that the brain’s wild randomness can be adequately simulated. Will brain emulation still attain its fullest success of perfectly replicating a specific person’s own identity? Bostrom & Sandberg recognize that neural noise will prevent precise one-to-one emulation. However, they think that the noise will not prevent the simulation from producing meaningful brain states. But to pursue further the personal identity question, let’s imagine that we want to emulate a certain casino slot machine. A relevant property is its unpredictability. So, do we want the emulation and the original to both give consistently the same outcomes? That would happen if we precisely duplicate of all the original’s relevant physical properties. But what about its essential unpredictability? The physically-accurate emulation could predict in advance all the original’s forthcoming read-outs. Or instead, would a more faithful copy of the original produce its own distinct set of unpredictable outcomes? Then we would be replicating the original’s most important relevant property of being governed by chance.

The problem is that the brain’s 1/f noise is wildly random. So suppose we emulate some person’s brain perfectly. And suppose further that the original person and her emulation have an identity merger where each one somehow mistakes themselves for the other. Yet, if both minds are subject to wild variations, then their consciousness and identity might come to differ more than just slightly. They could veer-off wildly. Perhaps our very effort to emulate a specific human brain results in our producing an entirely different mind altogether.

Whether this technology succeeds or fails, it still can advance a number of philosophical debates. It could tell us if our minds emerge from our brains; if the philosophy of artificial intelligence should take analog more seriously. We might learn whether our brain’s randomness is responsible for creativity, adaptation, and free choice; or, if this randomness is the reason our personal identities cannot be duplicated. The only failure, as I see it, is if we neglect this technology’s philosophical potential.

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The Wheel is Come Full Pixel: Analog vs. Digital; Bostrom and Sandberg's Brain Emulation, Examined and Critiqued. Section 4

Awake! Arise! or be Forever Disorganized; Bostrom and Sandberg's Brain Emulation, Examined and Critiqued. Section 3

by Corry Shores
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[The following is tentative material for my presentation at the Society for Philosophy & Technology Conference this summer.]

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Corry Shores

Do Posthumanists Dream of Pixilated Sheep?

Bostrom and Sandberg's Brain Emulation,

Examined and Critiqued

Section 3:

Awake! Arise! or be Forever Disorganized

Bostrom’s & Sandberg’s Roadmap presupposes a physicalist standpoint. So everything has a physical basis. Minds emerge from the brain’s pattern of physical dynamics. If you replicate this pattern-dynamic in some other physical medium, the same phenomena should likewise emerge. They write that “sufficient apparent success with [Whole Brain Emulation] would provide persuasive evidence for [this theory that consciousness may be realized in multiple distinct physical forms, or what’s called] multiple realizability.” (Bostrom & Sandberg 14)

Our mind’s emergence requires a dynamic process. Paul Humphreys calls it diachronic pattern emergence. (Humphreys 438)

According to emergentist theories, all reality is made-up of a single kind of stuff. But its parts aggregate and assemble into dynamic organizational patterns. The higher levels exhibit properties not found in the lower ones. Yet, the higher level would not exist were it not for its constituent lower level. (Clayton, 2-3)

Todd Feinberg suggests water, for example. The H2O molecule does not itself bear the properties of liquidity, wetness, and transparency. However, an aggregate does. (Feinberg, 125) Emergent features go beyond what we may expect from the lower level. Hence the higher levels are greater than the sum of their parts.

In our brains, no one single neuron is conscious. Yet our minds emerge from the complex dynamic pattern of all our neurons communicating and computing in parallel. Roger Sperry offers compelling evidence. There are "split brain" patients whose right and left brain hemispheres are disconnected from one another. Nonetheless, they maintained unified consciousness. But there is no good account for this on the basis of neurological activity. (Clayton 20)

William Hasker follows Sperry. He says that mental properties “manifest themselves when the appropriate material constituents are placed in special, highly complex relationships.” (Hasker, 189-190) He offers the analogy of magnetic fields, which he says are distinct from the magnets producing them. For, they occupy a much broader space. The magnetic field is generated because its “material constituents are arranged in a certain way – namely, when a sufficient number of the iron molecules are aligned so that their ‘micro-fields’ reinforce each other and produce a detectable overall field.” Once generated, the field exerts its own causality, which affects not only the objects around it, but even the very magnet itself. Hence Hasker’s analogy: just as the alignment of iron molecules produces a field, so too the particular organization of the brain’s neurons generates its field of ‘consciousness.’ (190) This emergent consciousness-field permeates and haloes our brain-matter, occupying its space and traveling along with it. (192)

Suppose whole brain emulation continually falls short. This could support Todd Feinberg’s argument that the mind does not emerge from the brain. He agrees with Searle that

the naïve idea here is that consciousness gets squirted out by the behavior of the neurons in the brain, but once it has been squirted out, then it has a life of its own (Searle, 1992) (qt. in Feinberg 126)

Feinberg does in fact think consciousness results from the interaction of many complex layers of neural organization. However, no level emerges, because none are more independent than any other. Our vision illustrates. We see a wide variety of stuff. But we can recognize singularities like our grandmother. Much visual information must be processed through many layers of neuron-circuits until finally arriving at the “grandmother cell.” Yet all layers must work together at once to achieve this recognition. The brain is a vast network of circuits far too interconnected to discern higher and lower levels of organization. (Feinberg 130-131)

But perhaps Feinberg, so to speak, looks too much among the iron atoms and so he never notices the surrounding magnetic field. Nonetheless, his objection may still be problematic for whole brain emulation. Bostrom & Sandberg write:

An important hypothesis for WBE is that in order to emulate the brain we do not need to understand the whole system, but rather we just need a database containing all necessary low-level information about the brain and knowledge of the local update rules that change brain states from moment to moment. (Bostrom & Sandberg 8)

But if Feinberg’s holistic theory is correct, we cannot only emulate the lower levels and expect the rest to spontaneously emerge. For, we need already to understand the higher-levels in order to program the lower ones. Thompson et al. write:

The brain is thus a highly cooperative system: the dense interconnections among its components entail that eventually everything going on will be a function of what all the components are doing. (Thompson, Varela, & Rosch 94a-b)

Thus the behavior of the whole system resembles a cocktail party conversation much more than a chain of command. (96a)

Consciousness results from neural activity. But it might do so in a way that is not perfectly suited to emergentist theories. Hence whole brain emulation might provide evidence indicating whether and how our minds relate to our brains.

[Next entry in this series.]

Clayton, Philip. "Conceptual Foundations of Emergence Theory." in The Re-Emergence of Emergence: The Emergentist Hypothesis from Science to Religion. Ed. Philip Clayton and Paul Davies. Oxford: Oxford University Press, 2006. More information and partial preview available at: http://books.google.be/books?id=KJF1ydg3HJQC&hl=en

Feinberg, Todd E. "Why the Mind is Not a Radically Emergent Feature of the Brain." in The Emergence of Consciousness. Ed. Anthony Freeman, Thorverton, UK: Imprint Academic, 2001. More information and partial preview available at: http://books.google.com/books?id=YBnLgsAOe6AC&printsec=toc&dq=Why+the+mind+is+not+a+radically+emergent+feature+of+the+brain&lr=&source=gbs_summary_s&cad=0#PPA136,M1

Hasker, William. The Emergent Self. London: Cornell University Press, 1999. More information and limited preview available at: http://books.google.be/books?id=dCW023Hc1q4C&hl=en

Humphreys, Paul. "Synchronic and Diachronic Emergence." Minds and Machines. Vol.18, Number 4, December, 2008, pp.431-442. More information and online text available at: http://www.springerlink.com/content/d442431150343t17/?p=f1cef51a00d346b582d2c3ad1386c814π=1

Sandberg, A. & Bostrom, N. (2008): Whole Brain Emulation: A Roadmap, Technical Report #2008‐3, Future of Humanity Institute, Oxford University. Available online at:http://www.fhi.ox.ac.uk/Reports/2008-3.pdf

Searle, J. R. The Rediscovery of the Mind. Cambridge: MIT Press, Bradford Books, 1992. (Cited in Feinberg)

Varela, Francisco J, Evan Thompson, & Eleanor Rosch. The Embodied Mind: Cognitive Science and Human Experience. Cambridge, Massachusetts: The MIT Press, 1991. More information and limited preview available at: http://books.google.be/books?id=QY4RoH2z5DoC&hl=en