8 Mar 2013

Andy Clark. Ch2 Supersizing the Mind “The Negotiable Body”

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[My own commentary is in brackets. All boldface and underlining is my own. Extra spacing between paragraphs follow the paragraph divisions in the original text.]

Andy Clark

Supersizing the Mind:

Embodiment, Action, and Cognitive Extension

The Negotiable Body

Very Brief Summary:
On account of neuroplasticity, our brains can rewire so that our bodily systems may incorporate tools and other technologies, which can then act as extensions of our body and mind.

Brief Summary:
Our minds and bodies are not locked into their current form and manner of operation but can rather incorporate tools and technologies so to extend our cognitive, sensory, and motor systems.

Interfaces are points of contact in a system, but in certain systems the contact is so intimate as to blur the boundary between those parts of the system, which in our case blurs the boundaries between body and world.

There are examples of robotic appendages affixed to humans and other primates where practiced usage led to them becoming transparent equipment. So while there was an interface between body and robotics, they together produce a new systemic whole. They become integrated because the brain’s neuroplasticity allows it to rewire itself so to function as if the tool were a part of the body it controls.

These integrations can also happen with our senses. Blind people can function as if seeing their surroundings by using devices that make a map of what their head is pointed at by using a grid of tactile sensations. Such sensory extensions become transparent equipment, and the users with their technology form new systemic wholes.

Some might object that transparency need not be a matter of creating new systemic wholes but rather of someone using something else as a tool. Clark notes that when using a stick, brains rewire so what is seen as the space around the stick is processed as if spatially immediate to the hand holding the stick. The stick then becomes incorporated into the body schema.

Primates (ourselves included) are deeply embodied, which means that we constantly seek opportunities to make the most of our body and world and the relation between them, by integrating resources deeply into our body-schema, and this creates whole new agent-world circuits. Our body is critically important for our problem solving but because of neuroplasticity and tool-incorporation our body is negotiable as well.



2.1 Fear and Loathing

Science fiction writer Bruce Sterling notes how forthcoming robotic technologies can aid the aging in their mobility, but the people operating these machines will be senile. (30)

Clark thinks technologies will be incorporating into our bodily and cognitive systems.

But such fears are rooted in a fundamentally misconceived vision of our own humanity: a vision that depicts us as “locked-in agents”— as beings whose minds and physical abilities are fixed quantities, apt (at best) for mere support and scaffolding by their best tools and technologies. In contrast to this view, I believe that human minds and | bodies are essentially open to episodes of deep and transformative restructuring in which new equipment (both physical and “mental”) can become quite literally incorporated into the thinking and acting systems that we identify as our minds and bodies (see, e.g., Clark 1997a, 2001b, 2003). [30-31]

When we use a stick [especially a blind person for ‘seeing’], our place of sensation extends past our hands to the stick’s end.

The typical human agent, circa 2008, feels herself to be a bounded physical entity in contact with the world through a variety of standard sensory channels, including touch, vision, smell, and hearing. It is a common observation, however, that the use of simple tools can lead to alterations in that local sense of embodiment. Fluently using a stick, we feel as if we are touching the world at the end of the stick, not (once we are indeed fluent in our use) as if we are touching the stick with our hand. The stick, it has sometimes been suggested, is in some way incorporated, and the overall effect seems more like bringing a temporary whole new agent-world circuit into being rather than simply exploiting the stick as a helpful prop or tool (see Merleau-Ponty 1945/1962 and Gibson 1979; for some more recent explorations of this theme, see Burton 1993; Reed 1996; Peck et al. 1996; Smitsman 1997; Hirose 2002; Maravita and Iriki 2004; Wheeler 2005). [31a.c]

Such enhancements can create new agent-world circuits.

In thinking about the case of stick-augmented perception, there would seem to be two key interfaces at play: the place where the stick meets the hand and the place where the extended system “biological agent + stick” meets the rest of the world. When we read about new forms of human–machine interface, we are again confronted by a similar duality and an accompanying tension. What makes such interfaces appropriate as mechanisms for human enhancement is, it seems, precisely their potential role in creating whole new agent-world circuits. But insofar as they succeed at this task, the new agent-tool interface itself fades from view, and the proper picture is one of an extended or enhanced agent confronting the (wider) world. [31c]

Clark will begin with the notion of an interface.

2.2 What’s in an Interface?

Clark begins with Haugeland’s (1998) explanation of interfaces. When analyzing interfaces, the “ goal is to uncover the underlying principles ‘for dividing systems into distinct subsystems along | nonarbitrary lines’ (211).” [31-32]
components: “those parts of a larger whole that interact through interfaces”. [32a]
interface: “ ‘a point of interactive ‘contact’ between components such that the relevant interactions are well-defined, reliable and relatively simple’ ” [32a]
systems: “ ‘relatively independent and self-contained’ composites of such interfaced components.” “(Haugeland 1998, 213).” [32a]

Clark agrees that interfaces are locations of contact between independent parts.

Haugeland is right to point to the nature of interactions as the key to the location of an interface. We discern an interface where we discern a kind of regimented, often deliberately designed, point of contact between two or more independently tunable or replaceable parts. [32]

But Haugeland is mistaken to say that the flow across the interface is simple. He needs this point so that he can say that human sensation is too complex for there to be interfaces between mind, body, and world and hence there is intimate intermingling of the three. (32b)

Clark agrees that sensation involves direct agent-environment couplings, but Clark is not ready to conclude that there are no interfaces. Haugeland thinks that sensation involves high-bandwidth communications, and interface low-bandwidth. But in a computer network with high-bandwidth connections, we have both interface and such intimate intermingling that the connected computers work like a single unified resource.

Nonetheless, we still think of it as a web of distinct but interfaced devices. And we do so not because the point of each machine’s contact with the grid is narrow (it isn’t) but because there exist, for each machine on the grid, very well-defined points of potential detachment and reengagement. We discern interfaces at the points at which one machine can be easily disengaged | and another engaged instead, allowing the first to join another grid or to operate in a stand-alone fashion. (32-33)

Thus we can have distinct entities that are nonetheless so intimately interactive that in their operations their boundaries are blurred. This means that the boundary between mind and world can likewise be blurred.

An interface, I conclude, is indeed a point of contact between two items across which the types of performance-relevant interaction are reliable and well defined. But there is no requirement that such interfaces be narrow-bandwidth bottlenecks. The way to argue for cognitive extensions and blurrings of the mind-world boundary is not by casting doubt on the presence of genuine interfaces (there are plenty of these within the brain, too, and that doesn’t stop us from distinguishing parts and roles) but by displaying special features of the flow of information across those interfaces and by stressing the novel properties of the new systemic wholes that result. It is to these tasks that we now turn. (33a.b)

2.3 New Systemic Wholes

Clark gives the example performance artist Sterlarc, who uses a robotic third arm, and has become so fluent in using it that it has become transparent equipment. [for more on transparent equipment, see
this section in
Natural Born Cyborgs]

Biological systems, from lampreys to primates, display remarkable powers of bodily and sensory adaptability (see Mussa-Ivaldi and Miller 2003; Bach y Rita and Kercel 2003; Clark 2003). The Australian performance artist Stelarc routinely deploys a “third hand,” a mechanical actuator controlled by Stelarc’s brain through commands to muscle sites on his legs and abdomen. Activity at these sites is monitored by electrodes that transmit signals (via a computer) to the artificial hand. Stelarc reports that, after some years of practice and performance, he no longer feels as if he has to actively control the third hand to achieve his goals. It has become “transparent equipment” (recall chap. 1), something through which Stelarc (the agent) can act on the world without first willing an action on anything else. In this respect, it now functions much as his biological hands and arms, serving his goals without (generally) being itself an object of conscious thought or effortful control. (33c.d)

Clark then discusses another example, an experiment in brain-machine interface (BMI) with a monkey and a robotic arm. [To clarify this experiment, we will draw both from Clark’s description and also from the paper itself.]

(from fig. 1 from
Carmena et al. 2003)

We see there is a monkey that is moving a joystick while looking at a screen. The joystick measures both grip and position. This translates into motions and changes in the position and size (grip intensity) of dot cursors on a computer screen.

(from fig. 1 from Carmena et al. 2003)

The first task has the monkey using the joystick’s pole to move the yellow dot to a green target dot. In the second task, the monkey need not move the pole, only squeeze it with the targeted amount of pressure. So its grip needed to be strong enough to make the  yellow circle expand outside the center circle, but not so hard it goes beyond the larger circle. Task three combines the first two: the monkey had to both move the cursor to the targeted location, and then afterward use the targeted amount of grip pressure. All the while, the researchers recorded the neural activity of the monkey’s brain, so that they could see what neural behaviors correlate with particular changes in the cursor. All this training happened during the “pole control” mode. So during this period, the monkey improves its abilities to manipulate the symbols. Eventually its ability reaches a maximal level, and its behaviors are coordinated consistently with its neural patterns. Then, the researchers disconnect the joystick wiring (while leaving the joystick in place), but now let the monkey’s neural activities control the cursor, on the basis of the correlations they found. When the monkey soon learns that the joystick is not working, the researchers remove it, and the monkey controls the dots using just its cognitive processes. After the monkey becomes accustomed to using just its brain, the researchers then add a robotic arm into the loop. The monkeys then are no longer directly controlling the screen cursors. Instead, they are controlling the position and grip of the robotic arm, whose parameters are then secondarily read and displayed on the screen. The results below show how the pole control period involved an increase in fluency, then when switching to brain control, fluency initially dropped a little but gradually reached maximal levels. But most notably when the robotic arm was introduced there was a steep drop in initial performance, but quick increase to maximal levels.


(from fig. 1 from
Carmena et al. 2003)

[Note that the authors write: “Figure 1C shows that because the intrinsic dynamics of the robot produced a lag between the pole movement and the cursor movement, the monkeys' performance initially declined.” But previously seemed to say they removed the pole and the monkey only used brain control. I will quote from the relevant passages.

In each recording session, an initial 30-min period was used for training of these models. During this period, monkeys used a hand-held pole either to move a cursor on the screen or to change the cursor size by application of gripping force to the pole. This period is referred to as “pole control” mode. As the models converged to an optimal performance, their coefficients were fixed and the control of the cursor position (task 1 and 3) and/or size (task 2 and 3) was obtained directly from the output of the linear models. This period is referred to as “brain control” mode. During brain control mode, animals initially produced arm movements, but they soon realized that these were not necessary and ceased to produce them for periods of time. To systematically study this phenomenon, we removed the pole after the monkey ceased to produce arm movements in a session. In each task, after initial training, a 6 DOF (degree-of-freedom) robot arm equipped with a 1 DOF gripper was included in the BMIc control loop. In all experiments, visual feedback (i.e., cursor position/size) informed the animal about the BMIc's performance. When the robot was used, cursor position indicated to the animal the X and Y coordinates of the robot hand. The cursor size provided feedback of the force measured by the sensors on the robot's gripper. The time delay between the output of the linear model and the response of the robot was in the range of 60–90 ms.


Behavioral Performance during Long-Term Operation of a BMIc

[…] In all three tasks, the levels of performance attained during brain control mode by far exceeded those predicted by a random walk model (dashed and dotted lines in Figure 1C–1E). Moreover, both animals could operate the BMIc without any overt arm movement and muscle activity, as demonstrated by the lack of EMG activity in several arm muscles (Figure 1G). The ratios of the standard deviation of the muscle activity during pole versus brain control for these muscles were 14.67 (wrist flexors), 9.87 (wrist extensors), and 2.77 (biceps).

A key novel feature of this study was the introduction of the robot equipped with a gripper into the control loop of the BMIc after the animals had learned the task. Figure 1C shows that because the intrinsic dynamics of the robot produced a lag between the pole movement and the cursor movement, the monkeys' performance initially declined. With time, however, the performance rapidly returned to the same levels as seen in previous training sessions (Figure 1C). It is critical to note that the high accuracy in the control of the robot was achieved by using velocity control in the BMIc, which produced smooth predicted trajectories, and by the fine tuning of robot controller parameters. These parameters were fixed across sessions in both monkeys. The controller sent velocity commands to the robot every 60–90 ms. Each of these commands compensated for potential position errors of the robot hand that resulted from previous commands. (Carmena et al.)

] The authors write in their conclusion:

Overall, the present findings demonstrate that it is reasonable to envision that a cortical neuroprosthesis for restoring upper-limb movements could be implemented in the future, following the basic BMIc principles described here. We propose that long-term operation of such a device by paralyzed subjects would lead, through a process of cortical plasticity, to the incorporation of artificial actuator dynamics into multiple brain representations. Ultimately, we predict that this assimilation process will not only ensure proficient operation of the neuroprosthesis, but it will also confer to subjects the perception that such apparatus has become an integral part of their own bodies. (Carmena et al.)

Hence we see the affinity between this experiment and Clark’s notion of transparence. Clark describes the experiment by writing:

Recent experimental work reveals more about the kinds of mechanisms that may be at work in such cases. A much publicized example is the work by Miguel Nicolelis and colleagues on a brain-machine interface (BMI) that allows a macaque monkey to use thought control to move a robot arm. In the most recent version of this work, Carmena et al. (2003) implanted 320 electrodes in the frontal and parietal lobes of a monkey. The electrodes allowed a monitoring computer to record neural activity across multiple cortical ensembles while the monkey learned to use a joystick to move a cursor across a computer screen | for rewards. As in previous work, the computer was able to extract the neural activity patterns corresponding to different movements, including direction and grip. Next, the joystick is disconnected. But the monkey is still able to use its neural activity, interpreted through the intervening computer, to directly control the cursor for rewards, and it learns to do so. Finally, these commands are diverted to a robot arm whose actual motions are then translated into on-screen cursor movements, including an on-screen equivalent of forceful gripping. This closes the loop. Instead of the monkey merely moving an unseen robot arm by thought control alone, the movement of the distant unseen arm now yields visual feedback in the form of on-screen cursor motion. (33-34)

As we noted, there was a drop in performance when the monkey began working through the robotic arm. Yet over time it gained fluency, because the monkey’s brain rewired (there was “cortical reorganization” as Carmena et al. term it) so that the two worked seamlessly together. This is neuroplasticity. Clark writes:

When the robot arm was inserted into the control loop, the monkey displayed a striking degradation of behavior. It took two full days of practice to reestablish fluent thought control over the on-screen cursor. The reason was that the monkey’s brain now had to learn to factor in the mechanical and temporal “friction” created by the new physical equipment: It had to factor in the mechanical and dynamical properties of the robot arm and the time delays (which were substantial, in the 60–90 millisecond range) caused by interposing the motion of the arm between neural command and on-screen feedback. By the time full fluency was achieved, it is reasonable to conjecture that these properties of the still unseen distant arm were in some sense incorporated into the monkey’s own body schema. In support of this, the experimenters were able to track real long-term physiological changes in the response profiles of frontoparietal neurons following use of the BMI, leading them to comment that

the dynamics of the robot arm (reflected by the cursor movements) become incorporated into multiple cortical representations . . . we propose that the gradual increase in behavioral performance . . . emerged as a consequence of a plastic reorganization whose main outcome was the assimilation of the dynamics of an artificial actuator into the physiological properties of fronto-parietal neurons. (Carmena et al. 2003, 205) [Clark 34b.d]

Certain creatures can incorporate new bodily structures in this way, and Clark calls such creatures “profoundly embodied agents.” They are able to “constantly to negotiate and renegotiate the agent-world boundary itself.” (34d)

But this is natural anyway, as evidenced in child development.

The human | infant must learn (by self-exploration) which neural commands bring about which bodily effects and must then practice until skilled enough to issue those commands without conscious effort. This process has been dubbed “body babbling” (Meltzoff and Moore 1997) and continues until the infant body becomes transparent equipment (see 1.6). Because bodily growth and change continue, it is simply good design not to permanently lock in knowledge of any particular configuration but instead to deploy plastic neural resources and an ongoing regime of monitoring and recalibration […]. (34-35)

2.4 Substitutes

Clark offers another example of such neuroplasticity. Blind subjects have a grid of nails fixed to their backs and parts of the grid stimulate the subjects back depending on information received from a video camera. Over time it is as if the subjects are able to see the things around them.

As a second class of examples of recalibration and renegotiation, consider the plasticity revealed by work in sensory substitution. Pioneered in the ‘60s and ’70s by Paul Bach y Rita and colleagues, the earliest such systems were grids of blunt “nails” fitted to the backs of blind subjects and taking input from a head-mounted camera. In response to the camera input, specific regions of the grid became active, gently stimulating the skin under the grid. At first, subjects report only a vague tingling sensation. But after wearing the grid while engaged in various kinds of goal-driven activity (walking, eating, etc.), the reports change dramatically. Subjects stop feeling the tingling on the back and start to report rough, quasi-visual experiences of looming objects and so forth. After a while, a ball thrown at the head causes instinctive and appropriate ducking. The causal chain is “deviant”: It runs via the systematic input to the back. But the nature of the information carried, and the way it supports the control of action, is suggestive of the visual modality. Performance using such devices can be quite impressive. In a recent article, Bach y Rita, Tyler, and Kaczmarek (2003) note that Tactile-Visual Substitution Systems (TVSS) have been sufficient to perform complex perception and “eye”-hand co-ordination tasks. These have included face recognition, accurate judgment of speed and direction of a rolling ball with over 95% accuracy in batting the ball as it rolls over a table edge, and complex inspection-assembly tasks. (287) [35b.d]

What is essential is that the head-mounted camera be under the subjects control, because this allows the brain to experiment by looking around and coordinating the nail stimulations with experiences of things around them.

The key to such effective sensory substitution is goal-driven motor engagement. It is crucial that the head-mounted camera be under the subject’s intentional motor control. This meant that the brain could, in effect, experiment through the motor system, giving commands that | systematically varied the input so as to begin to form hypotheses about what information the tactile signals might be carrying. Such training yields quite a flexible new agent-world circuit. Once trained in the use of the head-mounted camera, the motor system operating the camera could be changed (e.g., to a hand-held camera) with no loss of acuity. The touch pad, too, could be moved to new bodily sites, and there was no tactile–visual confusion: An itch scratched under the grid caused no “visual” effects (for these results, see Bach y Rita and Kercel 2003). [35-36]

These technologies have advanced quite a bit, and now have greater capabilities and are more compact. (36b)

These technologies can also be used for enhancement. They can give us night vision, and all sorts of signals, including television signals, can be directly feed into the brain, bypassing sensory peripheries. There is even a suit invented by the US Navy, where an inexperienced pilot can control a helicopter blindfolded, by reacting to air puffs from the suit that tell the pilot the helicopter’s tilts, so she can stabilize and fly it without vision.

While the pilot wears the suit, the helicopter behaves very much like an extended body for the pilot: It rapidly links the pilot to the aircraft in the same kind of closed-loop | interaction that linked Stelarc and the third hand, the monkey and the robot arm, or the blind person and the TVSS system. What matters, in each case, is the provision of closed-loop signaling so that motor commands affect sensory input. What varies is the amount of training (and hence the extent of deeper neural changes) required to fully exploit the new agent-world circuits thus created. (36-37)

What is important is that the circuits become transparent.

It is important, in all these cases, that the new agent-world circuits be trained and calibrated in the context of a whole agent engaged in world-directed (goal-driven) activity. One sign of successful calibration is, as we noted earlier, that once fluency is achieved, the specific details of the (old or new) circuitry by which the world is engaged fall “transparent” in use. The conscious agent is then aware of the oncoming ball, not (usually) of seeing the ball or (by the same token) of using a tactile substitution channel to detect the ball. In just this way, the tactile-vest-wearing pilot becomes aware of the aircraft’s tilt and slant, not of the puffs of air. (37b)

We see then that humans and other primates are highly capable of the sorts of neuroplasticity that allow for us to extend into external things.

In all these diverse ways, humans and other primates are revealed as constantly negotiable bodily platforms of sense, experience, and (as we’ll see in later chapters) reasoning, too. Such platforms are biologically primed so as to fluidly incorporate new bodily and sensory kit, creating brand new systemic wholes. This is just what one would expect of creatures built to engage in what we earlier (sec. 1.1) called “ecological control”: systems evolved so as to constantly search for opportunities to make the most of the reliable properties and dynamic potentialities of body and world. (37bc)

2.5 Incorporation Versus Use

One might say that tool use transparency is not such a controversial concept; it can be understood as a user in command of a tool rather than creating new systemic wholes. (37d)

Clark will begin by examining research on primate tool use. (37d)

Recently bimodal neurons in primate brains have been discovered. The respond both to somatosensory information from a bodily region and as well to visual information from the space adjacent to it. (38a)

“For example, some neurons respond to somatosensory stimuli (light touches) at the hand and to visually presented stimuli near the hand so as to yield an action-relevant coding of visual space.” The neurons then seem to be able to develop sensitivities extended through objects [so consider if we were to use a stick to feel for things beyond our normal reach. These neurons would coordinate visual information regarding what we see touching and affecting the stick with tactile information that we feel in our hand, such that we recode or reprocess this tactile information so that we can feel at the end of the stick.]

In a series of experiments, recordings were taken from bimodal neurons in the intraparietal cortex of Japanese macaques while the macaques learned to reach for food using a rake. The experimenters found that after just five minutes of rake use, the responses of some bimodal neurons whose original vRFs picked out stimuli near the hand had expanded to include the entire length of the tool, “as if the rake was part of the arm and forearm” (Maravita and Iriki 2004, 79). Similarly, other bimodal neurons, which previously responded to visual stimuli within the space reachable by the arm, now had vRFs that covered the space accessible by the arm-rake combination. After surveying a number of other related findings, including some fascinating work in which similar effects are observed after experience of reaching with a virtual arm in an on-screen display, Maravita and Iriki conclude: “Such vRF expansions may constitute the neural substrate of use-dependent assimilation of the tool into the body-schema, suggested by classical neurology” (2004, 80). (38a.c)

Another scientific study shows how our brains distinguish far space and near space, and when we use a stick as a tool, our brain treats the far space at its end as if it were space near our hand.

In human subjects suffering from unilateral neglect (in which stimuli from within a certain region of egocentrically coded space are selectively ignored), it has been shown that the use of a stick as a tool for reaching actually extends the area of visual neglect to encompass the space now reachable using the tool (see Berti and Frassinetti 2000). Berti and Frassinetti conclude

that the brain makes a distinction between “far space” (the space beyond reaching distance) and “near space” (the space within reaching distance) [and that] . . . simply holding a stick causes a remapping of far space to near space. In effect the brain, at least for some purposes, treats the stick as though it were a part of the body. (2000, 415) [38c.d]

So we see from these studies that there is a difference between mere use of an object and a true incorporation of it into our body scheme. Clark also distinguishes body image (“conscious construct able to inform thought and reasoning about the body”) and body schema (“a suite of neural settings that implicitly (and nonconsciously) define a body in terms of its capabilities for action action, for example, by defining the extent of ‘near space’ for action programs”.) [39a]

Clark has us imagine beings without the capacity to incorporate tools into their body schema. Instead, they would use conscious calculations and representations of the tools features and powers. We might also imagine them being so smart that they can make these calculations so fast they they use the tools just as well as humans would, as if the tools were incorporated into their body schemas when in fact they are not. The difference between humans and these beings would still be that human brains rewire so that the extensions the tool use enables are given automatically.

The contrast that would remain, even in the latter kind of case, would be between (a) the skilled agent’s first explicitly representing the shape, dimensions, and powers of the tool and then inferring (consciously or otherwise) that she can now reach such and such and do such and such and (b) agents whose brains were so constituted that experience with the tool results in, for example, a suite of altered vRFs such that objects within tool-augmented reaching range are now automatically treated as falling within near space. These are surely distinct strategies. The latter strategy might be especially recommended for beings whose bodies (like our own) are naturally subject to growth and change, as it seems designed to support genuine episodes of integration across change: cases that can now be defined as cases in which plastic neural resources become recalibrated (in the context of goal-directed whole agent activity) so as to automatically take account of new bodily and sensory opportunities. In this way, to paraphrase Varela, Thompson, and Rosch (1991), our own embodied activity enacts or brings forth new systemic wholes. [39b.c]

2.6 Toward Cognitive Extension

Clark now addresses the question of whether incorporation really creates new systemic wholes for our cognizing minds or if it is “just the same old mind with a shiny new tool?” (39d)

Clark thinks “we are not just bodily and sensorily but also cognitively permeable agents”, but it is not so clear what to look for in neural changes for cogniive extensions. Clark will just begin looking at instances of physical and sensory augmentation to find clues. (40a)

For one thing, cognitive enhancement does not require that we be aware of the cognitive operations at work. We are not now aware of our own cognitive operations and yet we think. And when our brain structures change, in growth and maturation), we do not need that the operations of the new structure be intelligible to those of the old structure. Rather, changes can create new wholes that are themselves (and not their prior forms) the determiners of what is intelligible to the agent. So nonbiological tools and structures can become sufficiently integrated into our problem-solving activity in such a way that it yields “new agent-constituting wholes.” (40c)

Clark has us consider when our neural systems learn a new complex problem solving routine, and this routine changes how we conceive of information around our body, like changing how our neurons work so we can feel at the end of a tool.

Consider the case when some existing neural system or systems learn a complex problem-solving routine that makes a variety of deep implicit commitments to the robust bioexternal availability of certain operations and/or bodies of information. This is the cognitive equivalent, I suggest, of the implicit commitments to details of bodily shape and potentials for action made (in the case of the rake) by rapidly retuning the receptive fields of key bimodal neurons and (in the case of the robot arm) by retuning key cortical representations (specifically, populations of frontoparietal neurons). (40d)

Clark mentions a study that he details later in the book. After “masking of motion transients” subject were unable to spot large and significant changes, even ones made in their field of focus. But we also think of ourselves as having a “rich visual contact with our surroundings”, so how could we miss such things? It could have something to do with us already feeling as we were are in intimate visual touch with the things we see, and because we can obtain details on demand when we need them, changes can slip our attention if we don’t recognize the need to focus on those details.

Clark also refers to a block-copying experiment from section 1.3. We find from it that

a problem-solving routine is delicately geared to automatically exploit, on pretty much an equal footing, both internal and (bio)external forms of information storage. Rather than drawing a firm line around the inner encodings, we thus expand the relevant forms of storage and retrieval to include inner biological resources, environmental structure, and the data (and operations) made available by cognitive artifacts such as notebooks and laptops. As we move toward an era of wearable computing and ubiquitous information access, the robust, reliable information fields to which our brains delicately adapt their inner cognitive routines will surely become increasingly dense and powerful, perhaps further blurring the boundaries between the cognitive agent and his or her best tools, props and artifacts. (41d)

2.7 The Grades of Embodiment

There are three grades of embodiment: a) mere embodiment, b) basic embodiment, and c) profound embodiment.
a) mere embodiment: “A merely embodied creature or robot is one equipped with a body and sensors, able to engage in closed-loop interactions with its world, but for whom the body is nothing but a highly controllable means to implement practical solutions arrived at by pure reason.” (42a)
b) basic embodiment: “A basically embodied creature or robot would then be one (we saw several in chap. 1) for whom the body is not just another problem space, requiring constant micromanaged control, but is rather a resource whose own features and dynamics (of sensor placement, of linked tendons and muscle groups, etc.) could be actively exploited allowing for increasingly fluent forms of action selection and control. Much (though by no means all) work in contemporary robotics has explored this middle ground of modest embodiment. Such systems are, however, congenitally unable to learn new kinds of body-exploiting solution ‘on the fly,’ in response to damage, growth, or change.” (42b)
c) profound embodiment: “By contrast, as we have seen, biological systems (and especially we primates) seem to be specifically designed to constantly search for opportunities to make the most of body and world, checking for what is available, and then (at various timescales and with varying degrees of difficulty) integrating new resources very deeply, creating whole new agent-world circuits in the process. A profoundly embodied creature or robot is thus one that is highly engineered to be able to learn to make maximal problem-simplifying use of an open-ended variety of internal, bodily, or external sources of order.” (42bc)

We cannot think of profoundly embodied minds as being like disembodied organs of control because they are not in any was disembodied.

Rather, they are promiscuously body-and-world exploiting. They are forever testing and exploring the possibilities for incorporating new resources and structures deep into their embodied acting and problem- solving regimes. They are, to use the jargon of Clark (2003), the minds of “natural-born cyborgs”—of systems continuously renegotiating their own limits, components, data stores, and interfaces. (42d)

The body in this sense is critically important because of its role in problem solving, but it is negotiable, because it is a machine constantly in flux.

On this account, the body is both critically important and constantly negotiable. It is critically important as a key player on the problem-solving stage. It is not simply the point at which processes of transduction pass the real problems (now rendered in rich internal representational formats) to an inner engine of disembodied reason. Instead, much of our successful performance depends | on constant and subtle trade-offs among morphology, real-world action and opportunities, and neural control strategies. But this empowering body is constantly negotiable, constructed moment by moment from the flux of willed action and resulting sensory stimulation. (42-43)

Recall from the first section of this chapter how Sterling was afraid of senile minds in control of sophisticated enabling machines. But this misses the point that our minds are not fixed but are fluid and can thus integrate and expand into the technology that it comes to incorporate within its systems.

Those first waves of fear and loathing now give way to something more rewarding. Sterling (sec. 2.1) saw frightening scenes of a merely superficially augmented agent within whom “the CPU is a human being: old, weak, vulnerable, pitifully limited, possibly senile.” Such fears play upon a deeply misguided image of who and what we already are. They play upon an image of the human agent as doubly locked in: as a fixed mind (one constituted solely by a given biological brain) and as a fixed bodily presence in a wider world. Fortunately for us, human minds are not old-fashioned CPUs trapped in immutable and increasingly feeble corporeal shells. Instead, they are the surprisingly plastic minds of profoundly embodied agents: agents whose boundaries and components are forever negotiable and for whom body, sensing, thinking, and reasoning are all woven flexibly and repeatedly from the accommodating weave of situated, intentional action. (43a)

Andy Clark. Supersizing the Mind: Embodiment, Action, and Cognitive Extension. Oxford / New York: Oxford University Press, 2008.

Carmena JM, Lebedev MA, Crist RE, O'Doherty JE, Santucci DM, et al. (2003) Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates. PLoS Biol 1(2): e42. doi:10.1371/journal.pbio.0000042


Copyright: © 2003 Carmena et al. This is an open-access article distributed under the terms of the Public Library of Science Open-Access License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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