#MeToo: The Ecology of Rhizo-Rhetoric

I'm examining the new kinds of documents that I see emerging on the Net, and I'm focusing on #MeToo, as it is currently the strongest and most visible of the kinds of documents I'm thinking about, but it certainly isn't the only one. In my last post, I looked at #MeToo as a hyperobject as Timothy Morton defines them. In this post, I want to begin thinking of #MeToo in terms of rhetoric. After all, I'm defining #MeToo as a text, and rhetoric should have something to say about any text.

As I recall from my years of reading rhetoric, most rhetoricians do not approach writing and communication from the view of complexity. Most in fact try to reduce writing to the simple or complicated domains, with fairly simple models and heuristics for producing useful texts. However, by the late 20th century, rhetoricians and literary theorists were beginning to push rhetorical thought into the complex domain. For instance, in his book A Counter-History of Composition: Toward Methodologies of Complexity (2007), Byron Hawk traces the emergence of complexity in modern thought, particularly in modern rhetoric, through the concept of vitalism. For Hawk, vitalism begins with Aristotle, takes a turn in rhetoric toward Romantic expressivism, and eventually flowers in the 20th century through the science and philosophy of complexity. Hawk does an admirable job of showing how a rich concept can influence science, philosophy, and rhetoric and how each of these disciplines can feed into and off of the other. Even if you are not so interested in rhetoric, his argument illuminates the history of an idea the informs much of modern thought.

I don't intend to recount Hawk's book, but I do want to explore several of his ideas in terms of #MeToo. The first idea is that complexity rhetoric, or what I have called in previous posts rhizo-rhetoric, is ecological rather than atomistic, or complex rather than simple.

Hawk starts his discussion of vitalism with Aristotle's concept of entelechy, which seems to lay the intellectual groundwork for ecological thinking for Hawk. For Hawk and Kenneth Burke, whom Hawk quotes, entelechy is "essentially a biological analogy. It is the title for the fact that the seed ‘implicitly contains’a future conforming to its nature, if the external conditions necessary to such unfolding and fulfillment occur in the right order. Were you to think of the circumstances and the seed together, as composing a single process, then the locus of the entelechy could be thought of as residing not just in the nature of the seed, but in the ground of the process as a whole" (Burke, The Rhetoric of Religion, 1961, pp. 246-247). Entelechy, then, embeds both seeds and rhetoric into a complex ecology, into a rhizome, and this idea that all things unfold through the interaction of internal resources and external environments begins to lay some groundwork for me to understand #MeToo.

To read #MeToo, then, I must frame it in a complex ecology, a rhizome, that considers both the text itself and the ecosystem of the text as a single process. #MeToo, of course has its internal resources, its DNA, arising from the experiences of millions of women and men and their abilities to express those experiences, but the unfolding and unpacking of that DNA happens within an ecosystem that seeks to express its own DNA and that may or may not support #MeToo. The environment is a co-creator of #MeToo, and this is made very clear when we learn that the MeToo meme was actually created a decade ago by a black activist named Tarana Burke who, according to Ebony magazine, started MeToo "as a grassroots movement to aid sexual assault survivors in underprivileged communities 'where rape crisis centers and sexual assault workers weren’t going.'” I don't know why #MeToo emerged now instead of 10 years ago. Perhaps because Tarana Burke is not a well-known movie star with thousands of Twitter followers. Maybe because Twitter was just created a decade ago and was not yet the force in social networking that it has become. Perhaps those reasons and many more, but the main point for me is that ten years ago the environment was not right for #MeToo. The #MeToo seed had fallen on dry, barren ground, and it did not emerge even though there were just as many millions of women who could speak to the issue. In 2017, the seed fell into fertile soil and sprouted. Then exploded.

Hawk says of this ecological frame for rhetoric:

The basic logic of entelechy is that the overall configuration of any situation, including both natural and human acts and forms, combines to create its own conditions of possibility that strive to be played out to completion. The combination of the four causes in nature is not just a push from behind but also a pull toward the future, the striving to develop potential. In more contemporary evolutionary terms, an ecological situation produces the structural conditions for certain types of plants or animals to develop and thrive and they strive to fill those gaps, to enact that potentiality. Humans as an efficient cause cannot be abstracted from this larger contextual ground set up by the other causes and the ecology or potentiality they enact. A human might have an internal, psychological, or intellectual motive, but a huge variety of cultural, linguistic, and material factors help create and enact that motive. As part of nature, humans can help realize the situational potential via the technê available to them through the complex ecological arrangement, and it is in this larger movement that rhetoric operates. (126)

First, note that an ecological understanding of rhetoric undermines the traditional concept of writing as an individual who through innate knowledge and skill creates texts that engage others, usually to meet some purpose of the writer, some need of the reader, or some issue in the world, or all three. Writers write to act on the world and to make things happen, and if I consider individual tweets and texts, then this can be a useful frame for thinking about #MeToo. For instance, consider Alyssa Milano's original tweet that kicked off the current #MeToo text:

I can describe this single tweet as Alyssa Milano writing a message to persuade her followers to express their own sexual harassment and the extent of sexual harassment in our society. Obviously, millions did, and the #MeToo text emerged and morphed around the world, far exceeding Milano's expectations. But this original tweet is neatly captured and usefully illuminated by a traditional rhetorical analysis that frames the communication as a writer writing to some reader about some issue to make something happen. You can easily model this with the communications triangle that I use in my college writing classes:

A writer, a reader, a subject, all joined by a text—in this case, a tweet. You can even change the terms to pull from different strains of communication theory:

I do not dismiss the immediate, though limited utility of framing writing like this, but for me, this frame is woefully inadequate for reading and understanding #MeToo. It's first problem is scale. It is too focused on the single tweet from Alyssa Milano, the single artifact of a single writer. #MeToo is much bigger than this one tweet. If this was the only tweet in #MeToo, then we would have no #MeToo, and I would be looking at other hyper-documents. I'm discussing #MeToo because it is a swarm of millions of tweets, texts, messages, articles, television discussions, and acceptance speeches that push #MeToo into the higher scale of hyperobjects. This higher scale makes #MeToo interesting and gives it its power, and the single Milano tweet is interesting to me only because of this higher level text. Indeed, I did not follow Milano on Twitter at the time of her tweet, so I would never have seen it had #MeToo not emerged.

The communications triangle also focuses too much on the individual writer. Yes, Milano wrote the first tweet in October 2017, and her voice is an integral, necessary part of #MeToo, but it is hardly sufficient to account for or to embody #MeToo. Milano's voice has been subsumed by the general hum that is #MeToo. While focusing on a narrow instance of #MeToo can be illuminating, it is ultimately distracting. Researching the behavior of a single neuron in the human brain can reveal much, but it doesn't reveal mind or consciousness, both of which emerge at a hyperscale above the single neuron. This is the scale at which I become aware of #MeToo, but traditional rhetoric inadequately frames this scale for me. I need a larger frame, a more ecological frame.

Hawk looks to artificial life studies to expand the frame of rhetoric from the single writer to a swarm of agents, both human and not:

In a paper delivered in 1992 at the third Workshop on Artificial Life, Mark Millonas wrote, “The notion that complex behavior, from the molecular to the ecological, can be the result of parallel local interactions of many simpler elements is one of the fundamental themes of artificial life. The swarm, which is a collection of simple locally interacting organisms with global adaptive behavior, is a quite appealing subject for the investigation of this theme” (quoted in Mark Taylor 153). … Essentially the study of life through artificial means extends the shift from examining characteristics of living beings to examining functions of living systems. (156, 157)

For me to read #MeToo, then, my rhetorical strategies must shift from framing the "characteristics of living beings", or individual writers, to framing the "functions of living systems", or swarms of writers. I have to see a complex, powerful document such as #MeToo as emerging from "simple locally interacting organisms with global adaptive behavior".

Many may object to characterizing the millions of people, mostly women, who wrote #MeToo as "simple locally interacting organisms with global adaptive behavior". It sounds demeaning—like ants in a pile—but I think that is a trick of scale.

Each individual #MeToo writer is simpler than #MeToo in two ways. First, they are simple in comparison to the complexity of the larger scale document that emerged from the aggregation of each tweet. An individual ant is a complex, capable creature at its own scale, but it is more simple and less capable in comparison to the ant colony. Likewise, the #MeToo writers are complex, capable people at the human scale, but they are more simple and less capable than the hyper-human scale that #MeToo works in. No single #MeToo text, not a tweet from Alyssa Milano or a speech from Oprah Winfrey, can match the power and force of a million tweets. #MeToo is a hyper-text that functions at a scale above the human, a hyper-human scale, a cyborg scale, and no individual human can measure against it.

But there is no need to measure against it. This is not an exercise in comparison, and it certainly isn't a denigration of individual people; rather, #MeToo is a celebration of the kinds of powerful texts emerging in a posthuman world.

Then each #MeToo document (tweets, text messages, posts, and more) is simpler than the #MeToo text. This is especially true of individual tweets. Most of the tweets are simple responses to Milano's call to retweet, and each can be characterized in simple terms of a stimulus-response as we might characterize the firing of a neuron in response to some stimulus. The responses, in turn, stimulate what becomes a cascade of responses across Twitter, and #MeToo emerges. I do not dismiss the benefits of learning about an individual stimulus-response pattern. Understanding stimulus-response is necessary for understanding the functions of the brain, for instance, or for understanding Twitter, but it is insufficient for understanding consciousness or #MeToo, both of which emerge at a scale beyond stimulus-response and create characteristics not inherent in the stimulus-response pattern. Mapping the trajectory of a single stimulus-response is enlightening and helpful, but it is not sufficient to map the trajectories of millions of stimuli-responses. One thing happens when a single neuron fires, but something else altogether different happens when millions of neurons fire.

Reducing #MeToo, or mind, to a single stimulus-response is both misleading and denigrating. First, it denigrates by denying the validity or even the possibility of emergent properties at the hyper scale. Many will deny the possibility of meaning emerging at the hyper-human level of #MeToo, and will insist that #MeToo is no more than, at best, a group of concerned women expressing their individual outrage or, at worst, a group of liberal fanatics bitching and moaning about not much. Even though the first opinion supports #MeToo and the second attacks it, they both undermine the real power of #MeToo which emerges at a scale above the individual.

Then, reducing #MeToo denigrates by glossing over the complexity of the individual interactions that we render simple. For instance, the term stimulus-response treats the complex behavior of an individual neuron as if it's no more than one billiard ball bumping into another. A single neuron firing is itself a complex event within a complex ecosystem. An individual neuron is a network of many parts, and it has the intelligence to collect data from its environment, to assess that data, and to change its internal state to respond to that data. It has many of the same complex characteristics at the neuronal scale that we humans have at the human scale. In other words, a neuron only looks simple from the great remove of the human scale. As Olaf Sporns proves in his 2010 book Networks of the Brain (not network singular), a neuron firing is a damned complex network in its own right, and we misunderstand it if we treat only as a simple mechanism. Likewise, a single woman retweeting #MeToo is a damned complex network in her own right. She is not just one more woman championing or complaining in a tweet but a complex constellation of experiences and knowledges, some too deep for words. To reduce these women to a simple group or to a simple response misleads and undermines our understanding of #MeToo.

This ecological approach drops both Hawk and me at the doorstep of post-humanism, which I will discuss in a next post.

Why Rhizomatic Learning, Pt. 2 #etmooc

In my last post, I said that networking is the lens through which I see most everything, or at least I try. I confess that I still have some old habits of mind, mostly that I'm unaware of, but when brought to mind, I do try to address them. I quoted Olaf Sporns comments that science is increasingly using the networking metaphor to guide both its subject matter and its research. I want to comment on a couple of points he made, and I quote:

Increasingly, science is concerned with the structure, behavior, and evolution of complex systems such as cells, brains, ecosystems, societies, or the global economy. To understand these systems, we require not only knowledge of elementary system components but also knowledge of the ways in which these components interact and the emergent properties of their interactions. (1)

Note first that he although he started with the term networks, mid-stream he switches to complex systems, the same term that Edgar Morin uses. In my reading, these terms have often been used interchangeably, and I will likely do so in my own discussion, unless I find some reason to distinguish between them.

Second, Sporns captures neatly the distinction between the reductionist, mechanistic clockwork type of science with its focus on "knowledge of elementary system components" and the evolutionary, organic networking type of science with its focus on "knowledge of the ways in which these components interact and the emergent properties of their interactions." This shift in metaphors, or paradigms if you prefer, is extremely important for me.

Although Sporns and I do not share similar disciplines—he studies and teaches neuroscience and I study and teach writing and literature—his work has a critical, core benefit for me: Sporns insists and demonstrates through exhaustive research that cognition is a network phenomenon. I accept his argument, as I have not found a better, more detailed, more precise description of how the brain works. I was pleased, then, to read James Zull's educational book The Art of Changing the Brain (2002) which applies the networking paradigm to learning and draws out some implications for teaching and pedagogy. Networking, of course, is at the heart of Connectivism. Throughout his writings, Stephen Downes makes a number of statements that express knowledge and learning as network phenomena. For instance, in the 2011 post What Networks Have In Common, Downes says, "the state we call 'knowledge' is produced in (complex) entities as a consequence of the connections between and interactions among the parts of that entity." I could no doubt find even more pointed pronouncements in Downes' writing, but this is sufficient. In his online book Knowing Knowledge (2006), George Siemens says, "Knowing and learning are today defined by connections ... connectivism is the assertion that learning is primarily a network-forming process" (15).

For me, then, learning is the ability of an entity to recognize, build, and traverse networks. Moreover, the tools entities use to recognize, build, and traverse networks are themselves networks, and knowledge is an emergent property of the interactions among and across those networks of neurons, sensory organs, sound waves, light waves/particles, classrooms, social groups, languages, the Universe … as far out or in as you wish to take it.

Anyone who has read Downes and Siemens will see that nothing I say here is new. Those fellows have already said it, and in general, I agree with them (details are always problematic, but that doesn't concern me here). Learning is networking across multi-scale networks, and that has huge implications for the way we teach, but is learning rhizomatic? Perhaps a better way to ask this question is what does the concept of the rhizome as developed in Deleuze and Guattari's book A Thousand Plateaus bring to connectivism that it doesn't already have? This is similar to a question I have heard Siemens ask of Cormier in some of our previous MOOCs, and it merits an investigation, if not an answer. I'll try to do that. Tomorrow.

Why Rhizomatic Learning? #etmooc

Okay, so I enjoyed the conversation about rhizomatic education over at Christina Hendricks' blog, You're the Teacher. In the conversation, I'm definitely championing rhizomatic, connectivist education, but why? I've been writing about this for a couple of years now, but can I state my point of view succinctly and reasonably clearly? Well, I can try.

Learning is a network phenomenon.

That's rather succinct, and owes deep apologies to neuroscientist Olaf Sporns, but I can say it with a bit more texture: learning is a function of our complex interactions across multi-scale physical, cognitive, technological, and social networks. For me, this is the DNA of a connectivist and rhizomatic view of learning, and everything else I say about learning will follow from this core idea. At least, I hope so.

But can I defend my claim that learning is a network phenomenon? I think so, but in some ways, starting points always carry with them assumptions that one either accepts or doesn't, and they carry assumptions that the believer is quite often unaware of. I think my use of networks falls into this category. However, I can point to some reasons why I use the concept.

Networks provide me a most useful model of how the Universe/Reality/Everything works, including learning. Of course, as soon as I say that, I am reminded of George E. P. Box's famous dictum that "essentially, all models are wrong, but some are useful" (Empirical Model-Building and Response Surfaces, 1987). I am convinced that, despite how right network thinking feels to me, eventually people will come to see the faults with the network metaphor just as we are coming to see the faults with the mechanistic clockwork metaphor that we inherited from Galileo, Newton, and Descartes. As Edgar Morin has pointed out in his book On Complexity (2008), the mechanistic, clockwork model of reality and the science and technology built upon it has been spectacularly successful, but over the past century, cracks have begun to appear as we have come to see more of Reality, especially at the macro and micro levels. As we peer into our scopes, bits of reality emerge that no longer fit the clockwork model. Reality is stubborn, so we change our model. But slowly, sometimes too slowly.

The model that appears to be replacing the mechanistic clockwork model is networking. Of course, not everyone uses that term. Edgar Morin speaks of systems, especially complex systems. In his book Interaction Ritual Chains (2005), Randall Collins defines the core sociological unit not an individual but the situation, a dynamic nexus of intersecting vectors which to my mind requires a network structure. James Lovelock calls it Gaia, the movies call it The Matrix. All of these sources have valid reasons for using the term that they do, but to my mind, networking (and here I'm using the verbal form intentionally to capture the complex dynamics in my concept) is the most convenient and natural-feeling term. I spent many years of my professional life building campus networks and connecting students, faculty, and staff to the Internet, so it just works for me; however, I also frequently use the term rhizomatics or rhizomics to play off Deleuze and Guattari's concept of the rhizome (A Thousand Plateaus, 1988), a more free-form, complex, dynamic, and inclusive form of networking that reveals some properties that I find particularly useful and fun.

So the networking model in all its various iterations and apellations appears to be the emerging model of how things work. I like Olaf Sporns' comments about this in his book Networks of the Brain (2011), so I'll end this post with a long quote that has a decidedly scientific bias that I think will inform our thinking in the humanities:

Over the last decade, the study of complex networks has dramatically expanded across diverse scientific fields, ranging from the social sciences to physics and biology. This expansion reflects modern trends and currents that have changed the way scientific questions are formulated and research is carried out. Increasingly, science is concerned with the structure, behavior, and evolution of complex systems such as cells, brains, ecosystems, societies, or the global economy. To understand these systems, we require not only knowledge of elementary system components but also knowledge of the ways in which these components interact and the emergent properties of their interactions. (1)

Simple vs Complex Definitions 2

In my last post, I started listing the problems I have with simple, reductionist, essentialist definitions and suggesting ways that complex definitions provide better, more workable results. The list of issues that I want to present is in no particular order, as my thinking is not yet ordered enough. I mentioned in that post that I have problems first with definitions as an end-point rather than a starting point and then with definitions that disregard the human point of view rather than incorporate it. My next problem with simple definitions is that they aim for the absolute.

In the extreme case, people want definitions that are true for everyone, everywhere, for all time. I was raised in a fundamentalist Christian family, so I deeply understand the attraction of this desire. Once we have our sock drawer arranged (well defined), then we want it to stay put. Once we know the formula for either eternal salvation or the speed of light, then we want it to stay put, and we become very unsettled when anything threatens that order. More to the discussion: once we know the formula for tenure, then we want it to stay put. We post-structuralist academics may pride ourselves on being open and fluid, but just mess with one of our primary relationships, our favorite word processor, or our retirement strategies, and see how quickly the knot of fascism swells in our hearts. We all want definitions that persist in the face of Life's flux, and we will move heaven and earth to make Reality conform to our definitions.

This very human desire for the absolute is, of course, a part of Reality, but Reality doesn't appear to take it too seriously. Reality won't stay put, or as Robert Frost says it: something there is that doesn't love a wall. We have come to see that a stable Reality is the result of truncated vision. We think that the arrangement of continents is stable only because we cannot see over long enough periods of time. When we extend our vision through technology, then we see that the very ground of our being is constantly shifting beneath our feet. This is not good. We want St. Louis to stay where we put it, damn it.

Complex definitions, then, incorporate the heuristics for change and development. This is very much the way DNA works: we have a set starting point for any human being, but the way that DNA unfolds and blossoms within its environment is critical to that human being. The starting point is necessary for the definition of a human, but hardly sufficient. The process of emerging, which is an interaction of both internal and external processes, is just as important. The starting point limits an entity (it prevents a given zygote from becoming a rabbit or a chimpanzee rather than a human, for instance), but it does not define the entity. Complex definitions allow for infinite variations in snowflakes and humans. They allow for black swans.

Then, simple definitions mishandle boundaries. In reductionist thinking, a boundary is a line that separates one kind of entity from all other entities, rendering an entity discrete from its environment. In complex thinking, a boundary is a zone of engagement between the entity and its environment. This is a radical difference in visualizing Reality that cannot be overstated. A simple definition isolates an entity from Reality, while a complex definition integrates an entity within its environment. Complex definitions take into account the dynamic exchange of energy and information at the boundaries of an entity, recognizing that what a thing is depends a great deal on the kinds of energy and information it exchanges at its boundaries. This is obvious at the biological level, but it is just as true for rhetoric. This post, for example, acquires most of its meaning from the information it exchanges with other posts (both my own and other bloggers) and conversations. This post has no discrete meaning. Its meaning comes only from the interactions with its environment.

Finally, at least for today, I have trouble with simple definitions because they ignore networks. This is perhaps another way of saying what I just said about mishandling boundaries, but I think the concept is worth introducing into this discussion, and anyway, it's been inherent in much of what I've already said. Meaning is a function of complex, multi-scale networking. As near as I can tell, all Reality is a function of complex, multi-scale networking, so definitions are as well. The definitions we have in our minds are networks of neurons firing in a fractal pattern, and this may seem dynamic enough, but thoughts are more dynamic than that. The neuronal network that represents the concept of, say, Christmas is dynamic. If Olaf Sporns is correct, each brain recreates the Christmas neuronal network with whatever neuronal resources it has available to it at the moment. Thus, Christmas is not associated with a fixed set of neurons firing in a fixed pattern—certainly not across all our brains, but not even within a given brain; rather, Christmas is a somewhat fresh, self-similar firing of available neurons each time I think it. The physical substrate for the thought Christmas is a dynamic, multi-scale networking (I'm using the verbal form rather than the nominal form of network to try to capture the dynamic nature of the concept).

Simple definitions, on the other hand, try to reduce entities to discrete chunks, whole within themselves. I don't think such chunks exist except in the coarsest level of Reality. They exist in conversation only as a convenient shorthand, a manner of speaking, but we should never be surprised when our simple definitions have slipped their moorings and we have to map reality all over again.

WAC: Writing to Learn across the Curriculum

I have the opportunity to design a new writing across the curriculum program for college, and I'm interested to see how I might translate theory into practice. I have been writing for three years now about networking, connectivism, and rhizomatic education, but it has all been rather abstract. So let's see what practice might look like.

Let's start with a statement about writing: writing is a network phenomenon. Writing's first job is to engage, develop, and render explicit internal, neural networks. As Olaf Sporns says, and as I have quoted often enough, "Cognition is a network phenomenon" (Networks of the Brain, 181). Writing is the networking tool that helps me to verbalize my cognition. It helps me to reinforce and reform my cognition. Writing is one of the best tools we have for cultivating thought and translating it into a text that helps the writer to clarify for herself what she means. This is the primary mode of writing that I am using in this blog. I am writing first for myself to explore my thoughts, mostly about networking, connectivism, and rhizomatic structures. I don't mean to insult any who might read this blog—indeed, many have made comments that have greatly helped me to clarify my thinking—but this blog is where I do my thinking and learning. I'm really my own first reader here, and I'm writing this blog first for my learning.

As a tool for learning, writing works in two different directions. First, writing works from the inside out. Writing helps me to engage, shape, and map my thoughts in a recursive probing that I can follow and remember. Writing helps me to make what I know explicit to myself, and that is of inestimable value to me. Once my thoughts are explicit, once they are mapped to a text, then I can interact with them more objectively and systematically than I can if they are just floating about in my head. I can keep my thoughts, remember them, much better and more reliably if they are mapped to words in a document than if I just keep them in my head. I do not mean to denigrate purely mental thought—often I think things through long before I put them in text, and I am aware that much cognition is not even available to the conscious, rational mind—but I want to speak for the added benefits of writing as a supplement to cognition. Writing affords me a conscious engagement with my thoughts that is difficult to get any other way. (This recursive dynamic between the external textual network and the internal neural network is worth much more investigation than I am giving it here, but that's for later.)

Thus as a learning tool, writing works first from the inside out, but it also works from the outside in. As I have experiences—most often through constant reading and conversation with others, but pretty sunsets are also included—I use writing to develop new knowledge networks and to engage my existing networks in different ways. This allows new knowledge to bloom in my mind, and writing about those experiences is a physical process that reinforces and cultivates those new mental blooms in ways that few other activities can do.

In short, writing is a very complex and wonderfully malleable boundary between the networks of knowledge in my mind and the networks of knowledge outside my mind. Writing is one of the key boundaries where knowledge leaks out of me and oozes into me. Writing is like a cell membrane, or the mechanism within the membrane that manages the exchange between the inside and the outside of the organism. It is a zone of engagement. As a tool for building and traversing knowledge networks, writing is one of the best technologies that humanity has invented. Writing makes me smarter and more knowledgeable than I am without writing. I think writing has this benefit for everyone, and I am convinced that the more students write about the things they are learning, the better they will learn those things.

ASIDE: The cell membrane analogy for knowledge exchange is problematic. It suggests too physical an activity. Actually, I don't think there is any physical exchange when I push knowledge into the world or the world pushes knowledge into me. There is no token, no chunk of knowledge, that is passed from you to me and back. Rather, there is an echo of patterns from you to me and back. This is much more like Deleuze and Guattari's concept of decalcomania, in which a pattern in one part of the rhizome (you) is echoed in another part (me) as we encounter each other either physically or via some media such as this blog post. Thus, this echo always happens at some boundary, or some pressing of one structure against another. A text is just such a boundary, or pressing, or zone of engagement. As James Gleick describes so well in his book Chaos: Making a New Science (2008), this boundary is where all the action takes place between or among structures. The boundary is where each structure (my mind and your mind, say) begins to vibrate under the presence of the other, and pattern in the one begins to echo in the other. This echoing of pattern has a definite physical ground, but the physical substrate is not sufficient to explain it.

Anyway, if I am thinking correctly, then the first goal for a writing across the curriculum program should be to enable students to learn more and better. Writing helps students to make better sense of the knowledge networks in their own minds, and it helps to feed those networks with new knowledge from outside.

Intentionality in the Rhizome, #cck12

Yesterday, I tried to explain why I think that Connectivism may be guilty of focusing too much on the network and not enough on the individuals in the network. I suggested that Edgar Morin may have the correct stance: it isn't either the network or the individual; rather, it's each that must be accounted for in the examination of the other.

This is not some philosophical compromise or a happy medium in which opposing viewpoints each get a little something to save face; rather, it's a radically different way of viewing reality. I'll explain by starting with an objection to something that Frances Bell said in reply to my comments about intentionality being another point of entry into the rhizome: "I agree that intentionality emerges from more than just an individual ‘forming intentions’ and to some extent may be seen as a local network effect." To be fair, this is basically an introductory statement to her real point about connectivism overplaying the network effect, so I do not suggest that it adequately expresses Bell's point of view, but I can say that it represents the common view about human cognition, including intentionality. For most people, intentionality is a function of the individual brain or mind, even if it involves some local neural networks. If we want to understand any given intention, then we need only look to the individual who has, or creates, or forms, or expresses that intention.

I disagree with this point of view. I insist that if we look only to the individual, then we simply cannot understand the intention. Why? Because as Olaf Sporns says in his book Networks of the Brain, cognition is a function of networks. Olaf devotes much of his book to the neural networks that are the most obvious scale of the networks that support cognitive activity such as intentionality, but he quite clearly opens the discussion to the networks functioning at higher and lower scales. Networks depend upon other networks and form the basis for yet more networks. Limiting a study to one scale can be a useful fiction that allows for great focus and parsimony, but it is not reality—it's a fiction. And I say that in the very best sense of the term and with the utmost respect for fiction. I happen to believe that good fiction is the best we humans can do.

But … I start to wander.

Back to intentionality as a function of the individual. We simply cannot reduce intention (or any other cognitive function, such as learning) to the given individual said to be forming the intention or doing the learning. We can understand any given intention only as the complex interaction of an open system (the individual) with its eco-system. As an open system, any individual is defined in great part by the flow of energy, matter, organization, and information between itself and its eco-system. While the brain of the individual forms a necessary substrate for intentionality, it is not sufficient for intentionality. Rather, intentionality requires the dynamic interaction—what Morin calls stabilized dynamics (On Complexity, 11)—between the individual and his physical, social, emotional environment. Morin goes on to say that "the intelligibility of the system has to be found, not only in the system itself, but also in its relations with the environment, and that this relationship is not a simple dependence: it is constitutive of the system. Reality is therefore as much in the connection (relationship) as in the distinction between the open system and its environment" (11). Thus, if we want to understand anyone's intentions, then we must understand not only their individual reasoning but also the dynamics between them and the world. It's an impossible task to understand even one single intention completely, a human condition for which I am most grateful. We will never have an end to learning. Never. There are simply too many connections to follow, and each intention is the nexus of innumerable arcs, trajectories, flows, and asignifying ruptures.

But, God, can we create some magnificent fictions, full of real insight, beauty, and helpful hints about reality. If Deleuze and Guattari are correct, then we use cartography and decalcomania to accomplish these fictions.

So to sum it all up: yeah, it's all in the connections. Probably even more so than Siemens and Downes know or can imagine. Those connections, of course, include non-human, even non-living, entities.

But the Oscars are on, and my wife wants me to watch them with her, so let's talk tomorrow about how cartography and decalcomania aid the individual in managing the flow of organization and information between the individual and her world.

Chasing Metaphors in CCK12

In a recent post entitled Week 3: Rhizomatic or biological neuralogical network?, my colleague in CCK12 Matt Bury writes that he's "been pondering the analogy of distributed networks of learners, i.e. Connectivism, as rhizomes" and concludes that he "wasn’t convinced by it from the start." You can read the rest of Matt's thoughtful comments on his blog, but if I understand him correctly, he doesn't see a tight fit between botanical rhizomes and networks of people. Actually, he seems to prefer neurological networks, which appear to be structured much more like social learning networks. I, too, have found neural networks to be most helpful in understanding networks in general and neural networks in particular, and I heartily refer interested scholars to Olaf Sporns' book Networks of the Brain.

Matt makes a fair point that others have made: the Deleusian rhizome doesn't match so well with botanical rhizomes which don't match so well with social networks. I see Matt's point, and I think it has some substance, but for me, it is somewhat beside the point for several reasons. First, Deleuze and Guattari use the rhizome mostly as a metaphor, or so it seems to me, and I don't think metaphors can be pushed to any great precision. Rather, a metaphor compares a more tangible thing to another less tangible thing to illuminate some aspect of the second thing, or sometimes both things. Love is a rose is a metaphor that suggests certain features of love that we might not have thought about before; however, if we press the metaphor too closely, we can quickly discover features of love that are not like a rose and vice versa. Metaphors shift our point of view so that we look at an object differently than has been customary. It doesn't map to the second thing precisely.

Then Deleuze and Guattari do not rely so much on any botanical definition of rhizomes; rather, they describe the rhizome as a linguistic and social structure mostly, providing a definition of sorts based on six features: connectivity, heterogeneity, multiplicity, asignifying ruptures, cartography, and decalcomania. I'm no botanist, but I don't think these features are prominent in any definition of botanical rhizomes. For example, Deleuzian rhizomes are heterogenous. Most botanical rhizomes are homogenous. Rather, these features describe much more closely the way language develops and spreads, and Deleuze and Guattari use them that way.

I have found Deleuze and Guattari's rhizome to be a particularly potent metaphor that has given me a new way of thinking about social networks and a new vocabulary to use in discussing those networks. The metaphor may not work for everyone, however. Love is a rose no longer works very well for most people, though at one time, it was quite the fresh and striking image.

I'm not sure why Deleuze and Guattari chose the rhizome upon which to construct their analysis. I think it was the visual features that appealed to them, but that's just conjecture. Or perhaps they wanted another botanical image to contrast with the tree image that they were using to describe hierarchical structures (another metaphor, and again, not so precise as some trees such as aspens could be classified as botanical rhizomes—a detail Deleuze and Guattari ignore or are unaware of). Anyway, I think the point I've been wandering toward is this: when we speak of the Deleuzian rhizome, or rhizomatics, we are not speaking of a botanical rhizome, but of a metaphor for that undifferentiated ground of being out of which structures emerge and into which all structures eventually return. To scale that down to something more practical, we are talking about how we humans are constantly trying to map a shifting landscape—to capture in language, mathematics, and social, political, religious, and economic structures those details of reality that for a time seem important, but which always shift away from our names and structures into something else. If that is how the complex world works, then how do we educate for that? That's rhizomatic learning.

Networks as Frames

I'm always thinking about writing as complex, network phenomena—to the point that I can hardly think of writing any other way. It doesn't seem to matter what level or scale I use to think about writing—the neuronal through the writing process of a single person to a book to the socio-historical movement of languages and genres—it's networks all the way down and up and across. Rhizomes, really. I do think Deleuze and Guattari have a most useful understanding of the structure of reality that they capture graphically in the term rhizome, but that term is a bit foreign to most, almost quirky, while network has a better exchange rate. Network, then, is for me one of those large concepts that frames and informs my other concepts.

And it seems useful to know how one is framing ones reality. Perhaps the biggest lesson, though, is to realize that one is, in fact, framing reality. Actually, that is not quite right. I am not framing reality, though I am absolutely necessary for reality's frames. Rather, I am part of the dialog that frames reality. This is an important aspect of rhizomatic thinking: any frame for reality emerges out of the interactions of nodes—the dialog of nodes—within that reality. Thus, the frame itself is an emergent, and temporary, part of the reality it frames. I see everything as networks because that kind of dialog and frame explains so many things to me. Things that were unclear are now clear. Things that were incoherent are now coherent.

Apparently, others find network structures useful for understanding their favorite slice of reality. In his book Networks of the Brain (2010), about which I have already written much in this blog, Olaf Sporns frames cognition and consciousness in terms of networks on multiple scales, from a chain of neurons, to the interactions of brain regions, and onward to the connections of brains to other brains and to human artifacts. As Sporns says plainly: "cognition is a network phenomenon" (181). In his book Interaction Ritual Chains (2005), Randall Collins frames microsociology in terms of networks at the micro and macro levels of human interaction. Collins says plainly that "the center of microsociological explanation is not the individual but the situation" (3). This situation is for Collins much like what I mean by networks: the interaction and retro-interactions of nodes within a system, across that system, and with other systems. Manuel Castells' book The Rise of the Network Society (2010), frames macrosociology in terms of networks, showing how the interactions of large social groups is a complex, network phenomenon.

Network ideas are certainly common in conversations about rhetoric and education. James Berlin says in his book Rhetoric and Reality that "Transactional rhetoric is based on an epistemology that sees truth as arising out of the interaction of the elements of the rhetorical situation: an interaction of subject and object or of subject and audience or even of all the elements—subject, object, audience, and language—operating simultaneously" (15). Other rhetoricians and academicians accept this way of structuring reality as a dynamic, complex network.

I find this idea that meaning, or reality, is an emergent property of communal dialog implicit and explicit in much that I've been reading about rhetoric, or the skillful use of language, especially in education. In his 1970 classic Pedagogy of the Oppressed, Paolo Freire refers to the "banking concept of education" that turns students "into containers, into receptacles to be filled by the teacher. The more completely he fills the receptacles, the better a teacher he is. The more meekly the receptacles permit themselves to be filled, the better students they are. Education thus becomes an act of depositing, in which the students are the depositories and the teacher is the depositor." This communal way of speaking about education, of course, frames what can be said and thought about education and what can be done in education. And the frame is so convincing that it feels as if Nature itself is structured this way. Of course students know next to nothing, and of course teachers know next to everything, and of course the teachers must deposit that knowledge into the students. What else could education be but this?

Freire spends much of his book poking holes in this language, but for me his salient point is that the reality of our educational system is an emergent property of the interactions among the constituent nodes within that system. Those interactions involve, of course, language and rhetoric as one, but not the only, type of meaning-creating interaction. Freire identifies the dominant language with the politically dominant group, and in this, I think he is mostly correct, and the heart of his message is that revolution depends in some part in over-turning this language. If we shift the meaning-creating interactions among ourselves and our artefacts, then we can shift our reality. This shift is accomplished partly, sometimes in large part, through a shift in language.

Like Freire, Nedra Reynolds also explores how community dialog can frame reality. In her 1998 essay Composition's Imagined Geographies: The Politics of Space in the Frontier, City, and Cyberspace, she shows how something as apparently concrete as geography is itself a product of communal dialog, especially when that language is applied to a specific discipline such as English Composition. "Spatial metaphors have long dominated our written discourse in this field ('field' being one of the first spatial references we can name) because, first, writing itself is spatial, or we cannot very well conceive of writing in ways other than spatial." Again, for me, the salient point is that the reality of English Composition emerges in large part from the dialog of the community that discusses it, lives it, and interacts within it.

This view of meaning and reality as emergent properties of a dialogic community seems to me to be at the heart of a connectivist rhetoric. I think such a frame brings some amazing explanatory power and a pleasing, aesthetically beautiful coherence to a swelter of confusing aspects of today's reality. I could easily climb atop my soapbox and proclaim the Gospel of the Net. This iGospel or eGospel has a certain appeal to it, and many of its evangelists are growing rich and powerful from it, but I must keep in mind that network is but one way of framing reality, one way among other ways. Network has a temporary advantage perhaps—a heightened currency—but in the end, network, too, will be supplanted by other ways of viewing the world. Better ways perhaps, though I can't see it at the moment.

The Extension of Neural Complexity

In the last chapter of his book Networks of the Brain, Olaf Sporns extends his neural processing and, thus, cognition beyond the brain and to the body and the body's environment. This is the feature of neurophysiology that finally destroys all my old ideas about cognition, thought, and knowledge, for no longer can I think of thoughts as belonging only to the brain. Thoughts and emotions – all forms of cognition – flash through the brain, through the body, into the environment, and then back through the body and into the brain. I have only to think of some of the lively and spirited conversations that I have had over the years to see how my thoughts at any given time were not my brain's alone, not even mine alone, but the reiterative, feedback process of patterns flashing through the conversational space from my brain to my colleague's brain and back to me and back to them, over and over. Sporns, of course, says it more scientifically precise:

By acting on the environment, the brain generates perturbations that lead to new inputs and transitions between network states. Environmental interactions thus further expand the available repertoire of functional brain networks. … The body forms a dynamic interface between brain and environment, enabling neural activity to generate actions that in turn lead to new sensory inputs. As a result of this interaction, patterns of functional connectivity in the brain are shaped not only by internal dynamics and processing but also by sensorimotor activity that occurs as a result of brain-body-environment interactions [which] can be conceptualized as an extension of functional connectivity beyond the boundaries of the physical nervous system. (306)

Sporns follows the argument of Andy Clark to say that

the minds of highly evolved cognitive agents extend into their environments and include tools, symbols, and other artifacts that serve as external substrates for representing, structuring, and performing mental operations. If this view of cognition as extending into body and world is correct, then cognition is not "brain bound" but depends on a web of interactions involving both neural and nonneural elements. The networks of the brain fundamentally build on this extended web that binds together perception and action and that grounds internal neural states in the external physical world. (309)

Those who are familiar with Stephen Downes' thoughts on this issue (for example, here) will quickly recognize his ideas about the extension of knowledge through a social network, so that anyone person's – say, Susan's – knowledge of the French capital Paris is a network of flashes across Susan's brain, body, and interaction within the general, historical discussion about Paris as well as with the actual city of Paris. For Susan, then, cognition of Paris is the interplay of patterns in her head, in her body, in her conversations with others (mediated by voice, text, image, networks, and other media) and with Paris itself. Indeed, the more sophisticated Susan's Paris network becomes, the richer is her repertoire of ways to think Paris. At any one time, Susan will likely never use the entire network of meaning available to her, but because she has such an extensive, rich network, then she can think significantly about Paris in almost any situation for any reason.

I have a couple of quick observations to make about this view of knowledge as a kind of cognition. First, we can only have personal knowledge. By that I mean that Susan must always view Paris from the center of her meaning network. With lots of training and hard mental work, she can perhaps learn to look at Paris from other points of view than her own, but she can never not think of Paris from her own point of view (I think that's the correct combination of negatives. Count'em). Even if she changes her mind about Paris, she is simply knowing Paris from a different center, but still her own.

Second, knowledge can never be merely personal. Yes, this contradicts my first observation, but there it is. Susan's knowledge of Paris always extends throughout her ecosystem to include shared language, shared social groups, shared experiences, and so forth. Susan must form her knowledge from the center, but she must also form it in dialog with others who are likewise working from their own centers. Any attempt by Susan to look at Paris from another's center is a sometimes useful exercise in fiction. It's a God's view that Susan can sustain for only a short time. Any attempt by Susan to look at Paris only from her own center is a fatal entrapment in fiction. Knowledge depends on what Morin terms the dialogic principle: the constant interaction of any entity from its own center with its environment and the other entities in that environment interacting from their own centers. Knowledge is that zone of tension between loss of self in its own center and loss of self in the centers of others. Susan interacts with her world – sometimes skillfully, sometimes not so skillfully – and that's what makes Susan who she is. Education is the attempt to help Susan interact more skillfully.

Complexity and Cognition

You might think that complex systems are complicated, but they often aren't.

That may be a bit too cutesy, but it does make a nice distinction between complexity and complication in network systems. Modern jet fighter planes and computer circuit boards are complicated structures – they are composed of millions of parts arranged in intricate ways for a myriad of purposes – but they are not complex. Why? Because they don't change, and if they do change, then that change usually breaks them. They are rather rigid structures, with regular, predictable, and reliable interactions among their parts. After all, you don't want a jet fighter that suddenly decides to start behaving differently in a dog fight.

On the other hand, complex structures such as the human body change constantly, acquiring new cells, functions, and capabilities and discarding old ones. They are dynamic, and not just in the sense of moving parts. They are dynamic in the ways the parts within the structure interact with each other and in the ways all those structural parts interact with the ecosystem that encloses them. And the trigger for this dynamism is sometimes quite simple. In Networks of the Brain, Sporns paraphrases Herbert Simon to say:

First … most complex systems can be decomposed into components and interactions possibly on several hierarchical levels. Second, complexity is a mixture of order and disorder, or regularity and randomness, which together account for the nontrivial, nonrepeating nature of complex structures and their diverse dynamics. (279)

Brains, then, owe their neural complexity to "the union or coexistence of segregation and integration expressed in the multiscale dynamics of brain networks" (278), to the mix and tension of "some degree of randomness and disorganized behavior with some degree of order and regularity" (281,282), and to "rich and dynamic contextual influences" (286). This dynamic complexity in the brain is what gives rise to the emergent property of consciousness, or as Sporns says it, "Consciousness emerges from complex brain networks as the outcome of a special kind of neural dynamics" (298).

I see, then, two elements that generate neural complexity and, thus, consciousness:

1. nodes and clusters of nodes – from single neurons to social networks and natural ecosystems – that are able to form meaningful patterns within any given scale and across all scales (segregation and integration of functions)
2. a fluid tension between regularity and randomness, order and chaos, as patterns form, fade, and reform across the web of nodes as nodes form their own patterns and then harmonize those patterns with the other patterns forming, fading, and reforming elsewhere in the neural network

I need a better picture, so I'll call again on the image of the brain as two musical groups: a left hemisphere orchestra and a right hemisphere jam band. Imagine the New York Philharmonic meets The Allman Brothers Band on the same circular, floating stage: the Philharmonic stage left, the Brothers stage right. The musicians can hear both each other and the speakers that circle the stage, filtering and focusing the sound from two omnidirectional microphones pointed out toward the world. (The musicians can also see, feel, smell, and taste, but let's not overcomplicate this metaphor. Sound will suffice, I think). Finally, they have microphones on stage through which they can play, or not, their sounds to the outside world.

Both bands are mature. They know their chops, their instruments, and each other. They know how to make music on their instruments and how to blend their individual music into the music being made by the other instruments on the stage AND to the music coming in over the speakers from the outside world. When they are all rested and focused, then they can make wonderful sounds that harmonize internally with the other sounds on the stage and externally with the sounds coming over the speakers from outside. When they are not rested or they've had too much to drink, then they make silly, discordant sounds, sometimes truly awful sounds.

Because they are mature musicians, they are dedicated to learning more about their instruments, each other, and their music, so much of the time they are focused on their internal, on-stage noodling, trying this new combination of instruments, this new musical motif or riff, or practicing and honing old motifs and riffs to have ready at hand when they need them. They have a huge repertoire of different sounds that they can call upon at an instance, and they know which among them can make which sounds. None of them can make all sounds, and some of them can make only a few sounds, but they all know how to group and regroup themselves as needed. Sometimes they group as strings, which will pull together the violins and guitars, sometimes as low register instruments, which pulls together the tubas and bass guitars.  The point is that they have a rich repertoire of established sounds, and they are constantly working to add to that repertoire.

But they are also keenly aware of the sounds coming from outside, and they will respond to sounds they hear. They can faithfully reproduce and harmonize with sounds that they know and other bands with which they've played before, creating a pleasing musical interlude, melodies and movements arcing back and forth between the different bands both on a single stage and across the different stages. 

This is where it gets fun. If you are lead guitarist Duane Allman (an individual neuron in the right hemisphere of the brain), then you are listening to your bandmates Dicky Betts, Gregg Allman, Butch Trucks, Jai Johnny Johnson, and Berry Oakley AND to the New York Philharmonic just across the stage with tonight's guest cellist Yo Yo Ma AND to the sounds coming from the other orchestra/jam band made up of the Boston Pops and the Grateful Dead. You are listening for a place for you to fit in. You at last hear a space for you and you make your sound. It's a particularly pleasing, clever riff, so Yo Yo Ma echoes it. You echo back. It's picked up by Phil Lesh of the Dead, reworked slightly, and comes back to you again. You restate it, then rework it again, expanding it by a few bars. The woodwinds in both orchestras join in, and the musical pattern soars. Everybody's happy. Everybody understands the same thing. The band has created a pattern of sound that you, Duane Allman, could not have produced alone but that could not have been produced without you.

Or perhaps you make an awkward sound, something that just doesn't work in the current flow. The musical pattern becomes chaotic for a moment until the other musicians ignore you. The music rights itself, and the bands move on as Brother Gregg leans over and whispers to you, "We're playing in G, dude."

Why did Gregg do this for you? Because – and this is the most important point – there is no conductor, no central processing unit, no boss. The musicians (the individual neurons) are all on their own, seeking a way to integrate their individually produced contributions into the whole. They are each guided by their own, unique abilities to produce unique sounds and by a shared interest in harmonizing, synchronizing, and otherwise fitting their sounds into all the other sounds to create a pleasing, workable whole.

In resourceful, well-tuned bands (brains), each musician finds a way to fit into the whole, most of the time playing a supporting, complementary role, sometimes taking the lead, but always looking to add her own unique sound to the group and its music. In damaged or deranged bands, the musicians are stuck playing the same tune over and over, or they cannot integrate with each other so that no coherent music emerges from their individual sounds.

If I understand Sporns, this is how cognition takes place: emerging, dynamic patterns of firings of individual nodes that group, fall apart, regroup in clusters across the left and right hemispheres of an individual brain AND across different brains, mediated by our actions and symbol systems.

So what does this view of cognition mean? Well, for me as an English teacher, it means that if I want to understand fully the meaning of Shelley's poem Ozymandias then I must be mindful of the complex interactions within Shelley's mind, the complex interactions of the symbol system he used to compose the poem (English language, poetry, sonnet, etc.), the complex interactions of Shelley with his ecosystem (natural, social, intellectual, etc.), the complex interactions of the poem with its ecosystem (production printing, distribution, consumption, etc.), the complex interactions of Shelley's readers with the poem and with each other, the complex interactions of all those interactions with me, and the complex interactions within my own mind.

This effectively describes an approximately infinite number of dynamic interactions that I must master in order to understand completely one fourteen-line poem, and it's why we can write about one poem for two hundred years and still not exhaust it. Basically, what it shows is that we can form a richer understanding of Ozymandias, but we cannot form a complete understanding. This is a crisis for students confronted with a regime of objective tests. In the face of this crisis, students do the only sensible thing: they either demand to know the correct answer, or they walk away. We force them to choose either to play the one correct tune over and over or to degenerate into chaos. Let us hope they are able to choose wisely.

The Hierarchy of Neural Complexity

Sporns says that "brain connectivity is organized on a hierarchy of scales from local circuits of neurons to modules of functional brain systems" (258). His use of the word hierarchy presents me with some problems as I have for the last few years contrasted hierarchical structures with network structures. In general, I have assumed that hierarchies were rigid, closed, traced, arboreal structures (to use terms from Deleuze and Guattari) while networks were flexible, scaleable, open, mapped, rhizomatic structures. Hierarchies admit only sanctioned, homogenous nodes within its structure and then fix them into a well-defined place with well-defined relationships to all other nodes; whereas,  networks admit heterogenous nodes within its open structure and allow nodes to develop relationships with any or all other nodes for any reason. For me, then, hierarchies and networks are not the same, and I cringe just a bit each time Sporns uses the term hierarchy to describe an aspect of neural networking.

But I think I have a resolution to my concern. I don't think Sporns is using hierarchy as I do; rather, he is describing various physical and functional layers of the brain and how they interact. As he says: "A recurrent theme in studies of collective behavior in complex networks, from epidemic to brain models, is its dependence on the network's multiscale architecture, its nested levels of clustered communities" (261). He is not so much describing a pyramidal structure as an onion structure. He's talking about layers enclosing layers enclosing layers and so on. This might be a mere quibble over visual metaphors but for his concepts of heterogenous coupling and multiscale dynamics, and some others like them. These principles prevent Sporns' neural hierarchies from calcifying into rigid hierarchies, as I have used the term. Indeed, heterogenous coupling in neurophysiology reminds me much of Deleuze and Guattari's first principle of the rhizome given in the first chapter of their book A Thousand Plateaus (1988): "any point of a rhizome can be connected to anything other, and must be. This is very different from the tree or root, which plots a point, fixes an order" (7). I think Deleuze and Guattari would be most comfortable with Sporns' concept of heterogenous coupling as very rhizomatic.

Sporns also talks about the brain's metastability, or tendency toward chaotic itinerancy:

the itinerant or roaming motion of the trajectory of a high-dimensional system among varieties of ordered states. Chaotic itineracy is found in a number of physical systems that are globally coupled, that are far from equilibrium, or that engage in turbulent flow. … It has also been observed in brain recordings … and neural network models … Over time, systems exhibiting chaotic itineracy alternate between ordered low-dimensional motion within a dynamically unstable "attractor ruin" and high-dimensional chaotic transitions. System variables are coherently coupled, their dynamics slow down during ordered motion, and they transiently lose coherence as the system trajectory rapidly moves between attractor ruins. (263, 264)

This chaotic itinerancy of neural networks with their roaming motions and trajectories and their constant transitions between coherent couplings and incoherent chaos is strongly reminiscent of another characteristic of rhizomes: the principle of asignifying rupture. Deleuze and Guattari say of asignifying ruptures:

Every rhizome contains lines of segmentarity according to which it is stratified, territorialized, organized, signified, attributed, etc., as well as lines of deterritorialization down which it constantly flees. There is a rupture in the rhizome whenever segmentary lines explode into a line of flight, but the line of flight is part of the rhizome.

Rhizomatic structures, then, deterritorialize and reterritorialize only to deterritorialize again. If I understand what Sporns is saying, then it seems that neural networks have a chaotic itinerancy that is at least a Rorschach of asignifying ruptures. Perhaps, Deleuze and Guattari's asignifying ruptures have deterritorialized and reterritorialized as chaotic itinerancy.

Seems possible. Anyway, chaotic itinerancy seems to fit nicely with D&G's whole idea about nomadology and motion in reality. Anyone know for sure?

Dynamic Hierarchies

So the brain is a dynamic system, if Sporns is correct. How does this dynamism arise?

I've just reread the chapter, and I confess that I am not yet ready to talk confidently about the physiology of the brain, but I think I have gleaned enough to make some general statements that might be useful. The brain's dynamics rest on heterogenous coupling and multiscale dynamics, both of which Sporns says are "ubiquitous features of the brain" (258).

Heterogenous coupling suggests that any given neuron, cluster of neurons, or brain region will connect to (couple with) most any other neuron, cluster, or region, and multiscale dynamics suggests that the neural activity at any neural level – neuron, cluster, or region – affects the activity of the enclosed and enclosing levels. Sporns says:

Brain connectivity is organized on a hierarchy of scales from local circuits of neurons to modules of functional brain systems. Distinct dynamic processes on local and global scales generate multiple levels of segregation and integration and give rise to spatially differentiated patterns of coherence …. Neural dynamics at each scale is determined not only by processes at the same sale but also by the dynamics at smaller and larger scales. (258)

Just as neural dynamics unfold across different spatial scales, they also unfold across different time scales "from fast synaptic processes in the millisecond range to dynamic states that can persist for several seconds to long-lasting changes in neural interactions due to plasticity" (262). As a neural pattern is created or expressed (or blooms, ripples, or flashes) across the spatial and temporal structures of the brain, it is invariably modified by endogenous brain activity and by external inputs. All neural "brain dynamics is inherently variable and 'labile,' consisting of sequences of transient spatiotemporal patterns that mediate perception and cognition" (262). Sporns calls these brain dynamics "that are neither entirely stable  nor completely unstable" metastable.

If I understand this correctly, then, all thoughts and emotions are metastable, always expressed somewhere on a scale between completely stable and completely unstable. To use my two bands model of the brain (left: orchestra, right: jazz band), each time I sound the thought Connectivism, I sound a recognizable but variable musical motif. Like Jimi Hendrix, I never quite play the song riff the same way, even if, like a classical guitarist, I'm trying to. And  even if I managed to play it exactly the same twice, it would still sound different if I were playing at the Fillmore East, at Woodstock, or in the studio.

It just won't ever be exactly the same. Thoughts, concepts, and emotions are dynamic, metastable expressions. So what's the point for education? Our effort to impart the same knowledge to 30 different students in a single class is pointless and impossible, so let's give it up. Let's shift gears and devise a different goal. Thirty students cannot learn the same thing, so let's quit teaching as if they can.

Okay, you rightfully ask, if we don't teach them all the same thing in the same way, then what do we teach them and how? I thought you might ask that. Fortunately, I've run out of time for writing today. Later.

Dynamics and 21st Century Education

In Chapters 12 and 13 of Networks of the Brain, Olaf Sporns tackles the issues of dynamics and complexity in neural networks. I have the feeling that I will be reading both chapters again to digest them, but what I understand so far is highly exciting. It's also beginning to feel somewhat natural to me. Nice that.

Sporns describes a very dynamic brain structure and function, radically different from the traditional views that "place much greater emphasis on serial processing, noise-free signal transmission, and reliable encoding and retrieval of information" (274). To me, this traditional view of neural activity sounds a lot like traditional education with its serial processing in a lock-step curriculum, noise-free signal transmission in a quiet classroom focused totally on the teacher, and reliable encoding and retrieval of information in rote memorization of a collection of facts and regurgitation on objective exams. Of course, as Sporns notes, the traditional approach to brains has "been remarkably successful in well-defined problem domains and in the absence of conflicting or competing demands" (274). The same with education, I think. So long as you are teaching a well-defined, fairly focused knowledge domain within a totally controlled environment with no conflicting demands, then traditional education, or training, works pretty darn well, but what happens when you encounter a rapidly shifting knowledge domain in a space with lots of conflicting demands—you know, like real life? Then the traditional models don't seem to work so well. What are the alternatives? Sporns suggests that the brain takes a dynamic path.

Why a dynamic path? First, Sporns suggests, for self-preservation, or self-expression. Apparently, diverse dynamics are important for self-organization and robustness within complex systems. Sporns notes W. Ross Ashby's law of requisite variety, which says that any system "must have a matching variety of responses at its disposal with which to counter [environmental perturbations] in order to maintain internal stability" (256). Thus, complex, dynamic environments demand complex, dynamic entities. Or, if historian David Christian is correct in his recent TED talk, then the universe is on a path of increasing complexity, and dynamic, complex environments and their composite entities are emerging together. In a marvelous dialogic, more dynamic and complex environments bring out more dynamic, complex entities which bring out more dynamic, complex environments. On and on, endlessly complexifying.

This has a strong lesson for education: the world our students live in today is much more dynamic and complex than the world of 19th Century industrialism that gave rise to modern education (see Sir Ken Robinson's RSA talk on this issue). It's time we moved on from that model to develop "a variety of responses" with which to counter the dynamic and complex environmental perturbations that bombard our students daily. Industrial thinking, while still valuable, has no response to iPhones, iPads, and Facebook-augmented revolutions. We must move education beyond its industrial mind-set, which does not have "a matching variety of responses at its disposal with which to counter [the 21st Century] in order to maintain internal stability." Just as an increasingly dynamic and complex Universe elicits an equally dynamic and complex consciousness, so too does an increasingly dynamic and complex society elicit an equally dynamic and complex educational system.

CCK11: Knowledge and Context

If you are like me, then you don't have too much trouble imagining cognition as a network phenomenon: all thoughts, visions, dreams, calculations are based on the spidery webs of firing neurons flashing in unique patterns like lightning through our brains. I can imagine, then, some peculiar and unique flash of lightning in my brain each time I think of, for instance, Connectivism. Each time that flash of lightning fires, my brain creates the concept Connectivism, and re-traces those routes along those particular neurons, across the various regions of my brain, traversing both hemispheres, so that I can think Connectivism. In this scenario, a particular flash of brain lightning equals a particular concept, and I can reinforce that concept by flashing it again and again in different contexts until I firmly etch the pathways into the circuitry of my brain. Nice image.

But wrong. If Sporns and his fellow researchers are correct, then this network of firing neurons is too regular and static. It appears that the brain is much more complex than that, and it is possible that any given idea such as Connectivism is not fixed to any specific network of firing neurons. Rather, the brain may use different neurons over different times to create the same pattern of meaning, depending on what else is already happening in the brain. It seems, then, that as we cannot find a specific chunk of knowledge in our brains, we also cannot find a specific network pattern of knowledge in our brains. The brain is far too dynamic for that. As Sporns points out, specific brain functions are not tied to specific brain regions, nor are specific regions tied solely to specific functions:

Different complex functions are accomplished by transient assemblies of network elements in varying conditions of input or task set. In other words, different processing demands and task domains are associated with the dynamic reconfiguration of functional or effective brain networks. The same set of network elements can participate in multiple cognitive functions by rapid reconfigurations of network links or functional connections. (182,183)

Sporns concludes that "functions do not reside in individual brain regions but are accomplished by network interactions that rapidly reconfigure, resulting in dynamic changes of neural context" (183). This suggests to me that any given bit of cognition depends very much on the interaction between the bit of cognition and the neural context within which it immediately finds itself seeking expression. Sporns says that "the functional contribution of a brain region is more clearly defined by the neural context within which it is embedded [and] this neural context is reconfigured as stimulus and task conditions vary, and it is ultimately constrained by the underlying structural network" (183,184).

So what is the takeaway lesson here? For me, it is this: the brain is a most complex orchestra of two parts—a right hemisphere jazz band and a left hemisphere classical orchestra, and each time it expresses the concept Connectivism, it chooses different instruments and different musical arrangements, depending on what the rest of the orchestra, and the conductor, and the audience are all doing. Sometimes, the concept Connectivism may find expression in my brain from the classical side, expressed mostly with woodwinds and a single flute. At other times, the concept may find expression from the jazz side, expressed through a wailing saxophone, a drum kit, and an electric fretless bass, with some contribution from the classical brass section. Either way, or in some other way, I can still recognize the musical motif: the idea Connectivism, but it is not the same network pattern expressed invariably each time. Each time it is expressed, Connectivism is something slightly different, even in my own mind.

This reminds me, of course, of Edgar Morin's view of complex thought, that "the intelligibility of the system has to be found, not only in the system itself, but also in its relationship with the environment, and that this relationship is not a simple dependence: it is constitutive of the system" (11). Sporns' research into neural networks reveals to me that complexity functions in the brain's production of a single thought which depends on the interaction of any defined unit with its enclosing unit and with all the other units that it encloses and that enclose it. A concept depends on the interaction of a given neuron with its brain region, that brain region with the other regions, all those regions with the brain, the brain with the rest of the body, the body with its enclosing groups, and so on.

CCK11: The Orchestra of Mind

In Chapter 9 of Sporns' book Networks and the Brain, I think we reach the heart of the issue for the discussion about Connectivism. In this chapter, Sporns is tackling the issue of cognition, or neural activity in all its various forms: learning, thinking, feeling, daydreaming, dreaming, etc., and he makes the bald, bold statement that "cognition is a network phenomenon" (181). This is the basis of Connectivism. It is certainly consistent with Stephen Downes 2008 statemtent in his Innovate article Connectivism & Connective Knowledge that "the term connectivism describes a form of knowledge and a pedagogy based on the idea that knowledge is distributed across a network of connections and that learning consists of the ability to construct and traverse those networks." Sporns lays a strong, well researched, authoritative foundation for the discussion of Connectivism, and I think it's helpful to consider three traits of this network concept of cognition as Sporns lists them at the end of Chapter 9:

1. "Cognition has an anatomical substrate" (205).
2. "Integration involves dynamic coordination (synchrony, coherence, linear and nonlinear coupling) as well as convergence" (205).
3. "Stimuli and cognitive tasks act as perturbations of existing network dynamics" (206).

Anatomical Substrate

I find it easy for my own New Age imagination to posit some cosmic Mind emerging from the idea of cognition as network, but Sporns assiduously avoids any hint of the New Age, keeping his conversation firmly grounded in the physical anatomy of the brain and its observable and verifiable behaviors. As he says, "All cognitive processes occur within anatomical networks, and the topology of these networks imposes powerful constraints on cognitive architectures" (205).

These neural networks have "small-world attributes" that are similar to other networks that are perhaps more familiar to us: social networks, the Internet, Wikipedia, or gene networks (Small-world network). Small-world networks are characterized by clustering, which groups nodes about a more well-connected node, thus facilitating quick connectivity to most any other node in the large-scale network. This is similar to the way Google works on the Net: it is a well-connected node that reduces the hops between us as individual nodes and most any other node on the Net. It may be the way an orchestra works with clusters of violins, bassoons, and drums, with a lead violinist about whom the other violins cluster and who connects those violins to the rest of the orchestra.

This small-world architecture means that cognitive processes are both segregated and integrated. An act of knowing relies on segregated clusters of neurons firing in different regions of the brain (different instruments sounding in different regions of the orchestra), and these segregated firings are then integrated (apparently within milliseconds) into a coherent thought, much like all the sounds of the orchestra. Which brings us to Sporns' second trait of cognition as network phenomenon.

Dynamic Coordination and Convergence

The brain apparently has a couple of mechanisms for integrating segregated patterns of neuronal firings into a coherent whole pattern. First, the brain can synchronize neuronal firings, or perhaps the better way to say this is that the neurons can synchronize themselves, much as a jazz band can find its way back to a  chord progression, time signature, or tune to synchronize the various flights of solo fantasy. Then, various firings can converge in a particular firing which takes the inputs and feedforwards them as a single input.

I do not understand neurophysiology well enough yet (perhaps never) to understand how synchrony and convergence work in the brain, but I understand the import: the brain is an incredibly rich orchestra of instruments which can sound or not, in unison or not, according to an internal rhythm, pitch, timbre, tone, and volume, and the brain's clusters of instruments—say, the woodwinds—can converge to create a single tone amongst the tones from the other regions of the orchestra. Every single instrument is vital to the orchestra, but no single instrument makes the orchestra. Every instrument must maintain its own integrity (the trumpet must not try to become a cello) just as surely as it must find its place in the orchestral network.

Stimuli and Cognitive Tasks as Perturbations

I think Sporns is using perturbation in its scientific sense as a variation or deviance in a system caused by some outside effect, and this is perhaps the most amazing trait of cognition as a network phenomenon. We in education typically think of external inputs (classroom lectures, for instance) as the most important aspect of learning, yet if Sporns is correct, then external inputs are best seen as an interruption of or as a more-or-less complimentary addition to the internal system. As I noted in a previous post, any external input must earn its place in the orchestra. An external input hardly ever totally supplants the music the orchestra of the brain is already playing, except in the case of trauma or other profound experiences. Of course, anyone who has ever tried to get the attention of twenty-five kindergarteners (or of twenty-five kindergarten teachers, for that matter) knows how difficult it is to keep them focused on your external input, whatever it is. This, it seems, is the natural state of things. Education is not the systematic, mechanical input of data into the blank data banks of students' minds; rather, education is an attempt to join and to modulate the tunes already playing in our students' minds. Surely these two processes are radically different and require radically different pedagogies.

Emergence in CCK11

Emergence is one of the key concepts in network theory that most attracts me to the conversation. In his article Emergent Biological Principles and the Computational Properties of the Universe, physicist Paul Davies defines emergence neatly as "the appearance of new properties that arise when a system exceeds a certain level of size or complexity, properties that are absent from the constituents of the system." Emergence, then, is an antidote to the reductionist idea that we can understand anything by reducing it to its basic parts and then thoroughly describing those parts and their interactions.

In Chapter 9 of his book Networks of the Brain, Sporns notes the strong impact of reductionist thinking in modern neuroscience and its ultimate shortcomings in accounting for mind:

There have been many false starts in the attempt to link brain and cognition. One such failure is neuroreductionism, a view that fully substitutes all mental phenomena by neural mechanisms, summarized in the catchphrase "You are nothing but a pack of neurons," or, put more eloquently, "'You', your joys and your sorrows, your memories and your ambitions, your sense of personal identity and free will, are in fact no more than the behavior of a vast assembly of nerve cells and their associated molecules" (Crick, 1994). The problematic nature of this statement lies not in the materialist stance that rightfully puts mental states on a physical basis but rather in the phrase "no more than," which implies that the elementary properties of cells and molecules can explain all there is to know about mind and cognition. Reductionism can be spectacularly successful when it traces complex phenomena to their root cause, and yet it consistently falls short as a theoretical framework for the operation of complex systems because it cannot explain their emergent and collective properties. (180)

Sporns' argument echoes arguments from Edgar Morin that I have noted in previous posts that reductionist science has been "spectacularly successful [in] tracing complex phenomena to their root cause," yet has consistently fallen "short as a theoretical framework for the operation of complex systems because it cannot explain their emergent and collective properties."

I think I detect a similar reductionism at work in most educational theories, which reduce knowledge and learning to the functions of a single mind. Even the social constructivists still limit knowledge created in a group to the knowing of a single mind. This concept of knowledge and learning can be spectacularly successful in helping us trace the complex phenomena of learning back to the behaviors of a single individual, but it fails to provide us with a theoretical framework to account for the ability of a network of strangers to so accurately guess the weight of an ox in a rural English fair, as James Surowiecki describes in his book The Wisdom of Crowds. How does the crowd know the weight of the ox when obviously so few of the individuals in the crowd had even the remotest notion of how much the animal weighed? What emergent properties were at work that produced knowledge that no one person had? For Sporns, this question might be phrased: what emergent properties are at work to produce mind when no one neuron has mind? Reductionism fails us just here. When we collect enough neurons in one place and interconnect them, then new structures and functions emerge—not out of nothing, but into something that wasn't there before. Likewise, when we collect enough students in one place and interconnect them, then do new structures and functions emerge?

I think Carl Bereiter is questioning this specific reductionist tendency in education when he says in the Preface to his book Education and Mind in the Knowledge Age (2002): "What is being challenged is the basic conception of the mind as a container of objects—beliefs, desires, conjectures, remembered events, and the like—which the mind works on in cognition" (1). If Bereiter is correct, then knowledge and learning cannot be reduced to a single mind containing a complicated collection of chunks of knowledge and other cognitive objects and upon which the individual mind operates to create its mental picture of the universe.

I'm somewhat curious that the concept of emergence is not more prominent in our CCK11 conversations. I wonder why.

#CCK11: Earning a Place in the Network

I continue to revel in Olaf Sporns' wonderful book about neural networks: Networks of the Brain. In Chapter 8: Dynamic Patterns in Spontaneous Neural Activity, Sporns examines the dominant model in brain studies that says brains are mainly structures for receiving, processing, and then responding to sensory impressions from outside the brain (eyes, ears, skin, etc.). As Sporns says, "This theoretical framework treats the brain as a system in which the essential neural process is the transformation of inputs into outputs" (149). If I understand Sporns correctly, then this view of the brain borrows heavily from computer information theory: signals from keyboard and mouse are input to the CPU, which then processes the signals and outputs a result in the form of another signal, or: I press the A key on my Macintosh PowerBook Pro's keyboard, then the internal 2.53 GHz Intel Core i5 chip processes that signal, and finally it outputs signals to the screen to display the letter A. In the view of traditional neuroscience, then, my brain is like the Intel Core i5 chip: a more or less efficient processor of sensory input into motor output.

This is a simple view of brain activity, and it is consistent with a Newtonian worldview that sees reality as a machine: a collection of parts with regular, reliable, and highly predictable interactions in which cause and effect are necessarily linked. If I press the A key on my computer keyboard, then the CPU will process that signal and it will cause A to appear on my screen. If this doesn't happen, then the machine is malfunctioning and requires repair. Or … if I teach (input) the technique for handling in-text citations according to the MLA Style Manual, then my students (CPUs) will process that input, and they will necessarily output papers that properly handle in-text citations according to the Modern Language Association's authoritative guide. If they don't, then their machines (brains) are malfunctioning and require repair (further teaching). If I'm a particularly sensitive and liberal teacher, then I might suspect that the teaching/learning process could be broken elsewhere—say, with my teaching technique—but I will not likely suspect that the entire mechanistic process itself does not adequately capture the reality of how students might learn to do in-text citations according to the MLA.

For two hundred years, both neuroscience and teaching have focused on a mechanistic model of input -> processing -> output, and this model has, indeed, taught us much about both brains and learning, but in the end, this model is too limiting. As Sporns says, "Until now, much of the interest in theoretical neuroscience has focused on stimulus-driven or task-related computation, and considerably less attention has been given to the brain as a dynamic, spontaneously active, and recurrently connected system" (149, 150). So what happens if we begin to consider students as dynamic, spontaneously active, recurrently connected systems?

Sporns explains why, despite its huge and continuing contributions to neuroscience, this mechanistic view does not adequately capture the reality of brains and, by my extension, the reality of students learning MLA techniques. Sporns provides strong research that suggests that the majority of neural activity is not in response to external input through eyes, ears, nose, tongue, and skin or output through motor and cognitive functions. As he says, "Whether considering individual neurons or entire brain regions, one finds that the vast majority of the structural connections that are made and received among network elements cannot be definitively associated with either input or output" (150). This raises at least the possibility that, similarly, most learning is not in response to either input through instruction or output through homework and papers.

Rather, the brain seems to spend much of its time self-organizing, partly in response to external inputs (nurture) and partly in response to internal physical structures (nature) but, perhaps, mostly through its own processes of maintenance and tweaking. As Sporns says, "Spontaneously generated network states form an internal functional repertoire. The observation and modeling of endogenous or spontaneous brain activity provide a unique window on patterns of self-organized brain dynamics—an intrinsic mode of neural processing that may have a central role in cognition" (151). The brain does not merely and automatically intake and process external inputs (say, teaching about MLA); rather, external inputs must "earn representation via their impact upon the pre-existing functional disposition of the brain" (151, Sporns quoting Rodolfo Llinás).

I like that: external stimuli don't just automatically find a place within our brains; rather, they have to earn a place among the stuff that is already there. Thus, merely showing up in class and lecturing about how MLA handles in-text citations does NOT guarantee a place for my lecture in any student's mind. It doesn't even suggest a place. Rather, my lecture must "earn representation via [its] impact upon the pre-existing functional disposition of the [student's] brain." What's more, while the relationship between my lecture and one student brain is complex and dynamic enough, I likely have thirty students, all with differing "pre-existing functional dispositions" in their brains. Now, we're really beginning to appreciate the complication and complexity of learning. How does one teacher awaken in the different minds of thirty people even so simple a concept as in-text citations according to the MLA? Does it even make sense to ask that question, putting the onus of learning simply on the teacher as if she can stamp out knowledge from brain to brain?

Well, it's Sunday morning, and I'm just getting to the essential questions for me, but I'm also in the middle of repainting the guest bathroom, a task my wife really wants me to finish. Her desires are definitely earning representation via their impact upon the pre-existing functional disposition of my brain.

I will, of course, talk more about neural networks and education later.