Rewiring the Classroom

In the last 5 chapters of his book The Art of Changing the Brain, Zull suggests the practical implications of neuroscience for the classroom, beginning with the use of sensory experience:

• SENSE LUSCIOUS: Using the power of the sensory brain to help people learn – The brain is wired for sensory experience, and while sight is the most powerful of the senses for most people, sensory experience involves the total body. In general, the more senses a lesson can engage, the better. Too many teachers seem to believe that their lecturing is a sufficient sensory experience for student learning. Lecturing is, in fact, a sensory experience, but when I think back to most of the lectures I endured, then I realize how displeased my senses were with the experience: a droning voice, dry, dusty chalk smells, drab rooms and lecture halls, an aching ass from sitting too long (I have lower back problems), my bouncing legs, the unexpected discovery of discarded chewing gum. The lecturer's words were seldom engaging enough to overcome all those other sensory impressions. The world simply drowned out the droning don, and god forbid a pretty girl should be in class—then I couldn't even take notes. If the lesson is not engaging the student's senses, then the class will. Fortunately, my writing classes have a built-in sensory experience: writing. Unfortunately, that doesn't work for everyone, so I use texting, drawing, discussing, games, reading, contests, group work, and more to engage students' senses. My classes are noisy, bordering on happy. This has troubled some of my superiors.
• WAITING FOR UNITY: Helping people comprehend their experience - "Reflection is searching for connections—literally!" Students need time to bounce their experiences around in their heads to build the connections necessary to integrate information into their own neural networks. This is how we create knowledge. Knowledge is not imparted, given, or transferred. That's a harmful metaphor. Knowledge is not even in our brains—that's an even more harmful metaphor. As Diane Laflamme says in her essay Ethics and the Interplay Between the Logic of the Exluded Middle and the Logic of the Included Middle (in Basarab Nicolescu's Transdisciplinarity: Theory and Practice, 2008), "The body does not contain consciousness" (150).  Rather, consciousness and knowledge are emergent properties, epiphenomena, of the interplay of neural networks. Better: it's what emerges when our brains fire in synchronicity with our body's sensing and acting within a rich ecosystem that is also firing. This bubbling of concepts is called reflection. The brain is looking for useful, recognizable patterns in its sensory data to integrate with the patterns that it already has. Even dreaming is an important part of reflection, but since we can't use dreaming as an instructional strategy in most classes (most of us work hard to counter daydreaming), then we use language for reflection. Actually, we should be encouraging our students to use language through discussion and writing, rather than falling into the easy habit of us talking all the time. Again, my writing classes have a built-in advantage, but only if I'm giving my students time to reflect on a lesson's sensory experience. 
• THE COURAGEOUS LEAP: Creating knowledge by using the integrative frontal cortex - Though related, short-term memory and long-term memory are not the same, nor are they necessarily linked. We can sometimes hold prodigious amounts of information in short-term memory without transferring that information to long-term, and sometimes we transfer powerful experiences directly to long-term memory, bypassing short-term. Students must be allowed to form their own long-term memory ideas, and this takes time and ownership. Short-term memory is powerful, but limited and easily replaced with new sensory data; thus, we cannot overload short-term with too many facts or too much feedback. To learn, students must hold limited information in short-term memory, and then have time to manipulate and play with that information to form connections in their own minds. Teachers who are in a briskly-paced rush to cover the material seldom allow the time and conditions for students to build the connections necessary for moving raw data from short-term memory into the knowledge centers of long-term memory. Students must have time and activities to attend to relevant bits of info and organize the learning task, task management, for themselves. They must develop a sense of probability, or an innate sense of statistical reasoning. This is critical thinking, and without it, most students don't learn, especially those who do well on the test.
• TEST BY TRIAL: Using the motor brain to close the loop of learning - Learning is active, action. Learners must be able to test their ideas to see if reality pushes back, or in Nicolescu's terms if reality resists our ideas. Good ideas bump up against something real, bad ideas don't. As Zull puts it: "It is this encounter with the reality of the world that leads to learning. The magic isn't in the action; it's in the testing" (219). What is active learning? "asking questions, drawing, writing, taking notes, checking out a reference, taking a test, and even reading. Anytime a learner tests out her ideas, she does it through action, and that action generates learning" (218). This is a good, educationist way of talking about Deleuze and Guattari's concepts of cartography, mapping, and decalcomania. This is perhaps the weakest part of my writing classes. I need to find ways to incorporate real-world writing into my classes: real writers writing to real readers about issues that are important to them. This is a tough one, and I welcome your suggestions.
• WE DID THIS OURSELVES: Changing the brain through effective use of emotions – Learning must involve the emotions. Engagement is an emotional response, depending on the learner's feelings about the importance and relevance of the lesson. We encourage engagement through arranging for success. When people repeatedly fail, they disengage; whereas, success (but not too easy) engages people. The balance between too easy and too hard is where the teacher's art comes in. Also, we encourage engagement when we enable a sense of control (emotional base) in students. Self-evaluation and ownership of task engage people emotionally; whereas, loss of control disengages people. When students have no control, then we teachers must resort to extrinsic motivations (which at heart are always violent uses of power) to get them to perform, and the performance is seldom satisfying.

I'm confident that I will refer to Zull's work a great deal in the future, and I recommend it to anyone interested in education.

Rewiring Student Brains

James Zull's book The Art of Changing the Brain contends that neuroscience can guide our teaching practice by revealing to us how our brains actually learn. I think his insight is reliable, and I'm particularly satisfied that he views the brain as a complex, multi-scale network and learning as changing, extending, and strengthening the connections within those networks. This fits quite nicely with connectivism, which defines learning in similar networking terms.

This definition of learning puts the student/learner at the center of the learning process, unlike traditional education, which puts the teacher/authority at the center of the learning process. Why? Because if learning is the development of new connections within existing neuronal networks, then learning depends overwhelmingly on the engagement of the student. No teacher can directly touch a student's brain. Development of neuronal networks absolutely depends on the student exercising her own brain, and her teachers cannot do it for her, any more than a fitness trainer can exercise her muscles for her. The student must sweat and exert herself and must want to sweat and exert. If the student is emotionally, physically, or intellectually incapable of learning a given lesson at a given time, then there is little the teacher can do. At best, teachers can create an environment that is engaging for a student and that encourages them to exert themselves, but the teacher cannot do it for them.

Then, each student comes with different neuronal networks. We teachers can often rely on rather gross similarities among student perceptions, neuronal processes, and responses, but the multi-cultural, inclusive nature of many modern classes shows how unreliable our dependence on these gross similarities can be. Our brilliant lectures and lessons, then, may engage one student and not the next. Neuroscience tells us why. If learning is a process of developing existing neuronal networks, then learning must start with each student's existing neuronal networks, and they ain't all the same. Some are positively alien, and ALL are different from the teacher's. Ground Zero for learning is NOT the teacher's knowledge, then, but her students' alien neuronal structures.

Traditional education views the teaching/learning process as a teacher writing a concept on the chalkboard of the student's mind. This is a radically false notion of education, and yet it is still the basis for too much instruction, even if the chalkboard is now a computer screen and the lecture involves a PowerPoint. The teacher's job is crucial but not essential to learning. The skillful teacher can create an environment that focuses, encourages, and enables students to stretch their minds to create new neuronal networks, but the teacher cannot create those neuronal networks for the student.

Moreover, the teacher cannot prevent students from learning. Most of the stuff that I remember learning in middle school—dealing mostly with sex—was never taught in the classroom. I suspect that most of what is learned in school is never taught from a lesson plan.

So what's the lesson for this teacher? First, I must start with the student and with their existing neuronal networks. That means that each program of study should begin not with what I know (the course content) but with what they know. I must build in to my classes time to discern what my students already know and do not know. The flipped classroom and just-in-time teaching techniques allow for this, and I use them.

Second, given the variety of neuronal structures I'm likely to encounter in any class, I must create a flexible environment that allows for a variety of engagements, processes, and responses. There is no one-size fits all. I know where I want my students to end-up, but I cannot assume one highway to travel or one vehicle. Some come from the cane fields near Belle Glade, some from the tenements of Lake Worth, and quite a few from Haiti, Jamaica, and Europe. Some take the bus, some walk, and some drive different cars. Some are here in a few minutes, some drive an hour or more each way. Getting everyone to West Palm Beach is a very messy business, and many teachers simply don't want to take that on. I think that's why they focus on simply delivering their content. It's much easier. It's also largely ineffective.

Third, I must monitor frequently, and not only to discern what they are not learning, but also what they are learning. I must check their progress along their different highways, monitoring where they are going and how fast they are travelling. If a particular lesson calls for a very specific destination, then I must be able to encourage those on-track to continue even if they can't see the destination, and I must be able to nudge those off-track to make new turn. If a particular lesson has no specific destination, as is the case with many cMOOCs, then I must delight in learning where they are going and encourage them to share their snapshots with me.

Rewiring the Neuron

My good friend Bruce recommended that I read James E. Zull's book The Art of Changing the Brain (2002), and I'm glad that I followed his advice. The book has some important implications for connectivist, rhizomatic thinking.

The first section of the book establishes a direct correlation between brain form and functions and teaching and learning. As Zull says in the very first sentence: "Learning is about biology." For Zull, learning is the process of changing neuronal structures, and good teaching is aware of and works with the brain's innate structures and functions to enable those changes. I'm a bit uneasy about reducing learning to physical changes in the brain, but I can accept this as a useful focus for better understanding this aspect of learning. So what does this connection between brain structure and learning imply for connectivism and rhizomatic learning?

First, Zull understands the brain as a network. This is implicit in the first part of his book, but he later makes it explicit, devoting Chapter 6 to an exploration of neuronal networks. Moreover, he conceives the brain as a multi-scale, complex networking structure. For instance, individual neurons are networked to process certain sensory inputs, but then those individual networks are networked into larger networks to perform various integrative and meaning-making functions. Zull does not extend those networks into the larger networks of the body or our social and natural ecosystems, but that may be a result of his particular focus for this book rather than a specific belief. I find nothing in what he says that would exclude such an extension of our neuronal networks. It's easy for me to say, then, that Zull, like connectivists, sees learning as a network phenomenon. For Zull, learning is the process of shaping neuronal networks that map more or less well to reality. This process of neuronal mapping is quite compatible with Deleuze and Guattari's notions of decalcomania and cartography, and network structures are at the heart of both connectivism and the rhizomatics.

Zull provides some specifics about neuronal processes that are useful for connectivism and rhizomatics, that make cartography and decalcomania more practical. First, he outlines the basic sequence of learning:

1. sensing,
2. integrating (2 parts), and
3. acting.

For Zull, then, learning is grounded in sensing the physical world, integrating those sense impressions into our existing neuronal networks first through reflection and then abstraction, and then acting on, or testing, that new knowledge. This is a dynamic, complex process because the results of acting/testing is then fed back into the loop as we sense the consequences of our actions/tests, integrate those consequences, and act/test again. Through this process, our brains develop themselves, strengthening those neuronal networks that lead to somehow satisfactory results and weakening those networks that lead to unsatisfactory results. Of course, the loop is not this simple. We humans are often confused about which neuronal networks map satisfactorily to reality and which don't, and our brains can create and adhere to some awful mappings, but mostly it seems to work for us, and according to the latest neuroscience, this is the process most of us use.

The takeaway for me is that the educational process is improved if it begins with a concrete, sensory experience, includes time for reflection and abstraction of the experience, allows for action/testing of the new knowledge, and allows for feedback of the testing results into the loop. A teacher doesn't need to know anything about connectivism or rhizomatics to follow this teaching process, but this process can usefully inform connectivism and rhizomatics.

First, at the start of any lesson, students favor concrete, sensory experience. This is what they are learning. Unfortunately, that means most of our students are learning whether or not they like their teachers, whether or not the teacher is boring, whether or not the classroom is comfortable, whether or not this class fits into their eating schedule, and so forth. Our brains are wired this way, and our brilliant lectures can seldom overcome this sensory bias. Perhaps, then, we should capitalize on the sensory bias, and begin there. For instance, in my writing classes, I could start by having my students text someone on their smartphones about what they are doing in my class, and then launching into a classroom discussion of why people write to each other.

Second, students need time to integrate their new sensory experiences into their existing neuronal networks. Implications: if we march tenaciously through the content, we will lose most students. At best they will manage to register the content into short-term memory, which is a necessary step in learning, but hardly sufficient for any useful learning. The brain quickly flushes short-term memory to make room for the next short-term memory. Reflection and abstraction are the neuronal processes that move sensory experience into long-term memory and integrate it into existing neuronal networks. Most of the classes that I took provided little to no time for reflection and abstraction or for feedback of tests; thus, I have forgotten most of what I learned. What a sad waste of everybody's efforts and investments. So in my writing classes, I might allow my students to blog about the concepts I'm trying to teach, encouraging them to make the connections between the new stuff and what they already know. Class discussion is also a good tool for this reflection. I might encourage abstraction by having student bring in writing assignments from other classes and plan in groups how they might solve those assignments. We might also allow for feedback from those assignments, assuming there is time.

Then, short, shared writings in class are a good way to test new knowledge and feed the results back into our brains. Actually, I don't see why this wouldn't work in most any class. Maybe I'm just biased, but I think that writing is one of the best tools we have for integrating new information into our existing neuronal networks, thereby turning it into knowledge.

A Coin Toss and Epistemology

On the drive to work this morning, I was thinking about the World Cup Final and lamenting that it ended in a most unsatisfying penalty kick shootout—not unsatisfying because the US lost, though I did want them to win, but unsatisfying because the shootout seems so random, more like a coin toss than a competitive decision. And in my mind (I was driving alone), I said, "A shootout is like a toss of the coin, it's like a coin toss." Or that's what I meant to say. What I actually said in the safe confines of my own brain was: "A shootout is like a toss of the coin, it's like a toin coss."

Of course, I made a common speech error in which I swapped the initial phonemes of two words. Most everyone has done something like this at some time, and some of us do it more often than others, but usually we simply chuckle or blush, correct ourselves, and move on with the conversation. However, I was conversing with myself this morning, and when I noticed what I'd done in my mind, I was reminded of the conversation I've been having about epistemology these past few weeks with Dave Cormier, David Wiley, Stephen Downes, and others. The conversation started when Wiley posted some observations about the value of MOOCs, and then George Siemens, Cormier, Downes, and others responded to Wiley's observations. The conversation was for me a wonderful chance to clarify my own thinking about knowledge and learning, and some of the ideas from that conversation help me clarify my phonetic shuffle this morning.

Why did I say toin coss rather than coin toss? Where did the non-words toin coss come from? I have not learned these near-words, so it makes no sense to say that they were stored in my brain in either short-term or long-term memory waiting for the right occasion to present themselves for use. The point is, until I used them this morning, these phonetic structures didn't exist, at least not for me. However, they are clearly related to two authentic phonetic structures—coin and toss—that did exist for me. I had just used those real words in the preceding sentence. But then I wonder if the real-words coin and toss also existed prior to my using them this morning, and I think that this may be an important question.

I could say—and perhaps most people would say—that the real-words existed in my mind because I have learned them, used them before, and know them; thus, they must be stored somewhere in the brain's memory that my mind can access when it needs that word. The image of a filing cabinet or a computer hard drive seems like a good image for this way of thinking about the brain, but this fails to explain the near-words. They clearly weren't stored in my neural filing cabinet. I could explain the near-words by saying, "Well, my brain just misread the words the second go-round, as brains are wont to do, and produced the near-words." But I think there is a more satisfying explanation that relies much more on the notion of complex, dynamic networks.

Neither the real-words nor the near-words existed in my brain in some storage system before I used them this morning. Rather, my mind created all of them on-the-fly as it helped me find the meaning I was searching for. In the very instance that I sought to express an idea about the value of penalty kick shootouts, a network of neurons and clusters of neurons in different regions of my brain began firing, assembling almost instantaneously phonemes, words, word clusters, sentences, and clusters of sentences. The brain uses both inherited and learned sets of rules to structure this network of neurons and the mind uses mostly learned set of rules to structure the physical network into phonemes and words and sentences and they do it so quickly and, usually, so reliably that I don't notice it. I'm too busy thinking my thoughts to notice how those thoughts emerge—at least, until I make a mistake. Then I notice.

So what have I learned from my mistakes this morning? First, knowledge is not some static thing stored in the brain. Even something that we might commonly think of as irreducible and as stable as a word is assembled, on-the-fly, as a network structure. It's likely also that the brain does not store or preserve even that network structure representing any given word. Rather, each word dynamically emerges each time I use it from and within a network of sounds and other sounds and words and sentences using a different neuronal substrate, depending on the total state of my mind and body and social situation, so that how I say the word coin this time is slightly, perhaps greatly, different from the way I said it last time or the way I may say it in the future. The coin I type here just now is different from the coin I thought this morning in the car.

Of course, the two instances of coin are also the same. Or perhaps it's better to say that they are receptive to definition from the center out so that they possess a core that keeps them recognizable from instance to instance of use. This core is the definition that we might find in the dictionary, and it is absolutely necessary for the word coin to be useful to us humans, but the definition is also just about the least we can say about the word coin. A definition artificially isolates an entity from its environment. It reduces a coin to one thing totally separate and distinct from all other things. This is a useful fiction, and we can learn things about coin this way that we might not see any other way, but the real value of coin usually arises when we embed coin in a conversation, an ecosystem, and the meaning begins to resonate across, through, and within other network structures.

I also see that I have separated brain and mind. That wasn't my intention when I started this post; however, in trying to say one thing, I have inadvertently said another thing. That often happens to me, and it is what editing is for: to correct your mis-statements or un-intended statements. However, I'm going to let this one stand, mainly because I don't know how to correct it. I know that lots of people want to make mind and brain synonymous, to reduce mind to a physical function of neuronal structures and processes. I'm not ready to say that just yet. I do believe that mind absolutely depends upon brain, but I'm not at all certain that it doesn't also depend on the body, on society, on speech, writing, and television, or on the spirit as well. That's another post.

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.

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.

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.