I’ve realized recently that one of my primary goals in my free time is to learn to learn efficiently. Thus, the stress I put on myself to learn a lot quickly is really a desire for methods that will help me (1) beat procrastination to focus on the things that really matter to me and (2) actually learn the material I go through, to the point where I can solve real problems and retain information long-term. I’d previously thought that I just had too long of a list of things I wanted to learn, and that stressed me out, so I started looking at people’s advice and research on how to get it all done. But I ended up not reading so much about organizational strategies, which I thought I needed to focus on because of my ADHD, but on how to revise the learning process itself–not how best to manage my list, but how to be effective at tackling each item on it. I’ve developed a professional interest in learning itself, not just what to learn.
There are three main prongs that I’ve been pursuing that take up most of my time of late:
(1) Thinking about pedagogy, and in particular how learning professionals can best support independent learning outside the classroom: I’ve read a lot about continuing and professional education, adult education, online classes and their effectiveness, and what makes successful online (and in-person) learning communities tick. A lot of this has been focused on a major shift in my thinking–from focusing on how to be a good teacher to how to be a good learner, and from fostering good learning by being a good subject-matter teacher to fostering good learning by being a good community builder. A lot of this has focused on observing various online lecture series and the forums that grow up round them (both officially supported, like the Coursera or MITx discussion forums, and unofficial communities of learners that band together online and otherwise to tackle learning together, like the StackExchange forums and the people who organize group read-alongs with discussion of a book).
(2) Researching and attempting to implement new learning strategies without the constraints of classroom instruction, and moving beyond the standard model of “lecture-take notes-read-revise notes-repeat-do problems” as a way to learn. At some point I abandoned my attempts to plow through lots of material and turned my attention to how that plowing can stick long term, and that meant that I couldn’t ignore the research on learning techniques anymore. It’s exciting, mostly because the research is so much more multifaceted than I thought–what started out as a corrective or defensive investigation for me to overcome the limitations ADHD and anxiety put on me became an active interest in the habits of highly effective learners for its own sake.
(3) Thinking about what it means to have a professional code of ethics in the teaching profession, and more broadly, what a Hippocratic Oath for teaching would look like and how to hold ourselves accountable outside of the assessment-based or value-added models that dominate the NYT Education section. This was sparked by a transformative encounter with Atul Gawande’s book on the Checklist Manifesto: what are a teacher’s professional obligations? How does professional development balance with daily interactions with student? To what extent is it ethical to try a “teaching experiment” with high risks and potentially high rewards, if it might mean your current class doesn’t do as well as it could? Partly this is my attempt to understand teaching as a profession, and to recreate it in my mind as a profession–society doesn’t exactly encourage us to think of teaching as a complex profession with its own societies, standards and ethics codes like law or medicine, but I think it should.
I’ll have more details on lots of this in future posts, but for now a few sites that have provoked my thinking on these things:
The StackExchange plaforms – check out math.stackexchange.com or physics.stackexchange.com, including their “Meta” sites, for examples of an online learning community and discussion of what direction it should go.
Scott Young’s extensive blog articles about effective learning strategies at: http://www.scotthyoung.com/blog, including, for example, his MIT Challenge (to learn the curriculum for 4 years of MIT courses more quickly and without the support of a formal school environment), and his extensive focus on the psychology behind how we learn best.
Physicsforums.com, for a perhaps older model of online learning in standard BBS format, where a community feel is created by prominent users but attention is more focused on discussion and less on answers to specific questions than on StackExchange sites.
Udemy, Coursera, MITx, Open Yale, and MIT OCW, for examples of online content offered by major universities.
Psychological research shows that giving external rewards can decrease the likelihood that someone will continue an activity longterm. This is an important principle for how to school effectively (or rather, how not to): conventional wisdom, which says that rewarding behavior that contributes to desired habit formation is good, is sometimes wrong.
It has been painful to realize how much of my motivation for learning, which I take to be an unqualified good thing, is external. It would still be painful, were it not for my newfound focus on not making my time-allocation decisions moralistic. Some examples: I signed up for MITx’s 6.002x course and stopped it about 6 weeks in, just before the midterm. A large part of the reason I stopped was simply time: I was working 50-60 hour weeks, and with that and family commitments, I felt the work just couldn’t get done (unless I cut out all my relaxing time, which is an ideal recipe for burnout). But my behavior after stopping is illuminating: I didn’t continue as I could, just learning a few topics or following along at my own pace, but abandoned the project entirely. The most painful reason for this I have to acknowledge is that I enjoyed being graded on the assignments and having the prospect of a certificate of completion on the horizon – that was definitely part of it, and I’m not proud of that. There were other reasons, though: (1) I was less enthralled by the specific material in 6.002 as I was intently curious about the MITx project, and the same impulse that meant I couldn’t pass up being part of the pilot meant that I didn’t need to see it all the way through as a subject learning experience to get what I wanted out of it. And (2) It is a big commitment to follow through on 10 hours or more a week of assignments for a course you’re taking alone over several weeks, and I chose to distribute those hours over several more relevant projects.
Which brings me to the second major pitfall in my self-educating methods, the tendency to plan extensively rather than dive into learning something. There’s a certain, odd, amount of fear here–it’s not like I’m going to set off a landmine by trying to learn about the role of tRNA before I’m ready. The worst that can happen with lack of planning in a learning context is that you have to go back to some more primitive concept or learn a concept more slowly, deviating from a plan. I’m still learning that, though, and finding that to really move forward I have to make it a habit to ignore my anxieties about starting a learning project. (This is a maximally general life principle for me: making it a habit to push aside limiting anxieties.)
In the past, I have often relied on standardized tests, syllabi, quals reading lists, and other conventional school benchmarks for measuring “progress” in my learning. For that matter, I’ve always had a lot of anxiety and a strong need to measure this progress–something I’d like to get out of the habit of doing. I have to keep reminding myself that I’m not in a degree program at the moment–there’s no benchmark I have to reach that’ll have consequences if I don’t reach it, learning-wise. The only way I can fail is by not learning what I want, in terms of the large-scale bucket-list style learning objectives I have for myself. The other major way I can fail, most dangerously, is failing and not realizing that I’m doing so, by speeding through drills and concepts and not understanding them as deeply as I’d like. While making lists encourages speedy and superficial learning, contributing primarily to the goal of appearing smart by serving as a proxy for having a credential in the subject (“it’s on my list of things I’ve learned”), I know in my heart of hearts that “wanting to look smart” is a false goal I’ve accidentally acquired out of anxiety. The real goal is to learn more in certain areas I’m fascinated by, and not even the ones I just tell people I’m fascinated by and havent’ followed up on.
So, back to studying MIT’s 7.014 intro biology course, with a TV break in between, because we all need them. For another post – how being in a teaching role can exacerbate anxieties about learning, but also how it can help achieve the deeper understanding that is the ultimate goal.
Before I write the following, I have to acknowledge my debt to the writings of Emily Rutherford, who’s prompted me to reflect in writing on my scholarship and why it matters to me–and later, in forthcoming essays, on how scholarship has changed my thinking on love.
I’ve discovered remarkable things about myself in my time off. A prime force in these discoveries is the freedom to read what I want to read, and think about what I want to think about. I mean that last in two ways: that I can think about whatever I want, and that I can decide carefully what it is I really want. This time has also been the first in a long while when I don’t feel too much pressure to produce–papers, solutions, screeds for a publication–and especially, to be engaged in new mathematical research. With the mountains of free time that release has opened up, despite working full-time and rehearsing often, I’ve done a lot of reading and thinking and loving, and not so much research.
In learning how to learn in an unstructured, entirely self-motivated environment, I’ve rediscovered the basic truth that one needs a driving question to learn naturally. I struggled for a while with a list of subjects I wanted to learn–some neurobiology here, some theory of computation there–but I didn’t get very far when the subject was on my to-do list as a word in itself, exhorting myself to open up a textbook and just read. I got a lot farther on my “side” projects that were driven by wanting a good answer to a question–what’s a gene? How is the concept of information related to thermodynamic entropy? How has the concept of a “species” changed through history? And so I rediscovered more forcefully what I should have known, that all information is useless except in a context, except as grist for interpretation. And also that there are more natural ways to learn than in a lecture hall or from a seminar reading list. Of necessity, I’ve learned to structure my own reading lists, and that doing so is an empowering act.
I’ve still been reflecting a lot about my desired professional path and my relationship to my studies. With distance and time, the patterns of my interest are easier to discern. I haven’t abandoned studying math and science–not by a long shot. But I’ve found what I’ve known for a while in my heart, that math and physics and biology are studies that add color to the world and feed my spirit and help me get up in the morning–but I don’t need to be doing research to get that boost from them, and in fact it’s better (for the moment?) if I’m not. It used to stress me out that I couldn’t imagine writing a monograph on symplectic integrators even though I find the subject fascinating, or that I had the disturbing thought I don’t care about advancing the state of knowledge. That thought seemed almost heretical for someone who is deeply invested in learning and even scholarship in a narrow sense; it made me feel like a pretender where genuine pursuit of knowledge is concerned.
Now I am gaining a better understanding of the multifarious ways in which smart, genuinely interested people make scientific study a part of their lives without being professional scientists. More importantly, I know my own heart better, and I trust and like myself better–and I know enough to say now that I (1) have an abiding love of science, and (2) don’t see it as a potential profession right now. I’ve read about the professionalization of science in the 19th century, and the deep ambivalence towards it amongst a group of committed natural philosophers and intellectuals. I’ve seen myself take joy in being able to explain a part of the world to myself–or to friends or children–using science, and take all that joy from the love of understanding people and the world involved, rather than the possession of knowledge. And I know that science has its greatest power for me as a humanizing force that connects me to long arcs of history, to a pursuit greater than myself or anyone but tied up with all of us. Whether or not it becomes a profession, I recognize its primary importance for me–which is for me as a human being, as one who seeks meaning and beauty and connection; not necessarily as a seeker of scientific truth.
Something else has been going on in my thinking on scholarship: my growing interest in doing all I can to make humanities scholarship my profession. I’ve been deeply drawn to it and deeply troubled by it for a long time, and my basic anxiety was that I’d love to put a sign that says “Theory-Free Safe Zone!” on my office door if I become a humanities scholar. Literary theory mostly sounds alarums of “bullshit!” or “why does this matter?” in my mind. Poststructuralism, feminism, Marxism, semiotics, psychoanalysis, ecocriticism, disability studies–anytime I come across an article that sets forth a “reading” of a text from a particular theoretical lens (especially 20th century theory, especially French), I usually feel either alarmed, wronged, or bored. I am deeply invested in the study of literature as a part of cultural history and for its internal logic: studies that affirm how literature matters and how it works from a writer’s-eye view. If I have any discernible theoretical orientation thus far, it is more in my allegiances to modes of historical practice than literary as such. This is so even though I’ve no idea how to do cultural history yet, I have deep anxieties about it as someone who focuses on the primary role of productive base over ideological superstructure, and in many ways I use literature to get away from study of society.
But at the same time, the study of literature means so much more to me than being able to spend my time with books I like. I had a graduate seminar last spring term where the eminent professor would end by asking if we liked the reading, asserting that we all come to the study of literature at the start out of a basic love of books. Well sure, I have that. But part of my anxiety about literature has been that I seem to want to use it to talk about everything but the books: I need my studies of literature to speak to something greater, and certainly not to a simple matter of personal gut reaction. I’m not so worried about that now that I understand myself as a humanities scholar better.
That same impulse that connects me to the study of science is what drives me to study literature and history: to feel connected to humanity throughout the ages, to be a part of something greater, to find my humanity and my better, better-loving self in wisdom in all the ways it has been recorded and communicated. I wish to understand how certain texts work as a technician, to study literary history partially as a history of technical and stylistic innovation, as one could comfortably do in the visual arts or music. That’s how I can do literary and cultural history (someday, when I have more tools to do it rigorously)–not by making larger claims about the actual, direct agency of art or culture as a productive or transformative force in society, which I will always be deeply uncomfortable with–but by understanding in its fullness our human, personal and collective reactions to art and its effects, why it is so glaringly inadequate to boil the study of art down to a question of liking.
The fundamental questions on which my studies turn are about how and why a work of art matters, or has mattered and been valued for various people or groups in other times, and how I as a scholar and teacher can cultivate that sense of mattering, wonder and love in others. I don’t wish to claim that it should matter to all people, or to enter the canon wars or the Culture Wars: I know that what I study, the reading of literate people with leisure time, has very little connection with how the mass of people spend their time. But I’ve also seen art transform people’s lives, whether in the prison writing workshops I’ve been a part of or the queer open mics or slam poetry. And I also know art matters deeply to me, it has been my education as a human, it has brought me out of innumerable darknesses: and I need to understand what allows these works of art and gems of science to do that, so that I may better pay forward the love I have learned and constantly renew my commitment to living well as these texts have taught me to do. That’s the difference that tips me towards the humanities: I wish to reap and sow the wisdom that we have to new ends in new people, and it matters less to me that I find a novel solution to a problem than that I engage with the complex, beautiful problem of life that we are each trying to solve, together and alone, and curate, preserve and teach some of the things that have helped some of us do that.
I just finished reading Stanislas Dehaene’s Reading in the Brain, and I’m already scrambling to figure out how I’m going to pitch it to the several people in wildly different areas who I think will find it fascinating. The book has two mutually supporting strengths that make for only a rare dull moment: meticulousness and willingness to speculate on the questions that really matter to us.
The basic claims underlying Dehaene’s neurological work are meticulously and transparently backed up with oodles of experimental results from different labs and different schools of neuroscience. Some of the details are really nitty-gritty ones, but I’m left with little doubt about the validity of his main claims vis a vis the brain: that it has a “letterbox” region that activates during reading only and is culturally invariant, and that this region is divided into cortical areas that together demonstrate a neurological basis for the psychologist’s model of top-down and bottom-up, hierarchical networks of visual processing that allow us to read.
In particular, Dehaene outlines a slow grapheme-to-phoneme route for decoding and a lexical route that develops eventually in expert readers, and describes how such things as invariant processing of multiple fonts and sizes and levels of abstraction in feature detection can arise from networks of neurons operating in parallel, the “bottommost” ones in the tree coding for line segments presented on the retina, middle ones that act as letter-detectors, and top ones that code for entire words. This perhaps wishy-washy model based on potentially naive neural network models that are currently in vogue is backed up with exhaustive anatomical information that confirms that these types of neurons do in fact exist in the “letterbox area,” the left occipito-temporal cortex, and they behave in a way consistent with the model. Further, this letterbox is culturally acquired and specific: Hebrew words don’t activate the letterbox area for English readers.
More surprising is the precision of this correspondence between written language categories and the letterbox’s division: there seem to be particular areas that code for different categories of words, within the area of the letterbox that processes meaning: different cortical regions for faces, people, animals, etc. And within verbs, the area of the premotor cortex that maps to the particular body part in question is activated when reading the verb: so reading “kiss” activates the area for the mouth, reading “kick” activates the area for the leg–words literally activate the motor areas they refer to. So the image of reading in the brain is simultaneously extraordinarily specific–as the reader learns, the cortical areas begin to be more and more subdivided and specialized in what they react to (one model of what learning looks like in the brain in general)–and extraordinarily diffuse, with the letterbox area connecting to the corresponding region in the other hemisphere, to the frontal cortex where we “synthesize” ideas and inputs, and all around the brain.
Dehaene is exacting about his brain science: he describes in detail what PET, fMRI, diffuse MRI, and magnetoencephalography (direct tracking of cerebral activation on the cortical surface itself, which lets us make movies in slow motion that show how a single read word travels through the brain) and lesion analysis have told us about the brain’s functioning, and what their limits are. But for me, the most interesting science was the slightly more speculative stuff, which came in (1) the research that indicates how the brain comes to specialize in reading for expert readers (development), and (2) the comparative studies of the region corresponding to the letterbox in primates.
The entire chapter on “Learning to Read” is worth keeping in mind for its pedagogical implications in the teaching of reading. In brief, the child first has a “pictorial” stage where the visual system attempts to recognize words as though they were objects or faces; next is the grapheme-to-phoneme mapping; and last is the acquisition of “a vast lexicon of visual units of various sizes” that includes frequency information about these units and their (written) neighbors. Two pedagogical points: grapheme-to-phoneme conversion MUST be explicitly taught and emphasized (phonics), and literacy rewires almost everything in the child’s brain. Moreover, there aren’t lots of ways to learn to read: the reading brain looks pretty much the same across cultures.
What stuck with me was a strikingly brain-based description of synesthesia, and an amazing hypothesis about “normal” reader’s brains that resulted. Synesthesia is real; it occurs exclusively for learned cultural objects; and it may be due to a mutation of the genes involved in synapse pruning during development. The experimental finding was that in synesthetes, the areas activated by letters and by color patches overlap noticeably, and they don’t in non-synesthetes: the synesthete is “stuck” with his cortex in an intermediate state of specialization. The amazing hypothesis is that all children may be synesthetes: during development, as cortical specialization unfolds, there may be a period of incomplete specialization that leads to effects comparable to those in adult synesthetes!
The chapter on “Inventing Reading” outlines several features common to all writing systems, but Marc Changizi’s stands out as a quantitative and highly suggestive commonality that links our reading to the apes’ “reading of nature.” He found that the frequency distribution of certain shapes was constant, considering only their topological attributes: L and T were most common, followed by X and F, then Y and (Delta). This distribution was the same as for natural scenes! For example, a T or L shape often occurs during object occlusion, and a Y can occur at the corners of objects.
Working in apes, Tanifuji found that monkey neurons respond to a remarkably sparse representation of some object exactly as they do when the object is fully rendered–as long as certain “proto-letters” that encode “non-accidental properties” are kept in the picture. If the Y, T, X, O and J’s in an image are left, the monkeys (and humans) still recognize the object: not so if an equivalent amount is erased from the image that includes some of those proto-letters. That provides strong evidence for how we manage invariance in object recognition: we have certain neurons (or at least, monkeys do) that respond to the presence of certain shapes on the retina that probably correspond to “non-accidental” features: shapes that arose because of some object or relation between objects, not the random placing of lines. In particular, these shapes are the ones that largely look the same when scaled, rotated, or lighted differently.
Lastly, Tanaka found some neurons that code for a black dot on a white background–posited to be an “eye detector,” which is essential for a social species–and there are other detectors corresponding to biologically useful features: but most correspond to simple geometric shapes.
Taken all together, we have here in Dehaene’s book material useful for all teachers, for neuroscientists, but also for linguists, anthropologists and scholars of the history and development of the English language. Exciting! Most importantly, we have an excellent model of how to do “soft” science rigorously and honestly, working on hot areas like culture without puffing up your claims. And a critical call to educators to experiment in the classroom: “Every teacher bears the burden of experimenting carefully and rigorously to identify the appropriate stimulation strategies that will provide students’ brains with an optimal daily enrichment.” (233)
There’s a point beyond which Dehaene goes into speculation: but it’s fascinating and appropriately qualified, so I went with him there, to some startling findings. One on symmetry: at the neuronal level, there’s a right-left brain symmetry, so that every neuron in the left region of the visual cortex goes through the corpus callosum and maps to another neuron in the right cortex. Before processing into the letterbox region, visual stimuli presented to one half of the visual field activate BOTH neurons in the pair: initial processing is symmetric. This lends credence to the claim that initial processing is focused on object identification, where spatial orientation (and in particular mirror reversals) and motion are less important. Only later, and only in the expert reader’s brain when viewing words in the reader’s language, is a “symmetry-breaking” observed, where only the left letterbox region responds.
There seem to be TWO visual pathways that are quite distinct operating in the visual cortex: the ventral one that is concerned with object recognition and exhibits symmetric brain activation, and the dorsal one that is concerned with motion and spatial orientation, and does not exhibit this symmetry. Here’s where the symmetry can be broken: and it’s aided by motor learning (since that’s the provenance of the dorsal cortical system attuned to motion in the environment), hence the effectiveness of children tracing sandpaper letters to aid their reading acquisition in the Montessori method. Neat!
Dehaene calls for a “neuro-anthropology” that he realizes is beyond our reach, but that he believes to be a viable research orientation and program. How might we look at cultures neuroscientifically? Structural anthropologists a la Levi-Strauss already do it to an extent, positing the existence of a kind of “deep grammar” of culture that gives rise, unconsciously, to universal cultural forms, among them religion, mathematics, music and arts, facial expressions, games, legal systems, etc. Dan Sperber frames it as, “the image that emerges from the cumulated ethnographic record is not at all one of indefinite variability, but rather one of extremely elaborate variations within a seemingly arbitrary restricted set.” There’s more scientific evidence (vs. anthropological) than you might think: consider the research of Paul Ekman, who painstakingly developed a system that can encode all facial “micro-expressions,” and demonstrated that people across cultures assign the same meanings to most of these facial expressions. In music, we see that infants across cultures are sensitive to octaves and fifths, though other intervals and scales vary.
Sperber and Ekman’s data, though, leads them to posit too much constraint on culture, in Dehaene’s view. Sperber thinks of the brain in terms of different modules that developed for certain evolutionarily helpful reasons and then can be stretched a little to apply in somewhat different domains: universal structures that allow a “fringe” of cultural variability. Dehaene thinks there’s more variation possible: art forms aren’t just a “super-stimulus” that work at the limits of cortical stimulation within each of these regions, but they arise out of associations between a wide selection of mental representations that elicit complex emotions. To the extent that there are cortical modules that artists exploit, those modules have nebulous boundaries, and education and cultural acquisition can rewire their connections and boundaries substantially (perhaps the best argument for early, intensive arts education I’ve ever heard).
Moreover, like Changeux thinks, masterpieces come from novel stimulation of many regions–but not just that novel stimulation: the interactions between those regions, and their synchronous and “harmonious” (reinforcing) activation, are key. So too are amplifications of reality: as Ramachandran puts it, in a way many aesthetic theorists would like, “the purpose of art…is not merely to depict or represent reality…but to enhance, transcend, or indeed even to distort it…to amplify it in order to more powerfully activate the same neural mechanisms that would be activated by the original object.” (310) These connections are uniquely amplified in the human brain, which developed neuronal connections between distant cortical areas in the course of tool learning, has an abnormally large frontal cortex that can synthesize novel ideas and associations like metaphors, and is remarkably sensitive–even in infancy–to the communicative intentions of others. I think there’s an interesting parallel to common descriptions of modernism here: art arising out of representing common forms in the most novel, non-traditional ways possible, art taken to the extremes of what has gone before along each possible dimension we can use to describe artistic forms.
Granted–as Dehane grants it–we’re a long way from any rigorous neuroscientific theory of aesthetics or complex cultural productions. But it’s fun and enlightening to speculate, and in the meantime, Dehaene gives us a core of very rigorous explication and a case study in how to exhibit a brain- and mind-based theory of a complex learned phenomenon without excluding its historical, social and cultural context.
As a final note in that vein–the New York Times review of this book criticized Dehaene for seeming to disagree with himself: at the same time saying that the mind is tightly constrained in its learning by the brain’s structure, and that it is remarkably plastic. I don’t think these things need to be in opposition. For one, Dehaene makes it clear that he’s positing constraints partly as a corrective to the “default social science model” of cultural acquisition via general learning mechanisms starting from a blank slate. More substantially, though, evo-devo and several other scientific developments have clearly shown us how tight constraints can produce enormous variety of mechanism and ultimate form. This is a sophisticated account of reading in culture that carefully avoids both reductionism and relativism, while incorporating both biology and historical and cultural context.