“You can’t ‘preserve’ a species.” – Grant and Grant
Passing through an experimental evolution lab this summer, I got used to the idea that we can experimentally test evolutionary theory and the operation of selection (only?) in small, simple systems, like the yeast the lab used, bacteria, or viruses. “Experimental evolution” to me meant evolution studied at the level of genes and especially single mutations, fitness determined by who remains standing in the petri dish. So the single most striking experiment described in Jonathan Weiner’s Beak of the Finch was the observed drastic decrease in elephant tusk length among populations subject to poachers looking for ivory. I don’t know how elephant tusks work–if growth is based on Bmp4 expression, it’s not too surprising to me that a “macro” trait like tusk length could quickly evolve in response to selection pressure at the genetic level–but with all the debate and discussion about the unit of selection and macroevolution, the tusk example was enlightening.
This book is the story of Peter and Rosemary Grant’s 20-year vigil on the Galapagos Islands, particularly Daphne Major. For being that, it’s amazing how much the book focuses on the research: there is a human drama that unfolds in these pages, but it’s almost entirely told through the long progression of experiments and scientific life in the field, with some recourse to Darwin’s history. I want to learn that narrative trick!
The acceptance of quantification as rigorization makes me a little uncomfortable in all these “experimental evolution” and especially ecological studies. Mostly I think it’s good–taking detailed measurements has surely led to striking demonstrations of the power and quick-acting scale of natural selection, and how big a difference a tiny variation in beak trait matters. That’s an important lesson for all of us who would constantly wonder how selection on variation, which many think of as a piecemeal, slow process of accumulating very slight benefits, could result in qualitative differences in who survives, the generation of a major phenotypical innovation, or the origin of species. And I can see how this minute empirical measurement is partly an antidote to Darwin’s florid Victorian prose and the geometric landscape theories of the great mathematical population geneticists of the early century. But it also seems that desire for illuminating numbers can blind researchers studying evolution like this to the need for sophisticated interpretation and to hold down the fort against the allure of trumpeting a murkily visible trend. For example, there’s a recurring theme of one researcher declaring that there’s been no natural selection in Darwin’s finches, and the Grants coming back with minute year-by-year measurements and saying that, while there’s little net change in characteristics, there’s actually strong selection oscillating rapidly. Certainly, that’s a vital distinction, especially as a corrective to the idea that nobody could ever observe natural selection in action in the timespan of a human lifetime. But I think there’s a danger of swinging too far in this shiny new direction: the fact that there’s little net variation is also very important.
Some highlights of the drama of observing Daphne: The description of the difficulty of landing on Daphne, everyone’s least favorite part of the trip–there’s only really one ledge to land on, and that after repeated partial offloadings as the swells move the boat above and below the ledge. The story-cum-legend about the scientist who was walking clad only in shoes on the island and got attacked by a barnacle that clamped onto his balls. The description of the Grants back in Princeton analyzing the data with such detailed, knowing affection, as they swap labelled stories: “‘He’s been a good producer of fledglings, 2666,’ Peter says, this time without even looking at the screen.” (118) The sense of a wunderkammer somewhere in two Princeton offices, full of vegetation and food and families and songs, all in tables of data. The times when the data really does seem to make a clear point: for example, how Darwin’s finches really are much more variable than most, a natural laboratory for natural selection–sparrows, closely related to finches, rarely deviate in beak length more than 10% from the mean on the remote island of Mandarte, B.C. (ostensibly similarly isolated conditions for accelerated evolution to take place), whereas in the Galapagos 4% of the cactus finches differ from the average beak by more than 10%. (p. 47) The sheer carefulness of the data collection and care taken in what to measure is stunning: there’s an index for difficulty-of-eating among the seeds (and every seed on the island is accounted for), and the Grants know just how much force it takes to crack the toughest seeds. The careful correlation of measurements and behavior: telling what kind of finch discovered the mericarp by whether the cover is peeled back or bitten through, and how many seeds are left. Careful experimental design: Peter Boag tested for heritability of beak dimensions, ruling out that big-beaked parents get more food for their babies (not with Darwin’s finches, but on Mandarte–the egg-swapping would be quite catastrophic in such a small, fragile ecosystem!)
I got to revisiting some of my half-remembered thoughts about the difficulties in Darwin’s theory while reading this book. Prime among them–and I look forward to going back to this when I read Grant & Grant’s book about their research, How and Why Species Multiply–is observing “the ever-turning sword,” the actual origin of new species. It was never quite observed on Daphne–but there were big steps. The image of the evolutionary tree as having loose, tangled webs, and not clean breaks, at the branching points, is a powerful one–and this summer, I learned how important the visualizations we rely on are in evolutionary thinking (witness the insidious, helpful idea of a fitness landscape). Dolph, one of the researchers, even made an actual empirical fitness landscape out of all the measurements for Darwin’s finches!!!!
Two particular points of interest: the light shed on Darwin’s gradations between kind, variety and species, and how they’re borne out on Daphne–many of these finches “are so intermediate in appearance that they cannot safely be identified…In no other birds are the differences between species so ill-defined.” There’s a saying at the Charles Darwin Research Station: “Only God and Peter Grant can recognize Darwin’s finches.” One of the impetuses of Grants’ research was a monograph published by David Lack, Darwin’s Finches, which put to rest the idea that prevailed for a while that they aren’t new species at all, but “a hybrid swarm” of varieties on the Galapagos, “offering no scope for natural selection.” Lack saw that the birds weren’t breeding together, but the ground finches were eating the same seeds–he saw that in a wet season, when seeds are plentiful. Then looking over the data at home, he saw that the closest together species in their beaks never live together on one island, and inferred competition. The Grants were the first to really see competition and the principle of divergence in play on the island, and not just infer it happened in the past.
On the problem of adaptation, a neat experiment with crossbills showed proof-of-concept that little fitness advantages can accumulate to make big adaptations: the beaks of the crossbills were filed down, and each generation grew a little more of a crossbill back; each generation was more fit than the last in terms of its ability to crack the pinecones it’s adapted to. The other one, more shocking, is the key role for hybrids that the Grants posit as a result of their observations: they found, contrary to all received wisdom, that sometimes the hybrids were successful far beyond what any of the “pure-breds” could do, and it wasn’t uncommon for two different species to attempt mating. Number 006, the tiniest fulginosa on the island, always pairs with a fortis, and is the most successful of her species on the island breeding-wise. (123) The numbers for bird hybridization are striking: there are 10,000 bird species known, and 1,000 are known to have mated with other species. In some cases the rates are even higher: 67 of 161 species of ducks and geese have interbred that we know–and probably more, given the patchy state of our knowledge (we didn’t even notice that the best-studied birds ever, on the Galapagos, were interbreeding until after a 20 year vigil). This extends the role of hybridization and its power, known to produce ne wplant species “literally overnight,” to the animal kingdom. At least half of the world’s flowering plant species came from interbreeding. Does a number on the biological species concept! Hybridization is also common among Bufo toads, many insects, and many fish. Evidence also points to the role of human motions and ecology in increasing the rate of hybridization: we disturb habitats and introduce invasive species that mix with the local gene pool, and hybrids can back-cross (“introgressive hypbridization,” Edgar Anderson called it) to mix the gene pool even more. Hybrids can fill special niches their parents can’t, and humans are creating such mixed up niches all over the planet: we see this in wildflowers in the Delta, where different species inhabit different fields, which the farmers have treated with different chemicals. Anderson also argues that “ecologically dominant” species, like humans currently are, could have driven the evolution of new niches in the past too: e.g. at the colonization of new islands or continents, the first land vertebrates invading terrestrial vegetation, the first large herbivorous reptiles or the first large land animals. (Stebbins and Anderson, “Hybridization as an Evolutionary Stimulus”)
On the modern speed of evolution, Weiner goes beyond the pat “we have lots of drug-resistant strains” line. He writes of two people who took antibiotics for a few days, and then sampled the bacteria in their body: almost all were drug resistant. As they note, it’s different seeing fast evolution in a lab and in our own bodies. There are examples of all the different ways a pest can avoid the effects of a pesticide: dodging it, not letting it get inside, developing an antidote, or inactivating it once it’s inside.
And there are interesting stories to liven things up (it’s really a page turner!) Not of Darwin’s personal life or the Grants’ per se, but personal anecdotes in the course of doing research. Darwin could’ve seen natural selection in action if he’d been of a mind to, as he kicked stones in his garden to count laps and noticed the death of 4/5ths of the bird species on his grounds one winter, something like the effect of an El Nino or a drought on the Galapagos. Darwin discovered 537 species of plant in 3 tablespoons of mud in his tabletop experiment. Two engineers created “two new letters to the alphabet of life,” adding synthetic X and K to A, C, T, and G. The book doesn’t get personal, but there’s a great sense of personal investment and the importance of these scientific stories in it. I’ll be trying to get my hands on Weiner’s other books.
“Direct Demonstrations of Natural Selection” in Natural Selection in the Wild, for more examples of experimental evolution studies
*Grant and Grant, How Species Multiply
I learned many things from this book, on the biology side. Roughgarden’s case studies of varied gender expression and sexuality in animals are fascinating, and she is careful to take you through what they might “mean”–the function of third (and further) genders, the failures of “deceit theory” to account for the presence of “effeminate males” (since birds can verifiably tell the difference between females and female-looking males), the usefulness of cooperation as a valid concept in evolutionary theory. Colorful examples abound: fishes (wrasses) that change sex based on environment (open sea or reef), plants that change sex based on time of day (with the excellent name “flexistyly”). I’m willing to allow the possibility that, to use her example, different species of Idaho squirrel have different mating habits during their 20-minute mating window (with the male inserting a sperm plug or not) according as which the female prefers, or is more pleasured by.
There are other interesting ideas to perhaps add to our thinking on mate choice: how can we study whether animals in a population acquire a “reputation”? It’s certainly true that courtship behavior and parental care is frequently carried out in full view of others in the group. The idea of a population’s reproductive skew as partially determined by the level of distributional inequity in reproductive opportunity is appealing, especially as an alternative to the pure selfish rhetoric in traditional literature: “stealing” mates, “wasting” resources on others, etc. Family structure does seem to be fluid in the animal world, and behavior towards other animals partially governed by the best strategy for getting others to help. There are detailed discussions of “extended families” in tamarins, African hunting dogs, birds, and several other groups: the tamarins in particular exemplify how polyandry can be necessary to raising young in a cooperative setting–matings take place in view of other males with no sign of aggression, and males cooperate to take care of the young. And maybe it couldn’t be another way: mothers usually birth twins, and they are each 50% of her weight by the time they can walk or climb on their own–a monogamous couple isn’t enough to raise them.
The point is also well taken that the words we use to describe animal behavior reflect entrenched beliefs, so that males can be described as “cuckolded” in peer-reviewed publications in respectable journals, and everyone deceives each other. This was partly affirmation of my confusion over the so-called “problem of cooperation” and why it’s a problem at all. I do find it hard to subscribe to Dawkins’ “genes are the fundamental unit of selection” selfish-gene model. There’s just too much going on in the environment, and I want a theory that imputes meaning to our time on Earth…
To me personally, the book was most disappointing in its discussion of homosexuality in humans–just because that’s what I was most looking forward to in it, I think. She’s unwilling to speculate much there (despite wild speculation re: trans issues), and her belief that there’ll never be a biological theory of homosexuality seems to run counter to the call for more study of homosexuality in other animals and for more nuanced studies in the social sciences.
But there was a lot to make up for that. The stories of animal homosexuality were delightful and convincing (even excluding “homosexual, heterogendered” courtship between individuals of the same sex and different genders). I don’t believe cohabitation and the alternating assumption of the “male” role in courtship qualifies certain asexual anolis species to be called “lesbian lizards” (too much human/social baggage there), but it’s fascinating of itself. And it’s nice to see the doubters get hit where it hurts: she points out that even geese marriages, the poster children for lifelong stable, monogamous couplings, are 15% male-male. (And they love each other, too: the male shows despondent grief after his partner dies, just as in straight couples.)
On the function of feminine males, I’m not sure I think Roughgarden’s theories are totally plausible, but they seem to be a good counterbalance to the deceit theorists. And at least they make nice stories (not, of course, a criterion for a good scientific theory): Consider the red-sided garter snake that makes Manitoba’s interlake region a hotbed of snake-watching. (There’s even a monument to garter snakes in the town!) Thousands of garter snakes live in a single den in the winter, then emerge, mate and disperse; mating occurs in “mating balls” with one snake courted by many others. At the den entrances, the ratio of males to females is 10:1. It turns out all males secrete feminine hormones: Roughgarden’s theory is that male garter snakes just emerging from the den want to roll around in the sun and “wake up,” so they can signal with female hormones–and males watching this would rather welcome this new guy than attack him, with so many others around who could jump on you. (Notable here is the lack of deceit theory–the males know the feminine male is indeed a male).
Part 2 of the book goes into development, where I think the treatment is a little weaker (it’s farther from Roughgarden’s area of expertise, which is ecology.) The most interesting thing I got from this was a BIOLOGICAL definition of gender: I’m used to thinking of “sex = biological characteristics, gender = how you feel.” Roughgarden takes the female to be the one with the larger gamete size, and that’s it. She then traces how sex (how a gamete matures) is influenced by gender (what type of tissue the gonad it lands in has) and vice-versa, and the complicated role of SRY in gender determination. She also solved a big mystery for me: how males and females can be so different while having only about 60 genes on their sex chromosomes (and about 4x that between two people on nonsex chromosomes). Two main culprits–evolution on the Y chromosome, and the extent of X inactivation–if there’s not much, the females have most of another chromosome to work with to effect these differences.
Continuing the theme of biological gender differences, the discussion of birds had a lot to offer: e.g. in speciese where males sing and females don’t, there’s a marked difference in the size of nerve cell clusters for learning and making song. Moreover, hormones control the size of these nerve cell clusters, varying with both age and time in the breeding season; they also control aspects of personality, often in very simple ways. Overall, the development section made me appreciate transgenderism as a biologically-based phenomenon, and not just something about “how you feel inside.”
The last thing of note for me was perhaps the “gay gene” discussion–basically, most biologists agree that homosexuality has some genetic basis, and the search for one gene is kind of pointless for a complex trait like this–we’ve only found things that sometimes make tiny differences.
Ultimately, though, despite all the things I learned about the biology, this book drove me a little crazy. Roughgarden has a clear axe to grind, and I think it weakens her book. What could’ve been a solid, scientifically-based, well-researched book of half the size on a coherent chunk of material became a bloated thing that’s half science and half poorly-defended manifesto. Sure, it’s good to challenge the social sciences to take a broader view of sex and gender, and to point out biases in the literature. But Roughgarden pulls some stunts that really weaken her credibility for me: for example, calling theories “diversity-repressing” as if that were the end of their credibility, bringing in the Bible, arbitrarily focusing on the T in LGBT, and laying out a trans agenda. When she does these things, suddenly the copious footnotes vanish. I’d rather she have done more with the science and less explicitly with a “feminist critique,” or that she publish the political implications as she sees them separately.
As it is, I can recommend the scientific insights, but not much of the editorializing woven in. And ultimately I’m not convinced that Darwinian sexual selection theory needs to be wholly overthrown: to me, its strongest point is that reproductive success can be selected for independently of survival probability, and we should look for reproductive explanations when a trait seems like it wouldn’t be naturally selected for. Yes, Darwin speaks of universal gender types; but he spoke of blending inheritance too, and the Modern Synthesis simply updated him with our new understanding of genetics; we could do the same with our understanding of sex and sexuality.
My favorite factoid: gay people’s otoacoustic emissions (little sounds emitted from our ears) are different than straight peoples’. Hello, gaydar! 😉
This title isn’t a straw man: I don’t mean by it the obvious claim that experimental methods are interesting. With all the current talk about the deluge of data we face, though, I am thinking about the adventure, creativity, and big science power that goes into data collection. Maybe we feel like we have too much data now–but we also have new methodologies and the promise of extracting quantitative data even from fields like literature and the arts. We start with the data in hand in most statistics classes and ask how we can analyze it: but behind bland terms like “missing data” and “spatial statistics” lie amazing stories about fieldwork, the invention of new experimental methods and the martialing of forces to organize huge data collection projects.
The first time this was brought home to me, I was sitting in the Harvard Statistics lounge waiting for a section to start and leafing through old issues of Chance, the magazine about statistics aimed at laymen. It’s a monument to how statistics really impacts our world, with articles on current policy and scientific issues, computing, and how statistics are presented. The most fun part is the human interest stories: what caught my eye that day was an article about the dangers of data collection in the tropics among poisonous creatures, and how to get the best data possible given the limits of tropical fieldwork.
Recently, a networks exhibit at the Hall of Science and my reading have given me a few more cool examples of data collecting tricks to think on. Walter Tschinkel is an entomologist at Florida State who has a unique way of studying ant colonies and their social structure: pouring molten metal into the colonies and waiting til it hardens to see the shape of the tunnels (more here). The resulting molds make for great data and great art–much like the cloud chamber tracks that grace another area of the museum exhibit hall. In my reading of Mayr, he mentions some other tactics for studying elusive populations: the most vivid of which is spraying pesticide into the forest canopy and doing a biodiversity census by classifying the bugs and birds that rain down from the trees.
Right now we have international data collection efforts that are changing the face of science: particle physics at CERN, genome projects, efforts towards a comprehensive Encyclopedia of Life. We’ve realized that our challenge now is to make data meaningful and usable, and we’re rising to that challenge. It reminds me of some of the very first international science efforts, like the British Navy’s collaboration with stations throughout the world to record the tides under Whewell’s direction (after he convinced everyone of how useful detailed tide data would be), and the days when logarithm and integral tables enabled all kinds of new science. Behind so many advances in science are new ways of looking at data that have an aesthetic interest all their own.
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.