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CAUTION: Confusingly enough, I have two new books in Autumn 1996. The other one is HOW BRAINS THINK.

click for enlargement of
this Mark Meyer painting

Thinking a Thought in
the Mosaics of the Mind

MIT Press
copyright ©1996 by
William H. Calvin
The Cerebral Code proposes a bold new theory for how Darwin’s evolutionary process could operate in the brain, improving ideas on the time scale of thought and action. Jung said that dreaming goes on continuously but you can’t see it when you’re awake, just as you can’t see the stars in the daylight because it is too bright. Calvin’s is a theory for what goes on, hidden from view by the glare of waking mental operations, that produces our peculiarly human consciousness and versatile intelligence.
     Shuffled memories, no better than the jumble of our nighttime dreams, can evolve subconsciously into something of quality, such as a sentence to speak aloud. The “interoffice mail” circuits of the cerebral cortex are nicely suited for this job because they’re good copying machines, able to clone the firing pattern within a hundred-element hexagonal column. That pattern, Calvin says, is the cerebral code representing an object or idea, the cortical-level equivalent of a gene or meme. Transposed to a hundred-key piano, this pattern would be a melody — a characteristic tune for each word of your vocabulary and each face you remember.
     Newly-cloned patterns are tacked onto a temporary mosaic, much like a choir recruiting additional singers during the Hallelujah Chorus. But cloning may “blunder slightly” or overlap several
patterns — and that variation makes us creative. Like dueling choirs, variant hexagonal mosaics compete with one another for territory in the association cortex, their success biased by memorized environments and sensory inputs.
     Unlike selectionist theories of mind, Calvin’s mosaics can fully implement all six essential ingredients of Darwin’s evolutionary algorithm, repeatedly turning the quality crank as we figure out what to say next. Even the optional ingredients known to speed up evolution (sex, island settings, climate change) have cortical equivalents that help us think up a quick come-back during conversation.
     Mosaics also supply “audit trail” structures needed for Universal Grammar, helping you understand nested phrases such as “I think I saw him leave to go home.” And, as a chapter title proclaims, mosaics are “A Machine for Metaphor.” Even analogies can compete to generate a strata of concepts that are inexpressible except by roundabout, inadequate means — as when we know things of which we cannot speak.

William H. Calvin is a theoretical neurophysiologist at the University of Washington in Seattle, and author of nine books.

A Bradford Book
Author photo by Doug vanderHoof

My other 1996 book is HOW BRAINS THINK in the Science Masters series (BasicBooks in the US, 12 translation editions). THE CEREBRAL CODE is primarily for scientists and only secondarily for intrepid general readers (though I’ve used an extensive glossary and cartoons to help the nonscientist).

Cartoons by Malcolm Wells

All rights reserved. Except for brief excerpts and personal photocopying of a single chapter, no part of this book may be reproduced in any form (including information storage and retrieval, photocopying, and recording) without permission from the publisher.

NOW AVAILABLE (Hardcover, US$22.50).
ISBN 0-262-03241-4
Chapter List


Act I

1     The Representation Problem and the Copying Solution
2     Cloning in Cerebral Cortex
3     A Compressed Code Emerges
4     Managing the Cerebral Commons
5     Resonating with your Chaotic Memories
6     Partitioning the Playfield

         Intermission Notes

Act II

7     The Brownian Notion
8     Convergence Zones with a Hint of Sex
9     Chimes on the Quarter Hour
10    The Making of Metaphor

11    Thinking a Thought in the Mosaics of the Mind

             Recommended Reading
             About the Author
             About the Artists

INSTRUCTORS: If you need glossary links from your own web page "handouts," you can make use of the glossary items in THE CEREBRAL CODE, e.g., to gloss postsynaptic, use
<a href=http://weber.u.washington.edu/~wcalvin/bk9gloss.html#postsynaptic>Postsynaptic</a>

If you would like to see a translation for your language, alert a relevant publisher and point them toward my literary agent:

Brockman, Inc., 5 East 59th Street, New York NY 10022 USA.
fax +1(212)935-5535 ....... e-mail john@brockman.com

Here is the card catalog listing (consider clipping and sending to your librarian):

Author: Calvin, William H., 1939-
Title: The cerebral code : thinking a thought in the mosaics of the mind
Pub. Info: Cambridge, Mass. : MIT Press, 1996
Year: 1996
Physical Descr: p. cm.
Notes: “A Bradford book.”
Notes: Includes bibliographical references and index.
Subject: Cognitive neuroscience.
Subject: Thought and thinking.
Subject: Cerebral cortex.
Subject: Natural selection.
Subject: Memory — physiology.
Subject: Thinking — physiology.
Subject: Brain — physiology.
Subject: Consciousness — physiology.
ISBN: 0262032414 (alk. paper)
Language: eng
OCLC No: 34564905
The book is vi+256 pages long and heavily illustrated, US$22.50.

The back cover of THE CEREBRAL CODE includes:
“This book is indeed of importance to specialists in the field, and is likely to be used as a source book for students to read in courses on the neural basis of cognition. Calvin proposes a model of a major part of cerebral cortical function and shows how it could apply to or ‘explain’ a number of examples of operations and cognitive achievements at various higher levels. The Cerebral Code is certainly original, readable, and of sound scholarship. It should appeal to an audience of professionals, students, and general readers.”
    Theodore H. Bullock
        Professor, Department of Neurosciences,
        University of California, San Diego

“Bill Calvin writes with elegance, economy, and authority. In The Cerebral Code, he has solidly embedded his ideas in experimental neurophysiology and neuropharmacology, deriving from his decades in the laboratory. He explores the ramifications of his insights into a wide range of cerebral functions, such as sleep, dreaming, awareness, problem solving, creative thinking, and the dynamics of nerve cell assemblies that make consciousness possible. Calvin has written primarily for his colleagues in neuroscience, as well as for lay readers. I believe he will achieve his aim, by recounting in adequate detail the basic concepts from which he is reasoning, and thereafter exploring ideas and issues that his reductionstically minded colleagues have largely ignored.”
     Walter J. Freeman
        Professor of the Graduate School,
        University of California at Berkeley

“Calvin’s single, simple purpose for The Cerebral Code is to propose a substantial hypothesis for how the human cerebral cortex might work. He suggests that the brain uses a selection algorithm, what he calls a ‘Darwin Machine,’ based upon small anatomical and physiological units, local spatiotemporal patterns of neuronal activity that should ‘tile’ regions of cortex with a hexagonal mosaic. Until this book, the concept of selection in the brain has not been so plausibly linked with real, empirically observable cellular structures and functions.”
    David G. King
        Anatomy, School of Medicine, and
        Zoology, College of Science
        Southern Illinois University at Carbondale.

“William Calvin writes with clarity and elegance about the brain. In an age when brain science is becoming increasingly fragmented and specialised Calvin is a ‘rara avis’... he provides a broad overview on the functions of the brain and a bold and novel conjecture about the most highly evolved — yet enigmatic of all biological organs — the human cerebral cortex. ”
    V.S. Ramachandran
        Professor of Neuroscience and Psychology,
        University of California, San Diego

Book reviews so far:

"... in The Cerebral Code: Thinking a Thought in the Mosaics of the Mind, Calvin lays out a wide-ranging and innovative theory linking the neural structure of the cortex to thought, language, and consciousness." "... a fascinating and readable presentation of a novel and radical approach to bridging the gap between mind and brain."

"[Calvin's] basic model can be applied to problems such as the sequences needed for body movements and in language, making associations, imagining, and thought pathologies. Finally, he goes for gold with a thought experiment, testing his [cortical Darwin Machine] theory on consciousness and a mechanistic outline for Universal Grammar.... [Calvin's is] a vision that is now all too rare. Right or wrong, his ideas should stimulate many to think more broadly about the dynamic processes of the cortex...."

--Jennifer Altman, in New Scientist 23 November 1996

"Ought to be a complete ban on global speculations about the brain... grandoise."

--Stuart Sutherland, in Nature 21 November 1996

"I was initially solidly sceptical about the central idea of The Cerebral Code, but my wariness diminished as it became clear that Calvin wasn't suggesting it as an explanation for everything (a trap into which many with novel ideas about brains and minds fall). His final chapter looks at how his ideas might connect with other approaches to the brain, approaches at different levels of explanation or addressing different aspects of brain function. Calvin's recognition that he doesn't have all the answers is refreshing. If his ideas are provocative, they are also testable (he suggests some experiments himself); and even if you aren't convinced, The Cerebral Code is still an enjoyable way to learn something about neurobiology and abstract Darwinian processes."

--Danny Yee book review

SUPPLEMENTARY MATERIAL: There are now animated illustrations for the spatiotemporal patterns.

Corrections and Supplements to the

are already incorporated into the web pages; they are also posted here in case owners of the first printing wish to update their copies.
Dust jacket author photo credit was left off: Doug vanderHoof, Modern Media.

p.82: "When chaos theory came along (which, for me, is dated to Otto Rössler’s famous paper of 1983), it started to become evident how basins of attraction lived in the connectionists' networks. When complexity theory and artificial life came along on their heels,..."

p.84: "to implement a shaping-up process, "

p.92: "boomtime survivals that would otherwise have been lost to juvenile mortality."

p.100: "that can be standardized in a crystalline manner (it's Dawkins' minimum replicable unit). "

p.102: "lingering after the active spatiotemporal patterns had died out. "

p.125: "sang a fifth (a 3:2 ratio of frequencies, seven semi-tones apart) or an octave (2:1, twelve semi-tones apart) higher"

p.126: " only certain semi-tones (7 of the 12 in an octave) are thought to go well enough together to make chords."

p.137: Addendum. As I was attempting to explain to John Maynard Smith (author of The Evolution of Sex, etc.) the Cerebral Code's analogies of corticocortical convergence to gamete dimorphism and the resulting numerical disproportion, he said, "You know, Calvin, the real reason why it takes so many sperm to fertilize a single egg? It's because none of the sperm will ask for directions."

p.161: "the little words of grammar"

p.171: "It’s meeting a high quality criterion that"

p.186: "An expert (someone who knows"

p.191: "meet some nonstandard criterion that itself evolves"


Protolanguage is a simple form of language lacking the fancier structure provided by syntax. It’s the language of the trained animals, children less than two years of age, speakers of pidgins, agrammatic aphasics, and American professors trying to communicate with Greek shopkeepers. With structureless protolanguage, it takes a lot of time to relate who did what to whom, even when supplemented by gestures.
p.193: "develop a mechanistic outline for some"

p.197: "Corticocortical coherence that became good enough to convey even novel spatiotemporal"

p.205: "chunking Collapsing multiple-word phrases into a single word, in the manner of acronyms."

p.213: "stellate neurons The nonpyramidal neurons in the neocortex, on the basis of anatomy. Physiologically, most of them have inhibitory actions, an exception being the spiny stellate neurons."

p.215: correct title to Derek Bickerton, Language and Human Behavior (University of Washington Press 1995). Add: Daniel C. Dennett, Kinds of Minds (BasicBooks 1996). Add: John Maynard Smith and Eörs Szathmáry, The Major Transitions of Evolution (Freeman 1995).

p.217: 4 William James's development of his darwinian theory of mind, see pp. 433ff of Robert J. Richards, Darwin and the Emergence of Evolutionary Theories of Mind and Behavior (University of Chicago Press 1987). The modern chapter of mental darwinism starts in 1965 with Dan Dennett's D. Phil. thesis, published as Content and Consciousness (Routledge and Kegan Paul 1969).

p.225: Add editor:

61 Donald T. Campbell, "Epistemological roles for selection theory," in Evolution, Cognition, and Realism: Studies in Evolutionary Epistemology, edited by N. Rescher (Lanham, MD: University Press of America 1990), pp. 1-19 at p. 9.
p.229: Add "100 Richard Dawkins, The Extended Phenotype (Oxford 1982)."

p.232: at 134, "p.105"

p.233: at 137, add title Origins of Sex

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There may be nothing new under the sun, but permutation of the old within complex systems can do wonders.
    Stephen Jay Gould, 1977

This is a book about thought, memory, creativity, consciousness, narrative, talking to oneself, and even dreaming. In a book that parallels this one, How Brains Think, I explored those subjects in a general way but here I treat them as some of the predicted outcomes of a detailed darwinian theory for how our cerebral cortex represents mental images — and occasionally recombines them, to create something new and different.

    This book proposes how darwinian processes could operate in the brain to shape up mental images. Starting with shuffled memories no better than the jumble of our nighttime dreams, a mental image can evolve into something of quality, such as a sentence to speak aloud. Jung said that dreaming goes on continuously but you can’t see it when you are awake, just as you can’t see the stars in the daylight because the sky is too bright. Mine is a theory for what goes on, hidden from view by the glare of waking mental operations, that produces our peculiarly human type of consciousness with its versatile intelligence. As Piaget emphasized, intelligence is what we use when we don’t know what to do, when we have to grope rather than using a standard response. In this book, I tackle a mechanism for doing this exploration and improvement offline, how we think before we act and how we practice the art of good guessing.

    Surprisingly, the subtitle’s mosaics of the mind is not just a literary metaphor. It is a description of mechanism at what appears to be an appropriate level of explanation for many mental phenomena — that of hexagonal mosaics of electrical activity, competing for territory in the association cortex of the brain. This two-dimensional mosaic is predicted to grow and dissolve, much as the sugar crystals do in the bottom of a supersaturated glass of iced tea. Looking down on the cortical surface, with the right kind of imaging, ought to reveal a constantly changing patchwork quilt.

    A closer look at each patch ought to reveal a hexagonal pattern that repeats every 0.5 mm. The pattern within each hexagon of this mosaic may be the representation of an item of our vocabulary: objects and actions such as the cat that sat on the mat, tunes such as Beethoven’s dit-dit-dit-dah, images such as the profile of your grandmother, a high-order concept such as a Turing Machine — even something for which you have no word, such as the face of someone whose name you haven’t learned. If I am right, the spatiotemporal firing pattern within that hexagon is your cerebral code for a word or mental image.

The other phrase in the book’s title that is sure to be mistaken for literary license is, of course, the cerebral code. The word “code” is often only a short way of saying “unlocking the secrets of” and newspaper headline writers love such short words. Neurobiologists also speak loosely about codes, as when we talk of “frequency codes” and “place codes,” when we really mean only a simple mapping.

    Real codes are phrase-based translation tables, such as those of bank wires and diplomatic telegrams. A code is a translation table whereby short abstract phrases are elaborated into the “real thing.” It’s similar to looking up ambivalence in a dictionary and getting an explanatory sentence back. In the genetic code, the RNA nucleotide sequence CUU is translated into leucine, the triplet GGA into glycine, and so on. The cerebral code, strictly speaking, would be what we use to convert thought into action, a translation table between the short-form cerebral pattern and its muscular implementation.

    Informally, code is also used for the short-form pattern itself, for instance, a nucleotide chain such as GCACUUCUUGCACUU. In this book, cerebral code refers to the spatiotemporal firing pattern of neocortical neurons that is essential to represent a concept, word, or image, even a metaphor. One of my theoretical results is that a unique code could be contained within a unit hexagon about 0.5 mm across (though it is often redundantly repeated in many neighboring hexagons).

    It was once thought that the genetic code was universal, that all organisms from bacteria to people used the same translation table. Now it turns out that mitochondria use a somewhat different translation table. Although the cerebral code is a true code, it surely isn’t going to be universal; I doubt that the spatiotemporal firing pattern I use for dog (transposed to a musical scale, it would be a short melody, perhaps with some chords) is the same one that you use. Each person’s cerebral codes are probably an accident of development and childhood experience. If we find some commonality, for example, that most people’s brains innately use a particular subset of codes for animate objects (say, C minor chords) and another subset (like the D major chords) for inanimate objects, I will be pleasantly surprised.

    An important consequence of my cerebral code candidate, falling out of the way in which cortical pattern-copying mechanisms seem capable of generating new categories, is that ascending levels of abstraction become possible — even analogies can compete, to help you answer those multiple-choice questions such as “A is to B as C is to D,E,F.” With a darwinian process operating in cerebral cortex, you can imagine using stratified stability to generate those strata of concepts that are inexpressible except by roundabout, inadequate means — as when we know things of which we cannot speak. That’s the topic of the book’s penultimate chapter, “The Making of Metaphor.”

As a neurophysiologist with long experience doing single neuron recordings in locales ranging from sea slug ganglia in vitro to human cerebral cortex in situ, I undertook this theoretical venture about a decade ago. I didn’t set out to explain representations, or even the nature of working memory. Like most people in neurobiology, I considered such questions too big to be approached directly. One had to work on their foundations instead.

    Back then, I had a much more modest goal: to seek brain analogies to the darwinian mechanisms that create higher-order complex systems in nature, something that could handle Kenneth Craik’s 1943 notion of simulating a possible course of action before actually acting. We know, after all, that the darwinian ratchet can create advanced capabilities in stages, that it’s an algorithmic process that gradually creates quality — and gets around the usual presumption that fancy things require an even fancier designer. We even know a lot of the ins-and-outs of the process, such as how evolution speeds up in island settings and why it slows down in continental ones.

    However attractive a top-down cognitive design process might be, we know that a bottom-up darwinian process can achieve sophisticated results, given enough time. Perhaps the brain has invented something even fancier than darwinism, but we first ought (so I reasoned) to try the darwinian algorithm out for size, as a foundation — and then look for shortcuts. In 1987, I wrote a commentary in Nature, “The brain as a Darwin Machine,” proposing a term for any full-fledged darwinian process, in analogy to the Turing Machine.

    Indeed, since William James first discussed the matter in the 1870s during Charles Darwin’s lifetime, darwinian processes have been thought to be a possible basis for mental processes, a way to shape up a grammatically correct sentence or a more efficient plan for visiting the essential aisles of the grocery store. They’re a way to explore the Piagetian maze, where you don’t initially know what to do; standard neural decision trees for overlearned items may suffice for answering questions, but something creative is often needed when deciding what to do next — as when you pose a question.

    When first discovered by Darwin and Wallace and used to explain the shaping up of new species over many millennia, the darwinian ratchet was naturally thought to operate slowly. Then it was discovered that a darwinian shaping up of antibodies also occurs, during the days-to-weeks time course of the immune response to a novel antigen. You end up with a new type of antibody that is a hundred times more effective than the ones available at the time of infection — and is, of course, far more numerous as well. What would it take, one asks, for the brain to mimic this creative mechanism using still faster neural mechanisms to run essentially the same process? Might some milliseconds-to-minutes darwinian ratchet form the foundation, atop which our sophisticated mental life is built?

    As Wittgenstein once observed, you gain insights mostly through new arrangements of things you already know, not by acquiring new data. This is certainly true at the level of biological variation: despite the constant talk of “mutations,” it’s really the random shuffle of grandparent chromosomes during meiosis as sperm and ova are made, and the subsequent sexual recombination during fertilization, that generates the substantial new variations, such as all the differences between siblings. Novel mental images have also been thought to arise from recombinations during brain activity. In our waking hours, most of these surely remain at subconscious levels — but many are probably the same sorts of juxtapositions that we experience in dreams every night. As the neurophysiologist J. Allan Hobson has noted:

Persons, places, and time change suddenly, without notice. There may be abrupt jumps, cuts, and interpolations. There may be fusions: impossible combinations of people, places, times, and activity abound.
Most such juxtapositions and chimeras are nonsense. But during our waking hours, they might be better shaped up in a darwinian manner. Only the more realistic ones might normally reach “consciousness.”

The mechanistic requirements for this kind of darwinian process are now better known than they were in the 1870s; they go well beyond the selective-survival summary of darwinism that so often trivializes the issue. Charles Darwin, alas, named his theory natural selection, thus leading many of his followers to focus on only one of what are really a half-dozen essential aspects of the darwinian process. Thus far, most “darwinian” discussions of the brain’s ontogeny, when examined, turn out to involve only several of the darwinian essentials — and not the whole creative loop that I discuss in later chapters.

    I attempted to apply these six darwinian attributes to our mental processes in The Cerebral Symphony and in “Islands in the mind,” published in Seminars in the Neurosciences in 1991, but at that time I hadn’t yet found a specific neural mechanism that could turn the crank. Later in 1991, I realized that two recent developments in neuroscience — emergent synchrony and standard-length intracortical axons — provided the essential elements needed for a darwinian process to operate in the superficial layers of our cerebral cortex. This neocortical Darwin Machine opens up a broad neurophysiological-level consideration of cortical operation. With it, you can address a range of cognitive issues, from recognition memory to higher intellectual function including language and plan-ahead mechanisms — even figuring out what goes with the leftovers in the refrigerator.

Despite the heritage from William James and Kenneth Craik, despite the recent interdisciplinary enthusiasm for fresh darwinian and complex adaptive systems approaches to long-standing problems, any such darwinian crank is going to seem new to those scientists who have little detailed knowledge of darwinian principles beyond the crude “survival of the fittest” caricature.

    For one thing, you have to think about the statistical nature of the forest, as well as the characteristic properties of each type of tree. Population thinking is not an easily acquired habit but I hope that the first chapter will briefly illustrate how to use it to make a list of six essential features of the darwinian process — plus a few more features that serve as catalysts, to turn the ratchet faster. Next comes a dose of the local neural circuits of cerebral cortex, as that is where the triangular arrays of synchronized neurons are predicted, that will be needed for both the coding and creative complexity aspects. This is also where I introduce the hexagon as the smallest unit of the Hebbian cell-assembly and estimate its size as about 100 minicolumns involving 10,000 neurons (it’s essentially the 0.5 mm macrocolumn of association cortex, about the same size as the ocular dominance columns of primary visual cortex but perhaps not anchored as permanently). This is where compressing the code is discussed and that puts us in a position to appreciate how long-term memory might work, both for encoding and retrieval.

    About halfway through the book, we’ll be finished with the circuitry of a neocortical Darwin Machine and ready to consider, in Act II, some of its surprising products: categories, cross-modality matching, sequences, analogies, and metaphors. It’s just like the familiar distinction we make between the principles of evolution and the products of evolution. The products, in this case, are some of the most interesting ways that humans differ from our ape cousins: going beyond mere category formation to shape up further levels of complexity such as metaphor, narrative, and even agendas. I think that planning ahead, language, and musical abilities also fall out of this same set of neocortical mechanisms, as I’ve discussed (along with their “free lunch” aspects, thanks to common neural mechanisms) in my earlier books.

Some readers may have noticed by now that this book is not like my previous ones. They were primarily for general readers and only secondarily for fellow scientists, but that order is reversed here. To help compensate, I’ve provided a glossary starting at page 203 (even the neuroscientists will need it for the brief tutorials in chaos theory and evolutionary biology). Consult it early and often.

    And I had the general reader firmly in mind as I did the book design (it’s all my fault, even the page layout). The illustrations range from the serious to the sketchy. In Three Places in New England, the composer Charles Ives had a characteristic way of playing a popular tune such as “Yankee Doodle” and then dissolving it into his own melody; even a quote of only four notes can be sufficient to release a flood of associations in the listener (something that I tackle mechanistically in Act II, when warming up for metaphor mechanisms). As a matter of writer’s technique, I have tried to use captionless thumbnail illustrations as the briefest of scene-setting digressions, to mimic Ives. I have again enlisted the underground architect, Malcolm Wells, to help me out — you won’t have any trouble telling which illustrations are Mac’s! Furthermore, a painting by the neurobiologist Mark Meyer adorns the cover. For some of my own illustrations, alas, I have had to cope with conveying spatiotemporal patterning in a spatial-only medium (further constrained by being grayscale-only and tree-based!). Although I’ve relied heavily on musical analogies, the material fairly begs for animations.

    I have resisted the temptation to utilize computer simulations, mostly for reasons of clarity (in my own head — and perhaps also the reader’s). Simulations, if they are to be more than mere animations of an idea, have hard-to-appreciate critical assumptions. At this stage, simulations are simply not needed — one can comprehend the more obvious consequences of a neocortical Darwin Machine without them, both the modular circuits and the territorial competitions. Plane geometry fortunately suffices, essentially that discovered by the ancient Greeks as they contemplated the hexagonal tile mosaics on the bathhouse floor.

Everyone knows that in 1859 Darwin demonstrated the occurrence of evolution with such overwhelming documentation that it was soon almost universally accepted. What not everyone knows, however, is that on that occasion Darwin introduced a number of other scientific and philosophical concepts that have been of far-reaching importance ever since. These concepts, population thinking and selection, owing to their total originality, had to overcome enormous resistance. One might think that among the many hundreds of philosophers who had developed ideas about change, beginning with the Ionians, Plato and Aristotle, the scholastics, the philosophers of the Enlightenment, Descartes, Locke, Hume, Leibniz, Kant, and the numerous philosophers of the first half of the nineteenth century, that there would have been at least one or two to have seen the enormous heuristic power of that combination of variation and selection. But the answer is no. To a modern, who sees the manifestations of variation and selection wherever he looks, this seems quite unbelievable, but it is a historical fact.
    Ernst Mayr, 1994

Looking back into the history of biology, it appears that wherever a phenomenon resembles learning, an instructive theory was first proposed to account for the underlying mechanisms. In every case, this was later replaced by a selective theory. Thus the species were thought to have developed by learning or by adaptation of individuals to the environment, until Darwin showed this to have been a selective process. Resistance of bacteria to antibacterial agents was thought to be acquired by adaptation, until Luria and Delbrück showed the mechanism to be a selective one. Adaptive enzymes were shown by Monod and his school to be inducible enzymes arising through the selection of preexisting genes. Finally, antibody formation that was thought to be based on instruction by the antigen is now found to result from the selection of already existing patterns. It thus remains to be asked if learning by the central nervous system might not also be a selective process; i.e., perhaps learning is not learning either.
    Niels K. Jerne, 1967

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