Mankind Rising: Why Evolutionary Developmentalism Will Inherit the Future

Evo Devo Universe - An Interdisciplinary Research Community

Evo Devo Universe – Exploring Models of Universal Evolution and Development

What is evolutionary development (“evo devo”)? It is a minority view of change in science, business, policy, foresight and philosophy today, a simultaneous application of both evolutionary and developmental thinking to the universe and its replicating subsystems. It is derived from evo-devo biology, a view of biological change that is redefining our thinking about evolution and development. As a big picture perspective on complex systems, I think evo devo models will be critical to understanding our past, present, and future. The sixty-some scholars at Evo Devo Universe, an interdisciplinary community I co-founded with philosopher Clement Vidal in 2008, are interested in arguing, critiquing and testing evolutionary and developmental models of the universe and its subsystems, and exploring their variations and implications.

Whatever else our universe is, and allowing that there are physical mysteries, like dark matter, dark energy, the substructure of quarks, and the nature of black holes still to be uncovered, reasonable analysis suggests that it is both evolutionary and developmental, or “evo devo”. Like a living organism, it undergoes both experimental, stochastic, divergent, and unpredictable change, a process we can call evolution , and at the same time, programmed, convergent, conservative, and predictable change, a process we can call development. Evo devo thinking is practiced by any who realize that parts of our future are unpredictable and creative, while other parts are predictable and conservative, and that in the universe, as in life, both processes are always operating at the same time.

Does our Universe have a  developmental life cycle? Evolutionary developmentalists think it may.

Like living organisms, our universe may have a developmental life cycle.

Our universe builds intelligence in a developmental hierarchy as it unfolds, from physics, to chemistry, to biology, to biominds, to postbiological intelligence. As physicists like Lee Smolin (The Life of the Cosmos, 1999) have argued, our universe may also be chained to a developmental life cycle, like a living organism. Since almost every interesting complex system we know of within the universe, from solar systems to cells, undergoes some form of replication, inheritance, variation, and selection to build its complexity, it is parsimonious (conceptually the simplest model) to suspect this is how the universe built its complexity as well, within a still poorly understood environment that physicists call the multiverse.

An evo devo universe answers theologian William Paley’s famous watchmaker argument, that only a God could have designed our planet’s breathtaking complexity, with the observation that any physical system that has both evolutionary  (variation) and developmental (replication, inheritance) features, and operates in a selective environment, will self-organize its own adaptive complexity as replication proceeds. Consider how replicating stars have advanced from the primitive Population III stars to the far more complex Population I solar systems, like our Sun and its complex planets, over galactic time. Replicating evo devo chemicals built up from nucleic acids to cells, over billions of years. Replicating evo devo cells created multicellular life with nervous systems,  again over billions of years.  Replicating evo devo nervous systems forged hominids, over roughly 500 million years. Replicating languages, ideas, and behaviors in hominid brains birthed nonbiological computing systems, over something like 5 million years. Now computing and robotics systems, whose replication is presently aided by human culture, are soon (within the next few decades, it seems) going to be able to replicate, evolve, and develop autonomously. As much as some might find comfort in believing in a God who designed our universe, it is perhaps even more comforting to believe, tentatively and conditionally, in the history and abilities of this evolutionary and developmental  self-organization process itself. Evo devo processes have apparently created both matter and mind, and have been astonishingly resilient to generating complexity and intelligence at ever-accelerating rates. These processes may even transcend our universe, and may have determined the first replicator, if such a thing exists. Then again, perhaps our physics and information theory will never reach back that far, and such knowledge may forever remain metaphysics. In the meantime we can say that Big History, the science story of the universe so far, is sufficiently awe inspiring, humbling, useful, and hopeful to give us guidance, once we place it in an evo devo frame. As we’ll suggest, we now know enough about evolution and development at the universal scale to begin relating these processes to our own lives, and most interestingly, to ask how we can make our values and goals more consistent with universal processes.

As our universe grows islands of accelerating local order and intelligence in a sea of ever-increasing entropy, physics tells us this process cannot continue forever. The universe’s “body” is aging, and will end in either heat death, or a big rip, or both. If our universe is indeed a replicating complex adaptive system that engages in both evolution and development, as it grows older it must package its intelligence into some kind of reproductive system, so it’s complexity can survive its death and begin again. Developmental models thus argue that intelligent civilizations throughout the universe are part of that reproductive system – protecting our complexity and ultimately reproducing the universe and further improving the intelligence it contains. In other words, growing, protecting, and reproducing personal, family, social, and universal intelligence may be the evolutionary developmental purpose of all intelligent beings, to the greatest extent that they are able.

Charles Darwin - Father of Evolutionary Theory

Charles Darwin, On the Origin of Species, 1859

Beginning in 1859, Charles Darwin helped us to clearly see evolutionism in living systems, for the first time. Discovering that humanity was an incremental, experimental product of the natural world was a revolutionary advance over our intellectually passive, antirational and humanocentric religious beliefs. But until we also understand and accept developmentalism, recognizing that the universe not only evolves but develops, the purpose and values of the universe, and our place in it will remain high mysteries about which science has little of interest to say. Our science will remain infantile, descriptive without also being prescriptive, and unable to deeply inform our morality and politics. That must and will change in coming decades.

Discovery_Channel_Curiosity

Curiosity – A Discover Channel TV Series

As an example of where we are today, I just watched a Discovery Channel program on evolution, Mankind Rising, available for $1.99 at YouTube It is Season 2, Episode 8 of Curiosity a new educational television series launched by Discovery founder and chairman John HendricksCuriosity is a five-year, multi-million dollar initiative to tackle fundamental questions and mysteries of science, technology, and society, in sixty episodes. There is also a commendable Curiosity initiative in American K-12 schools, to use the show to increase our children’s engagement in STEM education.

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Mankind Rising – Season 2, Episode 8 of Curiosity

Mankind Rising considers the question “How did we get here?” It tells the journey of humanity from the cooling of life’s nursery, Earth, 4 billion years ago, and the emergence of the first cell 3.8 billion years ago, to Homo erectus, anatomically modern humans, 1.8 million years ago. It does this in one 43 minute time-lapsed computer animation, the first time our biological history has received such a treatment, as far as I know. The animation is primitive, but it holds your interest enough to follow the story. And what an amazing story it is.  We see a lovely visualization of the Phylogenetic hypothesis, which proposes that human hiccups are a holdover from our amphibian ancestry, when we gulped air at the surface across our gills, which are now vestigial (think of pharyngeal pouches in human embryos), before we grew lungs. Human babies do a lot of gulping-hiccuping both in utero and when born prematurely, and both amphibian gill-gulping and human hiccups are stopped by elevated carbon dioxide, hence the folk remedy to breathe into a bag to stop them.

We also get to see the rise of the first tool users, Homo habilis, 4 million years ago, in a dramatic sequence where an early human strikes one rock against another and is fascinated to discover a sharp rock in his hands. H. habilis’ ability to hold sharp rocks and clubs in their hands, and use them imitatively in groups to defend against other animals was perhaps the original human event. The best definition of humanity, in my opinion, is any species that gains the ability to use technology creatively and socially to continually turn themselves into something more than their biological selves. We inevitably become a species with both greater mind (rationality, intellect) and greater heart (emotion, empathy, love), two core kinds of intelligence. I would predict the first collaborative rock-users on any Earth-like planet must soon thereafter become its dominant species, as there are so many paths to further adaptiveness from the powerful developmental duo of creative tool use and socially imitative behavior.

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Homo habilis, perhaps the first persistence hunters.

One clever thing that the first socially-adept rock- and club-holding animals on any Earth-like planet gain access to is pack hunting (and if good at sweating,  persistence hunting). Learning these two skills may have doubled our brain size by giving us our first reliable access to meat, a very high-energy fuel source. We may have begun with pack hunting by ambush, which chimpanzees do today, and then graduated to persistence hunting, or running down our prey. We primates sweat across our entire bodies, not just through our mouths like other mammals. Humans have developed our sweating and cooling ability the best of all primates by far. As a result, two or three of us can run to heat exhaustion any animals that can’t sweat, if we hunt them in the mid-day sun.  Some people persistence hunt even today, as seen in this seven minute Life on Earth clip of San Bushmen running down a Kudu antelope in the Kalahari desert. Mankind Rising ends with Homo erectus (“human upright”), possibly the first language-using humans, 1.8 million years ago. We don’t yet have fossil evidence their larynx was anatomically modern, but there are indirect arguments.  Language, both a form of socially imitative behavior and a fundamental tool for information encoding and processing, was very likely the final technology needed to push our species from the animal to the human level.

In Evolutionism, the Universe is a massive set of Random Events, Randomly Interacting.

In Evolutionism, the Universe is a Massive Set of Random Events, Randomly Interacting.

Unfortunately, there are serious shortcomings to Mankind Rising as an educational device. The show’s narrative, and the theory it represents, are the standard one-sided, dogmatically-presented story of life’s evolution, with no hint of life’s development. As a result, it treats humanity’s history as one big series of unpredictable accidents. This is the perspective of universal evolutionism, also called “Universal Darwinism” ”, which considers random selection to be the only process in universal change, ignoring the possibility of universal development. In evolutionism, all the great emergence events are told as happening randomly and contingently. The show even makes the extreme claim that life itself emerged “against the laws of probability.” The emergence of humanlike animals is also presented as a stroke of blind luck, because the K-T meteorite wiped out our predators, the dinosaurs. All of this is true in part, only from one set of perspectives, that of the individual, organism, or individual event. In other words this story, and evolutionism in general, is a dangerously incomplete half-truth.

When we look at the same events from the perspective of the universal system, the environment, or distributions of events over time, we can easily argue that many particular forms and functions appear physically predetermined to emerge. Consider two genetically identical biological twins, or two snowflakes. Most of what happens to them up close, at the molecular scale, is randomly, contingently, unpredictably different. The microstructure of all the twin’s organs, including their brain, fingerprints, and many other molecular features are as different as the designs on two snowflakes. But look at them from across the room, taking a system or environmental perspective, and you see that they achieve many of the same developmental endpoints over time. The twins have the same body and facial structure and many of the same personality traits, constrained by the organism’s developmental genes and the shared environment. The snowflake’s hexagonal structure is developmentally predetermined, constrained by the way water forms hydrogen bonds as it freezes.

Just like biological development, universal development happens because of the special initial conditions (physical laws, or “genes”) of our universe, the time constancy and environmental sameness (isotropy) of some physical law throughout the universe/system/environment, and the apparent commonness (ubiquity) of Earth-like planets in our universe, a suspicion that will hopefully be proven by astrobiologists in coming years. Examples of developmental processes and structures are easy to propose. We can see developmental physics in the motions of the planets, which are highly future-predictable, as Isaac Newton discovered. Other physical processes, such as the production of black holes in general relativity, the acceleration of entropy production, and of complexification in special locations, also appear highly predictable and universal. Other physics by contrast, such as nonlinear dynamics and quantum physics, looks highly evolutionary and unpredictable. As we move up the complexity hierarchy from physics to chemistry to biology, to society, our list of potential evolutionary and developmental forms and processes rapidly grows.

In Developmentalism, Certain Systemic Forms and Functions are Statistically Fated to Emerge in the Universe, as in Biological Development

In Developmentalism, Certain Universal Forms and Functions are Statistically Fated to Emerge, as in Biological Development

Convergent form and function in placental and marsupial mammals - a famous example of convergent evolution, or better, convergent evolutionary development.

Convergent form and function in placental and marsupial mammals – a famous example of convergent evolution, or better, convergent evolutionary development.

Other examples of inevitable, ubiquitous developments in our universe may include organic chemistry as the only easy path to complex molecular evo devo, iron-core, watery planets with carbon and nitrogen cycles and plate tectonics, and replicating nucleic acids, lipid membranes, amino acids, and proteins as the only easy path to cells, oxidative phosphorylation redox chemistry as the only easy path to high-energy chemical life, multicellular organisms, bilateral symmetry, eyes, skeletons, armor, jointed limbs, land colonization, wings, opposable thumbs, brains, language, social intelligence, written language, math, science, and various technology archetypes, from sharp rocks and clubs to levers, wheels, electricity, and computers. These potentially universal forms and functions may be destined to emerge, because of the particular initial conditions and laws of the universe in which evolution is occurring, and each are destined to become optimal or dominant, for their time, in environments in which accelerating complexification and intelligence growth are occurring.

On Earth, we have seen a number of these forms, such as eyes, emerge and persist independently in various separate evolutionary lineages and environments. Independent emergence, convergence, and optimization or dominance of developmental forms and processes is one good way to separate them from the much larger set of ongoing evolutionary experiments. Developmental forms and functions are those that will be more adaptive at each particular stage of environmental complexity, in more contexts and species. Think of two eyes for a predator, over three or one eye. Or four wheels for a car, over three or more than four wheels. Or all the body form and function types that converged in placental and marsupial mammals. Australia separated early from the other continents, yet produced many similar mammal types via marsupials, plus a few new ones, like the kangaroo. This is a classic example of convergent evolution, or more accurately, convergent evolutionary development, when we examine biological change from the planet and universe’s perspective.

Evolution is destined to randomly, contingently, and creatively but inevitably discover these optimal developmental forms and functions, in most environments. For more on evolutionary developmentalism, feel free to read my 50-page precis, Evo Devo Universe?, 2008, and let me know your thoughts. To see one place where evo devo theory may eventually lead us, you might also enjoy my 12-page paper, The Transcension Hypothesis, 2012, which argues that black holes are uniquely strong attractors for the future of intelligent life throughout the universe.

Would Raptors Have Led Inevitably to Dinosaur Humanoids, if the K-T Meteorite Hadn't Hit 65 Mya? - The Dinosauroid Hypothesis, one of several Developmentalist  proposals, yet to be tested.

Would Raptors Have Led Inevitably to Dinosaur Humanoids (Dinosauroids), if the K-T Meteorite Hadn’t Hit 65 Mya? That is the Dinosauroid Hypothesis, a Developmentalist Proposal.

As another example ignored by the show, several evolutionary developmentalists have independently proposed that our very-easy-to-create-yet-general-purpose  “humanoid form”, a bilaterally symmetric bipedal tetrapod with two eyes and two opposable thumbs, is a very likely outcome for all biological intelligences that first achieve our level of sophistication on all Earth-like planets. If we saw such “early” alien intelligences from across a dimly lit room, if they somehow had access to a way to reach us very quickly, before they turned postbiological (they apparently don’t, and postbiologicals apparently have other interests) they would they look roughly like us, as the astrophysicist Frank Drake, author of the Drake equation, has argued. Our mammalian history may have given us a unique evolutionary pathway to our developmental humanity, one with its own irreplaceable value, but if the K-T meteorite hadn’t hit, it is easy for an evolutionary developmentalist to argue that the dinosaurs would have independently and inevitably discovered the humanoid form, rocks, language, and tools. Why?

If you have seen the movie Jurassic Park, or have read up on raptors like Troodon, you know that they had semi-opposable digits and hunted in packs, in both the day and the night. It is easy to bet that the first raptor descendants that also learned how to hold sharp rocks and clubs in their hands in close-quarters combat would have forever after owned the role of top biological species. It would be game over, and competitive exclusion, for all other species that wanted that niche. Once you are manipulating tools in your hands, and speaking with your larynx, it’s easy to imagine that your body is forced upright, and your tail is no longer useful. You are engaged in runaway complexification of your social and technical intelligence – you’ve become human, and the leading edge universal intelligence has jumped to a higher substrate. Dale Russell, author of the Dinosauroid hypothesis in 1982, was scoffed at by conventional evolutionists back then, and the model is still largely ignored today, see for example Wikipedia’s short and evolutionist-biased paragraph on it. This treatment is most likely because of the hornet’s nest of implications that evolutionary developmentalism introduces. [I need to do an ISI Web of Knowledge search at a University to find all papers that have referenced Russell's paper in the 30 years since its publication, to review everyone who has looked seriously at the issue of dinosauroid evolutionary development since. Anyone want to volunteer for that?]

The hand of Stenonychosaurus inequalis, with a partly opposable digit.

The hand of Troodon inequalis, with a partly opposable digit.

Note the closeup of the hand of Stenonychosaurus (now called Troodon) inequalis, from Russell’s paper, “Reconstructions of the small cretaceous theropod Stenonychosauris inequalis and a hypothetical dinosauroid,” Dale A. Russell and Ron Séguin, Syllogeus, 37, 1982. The authors state the structure of the carpal block on Troodon’s hands argues that one of the three fingers partially opposed the other two as shown. The shape of the ulna also suggests its forearms rotated. It probably used its hands to snatch small prey, and to grab hold of larger dinosaurs while ripping into them with the raptorial claw on the inside of each of its feet. Troodon was a member of a very successful and diverse clade of small bipedal, binocular vision dinosaurs with one free claw on their feet, the Deinonychosaurs (“fearsome claw lizards”). These animals lived over the last 100 million years of the 165 million years of dinosaur existence, and were among the smartest and most agile dinosaurs known, with the highest brain-to-body ratios of any animals in the Mesozoic era. Most Deinonychosaurs had hands that were a useful combination of small wings and three long claws. Troodon was in a special subfamily that had lost the wings but retained the three long digits on each hand. According to Russell, Troodon’s brain-to-body ratio was the highest known for dinosaurs at the time. Because of their special abilities, I would argue that Deinonychosaurs  were not only members of an evolutionarily successful niche, they also occupied an inevitably successful developmental niche as well.

The assumption here, made by a handful of anthropologists and evolutionary scholars over the years, is that trees are a key niche, the “developmental bottleneck,” through which the first rock-throwing and club-wielding hominids will very likely pass, on a typical Earth-like planet. Swinging from limb to limb requires very dextrous hands, and just as importantly, a cerebellum and forebrain that can predict where the body will go in space. With their manipulative hands, with or without wings, their big, strong legs and multipurpose feet, yet their small size, Deinonychosaurs would have been impressive tree climbers, able to get rapidly up and down from considerable heights. If they were the largest and strongest animals physically capable of doing so, which seems likely, this argues that they would have permanently occupied the special niche that primates would later inhabit. Imagine primates trying to get into the trees later with Deinonychosaurs running about – I can’t. If  tree climbing and swinging is the fastest and best way to build grasping hands and predictive brains good at simulating complex trajectories and eventually, simulating and imitating the mental states of others in their pack, then if Deinonychosaurs dominated that niche, it is reasonable to expect a Deinonychosaur to be the first to make the jump to tool use. Troodon couldn’t swing in the trees, but he would have been very agile among them, able to use them for escape and evasion. He had two manipulative hands that would have been very useful both in killing and in avoiding being killed. This looks to me like a case for competitive exclusion. Tree environments may be the dominant developmental place on land to breed smart, socially-imitative and tool-using species, just as land appears to be the dominant developmental place for all species that use built structures, on any Earth-like planet, because water is a much less efficient and forgiving fluid than air. The irrepressible Jacques Cousteau discovered that octopi build small houses for themselves out of rocks, but tides and currents in water are very unforgiving, so it is logical that on any planet where land is also available, runaway tool use will happen on land first.

The universe, from its perspective, will use the first language-capable tool-using species to start selection for smarter and more social brains and ideas, in a new complexity space of memetic evo devo, using Richard Dawkin’s concept of the meme as any elemental mentally replicating behavior or idea, and also selecting for increasingly complex and self-aware technologies, in a new complexity space of technetic evo devo, using Susan Blackmore’s concept of the techneme as an elemental socially replicating technological form or algorithm. Once this explosive emergence happens, biological evo devo (genetic change) has now becomes so slow by comparison that its further changes are increasingly future irrelevant. Now memetic change (ideas) in concert with technetic change (tech forms and algorithms) drive the future.

Simon Conway Morris - A Leading Evolutionary Developmentalist (though he might not use that term :)

Simon Conway Morris – A Leading Evolutionary Developmentalist (but he might not use that term) :)

In the years since Russell’s indecent proposal, hundreds of other scientists, including the paleontologist Simon Conway Morris (Life’s Solution, 2001 and The Deep Structure of Biology, 2008) have proposed that humanity’s most advanced features, including our morality, emotions, and tool use, have all been independently discovered, to varying degrees, in other vertebrate and invertebrate species on Earth. If something happened to us, we can be confident that another species would very quickly emerge to become the dominant “human” tool-users in our place. In other words, local runaway complexification seems well protected by the universe. There is a developmental immune system operating, to ensure that human emergence, and remergence if catastrophes like the K-T meteorite occur, is both an inevitable and an accelerating event. Only the quality of our present transition to postbiological (not “posthuman”) status seems evolutionary, based on the morality and wisdom of our actions. Our pathway to and our subtype of humanity my thus be special and unique, but our humanity itself, in many of its key features, seems to be a product of the universe, far more than a product of our own free choice. Learning to see, accept, and better manage all this hidden development is the great challenge of our era.

Fortunately, these and other developmentalist hypotheses can increasingly be tested by computer simulation, as our computing technology, historical data, and scientific theory get progressively better. Run the universe simulation multiple times, and anything that appears environmentally dominant time and again is developmental. The rest, of course, is creative and evolutionary. To recap our earlier example, hexagonal snowflake structure will be developmental on all Earth-like planets with snow. But the pattern on each snowflake  will be evolutionary, and unpredictably unique, both on Earth and everywhere else. Nature uses both types of processes to build intelligence.

An Evo Devo Universe isn't a Ladder of  Life (above), or a Blind Watchmaker, but some  combination of the two.

In Evolutionary Development, the Universe is not just a Ladder of Nature (above), or a Random Experiment (standard Evolutionary theory), but some useful combination of the two simpler models.

Let me stress here that evolutionary development is no return to the Aristotelian scala naturae (Ladder of Nature, Great Chain of Being), where all important matter and process are predestined by God into some strict hierarchy of emergence. Only the developmental framework of universal complexification is statistically predetermined in evo devo models, not the evolutionary painting itself, which is the bulk of the work of art. Nor is it a Newtonian or Laplacian “clockwork universe” model, which proposed total physical predetermination. Each of these models of are universal development without meaningful evolution. They are as one-sided and incomplete as Darwinian theory is today. Nor is it the Blind Tinkerer that universal evolutionists like Richard Dawkins (The Blind Watchmaker, 1996) or the writers of Mankind Rising portray. It appears that our universe is more complex and interesting than these models suppose – it is predictable in certain critical parts that are necessary for its function and replication, and it is intrinsically unpredictable and creative in all the rest of its parts. Furthermore, unpredictable evolution and predictable development may be constrained to work together in ways that maximize intelligence and adaptation, both for leading-edge systems, and for universe as a system.

evo-devo

A Good Overview of Evo-Devo Biology

 Evo-devo biology is an academic community of several thousand theoretical and applied evolutionary and developmental biologists who seek to improve standard evolutionary theory by more rigorously modeling the way evolutionary and developmental processes interact in living systems to produce biological modules, morphologies, species, and ecosystems.  Books like From Embryology to Evo-Devo, 2009, and Convergent Evolution: Limited Forms Most Beautiful, 2011, are great intros to this emerging field. I expect most evolutionary developmental biologists would agree with the statement that evolution and development are in many ways opposite and equally fundamental processes in complex living systems, and that neither can be properly understood without reference to its interaction with the other.

If you doubt the idea of universal development, read this 2011 book!

If you doubt the idea of universal development, read this great 2011 book!

The best of this work realizes there are two key forms of selection and fitness landscapes operating in natural selection – evolutionary selection, which is divergent and treelike, with chaotic attractors, and developmental selection, which is convergent and funnel-like, with standard attractors. Thus evolutionary developmentalism is an attempt to generalize the evo-devo biological perspective to nonliving replicating complex adaptive systems as well, including solar systems, prebiological chemistry, ideas, technology, and in particular, to the universe as a system.

Let’s close this overview with one revealing example of the interaction of evolution and development. In biological systems, the vast majority of our genes, roughly 95% of them, are evolutionary, meaning they change randomly and unpredictably over macroscopic time, continually recombining and varying as species reproduce. Only about 3-5% of our genes control our developmental processes, and those highly conserved genes, our “developmental genetic toolkit“, direct predictable changes in the organism as it traces a life cycle in its environment. As I’ve argued before, as a 95%/5% Evo/Devo Rule, roughly 95% of the processes or events in a wide variety of complex adaptive systems, including organizations, societies, species, and the universe may turn out be creative bottom-up and evolutionary, and only 5% may be predictable top-down, and developmental, though this evo devo ratio must surely vary by system to some degree. The generic value of a 95/5 Rule in building and maintaining intelligent systems, if one exists, would explain why the vast majority of universal change appears to be evolutionary and unpredictable in complex systems, what systems theorist Kevin Kelly called Out of Control in his lovely 1994 book. Yet a critical subset of events and processes in these systems also appears to be developmental. Discovering that subset will make our world vastly more understandable, and show how it is constrained to certain future destinies, even as creativity and experimentation keep growing in the evolutionary domains.

So what do we gain from conditionally holding and exploring the hypothesis of universal evolutionary development? Quite a lot, I think:

First, we regain an open mind. Rather than telling humanity’s history from a dogmatic and one-sided perspective, and assuming that our past existence in the universe is predominantly a “random accident,” we remember that there are many highly predictable things about our universe, such as classical mechanics, the laws of thermodynamics, and accelerating change. This allows us to present life’s story as a mystery: What parts of its emergence are very highly probable, or statistically predetermined? What parts are improbable accidents? We lose our blind faith that neo-Darwinism explains all of life or the universe, and we realize that there appears to be a balance between evolutionary experiment and developmental predetermination in all things in the universe, as in life.

Second, we regain our humility. We no longer see ourselves as special, miraculous accidents. It is commonly suggested that we are incredibly unique in the universe, and that we emerged “against astronomical odds.” On the contrary, developmentalists suspect that many or all of the things we hold most dear about humanity, including our brains, language, emotions, love, morality, consciousness, tools, technology, and scientific curiosity, are all highly likely or even inevitable developments on Earth-like planets all across the universe. This kind of thinking, looking for our universals as well as our uniquenesses, moves us from a Western exceptionalism frame of mind to one that also includes an Eastern or Buddhist perspective. We may not only be unique and individual experiments, but we may also be members of a type that is as common as sand grains on a beach, instruments of a larger cycle of universal development and replication.

Third, we lose our unjustified fearfulness of and pessimism toward the future, and replace it with courage and practical optimism. The evolutionary accident story of humanity teaches us to be ever vigilant for things that could end our species at any moment. Vigilance is adaptive, but fear is usually not. We are constantly reminded by evolutionists that 99% of all species that ever lived are extinct (yes, but they were all necessary experiments, and their useful information lives on), and we live in a random, hostile and purposeless universe (no). Evolutionists conveniently forget that the patterns of intelligence in those species that died are almost all highly redundantly backed up in the other surviving organisms on the planet. Life is very, very good at preserving relevant pattern, information, and complexity, and now with science and technology, it is getting far better still at complexity protection and resiliency. When we study how complexity has emerged in life’s history, we gain a new appreciation for the smoothness of the rise of complexity and intelligence on Earth. Every catastrophe we can point to appears to have primarily catalyzed further immediate jumps in life’s accelerating intelligence and adaptiveness at the leading edge. Life needs regular catastrophe to make it stronger, and it is resilient beyond all expectation. What causes this resilience? Apparently a combination of evolutionary diversity and developmental immune systems, and we are still undervalue the former, and are mostly ignorant of the latter. If the universe is developmental, we can expect it has some kind of immune systems protecting its development, just as living systems do. The more we are willing to consider the idea that the universe may be both evolving and developing, the more we can open our eyes to hidden processes that are protecting and driving us toward a particular, predetermined future, even as each individual and civilization on Earth and in the universe will take its own partly unpredictable and creative evolutionary paths.

Fourth, we gain an understanding of universal purpose. Talk of purpose legitimately scares most scientists, who are so recently free of religion interfering in their work. They claim they don’t want to return to a faith-based view of the world, but we all have and should constantly revise a set of faiths, as our reason and intuition still leave so much unexplained. Unexamined faiths are the most dangerous kind. Evolutionists put a lot of unexamined and unrecognized faith in their purposeless universe model, so much that it can blind them to the value of admitting and exploring the unknown. Many scientists attack hypotheses of universal teleology wherever they find them – even as they live in a world that they clearly know is predictable in part. We must call that stance hypocrisy, as predictability is a basic form of teleology, or purpose. Evolutionary and behavioral psychologists are now proposing biologically-inspired scientific theories of human values. I recommend The Moral Landscape, by Sam Harris, 2011, which I’ve reviewed earlier.  But most of this work still is not deeply biologically-inspired, as it remains focused on evolution, ignoring development. We must recognize that a better understanding of universal evolution and development can help science derive more useful and more universal evolutionary and developmental values. I believe it is both the best definition and the purpose of humanity to use technology to continually reshape us, individually and collectively, into something more than our biological selves, and to do this in as deliberate and ethical a way as possible, using both evolutionary and developmental means. We can further realize that it appears to be our universal purpose to think, feel, act, and build in ways that maximize our intellectual and emotional intelligence, advancing our minds and hearts.

Fifth, we recognize that very important parts of the future are predictable. This benefit is the most useful to me as a professional futurist. Increasingly, we find foresight practitioners who accept the likelihood of developmental futures. Consider Pierre Wack at Royal Dutch/Shell’s foresight group, who proposed the inevitable TINA (There Is No Alternative) trends in economic liberalization and globalization in the 1980′s. Or Ron Inglehart and Christian Welzel, who have charted the inevitable developmental advance (with brief and partial evolutionary reversals) of evidence-based rationalism and personal freedom in all nations over the last 40 years.  Some leading recent books arguing for the inevitability of certain kinds of social development are Robert Wright’s Nonzero, 2000 (on positive sum rulesets), Steven Pinker’s The Better Angels of Our Nature (on violence reduction) and Ian Morris’sThe Measure of Civilization, 2013 (I have not read the latter but am very much looking forward to it). There are still far too many professional futurists who confidently and ignorantly claim that the future is entirely evolutionary (“cannot be predicted”). But a growing number of leaders, strategists, and futurists see regionally and globally dominant trends and inevitable convergences, make good predictions, and use increasingly better data and feedback to improve their models.

Great New Book on Prediction

Great New Book on Prediction

For a good recent book on this, read Nate Silver’s excellent The Signal and the Noise: Why So Many Predictions Fail But Some Don’t, 2012. I may review this book in a future post. As we learn take an evolutionary developmentalist perspective, at first unconsciously and later consciously, we will greatly grow our predictive capacity in coming decades. More of us will foresee, accept, and start managing toward the ethical emergence of such inevitable coming technological developments as the conversational interface and big data, deeply biologically-inspired (evo and devo) machine intelligence and robotics, cybertwins/digitwins and the values-mapped weblifelogs and peak experience summaries, the wearable web and augmented reality, teacherless education, and the metaverse. Professional futurists and forecasters are now developing our first really powerful tools and models that will keep expanding our prediction domains and horizons, and improving the reliability and accuracy of our forecasts. I believe evolutionary developmentalism is a foundational model that all long range forecasters and strategists need to embrace. Not only must we realize there are possible and preferable futures ahead of us, but we must be convinced that there are inevitable and highly probable futures as well, futures which can increasingly be uncovered as our intelligence, data, and methods improve. Such an effort, at a species level, is the only way we can map what remains truly unpredictable, at each level of our collective intelligence.

We’ve got a long way to go before modern science is willing to give the developmentalist perspective the same consideration and intellectual honesty that we presently give the evolutionist perspective. A lot of papers will have to be published. A lot of arguments will have to be made, and evidence marshaled. Courageous scientists will have to build the bridge from the developmentalist aspects of physics, chemistry, and biology to the highest aspects of our humanity, our ethics, consciousness, purpose, and spirituality. Convergent Evolution is one of several fields that will win lots of converts to developmentalism as it advances. Astrobiology will likely also play a big role, if it shows us just how common our type of life is in the universe, as many suspect it will.

A Classic in Foresight Literature - Parts of the Future are Quite Predictable

A Classic in Business Foresight – Parts of the Future are Quite Predictable. Ignore at Your Peril.

Fortunately, as futurist Alvis Brigis noted to me in a recent conversation, many of the world’s leading companies are already surprisingly developmentalist in their strategy and planning. We can trace this shift back at least to Pierre Wack’s strategy group at Royal Dutch/Shell in the 1980′s, as discussed in Peter Schwartz’s The Art of the Long View, 1996, a classic in business foresight. Wack realized that in order to do good scenario planning (exploring “what could happen”, and the best strategic responses to major uncertainties) one should first constrain the possibility space by understanding what is very likely to continue to happen in the larger environment.  To restate this in evo devo language, Wack recommended starting with developmental foresight, and then doing evolutionary foresight (exploring alternative futures) within a testable developmental frame. Treating both evo and devo foresight perspectives seriously is a key challenge for strategy leaders. Many management and foresight consultancies are good at one, but not the other, as it’s a lot easier to pick one perspective as your dominant framework than to have to continually figure out how to integrate two opposing processes. Yet both are critical to understanding and managing change.

I do technology foresight consulting for several companies, and follow foresight work at the consultancies, and I’m convinced that those companies with the best predictions, forecasts, and foresight processes interfacing with their strategic planning groups are winning increasingly large advantages in their markets every year. All the most successful companies realize there are many highly predictable aspects of our future, and collectively our business and government leaders are now betting trillions annually on their predictions. A few are using good foresight processes, but most are still flying by the seat of their pants.

The executives leading our most successful companies don’t see the world as a random accident, like an evolutionist, or some naive and self-absorbed postmodernist who lives off the exponentiating wealth and leisure of the very same science and technology that he argues are “not uniquely privileged perspectives” on the universe. Let’s hope our young scientists in coming years have the courage to be as developmentalist in their research, strategy, and perspective as our leading corporations are today. And as our biologically-inspired intelligent machines, destined to be faster and better at pattern recognition than us, will be a few decades hence. Will modern science recognize the evolutionary developmental nature of the universe before human-surpassing machine intelligences arrive and definitively show it to us? That is hard to say. But I believe we can predict with high probability that as mankind continues its incredible rise, our leaders, planners, and builders must become evolutionary developmentalists if we are to learn to see reality through the universe’s eyes, not just our own.

Saving the Titanic – Crowdsourcing to Find Hard Solutions, and Unlearning to Implement Them

A Good AH Collection

Hindsight is often 20/20. When we look to the past, we must guard against hindsight bias, where we assume more predictability to certain past events than actually existed. But rethinking the past is good practice. Not only does it allow us greater insight and self knowledge, as Mark Freeman argues in Hindsight, 2009, it helps us with foresight as well. Good hindsight helps us build better models of evolutionary and developmental process in the universe. We gain better ability to discriminate between those evolutionary interventions that could have changed the past, moving us to a new and different branch on the evolutionary tree of possibilities, and those which would not have been likely to change outcomes significantly, as there were developmental processes also at work, pushing the system in one predictable direction. The alternate history (AH) genre is concerned with these issues, looking for the leverage points in historical processes, and imagining “what if?” scenarios where those critical levers had been pulled in a different direction. What would the world be like today if Hitler and the Axis Powers had won WWII? What if Hero’s steam engine of 50 A.D. had been subsidized for R&D by an obsessed King, and steam-powered devices of increasing complexity had started competing against human labor two millenia earlier? Uchronia maintains a list over 3,100 AH novels, and lists annual awards. The Collected What If?, Robert Crowley (ed.), 2006 is a a great intro to the genre.

Debating alternative histories is good practice for envisioning alternative futures, one of the specialties of the foresight profession. Those who want to do more of that work will be glad to know that the U. Hawaii offers an MS in alternative futures, under futurist Jim Dator. There are of course evolutionary and developmental futures as well, as I argue in Evo Devo Universe?, 2008.  Evolutionary processes are those we can influence to multiple future states, like wars, competitions, or election results. Developmental outcomes are those where our interventions won’t change a future state at all, other than delaying or hastening it.  For example, every planet with intelligent biological life must eventually use rocks, spears, wheels, levers, and engines, if you believe in developmentalism. Each planet just takes different evolutionary paths to these outcomes. If any of us wanted to prevent the arrival of human-surpassing artificial intelligence, or the further acceleration of the world, there likely isn’t a damned thing any of us could do to prevent those developmental processes from going forward. They are going to happen. Most of us just aren’t aware yet how developmental they are. That apparent inevitability, on careful consideration, should be influencing us to focus our attention and resources on developing these particular outcomes well. The destination may be inevitable, but the path we take toward it is our evolutionary choice. We can fight it and get dragged to the future or we can ride the wave, its our choice.

As any future becomes increasingly predictable, the evolutionary choices for altering it must shrink and eventually disappear. At a certain point, there’s no choice left, only developmental certainty.  As the solution space shrinks, solutions may also get increasingly uncommon or “hard” — both hard to find and hard to implement. The only good way to quickly find and execute hard solutions may be crowdsourcing, or using collective intelligence, the power of a cognitively- and skills-diverse crowd, as social scientist Scott Page notes in his excellent book, The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools and Societies, 2008. At the same time, uncommon solutions may increasingly require unlearning existing habits, traditions, and protocols before they can even be implemented, as futurist Jack Uldrich notes in Higher Unlearning, 2011.

Here’s a quick  test: What could have been done, with a motivated crowd, after the collision with the iceberg to save many more of those who died on Titanic?

Recall the sinking of the RMS Titanic, a tragedy of great interest for over a century now, for so many cultural and psychological reasons that don’t need to be recounted here. She struck the edge of a very large iceberg at 11:40pm on April 15, 1912. The engines were stopped just prior to impact. After the impact, Captain Edward Smith summoned Thomas Andrews, one of the ship’s builders, to assess the damage. Around 12:10am, Andrews told the Captain that since five compartments were breached, the ship would sink with “mathematical certainty” in “an hour, or an hour and a half at most.” They also discussed the fact that there were only lifeboats for 1200 of the 2200 souls aboard. The Captain then sent the first wireless distress call, at 12:15am. PBS has a great new doc, Saving the Titanic, 2012, recounting story of the brave engineers, stokers, and firemen who worked to keep at least one of the boilers operational, for the lights and wireless, as long as possible. The ship sank at 2:20am, over two hours after the leaders knew the sinking was unavoidable.

Captain Edward Smith

Prior to the collision there were a number of small, common choices that could have prevented the tragedy. But after the impact, there were very few strategies left, if any, that might save the large majority of people on board. Captain Smith desperately needed some smart people looking hard for survival strategies around midnight on that fateful night. He needed ways to think beyond the box, to find any uncommon solutions. He needed a group of his best officers, crew, and passengers to work on that task. But Smith did not even advise many of his officers of the situation. As the wikipedia post on the sinking says, “he appeared to have become paralyzed by indecision. He did not issue a general call for evacuation, failed to order his officers to load the lifeboats, did not adequately organize the crew, and withheld crucial information from his officers and crewmen.” He also kept all the passengers in the dark as long as possible, to “avoid panic.” But this mushroom management strategy would just delay and eventually heighten total panic, not avoid it.

Those who’ve seen one of the films or read one of the books knows how poorly this approach worked. The first lifeboat wasn’t lowered until 12:45pm, 30 minutes after they were ordered to be uncovered, and that boat and many others were launched vastly under their capacity of 65. A “woman and children first” evacuation policy was proposed by Second Officer Charles Lightoller, but Captain Smith did not supervise the loading process, and no one had responsibility to maximize or expedite it. Passengers getting into the boats weren’t told the ship was sinking. Many wouldn’t get in the boats, preferring the apparent safety of the ship. Only eighteen of the twenty lifeboats were launched, over two hours. They ran out of time when they got to the last two collapsibles. Ultimately only 700 people were saved in the lifeboats, which had 1200 capacity. Even though Titanic’s crew manned them, only two of eighteen lifeboats went back to rescue survivors, pulling just nine people from floating wreckage. 1,502 people died. There have been countless safety improvements since, and six worse losses of life at sea in peacetime, but Titanic remains the most famous maritime disaster in history.

In addition to maximizing the lifeboats, which would have saved as many as 500 lives, a number of uncommon solutions for saving many more passengers have been proposed, in over a hundred years of hindsight. Experts and amateurs at sites like Encyclopedia Titanica, who know far more of seamanship, engineering, and Titanic history than I ever will, might heatedly critique what follows. But I think at least three are particularly excellent strategies, even if it may have taken an expert willing to think like an amateur to implement them, as we will discuss. Take a moment and see if you can think of any good solutions yourself, then look below the line.

Let me know if I’ve missed any, thanks.

_______________________________________________________________________________________________

One of the icebergs found near the Titanic

1. Return to the Iceberg. The iceberg that Titanic struck, perhaps this one found in the vicinity by the Minia shortly after,  had dropped ice on the forward deck on impact, so the ship’s officers knew, “with mathematical certainty” that they could get back to that point perhaps within twenty to thirty minutes, if they had navigated well. The berg they hit was described as like the Rock of Gibraltar, with at least one flat section. Hundreds of men might have been evacuated onto the berg. One of the ship’s deck cranes could have been used to drop a landing party, on a flat area or at the broken-up place where the ship had hit the berg, and they had steel cables, sledge hammers, anchor steel, and rope ladders on board. They could have used axes to hack holds and steel pitons to anchor some men to the ice. Perhaps the most critical solution, unless the iceberg were entirely flat, would have been to make modifications to the shoes and boots of several of landing party en route back to the berg, by nailing to the bottom small pieces of wood that in turn have nails protruding from their bottoms and nail heads from the sides, so their shoes would bite in the slippery ice. Notice how deep into solution space we need to get. Our innovation team needs to know about the specialized boots needed for ice climbing, and to have confidence in the ability of carpenters to make a few of these boots, in very limited time. The lifeboats could have been launched while returning slowly to the berg.

National Geographic’s / James Cameron’s Haunting Sinking Simulation http://www.youtube.com/watch?v=ARCEw7BdgLo

Restarting the engines for this, however, would have required the Captain to unlearn some of his many years of training. Titanic very likely had the ability to move either slow forward or slow reverse for at least an hour and a half after impact, but it was either stopped for good just before impact, or in another account, stayed stopped for ten minutes, then went slow ahead for ten minutes, then slow astern for five minutes and then stopped for good. In either case, the original iceberg would have been less than a mile behind them. There was probably also at least one other large berg nearby, and many smaller ones. An even more radical idea, and thus even more unlikely to be tried, would have been to ram the Titanic forward into one of the large icebergs, either slowly or quickly, to pin the sinking front end. This is the one solution that, in very unlikely circumstances, might have even saved the ship.

Unfortunately, unless a small crowd were charged with the task, these solutions were unlikely to be discovered, because of standard protocols. One would have been not move the ship after damage, for fear it would increase the rate of flooding. They might have tested this, if the alternative engine room account above is true. Unfortunately, it probably isn’t. Another protocol would have been to empty the hot boilers of their coal so they would not explode when cold seawater hit them. Thus protocols would have had to be modified for this solution to have been tried. Tradition, the way we think things must be done, is usually wise, but in uncommon situations, it can also be our greatest limitation.

SS Californian

2. Navigate to the Californian. Lookout Frederick Fleet saw a ship, the SS Californian, a small outline sitting next to a field of ice just five to ten miles away from Titanic, only ten minutes after she struck the iceberg. Fourth officer Joseph Boxhall then tried to signal this ship with Morse lamp and distress flares, and the Californian’s night crew, seeing the flares, almost understood the message, but not quite. The Californian was on the horizon the whole time, and they could have reached her, or gotten very close, if they had moved toward her, either forward or backward, at five to seven knots. At that slow speed they could have also launched lifeboats on the way.

Titanic’s crew could have made a bonfire on the top deck on the way there, for a visual signal. They could have used the ship’s horn to blow S.O.S., which would have carried in the calm night air for miles. They could have used their guns to make noise. Once they were around a mile away, they could have even reached the Californian with their bullets. At any point over the next two hours the Californian might have taken notice, heard the distress call on the wireless, and brought its lifeboats into action. Again, for this to work, thinking beyond the protocols against restarting the engines would have had to be done.

Adam and Jamie on a Makeshift Titanic Raft

3. Build Rafts. During the lifeboat evacuation, the crew and hundreds of male passengers could have been directed to find and tie as much floatable material together as possible, to make rafts. This is a truly crowdsourceable solution. In addition to all sorts of wood furniture, the ship had massive numbers of wood barrels, oil and food drums, and wood boxes that could have been emptied and lashed with rope, wire, and cable to make rafts. Anyone who’s made a raft knows how quickly you can get flotation if you have a very large number of wooden objects to pile together, as Titanic had. There was even a Mythbusters in Oct 2012 where Adam Savage and Jamie Hyneman, in picture left, show that if James Cameron’s Rose character had put her lifejacket under her plank, it would have supported Jack as well.

If the ship was not moving, rafts could have been lashed together in the water with rope, wire, and cable, and life vests, by a crew using one of or more of the deck cranes, to make a large floating island. You don’t want a lifejacket on you in the North Atlantic, at 28 °F (−2 °C), because you’ll die with full contact within 20 minutes, unless your lucky and drunk, like Charles Joughin, the baker who treaded water for at least an hour before being pulled onto the overturned Collapsible B lifeboat. You want your lifejacket under you, between you and the water, as part of your raft. Again that realization takes some unconventional thinking. It would have taken a bit more unconventional thinking at that point to realize that the women going into the lifeboats didn’t need the vests they were wearing. The men needed them, for rafts. Even if the ship had been moving toward either the iceberg or the Californian, the raft strategy could still have been done, in parallel with one of the two above. In that case the rafts could have been built on the top deck, and launched either by rope or later off the forward deck as the Titanic sank, bow first.

Could a motivated crowd have found any or all of these solutions? It is heartbreaking to learn that not a single raft was found in the wreckage. That is because the third class passengers were kept down below until the lifeboats were launched, and very few of the passengers knew the ship was sinking until the very end.

People are usually very effective at quickly coming up with solutions in tight time deadlines, if you trust them with the full news of the situation, however grim. When I googled “Titanic “return to the iceberg” ” I found that high school students came up with some version of both the raft and iceberg solutions, when challenged in a contest. That’s the power of the crowd. Recall NASA’s clever engineers improvising solutions to bring back Apollo 13 in 1970, or Sept 11, 2001, when just a few of United Airlines 93’s brave passengers, using their wits and their cellphones, identified and stopped the terrorists who had taken over their plane.

Certainly there are situations where the crowd doesn’t have the right mindset or training to handle collective power. Mob panic is real, and leaders may need to deny information for brief periods. But panic can also be managed, and justifications for not crowdsourcing solutions get rarer and briefer every year. Learning more conditions in which it makes sense to find and trust the crowd, and quickly build collective intelligence, is one of our greatest opportunities as managers. Here’s a KMPG and Manchester School of Business 2012 report on using the crowd, tamely defined here as bringing doctors and patients closer together in social networks, as a way to accelerate innovation in eHealth program deployment. This is is how it starts, but we can do so much more.

Innocentive’s Problem Solver Market

Whether we are talking about political, defense, economic, environmental, or social problems,  educated people usually deserve to know how bad the situation is, as quickly as possible, and to be empowered to come up with realistic, incremental, bottom-up solutions. To build their own rafts. For that to happen we need a lot more social transparency. We also need to help people become good raft builders. Think of Utah’s Mormons. We get the latter with better education and personal accountability, where irresponsible actions have consequences that are negative enough to influence behavior, while remaining noncatastrophic.

We all need to empower our crowds to come up with brilliant bottom-up solutions as often as possible. We can do this with our staff, our personal contacts, our customers, and the public. With proper education and guidance, cognitively diverse groups will usually find good solutions much faster than we will.

Henry Chesbrough, one of the pioneers of open innovation, has long advocated this. Think of Innocentive, and their growing community of technical problem solvers. Think of incentive prizes. Consider all the people on the web who self-identify as creative thinkers and problem solvers. How soon will LinkedIn or another dominant social network harness all the innovators and creative types into a general online platform that surfaces problems that need solving, and incentivizes competitions for the best solutions, with part of the payment being the growing reputation of the solver?

Spigit and Bright Idea are two new cloud-based innovation management platforms that use pairwise comparison ranking as a way to generate a better distribution of preferences among a set of competing ideas. Most of the other innovation and ideation platforms use simple voting, a crude algorithm that quickly becomes a popularity contest where early winners emerge, and later and potentially better ideas are rarely seen or evaluated. All of these tools are in their infancy, with little machine learning, collaborative filtering, or semantic analysis involved. Yet a few of them have crossed the chasm, as an early majority of cities and companies are now purchasing them, using them, and finding them valuable.

Futurists Venessa Miemis, Alvis Brigis and I just published an article, Open Foresight, in the Journal of Futures Studies, Sep 2012, 17(1): 91-98, where we argue that the best foresight projects in coming years will be based on open access, network-based, crowdsourced approaches. Using a cognitively diverse crowd will quickly generate a distribution of possible futures, and with good iteration and comparison algorithms, the best can rapidly filter to the top. I’m reasonably hopeful that the best of these innovation management platforms today will turn into the best of the open foresight platforms of tomorrow.

A plethora of ideas to be managed

But there’s another component to serious innovation, unlearning our old habits, that is equally necessary for our older managers and leaders, when you’ve got a good sized crowd presenting some great innovation ideas. Including more smart youth in leadership is one good way to avoid the tradition trap. Let’s hope we get other good tools for unlearning going forward as well.

Additions, critiques? Let me know thanks!

Preserving the Self for Later Emulation: What Brain Features Do We Need?

Let me propose to you four interesting statements about the future:

1. As I argue in this video, chemical brain preservation is a technology that may soon be validated to inexpensively preserve the key features of our memories and identity at our biological death.
2. If either chemical or cryogenic brain preservation can be validated to reliably store retrievable and useful individual mental information, these medical procedures should be made available in all societies as an option at biological death.
3. If computational neuroscience, microscopy, scanning, and robotics technologies continue to improve at their historical rates, preserved memories and identity may be affordably reanimated by being “uploaded” into computer simulations, beginning well before the end of this century.
4. In all societies where a significant minority (let’s say 100,000 people) have done brain preservation at biological death, significant positive social change will result in those societies today, regardless of how much information is eventually recovered from preserved brains.

These are all extraordinary claims, each requiring strong evidence. Many questions must be answered before we can believe any of them. Yet I provisionally believe all four of these statements, and that is why I co-founded the Brain Preservation Foundation in 2010 with the neuroscientist Ken Hayworth. BPF is a 501c3 noprofit, chartered to put the emerging science of brain preservation under the microscope. Check us out, and join our newsletter if you’d like to stay updated on our efforts.

As one of the themes of this blog I’ll try to explain why I’m optimistic about these technologies, and to enlist your help in pushing forward their validation or falsification as fast as feasible. If validated, I’ll be pitching to you for help in making the brain preservation option accessible and affordable around the world, as fast as feasible. To these ends, thank you for any frank and constructive feedback you can leave in the comments.

In this post, I’d like to try to provisionally answer a question relevant to the first three statements above:

To preserve the self for later emulation in a computer simulation, what brain features do we need?

We can distinguish three distinct information processing layers in the brain:[1]

1. Electrical Activity (“Sensation, Thought, and Consciousness”)
These brain features are stored from milliseconds to seconds, in electrical circuits.
2. Short-term Chemical Activity (Short- and Intermediate-term Learning – “Synapse I”)
These brain features are stored from seconds to a few days in our neural synapses (synaptome), by temporary molecular changes made to preexisting neural signaling proteins and synapses.
3. Long-term Molecular Changes (Long-term Learning – “Nucleus and Synapse II”)
These are stored from years to a lifetime in our neuron’s connectome, nucleus (epigenome) and synaptome, by permanent molecular changes to neural DNA, the synthesis of new neural proteins and receptors in existing synapses, and the creation of new synapses.

At present, it is a reasonable assumption that only the third layer, where long-term durable molecular changes occur, must be preserved for later memory and identity reanimation. The following overview of each of these layers should help explain this assumption.

1. Electrical Activity (“Sensation, Thought, and Consciousness”)

Our electrical brain includes short-distance ionic diffusion in and between neurons and their supporting cells (i.e., calcium wave communication in astrocytes), action potentials (how neurons send signals from their dendrites to their synapses), synaptic potentials (how signals cross the gaps between neurons), circuits (loops and networks) and synchrony (neurons that fire in unison, though they are widely separated). Electrical features operate at very fast timescales, from milliseconds to a few seconds, and are variable (not exact), volatile, and easily disrupted.

Neural Synchrony – Our Leading Model of Higher Perception and Consciousness . Image: Senkowski et.al., 2008

These features certainly feel very important to us. They include our sensations (sensory memory) and current thoughts (commonly called “short-term” memory by neuroscientists). Recurrent loops, special electrical circuits that cycle back on themselves, hold our current thoughts (when you rehearse some information to avoid forgetting it, you are literally keeping it “in the loop”). Neural synchrony creates our conscious perceptions, and when it happens in the self-modeling areas of our brain, it gives us self-aware consciousness.

Yet electrical features are also fleeting. When you sleep, or are knocked unconscious, or are given an anesthetic, your consciousness disappears, only to be “rebooted” later, from more stable parts of your brain. Our memories aren’t even recalled with precision but are rather recreated, as volatile electrical processes, from these molecular long-term stores, in ways easily influenced by our mental state and cognitive priming (what else is on our mind). That’s why eyewitness testimony is so variable and unreliable.

The electrical features of our self are thus like the “foam” on the top of the wave of our long-term memories and personality. They make us unique for a moment, as they hold only our most immediate thinking processes.[2] Amazingly, people who undergo special surgeries that stop their heart, and some who drown in very cold water, can have no detectable EEG (electrical patterns) for more than thirty minutes, and their brains successfully reboot after rewarming them. Essentially, these individuals are recovering from clinical brain death. Not only do they not have consciousness during this period, they have no unconscious thoughts. Yet because their deeper layers aren’t too disrupted, they can restart their electrical activities.

An excellent book about neural spikes, loops, and synchrony is Rhythms of the BrainGyorgy Buzsaki, 2006. It explains the emergent properties and integrative functions of these “highest order” electrical features of our brain. See also this recent discovery of electric field coupling among neighboring neurons, by leading neuroscientists Henry Markram, Christof Koch, and others, and reported by Peter Hankins on his great cognitive science blog, Conscious Entities. Ephaptic coupling is a way for neurons to synchronize spike timing in neighboring neurons, via a mechanism completely independent of synapses. Neurons are much more versatile in modes of communication and synchrony than previously thought.

My late mentor at UCSD, Francis Crick, and his Caltech collaborator, Christof Koch, call this topic the search for the Neural Correlates of Consciousness. It’s a great phrase. Consciousness is not a mystery we’ll never solve, but according to a number of neuroscientists it is a physical process of neural synchrony, in particular regions of your brain. These brief, rhythmic synchronizations share information between groups of neurons in distant regions of the brain by tightening up (“binding”) their interdependent sequences of action potentials. The synchronizations are controlled by the inhibitory neurons in our brain, which use the GABA neurotransmitter. Disrupt gamma synch, as with anesthesia, and you take away consciousness. Give a drug like zolpidem, which activates GABA neurons and increases gamma synch, to patients who are in persistent vegetative state, and amazingly, you will wake 60% of them up from their comas, to varying degreesWikipedia doesn’t yet have a good explanation of the gamma synchrony model of consciousness, but they will in a few more years. Laura Colgin at Kavli has found two reliable gamma synch mechanisms in rat hippocampus. She speculates that slow gamma makes stored memories available to current consciousness, and fast gamma integrates sensations to create conscious perceptions.  Though neuroscientists don’t yet all agree on the details, many have found neural correlates of sensations, thoughts, emotions, and consciousness in the electrical features of our brains. In conjunction with the short-term chemical changes we will describe next, these processes represent both our “highest” and our most volatile and impermanent self.

2. Short-term Chemical Activity (Short- and Intermediate-term Learning – “Synapse I”)

Short-term chemical activity is the next layer down. It involves all our short- and intermediate term learning and memory, everything beyond our sensations, current thoughts, and consciousness, but not including our long-term memories. We can call this layer “Synapse I.”

As our electrical experiences and thoughts race around the various circuits in our heads, we make a number of short-term learning changes in our neural networks to capture, for the moment, what we’ve just experienced and learned. These involve changes to preexisting proteins in our preexisting synapses (communication junctions), changes that last for minutes (short-term) to days (intermediate-term). These are changes in both the mechanics of neurotransmitter release and short-term facilitation (strengthening) or depression (weakening) of synaptic effectiveness. Synapses are temporarily modified by the precise timing and frequency of electrical signals (action potentials) received by the postsynaptic neuron, a process called spike-timing dependent plasticity. There are short-term changes in signaling molecules (neurotransmitters, cAMP, Ca++, CamKII, PKA, MAPK), and membrane receptors (NMDA). Phosphorylation states (chemical tags) are altered on some of these molecules, and a temporary equilibrium between kinases (enzymes that add phosphates to key molecules) and phosphatases (enzymes that take them away) is established in the synapse. [Note: In late 2012, Ye et. al. showed in Aplysia how precise spatiotemporal signaling in the synapse involving PKA holds short-term memories in synaptic electrochemical networks, and the interaction of PKA and MAPK holds intermediate-term memories in these networks, in a process called synaptic facilitation.] If any of these short- or intermediate-term memories or thinking patterns are selected to become long-term, communication with the cell nucleus must now occur, and new membrane proteins and synapses are then built, involving new or altered circuits in the connectome. If not, the new memory or pattern dies out.[3]

Every night, when we sleep, our short- and intermediate-term brain writes important parts of its experiences to our long-term memory, building durable new synaptic connections, where this learning can now stay with us for years to life, in a process called memory consolidation. This process moves a subset of our recent learning and memories, apparently the most relevant parts, from temporary spatiotemporal signaling states to permanent new synaptic structures, anchored to the cytoskeleton of each neuron. We can think of these new proteins, synapses, and circuits established in neural synapses and nuclei in a way that is very roughly like DNA, as they are long-term stable structures, encoded in a partly digital form, that will endure all the flux and variability of the biochemistry within each neuron, over a lifetime.  It is these unique synaptic and epigenetic networks that we must preserve, scan, and upload in creating neural emulations, as we will discuss. Long-term memory formation happens best when we are in slow wave (deep and dreamless) sleep, which we get in cycles during the night (and especially well if our sleeping room is dark and quiet) and also during a good nap (a great way to “lock in” what you’ve learned, after a demanding learning period that will naturally make you sleepy).

Neural dendrites, cell body,  action potential, and synapses. Image: Gallant’s Biology.

All our neurons work in circuits, and strengthen or weaken their connections based on chemical and electrical activity, in a process called Hebbian learning. Just like your muscles, which come in two sets that oppose each other around every joint, neural circuits are both excitatory and inhibitory at many decision points in the network. Perhaps most important decision points are the cell bodies of each neuron, where the nucleus is. The electrochemical current from all the dendrites (“roots”) of each neuron flows toward its cell body, and action potentials (current waves) flow from the cell body to its synapses (“branches”), along the axon (“trunk”) of each neuron. Glutamate is the main neurotransmitter we use to send excitatory current from a synapse to the dendrite of the next neuron in a circuit (the postsynaptic neuron). Glutaminergic synapses are thus called “positive” in sign, and they promote electrical activity throughout the brain. GABA is the main neurotransmitter we use to let inhibitory current leak out of a postsynaptic dendrite. GABAergic synapses are thus called “negative” in sign, and they depress circuits throughout the brain.

Each neuron sums the net result of the positive and negative inputs it receives from its dendrites, over milliseconds to seconds. If the current exceeds that neuron’s threshold,  it sends an action potential (depolarizing electrochemical signal) to all its synapses. As the brain learns, our synapses enlarge or shrink, giving them greater or lesser excitatory or inhibitory effect, and we may grow more or lose our synapses. With few exceptions, each neuron also uses just one type of neurotransmitter (eg., glutamate or GABA), or the same small set of neurotransmitters, at all its synapses.

The architecture of memory, thought, emotion, and consciousness may thus be reducible to a surprisingly simple set of algorithms, connections, weights, signaling molecules and electrical features in each neuron, working together in a massively parallel way to create computational networks that are far more complex than the individual parts.

Hippocampus and frontal lobes. Image: NIH

In higher animals, the neurons in our hippocampi (two c-shaped areas of ancient, primitive, three-layer cortex in each hemisphere of our brain), and the connections they make to the rest of our cerebral cortex (especially to our frontal cortex), store all kinds of episodic (experiential) and declarative (fact-based) information, all from our last few days of life. At the same time, neurons in our cerebellum (a more primitive, “little brain” at the base of our skull) store procedural learning and memory (how to move our bodies in space). Experiments with rats and primates tell us that each hippocampus makes perhaps tens of thousands of new neurons every day, from neural stem cells. Other than for repair after certain kinds of injury, no other part of the adult brain is able to use stem cells in detectable numbers, as far as we know. The rest of our brain is postmitotic (unable to use cell division to maintain its structure), as neuroscientists demonstrated in an elegant experiment in 2006. Our neurons must be maintained by our immune and repair systems, and as they die via natural aging, or kill themselves in apoptosis, memories start to die.

Hippocampal dendritic spines. Image: Fiala & Harris, 2000.

Our hippocampal neurons have the very tough job of temporarily holding, in their uniquely dense synapses, and via their connections to the rest of the cortex, much of the new information we have learned over the last day or two, during our entire adult life. Here is a picture of a computer reconstruction of a small section of ten columns of synapse-rich “spiny dendrites”, from the CA1 (input) region of the hippocampus. CA1 contains areas like place cells, imprinted genetically with detailed maps of 3D space. Like the digestive cells lining our gut, and the skin cells at our fingertips, certain hippocampal neurons appear to get worn out on a regular basis by this demanding short-term memory holding function, and so some neuroscientists think new ones must regularly grow and mature to replace them.

People whose hippocampi are both surgically removed, like the memory disorder patient Henry Moliason, who had this done at the age of 27, can’t update their long-term episodic and declarative memories. H.M.’s long-term memory and personality was mostly “frozen” at 27. He could occasionally add bits of new information to long-term memories of the same type he’d built before the surgery, and he could learn new procedural (spatial and muscle) memories in his cerebellum, but he had no cerebral knowledge that he’d added these memories. H.M.’s amazing life suggests that if the brain preservation process damaged our hippocampi, but not the rest of our brain, we’d come back without our most recent experiences (two-day amnesia), but all our older memories and personality would still be intact.  Ted Berger at USC managed to build a simple version of an artificial electronic hippocampus for mice in 2005, so there’s a good reason to believe that this part of our brain, though important, isn’t irreplaceable. As long as you could install an artificial hippocampus in the computer emulation constructed from your scanned brain, you’d be back in business as a learning organism, with only some of your more recent memories and learning erased. This all helps us understand that what cognitive scientist Daniel Dennett would call our center of narrative gravity, our most unique self, is our long-term memory.

The fact that only special areas of our hippocampus can add new cells during life exposes a harsh reality about our biological brains. We are all born with a very large but fixed long-term memory capacity, and this capacity gets increasingly used up, pruned and potentiated, the older we get. Anyone over 40, like myself, knows they are considerably less flexible at learning new things than they were at 20. It’s far easier for older people to add more twigs to branches of knowledge we’ve previously built in our “tree of experience” than to form new branches. We can do it, but gets progressively tougher and slower the older we get.

This means, if we want to be lifelong learners in a world of accelerating technological and job change, it is critical to get an early education that is as categorically complete (global, cosmopolitan, and scientific), moral (socially good, positive sum) and evidence-based as possible. Our children need the best mental scaffolds they can get early on, or they’ll spend the rest of their lives trying to prune away harmful and untrue thoughts and beliefs acquired in their youth. Psychologists have long known that it is much easier to add increasing specificity to a neural network than it is to unlearn (depress) any branch, once it’s built. We need to be careful about what we allow into our memory palaces.

That said, children also benefit greatly from freedom, early on in life, to study what they themselves desire to learn, and to have a good degree of control over learning outcomes and style. This freedom, and appropriate rewards for effort of any kind, induce them to build intricate mental specializations in areas they are personally passionate about. For those who want to know how to implement a 50/50 balance of broad, state-mandated learning in future-critical STEM fields, analytical thinking, and civics (the “hilt of the sword”, basic protective world knowledge), and a personalized program of student-directed specialized learning, creativity, and play in the other half of the time, mastering whatever they can convince their teachers is worth studying (the “blade of the sword”, passionate specialization allowing them to cut their own unique path of new knowledge and value in the world), I strongly recommend The Finland Phenomenon, 2010 . This exceptional film, and to a lesser extent Tony Wagner’s book Creating Innovators, 2012, demonstrate key elements of the future of learning for enlightened societies, in my opinion. It may take 20 years for the evidence to be incontrovertible, but you can give it to your child now, if you find it appealing. The US will eventually realize that if the Finns did it, rejuvenating their previously failing education system over a twenty year period, we can too.

Cybertwin – Virtual Assistants With Simple Models of Our Interests Will Be Useful for Many of Us By the Early 2020′s. Image: MyCyberTwin.com

It is also liberating to realize that while our biological brains are less able to learn fundamentally new things as they age, all the digital technologies we use, technologies which will bring our emulations back at an affordable price later this century, will continue to get exponentially more powerful every year. Most of us don’t realize this, but everyone who uses a social network, email, or any other technology to capture things they say, see, and write about is also creating a digital simulation of themselves. By 2020 we’ll all be talking to and with our best search engines in complex sentences (the conversational interface), and shortly thereafter, we’ll all be able to use simple software agents, Cybertwins, or “Twins,” which will have crude maps of our interests and personality, so they can serve us better. Computational linguists know that if you capture what a person says for just two years, we are so repetitive about what we care about that a cybertwin could whisper into our ear the word that natural language processing algorithms predict we want when we are having a senior moment, and they’ll be right most of the time. That’s how repetitive we are, and how good web search will be by 2020. As I wrote in 2005, people who don’t run cybertwins will be much less productive, so they’ll be very popular, even though they’ll bring lots of new social problems in their first generation.

Now here’s the kicker: These simulations won’t be turned off by our loved ones when we die. Our children, friends, and colleagues will use them to interact conversationally, and only in appropriate contexts, with these semantic simulations of us, to keep the best of our thoughts, experiences and personalities accessible to them when desired. Once folks realize that their Twins really are a “digital immortalization” of parts of themselves, and once neuroscience has proven – in the next ten to twenty years perhaps – that we can read (“upload”) particular memories from preserved and scanned animal brains, even very primitive ones, at that point preserving one’s brain for later uploading into a Twin, to improve the quality of the simulation at least, and perhaps even to come back in a self-aware state, at best, will be an increasingly obvious and responsible choice for dying individuals, especially if the cost to do so is quite affordable. What’s more, recent advances in molecular scale MRI scanning strongly suggest that future scanning technologies should be able to nondestructively scan entire preserved brains, to upload their molecular states, memories, and higher functions into a Twin. So if the first scan isn’t perfect, it can always be updated later, from the preserved, “immortal” brain.

Given all this, we can see that teaching our children and ourselves to be digital natives and digital activists, to use the social web and the first affordable commercial lifelogs, like Google glass, when they arrive, is an important way for us to build an ever more capable cybertwin for ourselves and for our loved ones when we die (and ideally, are preserved), even as our biological self naturally slows down and simplifies (prunes away branches of knowledge and memories we once had ready access to) with advancing age.

Now we arrive at our truest self, the part we care most about preserving and sharing with our loved ones and society. It is this self that I expect will later merge with the Twin that many of us will leave behind in the 2020′s, as strange as that might sound today.

Experience-based learning. Image: Graham Paterson, Children’s Hospital Boston

3. Long-term Molecular Changes (Long-term Learning – “Nucleus and Synapse II”)

The production of long-term memory, personality, and identity requires all the short-term synaptic changes above, plus permanent molecular changes in the neuron’s Nucleus (DNA and its histones, or wrapping proteins), and the permanent creation of new cellular proteins, synapses, and circuits (Synapse II). Here’s a brief summary of our understanding of the process[4]:

Nucleus (“Genome, Transcriptome, and Epigenome”)
1. Retrograde transport and signaling from the synapse to the nucleus
2. Activation of nuclear transcription factors and induction of gene expression
3. Chromatin alteration and epigenetic changes in gene expression (gene-protein networks)
Synapse II (“Connectome and Synaptome”)
4. Synaptic capture of new gene products, local protein synthesis, and seeding of new synaptic sites
5. Permanent synaptic changes, activation of preexisting silent synapses, formation of new synapses.

We used several “-ome” words above. Let us briefly consider each. They are very roughly ordered below in terms of their likely contribution to our unique self, from least to most important:

The Genome. These are inherited genes and gene regulatory networks that control instinctual behaviors. Our genome includes the unique alleles we received from our parents. It is easy to preserve, as it is the same in all cells. With one tissue sample we can create a clone later, either physically, or far more likely, in a computer simulation. But this clone has only our inherited uniqueness. We’ll need contributions from the next four “omes” to add our life memories and learning to the emulation.

The Transcriptome. This is the set of proteins made (transcribed) by cells. While proteomics (another “ome” word) is in its infancy, scientists estimate each of our cells has the DNA to express ~20,000 basic protein types. Each type can be further modified after creation by adding or removing chemical tags like phosphate, methyl, ubiquitin, and other small molecules, so that more than a million protein subtypes may exist in a typical human body. Fortunately, each of our ~220 cell types only uses around 5,000 of these 20,000, and perhaps less than 2,000 of the 5,000 are unique to each cell type. Neurons and glia, the cell types we are most interested in, may use just a few hundred protein types to store our higher learning and memory in the nucleus and synapses. The other proteins are there to keep all of our cells alive, which is a critical precondition to being able to store long-term memories in a special subset of neural structures. All this suggests the proteomics of memory and identity, and of later memory and identity reconstruction from scanned brains, are not impossibly complex, but rather highly challenging, fascinating and eventually solvable problems.

The Epigenome. These are learning-based changes in gene-protein networks that happen in the nucleus of each neuron, mostly during the life of the organism. The Dutch famine of 1944 and the Överkalix study in Sweden tell us that some epigenetic changes can be inherited in humans, so we all should seek good nutrition and avoid toxin exposure, as we may pass some of that to our children in the form of compromised and undermethylated epigenomes. But there is a lot more to the epigenome story still to be uncovered, as this 2011 article on epigenetic regulation in learning and memory in Drosophila makes clear. Our epigenome is a gene-regulatory layer that involves chemical changes, mostly methylation, to DNA and to the histone proteins that wrap and expose DNA in the cell nucleus. These changes determine how DNA, RNA, and protein are expressed in the nucleus, and thus may affect, at least to some degree, how neurons uniquely manage their synapses as they grow and learn.

The Connectome. This is a map of our neural cell types, and how they connect. Our connectomes and much of our dendrite structure is very similar in all of us. This shared developmental structure makes it easy for us to communicate as collectives, for ideas or “memes” to jump from brain to brain. Yet with 100 billion neurons making an average of 1,000 connections to other neurons, and most of these not being developmentally controlled, we’ve got the ability to make 100 trillion connections, the large majority of which will be unique to each individual.

The Synaptome. These are key features of the ~1,000 synapses that each neuron makes to others. They are the particular long-term molecular features that determine the strength and type of each synapse, its signaling states and electrical properties, as we’ve described them above. The synaptome is the weight and type of the 100 trillion connections described above, and this information may be the most important “recording” of our unique self. Fortunately, because memories are stored in a highly redundant, distributed, and associative manner in our synaptic connections, our synaptome is to some degree fault tolerant to cell death. Both artificial and biological neural networks experience graceful degradation (partial recall, incremental death) of higher memories as individual neurons die. We also know the molecular code of long term memory is fault tolerant to the noise, deformations, and chaos of wet biology. The feedback loops between the electrical and gene-protein network subsystems interact somehow to stabilize long term memories in a special subset of durable molecular changes, in spite of all the other biochemistry furiously going on to keep the cell alive.

Single-celled animal. Image: Anthony Horth

I am sure the distinguished futurist and technologist Ray Kurzweil will have a lot more to say about these topics in his next book, How to Create a Mind, which comes out next month. You can preorder a copy here. To understand how these subsystems interact in a living organism, let’s start in as simple a model organism as we can find, single-celled animals, organisms that don’t even have nervous systems as we know them. Wetware, Dennis Bray, 2009 is a great tour of these animals. Single-celled eukaryotes like Stentor, Paramecium, and Amoeba do complex information processing, and hold short-term memories in their chemical networks. In 2008, we learned that Amoeba remember and anticipate cold shocks, for example. These networks include the cell’s genome, epigenome, cellular proteins, cytoskeleton, receptors, and cell membrane. They are true computational networks, with both neural-network like and Boolean logic properties. Genes and proteins integrate signals from other genes and proteins, and selectively switch and transmit signals, just like neurons do. The genes in each cell, via RNA, determine which proteins are made, when and where. Most protein changes are part of the short term computation being done in a cell, but a special few will lead to lasting changes in the epigenome and the cytoskeleton and receptors in and on the surface of the cell. These long-term changes are the ones we care most about, as they store the cell’s unique memory and identity.

Until computational neuroscience[5] can predictively model how the gene-protein networks in a Paramecium allows these animals to evaluate options, assign priorities, regulate their moment-by-moment computational attention, continually vary strategies for chasing prey and avoiding toxins, and chemically store their representations, habituations, and memories in an intracellular environment, all within a single cell that has no proper nervous system, the field will be missing its Rosetta Stone. Electrical waves exist in these single-celled animals, but with the exception of mitochondrial energy production, they are of the most primitive, diffusion-based kind. All the considerable intelligence in these animals is coursing, moment by moment, through their gene-protein networks.

In multicellular organisms with neurons, the cytoskeleton and receptors have specialized into the synaptome, the pre-and post-synaptic molecular modification of our synapses, including phosphorylation of switching proteins like calmodulin kinase II. While there are over 50 known neuromodulators and 14 neurotransmitters in our brain, only six neurotransmitters have been regularly implicated in long term learning and memory in our synaptome. It is these and their partner molecules in the synapse and nucleus that are probably most important to understand and model to crack the long-term memory code.

C. elegans connectome. Image: OpenWorm.org

Fortunately, even with our very partial molecular and functional maps today we have still managed to work out some basics of neural network interaction in very small neural ensembles, like the somatogastric nervous system (~30 neurons) in lobsters. We’ve even created early maps of very small whole-animal neural systems, like the nematode worm C. elegans, with its 302 neurons and ~6,000 synapses. We mapped the C. elegans connectome in 1986, but we still know just pieces of its synaptome and transcriptome, and even less about its epigenome. Fabio Piano et. al. give us an overview of the state of C. elegans gene-protein network knowledge in 2006. Note their subtitle is “A Beginning.” Jeff Kaufman has recently summarized the very early status today of whole brain emulation in nematodes. David Dalrymple in Ed Boyden’s lab at MIT is working on C. elegans simulation, and he is optimistic about new tools in neural state recording, optogenetics, and viral tagging for characterizing each neuron’s function. As Derya Unmatz reports in a blog post that sounds like science fiction,  Sharad Ramanathan et. al. at Harvard can now take control of C. elegans locomotion by firing precisely targeted lasers at individual neurons in an optogenetically modified worm’s brain, controlling its chemotactic behavior and convincing it that food is nearby.

A small international collaboration exists to emulate the C. elegans nervous system, called OpenWorm. There’s even a Whole (Human) Brain Emulation Roadmap, started in 2007 by Anders Sandberg and Nick Bostrom at Oxford, and a few other visionary folks in biology, computer science, and philosophy. These important projects are quite early and extremely underfunded at present. The biggest problem today is getting more funded people working on them.

To emulate how C. elegansDrosophilaAplysiaDanioMus, and other neural networks actually work, and to begin to extract even crude and partial memories from the scanned brains of any of these and other model organisms, we’ll need a better understanding of behavioral plasticity, and the way the synapse, the nucleus, and neuromodulators bias the pattern generators in neural circuits into a particular set of behavioral patterns. This may require not only better neural circuit maps, but better maps of several still partly-hidden intracellular systems involved in long-term memory formation: gene regulatory networks, the transcriptome, and the epigenome[6]. There are gene-protein networks controlling human neural development, neural evolution, and our long-term learning and memory. A special few of these regulatory networks, their proteins, and the epigenomic changes these networks store during a lifetime of human learning may be as important as the synapse, if not more, in determining how our brain encodes and stores useful information about the world.

A great textbook on gene regulatory networks is The Regulatory Genome: Gene Regulatory Networks in Development and Evolution, Eric Davidson, 2006. It will amaze you how much Davidson’s group has learned about these networks, primarily by studying the evolutionary development of one simple organism, the sea-urchin, over several decades. Last month, Isabelle Peter and others in Davidson’s group at Caltech published the first highly predictive model of how these networks control all the steps in sea urchin embryo development over the first 30 hours of its life. 50 genes are involved, and their regulatory interactions can be fully described in Boolean logic. Now they want to model all of development, and some of the networks controlling its variational processes. Consider the magnitude of their achievement: Davidson et. al. have reduced an incredibly complex biochemical process down to a far simpler algorithm. This is what must happen in long-term memory, if we are to use scanned brains to abstract the key subsets of molecular structures that reliably encode it in our neurons.

Protein Microarrays – An Exciting New Tool. Image:  Eye-Research.org

Neural proteomics and the transcriptome are entering an exciting new phase as we use DNA and RNA microarrays, and now protein microarrays to catalog neural transcriptomes and compare them to other types of human cells, and to other primate and mammal neurons. In August, Genevieve Konopka and colleagues published an exciting paper comparing human, chimpanzee, and rhesus monkey neural transcriptomes. We’re finding genes and proteins unique to particular areas in human brains, especially our frontal lobes. We’re building our first maps of the critical differences in the gene and protein regulatory networks that allowed us to wake up, make tools, and walk out of Africa less than two million years ago.

Epigenome (methylated DNA and modified histones). Image: RoadmapEpigenomics.org

We recently learned that what was long called “Junk” DNA, the 98% of each cell’s non-exonic DNA (DNA that doesn’t code directly for proteins), participates at various levels in gene regulatory networks, and through epigenomics these networks can change to some degree over the life of the cell. We’re learning now to map gene-protein interactions in these networks, including epigenomic changes, using tools like Chromatin ImmunoPrecipitation and sequencing (ChIP-seq). Unfortunately, this work is also seriously underfunded. We’ve known about the importance of the epigenome for over a decade. Epigenomic changes can be inherited (watch what you do with your body, as your kids will inherit a record of some of your bad or good life habits in their epigenome), and thus record unique learning in each cell over its lifetime, in ways we are still uncovering.

The NIH started a Roadmap Epigenomics Project for mapping the human epigenome in 2008, but the funding is a pittance, roughly $40 million a year. There is also a global collaborative research database, ENCODE, for sharing what is presently known about all the functional elements in the human genome. We give it roughly $20M/year, barely life support. There are also various Human Proteome Projects under way, but no one seems to be funding any of these seriously, either. None of the politicians or key philanthropists who could make the Human Proteome and Epigenome into national research priorities have proposed any big initiatives, as far as I know. Even our science documentaries don’t adequately convey the promise of these fields. The scientific community is tooling along as best it can in spite of the fact that the public still hasn’t gotten the clue on how much better medicine would be in ten years if we were spending a whole lot more money on this right now.

Recall by contrast the Human Genome Project, which began with fanfare in 1990 and was rough draft completed in 2000, for $3 billion, a price gladly paid by the U.S. and four other motivated nations. The Human Genome Project was, to put it in proper perspective, our planet’s Moon Shot in the 1990’s, our species latest great leap into “inner space.” As those who’ve read my Race to Inner Space post know, I think understanding the machinery of life and intelligence, and nanotechnology in general, is a destination far, far more valuable to us than outer and human scale (as opposed to cell and molecule-scale) space. We need an international Human Proteome and Epigenome Project race. With good funding and leadership, we might nail our first good maps of the neural gene-protein interaction layer in a decade. With business-as-usual, it will likely take much longer.

As we learn the languages of gene regulatory networks, the transcriptome, and the epigenome in coming years, we should learn how to influence these networks in many powerful ways. Do you think the trillion dollar global pharmaceutical industry is big now? Wait for the therapeutics that may start to arrive in the late 2020s, as we begin to learn how to intervene in these networks. I think it is only when we have good maps of these gene-protein networks that we can finally expect medical advances like better learning and memory formation, elimination of a vast range of diseases including cancer and Alzheimer’s, immune system boosting, aging reduction (epigenomics repair), and perhaps even the uncovering of genetically latent skills like tissue regeneration and hibernation. We are not talking about gene modification (inserting new genes in the germline, or in an adult), but rather about improving dysfunctional gene network regulation, and learning how to assay and minimize important parts of the network dysregulation that goes wrong in each of us as we get older and get various diseases.

Ken Hayworth

There’s a nice analogy here, pointed out by my Brain Preservation Foundation co-founder, Ken Hayworth. The Human Genome Project gave the world affordable gene sequencing in the mid-2000’s, and ten years later, we are beginning to see the major fruits: the uncovering the previously hidden worlds of gene regulation networks, the transcriptome, and the epigenome. Likewise, the Human Connectome Project and the still-unfunded Human Proteome and Epigenome Projects could get us affordable neural circuit tracing and functional gene regulatory network modeling in the late 2010s. Just as the Human Genome Project showed us we had a lot fewer genes than we thought (~21,000 rather than 100,000) the Human Epigenome Project may tell us that our gene regulatory networks are functionally simpler than we currently think, and that of the ~5,000 proteins in a typical cell, there are just a handful that matter to our long-term self. With luck, the remaining hidden layers of the neural transcriptome and epigenome will be functionally understood in the late 2020s. In that exciting time, our ability to understand memory and learning, to read memories from the scanned brains of model organisms, and to build biologically-inspired computer models, will all be greatly enhanced.

So to answer our original question, we need to find out if both chemical preservation and cryopreservation will preserve the connectome, the synaptome, and any long-term memory-related changes in the epigenome in a living brain.

Our Brain Preservation Technology Prize, which focuses on the connectome and many but not all features of the synaptome, is an important start down this road. As we understand better what molecular features in the synaptome and epigenome need to be preserved to capture and later retrieve memories, we’ll also need to find out if either chemical or cryopreservation, or ideally both, will reliably preserve those structures at the end of our biological lives, and whether it will be possible for future scanning algorithms to repair any damage done by the preservation process. We’re too early to answer such questions today, but it is encouraging to remember that long-term memory is a very redundant, resilient and distributed system.  Extensive neural destruction can occur in brains via Alzheimer’s, stroke, and other diseases before our memories are substantially erased and cognitive reserve is no longer available.

Sixty years of histology practice tells us that good perfusion of special chemical fixatives such formaldehyde and glutaraldehyde at death will immediately preserve everything we can see by electron microscopy in neurons. A great book on how this works is John Kiernan’s Histological and Histochemical Methods: Theory and Practice, 4th Ed., 2008. Kiernan has been publishing since 1964, and is a leader in the theory and practice of chemical fixation. There are even a few published fixation methods for whole mice brains. Here’s a 2005 paper by Kenneth Eichenbaum et.al. demonstrating a whole brain fixation technique that claims “complete preservation of cellular ultrastructure”, “artifact-free brain fixation” and “no signs of cellular necrosis” in an entire mouse brain. Presumably these methods also protect DNA methylation and histone modification in the epigenome, the phosphorylation of dendritic proteins like CamKII, the anchoring of AMPA receptors in the synapse, and any other elements of long-term memory formation. Presumably these molecules are protected today for years just by aldehyde fixation, if kept at low temperature (4 degrees).  Companies like Biomatrica have even developed ways to store human and bacterial DNA and RNA at room temperature for years. Long term storage of whole brain connectomes, synaptomes and epigenomes at room temperature, an ideal outcome for simplicity and affordability, may work today via additional chemical fixation steps like osmium tetroxide, a process that crosslinks fats and cell membranes, and plastination, a process that draws all the water out of a preserved brain and replaces it with resin.

But all this remains to be proven. If you know of experts who have done work in this area who would be willing to help BPF write position papers on these topics, and who can envision research projects that will answer these questions more definitively, please let me know, in the comments or by email at johnsmart{at}gmail{dot}com. Thanks.


Footnotes:

1. There is a much older layer of unique learning in each of us that is also important, the intelligent behaviors that gene networks have recorded in each of us over evolutionary time, as instinctual programs, and the unique assortment and variants of genes we each received at birth. Such networks determine our inherited neural programs, instincts and behaviors that are executed mostly unthinkingly and robustly, and during which other forms of learning, like short-term learning, often does not even occur. To preserve this layer we just need a DNA sample of the preserved person, and that particular uniqueness can be incorporated in any future emulation, assuming future computers are up to the task.

2. Some scientists working on brain emulation, like BPF Advisor Randal Koene, suspect that measuring and modeling the brain’s electrical processes, a topic called Computational Neurophysiology, will give us powerful new insights into artificial intelligence. There are new tools emerging for in situ functional recording of electrical features of the neuron. These may be critical to establish the “reference class” of normal electrical responses, for each type of neuron and neural architecture, the class of electrical representations of information. But if the model I’ve presented here is correct, we won’t need to record any electrical features of individual brains in order to successfully reanimate them later. We’ll see.

3. In Aplysia (sea slug), the sensory neuron neurotransmitter serotonin (5-HT) binds to postsynaptic receptors, activates adenylyl cyclase (AC) in the cell to make the second messenger cAMP, causing a short-term facilitation (STF) in strength of the sensory to motor neuron connection. More of the excitatory neurotransmitter glutamate is released by the neuron to its follower motor cells, and Aplysia pulls away harder from its shock. The neuron is also sensitized: K+ channels are depressed, more Ca++ enters the presynaptic terminal, and the action potential spike broadens. Kinases and phosphatases (phosphate adding and removing enzymes) including cAMP-dependent PK, PKA, PKC, and CamKII control duration and strength of these changes. In facilitation, the spike broadens temporarily, as both pre- and post-synaptic Ca++ and CamKII make molecular changes that temporarily strengthen the electrical signal across the synapse. In short-term depression (STD), the same mechanism temporarily weakens the signal. If water is gently shot at Aplysia’s gills ten times in a row, it temporarily learns not withdraw them, via synaptic depression of motor circuits. This short-term memory lasts for ten minutes, and involves a short-term reduction in the number of glutamate vesicles that are docked at presynaptic release sites in sensory neurons (undocked vesicles can’t be immediately used). Repeat this training four times and the slug will turn this into an intermediate-term memory, making chemical and electrical changes in the synapse that now last for three weeks. Again, all this involves changes only to preexisting proteins and synaptic connections in neurons.

4. In rat and human hippocampus, the primary excitatory neurotransmitter is glutamate. This causes Ca++ influx through NMDA receptors at postsynaptic membranes, and activation of CamKII, PKC, and MAPK. Permanent synaptic changes (Early LTP) include increased insertion of AMPA receptors in the membrane, and phosphorylation of proteins to change the properties of the channel. These receptors are anchored to the neural cytoskeleton, so they have reliable long term effects. Later LTP involves recruitment of pre- and postsynaptic molecules to create new synaptic sites. A few key gene-regulatory networks are involved, with transcriptional and translational control at both the nucleus and the synapse, and control molecules including BDNF, mTOR, CREB, and CPEB. We’ve recently found a memory encoding master control gene, Npas4, that encodes nuclear transcription factors (the copying of other genes into messenger RNA) which interact with hippocampal neurons to encode episodic memory. When Npas4 is knocked out of mice, they can’t learn. We’ve found RNA binding proteins like Orb2, that bind to genes involved in long-term memory. A great and reasonably current text on the molecular basis of memory and learning is Mechanisms of Memory, David Sweatt, 2009. We’re still figuring out the epigenomic regulation that occurs in long-term learning and memory, so you’ll need to go to journals for most of that story, like this 2011 PloS Biology paper on epigenetic regulation of learning and memory in Drosophila. The full size of the memory puzzle is becoming clearer every day. Now we just need to fund the work to complete it. We sure could use this knowledge in all kinds of good ways today, if we had it. Here’s a cartoon of long-term memory formation in both Aplysia and rat hippocampus, from Learning and Memory, John Byrne (Ed.), 2008 (Vol 4., David Sweatt, p. 14):

5. Computational Neuroscience seeks to model brain function at multiple spatial-temporal scales. The brain uses a vast range of different schemes for representation and manipulation of information, and it passes some of this information from one system to another all the time. Consider the way neurons integrate signals from the receptors at their dendrites, the timing and shape of their action potentials, the way synapses interact with postsynaptic dendrites from other neurons, how neurons encode and store associative memory, specialize for perceiving and storing certain types of information (edge detection, grandmother cells), do inference and other calculations, work in functional subunits like cortical columns, and organize receptive fields. It all seems formidably complex, but useful simplifications exist, as we’ve described above.

6. Most folks in the neural emulation community don’t talk much about modeling gene regulatory networks or the epigenome and its interaction with the synaptome, and I think that’s their loss. Some focus only on easier stuff to see, like electrical features, and assume that might be enough to get a predictive model. But I think that’s like looking for your keys under the streetlights when they are in the shadows. If spikes, loops, and synchrony are a network layer that has grown on top of cell morphology and gene-protein networks, the way single-celled animals eventually grew neurons, we may learn surprisingly little by measuring and modeling electrical features. Attempting to do so may be like trying to infer the structure of hidden layers in a very large neural network [genome, epigenome, connectome, synaptome, and electrical features] by analyzing just the input/output layer, electrical features. We need all the hidden layers if we expect to have enough computational complexity to predictively characterize learning, memory, and behavior.

Chemical Brain Preservation: How to Live “Forever” – A Personal View

Here’s my 45 minute talk on Chemical Brain Preservation at World Future Society 2012. Given the progress we’ve seen in the relevant science and technologies it’s a topic I’m presently very optimistic about. I had a great audience with lots of questions at the end, but in the interest of brevity I’m just uploading the talk. Let me know your thoughts in the comments, thanks!



A number of neuroscientists, working today with simple model organisms, are investigating the hypothesis that chemical brain preservation may inexpensively preserve the organism’s memories and mental states after death. Chemically preserved brains can be stored at room temperature in cemeteries, contract storage, even private homes. Our 501c3 nonprofit organization, the Brain Preservation Foundation, is offering a $100,000 prize to the first scientific team to demonstrate that the entire synaptic connectivity (“connectome”) of mammalian brains can be perfectly preserved using either chemical preservation or more expensive cryopreservation techniques.

Such preserved brains may be “read” in the future, analogous to the way a computer hard drive is read today, so that either memories or the complete identities of the preserved individuals can be restored or “uploaded” in computer form. Chemical preservation techniques are already being used to scan and upload the connectomes of very small animal brains (C. elegans and OpenWorm, zebrafish, soon flies). Though these scans are not yet sufficiently complex to extract memories from the uploaded organisms, give them a little more time, we’re very close now to cracking long-term memory. We just need to know a bit more about this process at the protein/receptor/gene level: http://en.wikipedia.org/wiki/Long-term_potentiation

Amazingly, if information technologies continue to improve at historical rates, a person whose brain is chemically preserved in 2020 might have their memories read or even fully return to the world in a computer form not centuries but just a few decades from now, while their children and loved ones are still alive. Given progress in electron microscopy and connectomics research to date, we can even forsee how this may be done as a fully automated and inexpensive process.

Today, only 1% of people in developed societies are interested in living beyond their biological death (see When I’m 164, David Ewing Duncan, 2012). With chemical brain preservation, this 1% may soon have a validated, low-cost method that will allow them to do just that. Once it becomes a real option, and recovery of simple memories has been demonstrated in model organisms, this 1% may grow larger as well.

I am particularly excited by chemical brain preservation’s ability to improve the social contract: what benefits we may reasonably expect from the universe and society when we choose to live a good and moral life. I believe that having the option of chemical brain preservation at death, if the science is validated, may help all our societies become significantly more science-, future-, progress-, preservation-, sustainability-, truth and justice-, and community-oriented in coming years.

Would you choose chemical brain preservation at death if it was widely available, validated, and inexpensive? If not, why not? Would you do it to donate your brain to science? Your memories to your children or others who might want them? Would you be willing to come back in person, if that turns out to be possible? If it is sufficiently inexpensive, would it be best to preserve your brain at death, and let future society decide if either your memories or your identity are “worth” reanimating? Please let me know what you think in the comments, thank you.

The Moral Landscape – A Four Part Review (Part 4)

More thoughts on Sam Harris’s insightful new book, The Moral Landscape: How Science Can Determine Human Values, 2011. I read it with two friends, and interpreted it through an evo devo universe lens. I originally planned to critique the entire book but I’ve since moved on to other readings, so this will be it for now.

Chapter 3 follows:

The Moral Landscape, Chapter 3 – Belief

Agreements (and my rewording/additions in italics):

Harris uses the OED definition of belief, particularly “mental acceptance of a proposition, statement, or fact as true.”

This is helpful, but we can get more specific. I prefer the way the great 20th century philosopher, historian and science writer Jacob Bronowski approaches belief, in Science and Human Values, 1965 and The Origins of Knowledge and Imagination, 1979. As I recall him, Bronowski talks of 1. “intuition/faith”, 2. “philosophy/experience” and 3. “science/experiment” as three fundamental types of thinking. We accept propositions based on our intuition or faith, based on our philosophy or experience, or based on our science or experiment. Bronowski concludes the first book above with a Platonic dialog between an intuitive artist, a practical public servant, and an experimentally-driven scientist, and uses them to represent three potentially fundamental and complementary thinking styles: 

1. Experimental, creative, intuitive, and faith-based (evolutionary*) thinking
2. Adaptive, practical, logical/philosophical, experience-based (evo devo*) thinking
3. Scientific, factual, replicable experiment-based (developmental*) thinking 

*The labels in parentheses are my additions to Bronowski’s model. I’m not sure, but I believe :) he would have approved. As Harris reminds us, all of these are technically beliefs, but as Bronowski reminds us, the first category of thinking styles is the most common connotation for belief, the second is rational argument or experience, the third is science. This is a very practical categorization system for our thinking.

In these books, and in his sublime BBC documentary series and book, The Ascent of Man, 1976, Bronowski regularly visits these three categories of thought, and convinces us that we use and need all of these types of thinking to survive and thrive. In common parlance, beliefs are thoughtful intuitions and faiths that we have little justification for, beyond gut feeling or social custom. Thinking them to be true is an individually and socially creative act. We also have thoughts that have some practice, experience, logic, or philosophy to guide them. Finally, we have thoughts that have been to some degree validated via experiment, replication, scientific method. Bronowksi argues that we always need intuition, but as society matures, we increasingly gravitate away from pure faith-based thoughts to ones more informed by philosophy and experience, and in special cases, scientific knowledge, to the great benefit of civilization. But intuition, and a modicum of faith, must always remain, no matter how complex we become. Thus religion never goes away, nor should it, but it does get continually reformed.

“The less competent a person is in a given domain, the more he will tend to overestimate his abilities.”

This has been described as the Dunning-Kruger Effect, and is one very important source of cognitive bias. Ignorance and certainty often go hand in hand. One hallmark of complex thinking is when we qualify our statements, and are aware of places where we have a number of competing theories, all of which have some merit, and where we presently have insufficient data to form a judgment. We need to be tolerant of uncertainty and ambiguity, as it is a key component of nature itself, with its profusion of evolutionary experiments, many yet to be judged by the environment. In fact, we have to move beyond tolerance to actively championing diversity and experiment, especially in those controversial and uncertain areas where the right way or ways are not yet clear.

“The level of humility in scientific discourse is one of its most striking characteristics.”

Well said. The way that even a Nobel laureate usually speaks about subjects outside their expertise (there are of course exceptions) is something we should all strive for, in our discourse about the deepest and most important things, like our beliefs and values.

“Political conservatism… is a fairly well-defined perspective characterized by a general discomfort with societal change and a ready acceptance of social inequality… The psychologist John Jost and colleagues analyzed data from twelve countries, acquired from 23,000 subjects, and found this attitude [political conservatism] to [also] be correlated with dogmatism, inflexibility, death anxiety, need for closure, and anticorrelated with openness to experience, cognitive complexity, self-esteem, and social stability.”

Brilliant diagnosis! Yet we must recognize that liberals are equally “conservative” (parochial, protectionist, change-averse) on the economic dimensions of society. They gravitate to trade restriction, to onerous economic guarantees,  to high trade barriers, to change-averse unions, jobs for life, etc.

Liberals, in other words, are socially evolutionary (freedom oriented) and economically developmental (constraint oriented, tariffs, unions, guaranteed wages). Conservatives are socially developmental (constraint oriented) and economically evolutionary (freedom oriented).

Conservatives are the natural leaders in socially developmental aspects of our society (defense, security, intelligence, rulemaking, social norms and traditions) and in the economically evolutionary (market, innovation oriented) aspects as well. Liberals are natural leaders and key players in all the social innovations of modern societies, and in all positions of power involving constraint and regulation of economic activities. Both play critical evo and devo roles. Demonize either and you miss seeing why the system works as it does.

“If a person’s primary motivation in holding a belief is to hew to a positive state of mind—to mitigate feelings of anxiety, embarrassment, or guilt, for instance—this is precisely what we mean by phrases like “wishful thinking” and “self-deception.” Such a person will, of necessity, be less responsive to valid chains of evidence and argument that run counter to the beliefs he is seeking to maintain.”

Well said. We must be willing to undergo mental disruption and discomfort, to unlearn bad beliefs, if we seek to live an evidence-based life. Ideally, we will allow such disruption to become increasingly frequent the older we get, as there is more known, and more we have to unlearn, at least in particulars. If we can live with this disruption, we can be the kind of elderly that grow in wisdom and stay relevant, even as our knowledge must become both increasingly general and conditional, and our ability to change the world gets increasingly narrowly defined. Fortunately, our electronic extensions are continually rejuvenating themselves, and the more we embrace them, the more resilient we become.

The neurologist Robert Berton, On Being Certain, 2008, says schizophrenia is a disorder of pathological certainty, and obsessive compulsiveness is a disorder of pathological uncertainty. Certainty is primarily an emotional process, and is connected to but different from the chains of evidence and argument that determine the correctness of any belief.

Lovely insights.

There are genetic differences in the types and quality of human reasoning. “People who have inherited the most active form of the D4 [dopamine] receptor are more likely to believe in miracles and to be skeptical of science; the least active forms correlate with rational materialism.” There are also genetic differences in our innate risk tolerance, which in turn greatly influence our reasoning and conclusions. Does this variation mean that we cannot identify unproductive extremes? No.

Enlightening! Nurture’s contribution to human mental life gets steadily clearer.

I expect that in the future, we will come to understand two fundamental things about the genetic differences in human reasoning and belief systems: 1. There is a developmentally healthy envelope of variations in risk tolerance, willingness to believe strange things, and other thinking parameters. The vast majority (usually 99%?) of humans are almost always functioning within this envelope, and those times when they aren’t we can define as deprivation or disease. 2. Within this envelope, there is no developmental “optimum” that we can usefully define. Having a healthy evolutionary variety and distribution within the envelope of normal function will turn out to be as important as having developmental bounds on the size of the envelope.

This is all that will be left of the “eugenics” visions of the 20th century reductionists: just a better definition of the exceptional cases of disease, not discovery of an optimal configuration among a great variety of healthy norms. As healthy thinking is an evolutionary process, adaptation will always remain contingent and dependent on local context, and impossible to globally predict or define.

“Skeptics given the drug L-dopa, which increases dopamine levels, show an increased propensity to accept mystical explanations for novel phenomena. The fact that religious belief is both a cultural universal and appears to be tethered to the genome [and dopamine levels in the brain] has led scientists like [Robert] Burton to conclude that there is simply no getting rid of faith-based thinking.”

Absolutely! I doubt Harris would agree with this, but I see these dopamine experiments as beautiful evidence that our very brain machinery is biased to make us do: 1. Intuition/faith-based thinking, 2. Argument/experience-based thinking, and 3. Scientific/experimental-based thinking. All three are fundamentally necessary processes for thinking creatures in our universe, in my evo devo view. 

“Reason can bridge the gap between believers and nonbelievers.”

Harris explores how we purged such harmful beliefs as the belief in Witchcraft, and the cruel punishments that purported witches received in the West a few hundred years ago, and which they still receive in some African nations today. Reason can help us sort out harmful and regressive from progressive beliefs. But while I agree strongly with him here, I also think our human need for a rather large set of faith based-beliefs remains fundamental, in a world where complexity, for now, remains far greater than our minds.

Disagreements:

“There does not seem to be a process in nature that allows for the creation of new structures dedicated to entirely novel modes of behavior and cognition.”

Disagree. We can’t yet say this definitively, but I’d bet universal evolutionary development guarantees regular emergence of new behavioral and cognitive novelty. I’d bet the consciousness and behavior modes of a human are qualitatively novel vs. that of an insect, and I’d predict the AI’s hyperconsciousness, given its new level of structural freedoms, will be qualitatively novel yet again.

“Much of our behavior and cognition… has not been selected for at all.”

Strongly disagree. All our behavior and thought undergo memetic selection. You just choose not to see or discuss it. You seem to be in the Denial phase of the death of an ultra-Darwinian world view (Denial, Anger, Bargaining, Death, and Acceptance being the full progression). You don’t even confront or critique the 35 years of literature on memes, in your entire book. I recommend Bob Aunger’s Darwinizing Culture: The Status of Memetics as a Science, 2001 as a start into that literature.

“I have argued there is no gulf between facts and values, because values reduce to a certain type of fact.” [Harris found both constructs used the medial prefrontal cortex (MPFC) and emotional areas in his research].

There is no such “reduction” occurring. It’s interesting that both scientific and ethical constructs use the MPFC and emotion, but that doesn’t make them the same. Ethical judgments are a subset of scientific judgments. Some ethical judgements are factual-scientific (developmental) others are creative (experimental) and they may or may not be or turn out to be factual. 

On Lie Detection science: “Whether or not we ever crack the neural code, enabling us to download a person’s private thoughts, memories, and perceptions without distortion, we will almost surely be able to determine, to a moral certainty, whether a person is representing his thoughts, memories, and perceptions honestly in conversation.”

I don’t share Harris’s faith in the perfectability of this science, by imperfect humans at least. Better lie detection will surely weed out the amateurs, but it will also breed better liars. The best liars, the ones that beat polygraphs today, are capable of amazing feats of self-deception, and belief in their own lies. Those that dissociate (create multiple personalities) may be detectable by neuroimaging, but what of those that learn to believe their own lies with their “whole mind”?

“Choosing beliefs freely is not what rational minds do.” [On his debate with Philip Ball].

Strongly disagree. In the realm of our intuition and faith-based thoughts, quite a number of these beliefs are freely and consciously or unconsciously chosen, based on how they make us feel, as Philip Ball apparently argues, and it is rational to do this. When we get argument or experience, or even science to constrain these beliefs, it is also rational to revise our beliefs. But we often don’t have even argument or experience to guide our first beliefs in an abstract or new area of thought. The act of intuitive or faith-based belief, the search for propositions that we think might be true, is a creative action, a necessary evolutionary step toward greater adaptive complexity and development.

“Believe a proposition because it is well supported by theory and evidence; believe it because it has been experimentally verified; believe it because a generation of smart people have tried their best to falsify it and failed; believe it because it is true (or seems so). This is a norm of cognition as well as the core of any scientific mission statement.”

Yes, but this is only a subset of the beliefs we use and need! Many of our beliefs are intuitive, or faith-based, and may not yet even be conscious, much less supported by argument or evidence. Consider our faith that the universe is comprehensible, or amenable to life, or people mostly moral. Most of our thinking may be based on such bottom-up, neural-net constructed beliefs. They are the foundation on which the tip of our conscious beliefs, argument, evidence, and science has emerged. We shouldn’t ignore them. At the same time, we can marvel that even with this sea of intuitive thinking as our inheritance, so much rationality emerges so predictably in all of us. Developmental psychology is yet another amazing example of the power of universal development.

Thoughts? Comments? Let me know, thanks. 

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