The Ravenous Brain: How the New Science of Consciousness Explains Our Insatiable Search for Meaning (13 page)

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Authors: Daniel Bor

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And although talking about evolution beyond the realm of the organism is controversial, wherever there are competing ideas between information carriers capable of change, something resembling evolution in all but name may well occur.
It’s possible that some particularly complex ideas can really only be supported by a group of organisms, and that if an idea helps keep them all alive, then, again, evolution could, in principle, step in to favor this information chunk. Although the transmission of that information down the generations still involves genes, the point of evolutionary pressure essentially resides at the level of the concept, in this case the group of creatures, as if they were a single system. In similar fashion, we wouldn’t claim that the stock market rose 1 percent today because of the physical laws that govern how fundamental particles interact, even though the stock market wouldn’t exist without such particles.
So in this way, from the level of short sections of DNA all the way up to ecosystems and beyond, evolutionary pressures could in principle weed out those ideas incongruent with survival, while favoring any concepts that capture something accurate and crucial about the world. And one part of this process may well be the encouragement of the complex combination of ideas at the lower level to form more enlightened blind concepts a level above.
GENIUS CELLS
 
So far I’ve only discussed information management within DNA. But if the other organisms around you are performing the same genetic informational tricks, how do you inch ahead in the evolutionary arms race? One potential way is to start storing and changing information using other tools within a cell, to build additional layers, not just in terms of the domain and structure of ideas, but also in their computation.
In almost every realm imaginable, science has been revolutionized by computers. For a couple of days last week, I analyzed a large fMRI dataset—or rather my computer did, since there were well over 3 billion calculations needed to reach the results. Computers are unrivaled tools for the scientist, helping enormously in both the collection and analysis of information.
Similarly, if an organism won the random mutation lottery and got its hands on a better biological form of computation, the rewards would be enormous.
So far, DNA-based ideas can only get updated by evolution; in other words, by generations of organisms passing by, and those with genes—or collections of genes—that are able to persist over time being selected over those that cannot. That is in some ways a painfully inefficient way to learn something about the environment, with many millions of life-forms extinguishing before the lesson is fully learned. A far better approach would be to gather relevant knowledge about the surroundings
within the lifetime of the organism
.
This sounds like the realm of animals, but in fact many single-celled organisms, including bacteria, process information in this dynamic way.
The main mechanism available is the proteins that genes create. Some proteins can interact with each other to follow the rules of logic in order to perform rudimentary calculations. Other proteins help sense details of the environment, while some even turn back to the DNA that created them, and turn on and off various genes, thus changing the production levels of other proteins. These new layers of information communication allow for a very complex cascade of activity, and surprisingly intelligent forms of learning and ideas.
One collective example of the generation of a complex concept are Hox genes, which control the location and number of developing limbs in animal embryos by deciding whether other genes are activated. Some of these controlled genes, one step down in the hierarchy, themselves regulate the activity of other sets of genes.
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This is highly reminiscent of many aspects of human life, such as the network of staff in a large corporation, or the many layers of categorization we mentally learn (for instance, my laptop is a kind of computer, which is a type of electronic device, which belongs to the set of machines, all of which are a form of tool, which are inanimate objects, and so on). Most complex systems benefit hugely from a hierarchy of knowledge and management, single cells included.
Far more impressive, though, is the facility for learning that microbes can demonstrate, usually via these protein-based computations. Bacteria, for instance, can communicate with each other using chemicals to indicate a lack of food, and thus each bacterium will spread out in a region to maximize consumption of what little food is available.
Protozoa and bacteria even use rudimentary forms of learning and memory when faced with different types of food or possible threats. For instance, if gut bacteria find some appropriate food, they will ready themselves to digest related food that’s likely to be nearby, as if making a kind of prediction, but will stop this behavior if they do not find it soon enough.
INTERNAL EVOLUTION
 
So evolution favors an accurate internal picture of the world via effective learning. But there are important limiting factors to this process. For one thing, as your internal model of the environment increases in accuracy, more energy is required to maintain this growing set of knowledge, and you become more vulnerable when food supplies fall short. And generating an increasingly large set of ideas requires an increasingly complex organism, so your reproductive rate slows down. However accurate your set of internal beliefs about the world are
right now
, the world can change catastrophically and instantaneously, and if you are sluggish at making copies, there’s little chance the critical DNA component of your ideas (if you have others, such as mental memories) will be able to update fast enough to track the changes, making extinction far more likely. Finally, if you have to become a larger, more complex organism to store all these extra ideas, then your bulkier biological machinery is also more likely to break down.
Bacteria hit that sweet spot of just enough complexity, but without it being an undue burden on survival. Consequently, they are capable of surprisingly clever information processing, but they are otherwise simple and small enough to replicate quickly and efficiently. They are the most successful type of creature on the planet by any yardstick you’d care to use: by numbers, because there are a staggering 10
30
of them; by diversity, because they live not just on all continents and in all climates of the world, but also in acid, in radioactive waste, and deep in the earth’s crust; and even by longevity, as bacteria have been known to spring back to life after lying dormant for tens of thousands or even millions of years. Bacteria existed in vast quantities across the earth billions of years before animals turned up, and it’s very likely that they will still be around long after humans have perished. Based on this evidence, it seems highly plausible that there was an active trend, via evolution, from the origins of life onward, to favor those creatures that could process information most dynamically and accurately—but only up to the complexity of bacteria.
So why do animals exist in the first place? Part of the explanation is that they arose and succeeded by chance: Given sufficient evolutionary probing over sufficient time, with the right conditions, ever-increasing possible niches of survival will be explored, or, in other words, ever-increasing sets of biological ideas will be entertained. Animals are just one random set of strategies for survival. Of course, humans are a fascinating, wondrous example of what an organism can become, with our rich consciousness and deep intellect, but evolutionary success is a different matter. Having a brain such as ours, for instance, seems to lead to runaway processes that endanger our own existence—excessive CO
2
emissions being one catastrophic example of a set of damaging products of our great collective consciousness.
 
Leaving these caveats aside, I now want to explore the details of this niche that animals exploit. Modern bacteria can form and adapt ideas immediately by encoding information not just in DNA, but further afield within the cell, mainly by using protein to represent additional ideas, or, even more powerfully, by building many computational links between DNA and proteins. Although ingenious, this system is also terribly limited, since only incredibly rudimentary information can be learned, moment to moment. So what else can be co-opted to manage even more information, if better computational power is a potential evolutionary niche that is to be exploited? With bacteria combining to represent ideas about food as one primitive example, the next logical step is to move beyond the confines of the cell wall.
We are now in the realm of multicellular organisms, with cells specialized for specific functions within the organism. Bundles of nerve cells making a brain are one route nature took, with the evolutionary “hypothesis” that learning and storing even more information on the fly would compensate somewhat for the greater investment of time and resources required to maintain this organ.
Some basic change in the world may take non-animals—including the cleverest bacteria—generations to encode via natural selection and DNA. But even the simplest of animals can, strikingly, learn a wide range of lessons from the environment over just a few seconds. Other more complex features of the world, assimilated easily by animals, may never be captured by DNA alone. In this way, a threat that would have destroyed a non-animal organism, or even a whole non-animal species, because of its limited capacity to process information, might not even harm an animal.
If you view evolution essentially as the competition between ideas, with the best ones eventually claiming victory, then animals are in a sense clamping on an additional, internalized version of evolution in order to enhance their chances for survival.
Thus all life undergoes genetic hypothesis-testing via evolution. The feedback about whether your concepts are right or wrong usually comes from the environment directly, which selects those concepts for persistence across the generations, and the bad concepts for death. If you happen to be a sophisticated type of bacteria, then a small but vital component of the feedback you receive can come from the intermediate steps of proteins, which help sense and adapt to very crude features about the world on the fly.
But for animals there is an additional buffer to process an important subset of beliefs that really matter for survival and reproduction. Feedback still comes from the environment, but much of that feedback need not affect DNA at all, since it can merely change the ideas stored in brain cells. And, in combination with movement, animals can now actually interact in pointed ways with the world in order to test beliefs very actively. The number of possible ideas an animal can entertain in a lifetime is effectively infinite, especially since wrong ideas no longer risk death.
Moreover, the more mentally complex the animal, the more elaborate its internal model of the world is. Thus, much of the environmental feedback that used to be required to change a belief, whether genetically or neurally stored, can now occur entirely within the complex, structured, internal environment of the animal’s brain.
Animals with particularly complex brains could even test many competing ideas without moving a muscle. For instance, in the middle of the night, unable to turn off my consciousness sufficiently to fall asleep, because I’m obsessively thinking about consciousness science, I feel a sharp hunger pang and conclude that the best course of action is to obtain a very large bag of cashew nuts. I initially decide to visit the kitchen, but then recall that a now rather irritating spring-cleaning the previous day cleared out most of the food. I then imagine the usual situation of going to the supermarket, but realize that my standard one is closed after 9 p.m. So I either could go to the 24-hour supermarket, which is a 15-minute drive away, or a gas station a kilometer away. I can work out the optimal way to obtain a much-needed, intensely fattening snack, potentially from miles away, without ever leaving my bed, which is in some ways incredible.
This illustrates that evolution has begotten a form of internal evolution, and this internal evolution becomes ever more apparent the more intelligent the animals are, to the extent that we humans have brains that very much behave like internal evolutionary worlds.
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We represent the world so fully, so accurately, that we can play out scenarios in our heads and explore a large range of options—all while hardly expending any physical effort. Such experimentation is now as safe as it’s possible to be—we don’t risk anything whatsoever by searching through the options in the mental realm—not survival by genetically betting on a loser, not even physical damage by learning painfully from our mistakes. This seems a universe away from the proto-life “ideas” with which we began this chapter, but it’s not. It is merely a sequence of connected evolutionary steps, all based on the theory that effective information processing naturally confers an advantage.
THE COMPUTATIONAL LANDSCAPE OF A BRAIN
 
Given the last few paragraphs, I should emphasize again that animals are not necessarily superior to other organisms in terms of evolutionary success. An oak tree, for instance, with its working hypotheses that physical toughness is highly protective and that the sun is a plentiful source of energy, may be just as long-lived and populous a species as a mouse. It’s just that animals have a fascinating, powerfully pointed set of advantages converging on complex information processing, and the overall genetic assumption of these organisms is that these few profoundly superior traits outweigh the many limitations.

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