Sperber’s idea also suggests that increased access to public presentation of ideas will increase the dynamic range of culture overall. Some publicly available representations will take hold among the widest possible group of participants in history, in both absolute numbers and as a percentage of the human race. (Consider, for example, the number of people who can now understand the phrase “That’s killing two pigs with one bird.”) It is this globally wired possibility for global cultural imitation that evolutionary theorist Mark Pagel worries about when he talks about the Internet enabling “infinite stupidity.”
At the same time, it has never been easier for members of possible subcultures to find one another and create their own public representations at much lower cost, longer life, and greater reach than ordinary citizens have ever been able to. The January 25, 2011, protests in Egypt hijacked the official public representation of that day as National Police Day; this was possible only because the dissidents could create alternate public representations on a scale similar to the Egyptian state.
Actual reductionism—the interpretation of a large number of effects using a small number of causes—is rare in the social sciences, but Sperber has provided a framework for dissolving large and vague questions about culture into a series of tractable research programs. Most of the empirical study of the “precipitate of cognition and communication” is still in the future, but I can’t think of another current idea in the social sciences offering that degree of explanatory heft.
METAREPRESENTATIONS EXPLAIN HUMAN UNIQUENESS
HUGO MERCIER
Psychologist; cognitive scientist; postdoc, University of Neuchâtel
Humans alone fluently understand the mental states of others. Humans alone rely on an open-ended system of communication. Humans alone ponder the reasons for their beliefs. For each of these feats, and for others too, humans rely on their most special gift: the ability to represent representations—that is, to form metarepresentations. Hidden behind such mundane thoughts as “Mary believes that Paul believes that it’s going to rain” is the explanation of human uniqueness.
There are two ways to represent representations, one immensely powerful, the other rather clumsy. The clumsy way is to create a new representation for every representation that needs to be represented. Using such a device, Mary would have to form a representation “Paul believes that it’s going to rain” completely independent of her representation “it’s going to rain.” She would then have to learn anew all of the inferences that can be drawn from “Paul believes that it’s going to rain,” such as the negative impact on Paul’s willingness to go for a jog or the increased probability that he will fetch an umbrella. This cumbersome process would have to be repeated for each new representation that Mary wishes to attribute, from “Peter thinks the weather looks lovely” to “Ruth is afraid that the Dow Jones is going to crash tomorrow.” Such a process could not possibly account for humans’ amazing abilities to attribute just about any thought to other people. How then can we account for these skills?
The explanation is that we use our own representations to attribute thoughts to others. When Mary wants to attribute to Paul the belief “it’s going to rain,” she simply uses her representation “it’s going to rain” and embeds it in a metarepresentation: “Paul thinks that ‘it’s going to rain.’ ” Because the same representation is used, Mary can take advantage of the inferences she could draw from “it’s going to rain” to draw inferences from “Paul believes that ‘it’s going to rain.’ ” This trick opened for humans the doors to an unparalleled understanding of their social environment.
Most of the beliefs we form about others are derived from communication: People keep telling us what they believe, want, desire, fear, love. Here again, metarepresentations play a crucial role, since understanding language requires going from utterances—“It’s going to rain”—to metarepresentations—“Paul means that ‘it will soon rain here.’ ”
Mentalizing—attributing thoughts to others—and communicating are the best-known uses of metarepresentations, but they are not the only ones. Metarepresentations are also essential for us to be able to think about reasons. Specific metarepresentations are relied on when people produce and evaluate arguments, as in: “Mary thinks ‘it’s going to rain’ is a good argument for ‘We shouldn’t go out.’ ” Again, Mary uses her representation “it’s going to rain,” but instead of attributing it to someone else, she represents its strength as a reason to accept a given conclusion.
Several other properties of representations can be represented, from their aesthetic value to their normative status. The representational richness made possible by recycling our own representations to represent other people’s representations, or to represent other attributes of representations, is our most distinctive trait—one of these amazingly brilliant solutions that natural selection stumbles upon. However, whereas it is much simpler to rely on metarepresentations than on the cumbersome solution of creating new representations from scratch every time, we still face a complex computational task. Even when we use our own representations to attribute representations to other people, a lot of work remains to be done—it cannot be metarepresentations all the way down. At some point, we need other inputs—linguistic or behavioral cues—to attribute representations. Moreover, when a representation is represented, not all the inferences that can be drawn from it are relevant. When Mary believes that John believes it’s going to rain, some of the inferences that she would draw from “it’s going to rain” may not be attributable to John. Maybe he doesn’t mind jogging in the rain, for instance. And Mary may not draw other inferences: Maybe John will be worried because he left his book outside. Still, without a baseline—Mary’s own representation—the task would jump from merely difficult to utterly intractable.
Probably more than any other cognitive trait, the ability to use our own representations to represent representations is what explains humankind’s achievements. Without this skill, the complex forms of social cognition that characterize our species would have been all but impossible. It is also critical for us psychologists to understand these ideas if we want to continue our forays into human cognition.
I leave the last word to Dan Sperber, who more than any other cognitive scientist has made metarepresentations the most central explanation of our unique cognition: “Humans have the ability to represent representations. I would argue that this meta-representational ability is as distinctive of humans, and as important in understanding their behaviour, as is echolocation for bats.”*
WHY THE HUMAN MIND MAY SEEM TO HAVE AN ELEGANT EXPLANATION EVEN IF IT DOESN’T
NICHOLAS HUMPHREY
Emeritus professor, London School of Economics; author, Soul Dust: The Magic of Consciousness
On reading The Origin of Species, Erasmus Darwin wrote to his brother Charles in 1859: “The a priori reasoning is so entirely satisfactory to me that if the facts won’t fit in, why so much the worse for the facts.” Some of the facts—such as Kelvin’s calculation of the age of the Earth—looked awkard for Darwin’s theory at the time. But the theory of natural selection was too beautiful to be wrong. The brother was sure the troublesome facts would have to change. And so they did.
But it doesn’t always work that way. Elegance can be misleading. Consider a simple mathematical example. Given the sequence 2, 4, 6, 8, what rule would you guess is operating to generate the series? There are several theoretically possible answers. One would be the simple rule: Take the previous number, x, and compute x + 2. But equally valid for these data would be the much more complicated rule: Take the previous number, x, and compute
For the sequence as given so far, the first rule is clearly the more elegant. And if someone, let’s call her Tracey, were to maintain that since both rules work equally well she was going to make a personal choice of the second, we would surely think she was being deliberately contrarian and anti-elegant. Tracey Emin, not Michelangelo.
But suppose Tracey were to say,
“I bet if we look a little further, we shall find I was right all along.” And suppose, when we do look further, we find to our surprise that the next number in the sequence is not 10, but 8.91 and the next after that not 12 but 8.67. That is, the sequence we actually discover goes 2, 4, 6, 8, 8.91, 8.67. Then what had previously seemed the better rule would no longer fit the facts at all. Yet—surprise, surprise!—the second rule would still fit nicely. In this case, we should be forced to concede that Tracey’s anti-elegance had won the day.
How often does the real world tease us by seeming simpler than it really is? A famous case is Francis Crick’s 1957 theory of how DNA passes on instructions for protein synthesis using a “comma-free code.” As Crick wrote many years later, “Naturally [we] were excited by the idea of a comma-free code. It seemed so pretty, almost elegant. You fed in the magic numbers 4 (the 4 bases) and 3 (the triplet) and out came the magic number 20, the number of the amino acids.”* But alas, this lovely theory could not be squared with experimental facts. The truth was altogether less elegant.
A tease? I’m not, of course, suggesting that nature was deliberately stringing Crick along. As Einstein said, God is subtle but he is not malicious. In this case, the failure of the most elegant explanation to be the true one is presumably just a matter of bad luck. And, assuming this doesn’t happen often, perhaps in general we can still expect truth and beauty to go together (as no doubt many of the other answers to this Edge Question will prove).
However, there is one class of cases where the elegance of an untrue theory may not be luck at all—where, indeed, complex phenomena have actually been designed to masquerade as simple ones, or at any rate to masquerade as such to human beings. And such cases will arise just when, in the course of evolution, it has been to the biological advantage of humans to see certain things in a particularly simple way. The designer of the pseudo-elegant explananda has been not God but natural selection.
Here is my favorite example. Individual humans appear to other humans to be controlled by the remarkable structures we call minds. But the surprising and wonderful thing is that human minds are quite easy for others to read. We’ve all been doing it since we were babies, using the folk theory known to psychologists as “Theory of Mind” (or sometimes as “belief desire psychology”). Theory of Mind is simple and elegant, and can be understood by a two-year-old. There’s no question that it provides a highly effective way of explaining the way people behave. And this skill at mind-reading has been essential to human survival in social groups. Yet the fact is, Theory of Mind could never have worked so well unless natural selection had shaped human brains to be able to read—and to be readable by—one another in this way. Which is where the explanatory sleight-of-hand comes in. For as an explanation of how the brain works, Theory of Mind just doesn’t add up. It’s a purpose-built, oversimplified, deep, elegant myth—a myth whose inadequacy may not become apparent, perhaps, until those “extra numbers” are added by madness or by brain damage—contingencies that selection has not allowed for.
I find this explanation of the elegance of Theory of Mind beautiful.
FITNESS LANDSCAPES
STEWART BRAND
Founder, Whole Earth Catalog; cofounder, The Well; cofounder, The Long Now Foundation; author, Whole Earth Discipline: An Ecopragmatist Manifesto
The first time I saw a fitness-landscape cartoon (in Garrett Hardin’s Nature and Man’s Fate, 1965), I knew it was giving me advice on how not to get stuck overadapted—hence overspecialized—on some local peak of fitness, when whole mountain ranges of opportunity could be glimpsed in the distance. But getting to them involved venturing “downhill” into regions of lower fitness. I learned to distrust optimality.
Fitness landscapes (sometimes called “adaptive landscapes”) keep turning up when people try to figure out how evolution or innovation works in a complex world. An important critique by Marvin Minsky and Seymour Papert of early optimism about artificial intelligence warned that seemingly intelligent agents would dumbly “hill climb” to local peaks of illusory optimality and get stuck there. Complexity theorist Stuart Kauffman used fitness landscapes to visualize his ideas about the “adjacent possible” in 1993 and 2000, and that led in turn to Steven Johnson’s celebration of how the “adjacent possible” works for innovation in Where Good Ideas Come From.
The man behind the genius of fitness landscapes was the founding theorist of population genetics, Sewall Wright (1889–1988). In 1932, he came up with the landscape as a way to visualize and explain how biological populations escape the potential trap of a local peak by imagining what might drive their evolutionary path downhill from the peak toward other possibilities. Consider these six diagrams of his:
© Sewall Wright, The Role of Mutation, Inbreeding, Crossbreeding, and Selection in Evolution, Sixth International Congress of Genetics, Brooklyn, NY: Brooklyn Botanical Garden, 1932.
The first two illustrate how low selection pressure or a high rate of mutation (which comes with small populations) can broaden the range of a species, whereas intense selection pressure or a low mutation rate can severely limit a species to the very peak of local fitness. The third diagram shows what happens when the landscape itself shifts, and the population has to evolve to shift with it.
The bottom row explores how small populations respond to inbreeding by wandering ineffectively. The best mode of exploration Wright deemed the final diagram, showing how a species can divide into an array of races that interact with one another. That jostling crowd explores well, and it can respond to opportunity.
Fitness landscapes express so much so economically. There’s no better way, for example, to show the different modes of evolution of a remote oceanic island and a continental jungle. The jungle is dense and rugged, with steep peaks and valleys, isolating countless species on their tiny peaks of high specialization. The island, with its few species, is like a rolling landscape of gentle hills with species casually wandering over them, evolving into a whole array of Darwin’s finches, say. The island creatures and plants “lazily” become defenseless against invaders from the mainland.
You realize that the landscape for each species consists almost entirely of other species, all of them busy evolving right back. That’s coevolution. We are all each other’s fitness landscapes.
ON OCEANS AND AIRPORT SECURITY
KEVIN P. HAND
Planetary scientist and astrobiologist; deputy chief scientist, Solar System Exploration, NASA Jet Propulsion Laboratory, California Institute of Technology
It may sound odd, but much as I loathe airport security lines, I must admit that while I’m standing there, stripped down and denuded of metal, waiting to go through the doorway, part of my mind wanders to oceans that likely exist on distant worlds in our solar system.
These oceans are sheltered beneath the icy shells that cover worlds like Europa, Ganymede, and Callisto (moons of Jupiter), and Enceladus and Titan (moons of Saturn). The oceans within these worlds are liquid water, just as we know and love it here on Earth, and they have probably existed for much of the history of the solar system (about 4.6 billion years). The total volume of liquid water contained in them is at least twenty times that found on Earth. From the standpoint of our search for life beyond Earth, these oceans are prime real estate for a second origin of life and the evolution of extraterrestrial ecosystems.
But how do we know they exist? The moons are covered in ice, and we can’t just look down with a spacecraft and see liquid water. That’s where airport security comes into play. When you walk through an airport-security portal, you’re walking through a rapidly changing magnetic field. The laws of physics dictate that if you put a conducting material in a changing magnetic field, electric currents will arise, and those electric currents will create a secondary magnetic field. This secondary field is often referred to as the induced magnetic field, because it is induced by the primary field of the portal. Within the portal are detectors that can sense when an induced field is present. When the
y do, the alarm goes off, and you’re whisked over to the special search line.
The same fundamental physics is largely responsible for our knowledge of oceans on some of these distant worlds. Europa provides a good example. Back in the late 1990s, NASA’s Galileo spacecraft made several flybys of Europa, and the magnetic field sensors on the spacecraft found that Europa does not have a strong internal field of its own. Instead, it has an induced magnetic field, created as a result of Jupiter’s strong background magnetic field. In other words, the alarm went off.
But in order for the alarm to go off, there needed to be a conductor. And for Europa, the data indicated that the conducting layer must be near its surface. Other lines of evidence had already shown that the outer 150 kilometers or so of Europa was water, but those datasets could not help distinguish between solid ice and liquid water. For the magnetic-field data, however, ice doesn’t work—it’s not a good conductor. Liquid water with salts dissolved in it, like our own ocean, does work. The best fits to the data indicate that Europa has an outer ice shell about 10 kilometers thick, beneath which lies a global ocean about 100 kilometers deep. Beneath that is a rocky seafloor, which may be teeming with hydrothermal vents and bizarre otherworldly organisms.
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