Imagine an experiment that proceeds in the following fashion: there is a stage, and onto that stage researchers place boxes that are nondescript other than that each box has a small hole near the bottom from which water can freely drain. In the experiment, each box is left on the stage for precisely one hour, during which time the drained water is collected and measured.
The experiment consists of a broad sampling of such boxes, and it is discovered that there is a fair amount of variation in the results—some boxes produce more water, some produce less. The distribution of results comes out to be nearly normal, with a mean collection of 500 ml and a standard deviation of 100 ml. To provide some visualization for these results, the researchers summarize the experiment by placing three representative boxes onto the stage: a left box producing 400 ml per hour, one standard deviation below the mean; a middle box producing 500 ml per hour, exactly at the mean; and a right box producing 600 ml per hour, one standard deviation above the mean.
The study of these boxes is of importance to the researchers because the boxes serve useful purposes within the community. For instance food placed on top of a box does not spoil as quickly as it would otherwise. Furthermore, other experiments have shown that a box’s usefulness is often proportional to the box’s water production score, and although the correlations are not always exact they do tend to be statistically significant and emerge in all kinds of usefulness experiments. Based upon these strong correlations, the researchers define a box’s usefulness to be the equivalent of its water production ability.
Curious about the variation in results and wanting to learn more about the underlying cause of the differing water production scores, the researchers conduct investigations that focus on the physical/generational characteristics of each box. Some of these characteristics, such as surface material and factory of origin, emerge as promising candidates, because variations in these characteristics correlate with variations in water production scores. Again, the correlations are not always exact but they do tend to be statistically significant and they do allow the researchers to predict water production scores to a reasonable degree of accuracy given any box’s overall characteristics. These findings are strong enough to convince the researchers that a box’s water production score and its physical/generational characteristics are tightly linked. The researchers begin to formulate theories about the nature of this linkage.
These experiments are repeated frequently, at least once each year, and the researchers notice that the overall results remain extremely consistent—same variation, same distribution, same correlations. All the prior years’ findings are regularly verified, no theory gets overturned.
And yet over time, there does arise one nagging problem.
Despite the fact that almost every feature of the experiment remains exactly the same—same set up, same variation in results, same distribution, same usefulness, same correlations—despite all this remarkable consistency, the water production scores keep going up. They keep going up every year and they keep going up for all the boxes. The amount of increase each year is not overwhelming but it is large enough that it cannot be ignored. For instance, the collection containers that were used in the early years of the experiment eventually have to be replaced with larger containers to prevent spillage. After ten years of these experiments, when the researchers place the three representative boxes onto the stage to help visualize and summarize the results, the left box is now producing 480 ml of water per hour, the middle box is producing 600 ml, and the right box is producing 720 ml. The researchers recognize, with a good deal of consternation, that an average box now possesses the same water production ability as did a box one standard deviation above the mean from just ten years prior.
Many explanations for this phenomenon are proposed and investigated, beginning with a focus on the boxes themselves. Has there been a change in the surface material? The researchers discover that for a small number of boxes some slight alterations have indeed been introduced. Has there been a change in the construction process? Again the researchers find that one factory of origin has been mothballed and another has been remodeled, although the majority of the production facilities remain the same as before. The researchers look for still other clues, such as changes in size or weight, and although particular instances can be found, such changes are not pervasive. Indeed that becomes the telltale defect against all these explanations—each explanation accounts for only a small number of cases at best and appears entirely inadequate in the face of the widespread water production increase across boxes and across time.
The researchers then focus on the boxes’ environment, thinking that this line of attack might uncover a more all-encompassing solution. For instance, a few of the storage facilities where the boxes are housed are now located at higher altitudes than they used to be. Other storage facilities have been reconstructed out of metal whereas they were formerly made out of wood. And some storage facilities have had a ventilation system installed. But here too, such circumstances account for only a limited number of cases, and furthermore, these environmental explanations suffer from a still more troubling defect, namely that no one can explain how an environmental change would translate into the necessary and corresponding change in the physical/generational characteristics of each box. Everyone agrees, as a consequence of the experiments conducted each year, that water production scores and the physical/generational characteristics of each box are tightly linked, so that any significant change in water production scores must of necessity be accompanied by significant changes in the boxes’ characteristics. But how does an environmental change produce such an effect? How does a higher altitude, metallic surroundings or a ventilated facility produce the requisite alteration in surface material or point of origin? To many of the researchers, the connection seems implausible.
One researcher attempts to solve the dilemma by demonstrating how an environmental change and a physical/generational change can feed off each other with an amplifying effect. He uses terms such as multiplier and feedback loop and produces an impressive array of mathematics. “For instance a small change in surface material or production quality can generate a subtle difference in air flow around the corners of the box, which can alter the currents in the room, producing a multiplicative effect” he begins. “Then at the point of maximum air flow differential, a powerful feedback vibration is generated inside the box, and this vibration amplifies the rearrangement of surface material and boosts the air flow still further, which causes…”
Another researcher, noting the large number of explanations generated so far and the inability of each one to account for more than just a few cases, suggests that perhaps there is not just one explanation for the rising water production scores but rather the solution is to be found in a combination of explanations. This approach seems appealing to the frustrated researchers, although they have to agree it is not the kind of definitive answer they originally had in mind.
Then one day a visitor appears and has a suggestion for the researchers. “I don’t know about you, but it seems to me that the weather keeps getting warmer all the time—I’ve been coming to your experiments for several years now, and every time I visit, it feels hotter to me than it did the last time. Then it occurred to me, that would make for an elegant explanation to your rising water production scores.”
“How so?” ask the researchers.
“A general rise in temperature is the perfect match to what you’re looking for, it has all the essential characteristics. For one, an increase in temperature would mirror the increase in water production scores. Two, an increase in temperature would be continuous over time—just as with the rise in scores. And three, a general increase in temperature would be ubiquitous, it would affect all the boxes nearly the same.”
The researchers are not convinced.
“Here are the shortcomings in your explanation,” they point out to the visitor. “In the first place, your explanation is not germane to the problem. We are investigating a box’s usefulness, as measured by its water production score and contained in its physical/generational characteristics. It is hard to envision how ambient temperature can even be relevant to that discussion. But assuming it were somehow relevant, your explanation has an even bigger defect: you can’t provide a plausible description for how a change in environmental temperature would alter the physical/generational characteristics of each box. Are you trying to suggest that a slight increase in temperature would somehow rearrange a box’s surface material or reset a box’s factory of origin? That would be ridiculous. If a change in ambient temperature were somehow the cause of a change in water production scores, then that change in temperature must also impact the physical/generational characteristics of each box, because those characteristics are the source of a box’s water production ability.”
The visitor ponders this statement for a moment, then gives a lengthy reply:
“I believe you’re working under a mistaken assumption. Listen, I agree with you that variations in a box’s physical/generational characteristics produce corresponding changes in a box’s water production score—you have plenty of experimental evidence for that, and the results are strong and compelling. But your results are so strong and compelling that they seem to have convinced you that the inference is valid also in the other direction—that is, that every change in water production score is necessarily accompanied by a change in a box’s physical/generational characteristics. But actually, you have no evidence that the inference is valid in that direction, all your evidence runs only the other way. Moreover, the increase in water production scores across time and across all the boxes suggests quite strongly that such an inference would be mistaken.
“Here is how I would describe the situation. We have three different quantities in play: a box’s capacity to produce water, the water production score, and the ambient temperature. Let’s let letters stand for each of these quantities:
C = a box’s CAPACITY to produce water,
S = a box’s water production SCORE, and
T = the ambient TEMPERATURE.
“A box’s water production score (S) is the combination of the two other factors (C and T) working independently. This can be expressed in a simple relationship:
S = C x T.
“That relationship fits your experimental results perfectly. At any given point in time, the ambient temperature (T) will be constant, so that when experiments are run at that time, all the variation in water production scores (S) will be the direct result of the differing physical/generational characteristics of each box, because those characteristics are what determine a box’s relative capacity (C) to produce water.
“But over time it is the orthogonal effect that holds sway. Over time, it is C that remains constant. Your investigations have already told you this, because when you went looking for physical/generational changes across time that would help explain your results, you discovered that physical/generational changes across time are minimal at best, hardly worth the notice. But if C remains essentially constant, then all the increase in S over time is explained solely by an increase in T. The rising ambient temperature causes water production scores to increase over time and does so without impacting any of the boxes’ physical/generational characteristics.”
But it has to impact those characteristics,” the researchers insist. “A box’s physical/generational characteristics embody its water production ability, and therefore embody its usefulness too.”
“No, that’s just it,” the visitor answers. “Those physical/generational characteristics tell only half the story at best. If you want to fully understand the nature of water production ability, as well as fully understand the nature of usefulness, you must also take into account the ambient temperature. And if it’s the increase in water production ability you’re trying to explain, then all the focus has to go towards the ambient temperature, because that’s the only factor that changes over time.”
The researchers are polite but end the discussion by saying they cannot argue established fact—plus they have to get back to combining explanations and attending to impressive mathematical formulas.
The above analogy forms a nearly exact isomorphism to the current situation regarding intelligence and the Flynn effect.
IQ scores are like water production scores, and individual people (or individual brains, if you will) are like boxes. Scientists have built up a large and compelling cache of evidence that variations in IQ scores among individuals are driven by a presumed set of neuronal/genetic characteristics, the idea being that if researchers had good knowledge of an individual’s neuronal/genetic background, they would be able to predict within a reasonable degree of accuracy the individual’s intelligence score and the corresponding likelihood of success within the community. The correlations are not always exact but they are strong enough to be informative across both individuals and groups.
If that is all there were to it, then intelligence would be essentially explained. However, that is not all there is to it. In addition to the experimental evidence outlined so far, scientists have also discovered that raw intelligence scores keep increasing over time, a phenomenon that has been named the Flynn effect. Intelligence scores keep increasing each year and they keep increasing for essentially every population.
Many explanations for the Flynn effect have been proposed and investigated. Many of these explanations focus on possible improvements to humanity’s neuronal/genetic underpinnings—through better nutrition for instance, or assortative mating. Other explanations target specific changes to the human environment, such as increasing amounts of visual stimulation or broader access to advanced education. However all these explanations have only limited scope, and therefore none have been able to account for the ubiquitous and relentless reach of the Flynn effect. Plus the environmental explanations have been perceived as suffering from a still further difficulty, namely that they must be translated into more or less permanent changes in a person’s neuronal/genetic characteristics, because everyone agrees that those neuronal/genetic characteristics are what ultimately underlie intelligence. The translation often seems implausible.
Dickens and Flynn (2001) have attempted to solve this dilemma by demonstrating that neuronal/genetic characteristics and environmental factors can resonate off each other with amplifying effect. They have introduced concepts such as social multipliers and feedback loops and have developed complex mathematical formulas to show how their mechanism can be tuned to experimental results. Other researchers, perhaps frustrated over the lack of a definitive answer, have suggested that the Flynn effect cannot be explained by any one factor alone, but that instead a large combination of factors must ultimately be brought to bear.
The essay The Flynn Effect’s Unseen Hand proposes an entirely different approach to the problem, suggesting that the perceived difficulty in solving the Flynn effect is actually being produced by a widespread misunderstanding of the problem’s context, in particular by a widespread misunderstanding of what constitutes intelligence. The essay defines the term environmental intelligence as the total amount of non-biological pattern, structure and form tangibly contained within the human environment and associates human intelligence to this ambient pattern, structure and form, and repudiates the notion that intelligence must be invariably tied to the workings of the human brain. That there has been a steady increase in environmental intelligence can be seen readily enough from a survey of human history: from the beginnings of the human great leap forward, through the transformations of the agricultural revolution, through the development of civilizations such as Mesopotamia, Egypt and Greece, through the fast-paced innovations of the Renaissance, and finally culminating in the explosion of technologies and constructions that humans encounter today. As humans have progressed through their many changing epochs, they have been navigating an increasingly complex framework of pattern, structure and form, and they have been navigating this framework at a faster and faster pace.
This increasing amount of pattern, structure and form makes for an ideal and elegant explanation of the Flynn effect, it has all the essential characteristics. For one, the increase mirrors the increase in raw intelligence scores. Two, the increase in environmental intelligence is continuous over time, just as with the rise in test results. Three, the increase in environmental intelligence is ubiquitous, people are exposed to it everywhere nearly the same. And finally, in considering the content of an intelligence exam—all those questions formed out of pattern, structure and form—one recognizes that navigating an intelligence exam is not unlike navigating the framework of the surrounding world, and thus it cannot be all that surprising that ambient pattern, structure and form must have something to do with human intelligence.
The situation can be described like this: there are three different quantities in play—a person’s neuronal/genetic capacity for intelligence, the raw intelligence score, and the total amount of pattern, structure and form contained within the human environment. Letters can be used to stand for these quantities:
C = a person’s neuronal/genetic CAPACITY for intelligence,
S = the raw intelligence SCORE, and
T = the TOTAL AMOUNT of pattern, structure and form contained within the human environment.
A person’s raw intelligence score (S) is the combination of the two other factors (C and T) working independently. This can be expressed in a simple relationship:
S = C x T.
This relationship fits the experimental results perfectly. At any given point in time, the total amount of ambient pattern, structure and form (T) will be constant, so that when experiments are run at that time, all the variation in intelligence scores (S) will be the direct result of the differing neuronal/genetic characteristics of each person, because those neuronal/genetic characteristics are what drive a person’s relative capacity (C) to demonstrate intelligence.
But over time it is the orthogonal effect that holds sway. Over time, it is C that remains constant; scientists have little in the way of evidence to suggest that profound neuronal/genetic changes occur over time, just as to be expected under the tenets of biology and evolution. But if C remains essentially constant, then all the increase in S over time is explained solely by an increase in T. The increasing amount of pattern, structure and form contained within the human environment causes intelligence scores to go up over time and does so without impacting any person’s neuronal/genetic characteristics.
Scientists have a hard time considering, let alone accepting, this new description of intelligence because scientists are working under a mistaken assumption. Their evidence has been so strong and compelling that variations in neuronal/genetic characteristics lead to corresponding differences in intelligence scores that the scientists have somehow become convinced that the inference is valid also in the other direction—that is, that every change in intelligence score is necessarily accompanied by a change in neuronal/genetic characteristics. That unsupported assumption is what leads everyone astray. For instance, all the complexity of the Dickens‑Flynn model is being driven by a perceived need to have environmental influences and neuronal/genetic characteristics interact. But in reality that interaction is not called for at all, all the evidence clearly indicates that environment influences and neuronal/genetic characteristics are essentially independent.
If there is one disruptive consequence to this new and straightforward accounting of the Flynn effect, it is that it compels a complete reassessment of the word intelligence. Because of the perceived (and mistaken) bi-directional linkage of IQ scores and neuronal/genetic characteristics, scientists have been restricting use of the word intelligence to the domain of that linkage alone. But neuronal/genetic characteristics tell only half the story at best. If scientists hope to understand fully and accurately the nature of intelligence, then they must also take into account the total amount of non-biological pattern, structure and form tangibly contained within the human environment. And if it is the increase in intelligence that scientists are trying to explain, then all their focus must go towards the structural human surroundings, because that is the only factor that changes over time.
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