Intelligence as Field

[Note: if the data charts below are difficult to read, try reading the essay at this alternative location.]


Let me begin with a chart of data:

Age	Raw Intelligence Scores by Age and Year
	95	40.8	41.6	42.5	43.3	44.2	45.1	46.0	46.9	47.9	48.8	49.8
	85	45.6	46.5	47.5	48.4	49.4	50.4	51.4	52.4	53.5	54.6	55.7
	75	50.4	51.4	52.5	53.5	54.6	55.7	56.8	58.0	59.1	60.3	61.5
	65	54.0	55.1	56.2	57.3	58.5	59.7	60.9	62.1	63.4	64.6	65.9
	55	55.8	56.9	58.1	59.2	60.4	61.7	62.9	64.2	65.5	66.8	68.1
	45	57.6	58.8	59.9	61.2	62.4	63.7	64.9	66.2	67.6	68.9	70.3
	35	58.8	60.0	61.2	62.4	63.7	65.0	66.3	67.6	69.0	70.4	71.8
	25	60.0	61.2	62.4	63.7	65.0	66.3	67.6	69.0	70.4	71.8	73.3
	15	51.0	52.0	53.1	54.2	55.2	56.4	57.5	58.7	59.8	61.0	62.3
	5	15.0	15.3	15.6	15.9	16.2	16.6	16.9	17.3	17.6	18.0	18.3
		105	115	125	135	145	155	165	175	185	195	205
	Year

This chart represents the results from an idealized experiment. Every ten years, beginning in year 105 and running to year 205, an intelligence test is administered to sample members from a population. They are recruited to match the *5 age points—that is, the test is administered each time to 5 year-olds, 15 year-olds, 25 year-olds, and so on up to 95 years of age. This allows us to follow each generation born at the decade boundaries: for instance, those born in the year 100 would take the exam as 5 year olds in the year 105, as 15 year olds in the year 115, and so on. Successive generations can be followed accordingly.

The test given is always the same—it does not change from decade to decade or from age group to age group. Furthermore, the test is administered in such a way as to make each trial independent of all previous outcomes. In practical terms, this would mean that each member of the population could be given the exam at most once during their lifetime, and there would be no such thing as cheat sheets, practice exams, or other extraneous factors that might influence scores across time. Admittedly, this requirement would be difficult to enact in reality, which is one of the reasons why we must resort to an idealized experiment.

The exam is considered to be a good measure of general intelligence abilities—in modern parlance, it has a high g loading. The values shown in the chart reflect raw scores on the exam and thus are directly comparable. We might think of the exam as consisting of one hundred questions, ranging from simple to difficult, with one point given for each correct answer and with an individual score above 95 considered to be nearly impossible, thus reducing or eliminating any ceiling effects.

The scores shown are the mean for the population, but it can also be assumed that the temporal patterns evident in these scores remain invariant across sub-populations. For instance, a given ethnic sub-population might have mean scores that are higher or lower than those shown in the above chart, but the pattern of scores from decade to decade and from age group to age group will remain quite similar. The same can be said for a sub-population of the highly intelligent or a sub-population of the less intelligent (or a sub-population of the middling intelligent, for that matter). The temporal patterns evident in the mean scores are indicative of similar temporal patterns that appear in nearly every significant sub-population that comes under study.

Although this experiment and its results have been idealized, they are intended to reflect current reality. In particular they reflect the two most experimentally agreed-upon temporal patterns witnessed in human raw intelligence scores. First there is the age-based pattern of intelligence performance, which can be seen by reading up the chart for any given year. At each time period, raw intelligence ability across age groups is seen as low but quickly increasing during the formative childhood years, peaking and plateauing during early adulthood, and finally diminishing gradually with advancing age. In the real world, the degree of these age-related changes in intelligence ability might differ from experiment to experiment or from exam to exam, but the pattern of intelligence growth, peak and gradual decline tends to show up in nearly every experiment, across nearly every intelligence exam, and for nearly every sub-population that comes under study.

The second experimentally agreed-upon temporal pattern in human raw intelligence scores can be seen by reading across the chart. At each age level, raw intelligence scores gradually and consistently increase with each passing decade. This is a demonstration of the well known Flynn effect, and once again, in the real world, the degree of these time-related changes might differ from experiment to experiment or from exam to exam, but the pattern of widespread intelligence growth tends to show up in nearly every experiment, for nearly every intelligence exam, and across nearly every sub-population that comes under study.

It would be helpful if there were a real-world gathering of intelligence data that might be compared to our idealized chart—and maybe one day there will be—but as of today there are difficulties in the path of this realization. For one, intelligence exams are changed from time to time and different levels of exam are often given to different age groups, making direct comparisons problematic. Furthermore, widespread awareness of intelligence tests has become established within the culture, so that independence of test-taking results is not as assured as it once was. Finally, at this point in human history, collecting a hundred years worth of data on any particular intelligence exam, especially for all age groups, would be unrealistic. Perhaps the Raven's test might be the most amenable to the type of data gathering being imagined here, but even in that instance, the data set would have to be described as incomplete at best.

Nonetheless, there remains compelling reason to accept the idealized chart as being essentially accurate. The chart was after all constructed specifically to reflect two of the most widely held and experimentally backed assumptions regarding human intelligence scores, namely the age-based pattern of growth, peak and gradual decline, and also the Flynn effect. If the real-world counterpart to our idealized chart were to somehow be essentially different in its temporal patterns, then the phenomena themselves would have to be called into question. If you accept the age-based pattern of growth, peak and decline, and if you accept the reality of the Flynn effect, then you would also have to accept the essential accuracy of the idealized chart of data—it reflects (ideally) the impact those phenomena must have on raw intelligence scores.


One of the reasons I have taken the time both to explain and to justify this chart of data is that I want to make use of it to dispel a common myth surrounding the Flynn effect, a myth regarding generational comparisons of human intelligence ability. This myth is usually presented along the lines of saying that a younger generation, by virtue of the Flynn effect, is more intelligent than an older generation (or for those of a more pessimistic bent, by saying that the older generation is generally less intelligent than the newer generation). An echo of this sentiment can be heard in the following passage from James Flynn's book What is Intelligence?

“If huge IQ gains are intelligence gains, why are we not struck by the extraordinary subtlety of our children's conversation? Why do we not have to make allowances for the limitations of our parents? A difference of some 18 points in Full Scale IQ over two generations ought to be highly visible.”

Such statements, when taken at face value, suggest that the Flynn effect should be producing a discernible distinction in intelligence ability across near generations, with younger generations emerging in real time as the more intelligent. But a quick perusal of the above chart (not to mention a careful observation of everyday experience) reveals the idea to be complete nonsense. If, for instance, we compare raw intelligence abilities of 15 year-olds against 55 year-olds, the 55 year-olds always emerge as the more intelligent, and furthermore, the relative distinction never changes. At each decade of measurement, the intelligence advantage of 55 year-olds over 15 year-olds remains exactly the same, driven by each group's respective position along the age-based pattern of human intelligence, and this continues despite the fact that the Flynn effect remains in full evidence across the entire time period of measurement:

Age	Raw Intelligence Scores by Age and Year
	95	40.8	41.6	42.5	43.3	44.2	45.1	46.0	46.9	47.9	48.8	49.8
	85	45.6	46.5	47.5	48.4	49.4	50.4	51.4	52.4	53.5	54.6	55.7
	75	50.4	51.4	52.5	53.5	54.6	55.7	56.8	58.0	59.1	60.3	61.5
	65	54.0	55.1	56.2	57.3	58.5	59.7	60.9	62.1	63.4	64.6	65.9
	55	55.8	56.9	58.1	59.2	60.4	61.7	62.9	64.2	65.5	66.8	68.1
	45	57.6	58.8	59.9	61.2	62.4	63.7	64.9	66.2	67.6	68.9	70.3
	35	58.8	60.0	61.2	62.4	63.7	65.0	66.3	67.6	69.0	70.4	71.8
	25	60.0	61.2	62.4	63.7	65.0	66.3	67.6	69.0	70.4	71.8	73.3
	15	51.0	52.0	53.1	54.2	55.2	56.4	57.5	58.7	59.8	61.0	62.3
	5	15.0	15.3	15.6	15.9	16.2	16.6	16.9	17.3	17.6	18.0	18.3
		105	115	125	135	145	155	165	175	185	195	205
	Year

So where did the Flynn effect go? Why is it not more apparent in these generational comparisons? Well, of course the Flynn effect did not go anywhere, the problem is actually in the comparison itself. The myth is essentially caused by the mistaken notion that a Flynn effect distinction can be discerned by comparing up and down the chart, that is by making a generational comparison at a particular point in time—for instance, by comparing 15 year-olds to 55 year-olds at time 175 and expecting a Flynn effect distinction will somehow emerge. But in fact, comparisons up and down the chart will never reveal any such thing.

The only proper Flynn effect comparison that can be made is across the chart. For instance, all we can accurately say about 15 year-olds at time 175 is that they reveal more raw intelligence ability than did the 15 year-olds at say time 135. But it is just as important to note that the 55 year-olds at time 175 (who of course were the 15 year-olds at time 135) also reveal more raw intelligence ability than did the 55 year-olds at time 135, and this difference, along with the normal age-related distinctions, has a predictable and consistent impact. The 55 year-olds at time 175 (along with the 55 year-olds at any time period) are not generationally lacking in raw intelligence. Quite the opposite.

Let's take one of the 55 year-olds at time 175, the ones being inaccurately characterized as somehow less intelligent than the younger generations from that same time period, and let's call him person A. One of the drivers behind the myth that Person A must be less intelligent than those of the younger generations is the mistaken sense that Person A's intelligence is irrevocably tied to the year of his birth. It is as though the intelligence characteristics of Person A are being determined by reading up the chart from the year 120 (which would be formed roughly from the average of the years 115 and 125):

Age	Raw Intelligence Scores by Age and Year
	95	40.8	41.6	42.0	42.5	43.3	44.2	45.1	46.0	46.9	47.9	48.8	49.8
	85	45.6	46.5	47.0	47.5	48.4	49.4	50.4	51.4	52.4	53.5	54.6	55.7
	75	50.4	51.4	51.9	52.5	53.5	54.6	55.7	56.8	58.0	59.1	60.3	61.5
	65	54.0	55.1	55.6	56.2	57.3	58.5	59.7	60.9	62.1	63.4	64.6	65.9
	55	55.8	56.9	57.5	58.1	59.2	60.4	61.7	62.9	64.2	65.5	66.8	68.1
	45	57.6	58.8	59.3	59.9	61.2	62.4	63.7	64.9	66.2	67.6	68.9	70.3
	35	58.8	60.0	60.6	61.2	62.4	63.7	65.0	66.3	67.6	69.0	70.4	71.8
	25	60.0	61.2	61.8	62.4	63.7	65.0	66.3	67.6	69.0	70.4	71.8	73.3
	15	51.0	52.0	52.5	53.1	54.2	55.2	56.4	57.5	58.7	59.8	61.0	62.3
	5	15.0	15.3	15.4	15.6	15.9	16.2	16.6	16.9	17.3	17.6	18.0	18.3
		105	115	120	125	135	145	155	165	175	185	195	205
	Year

There! You can practically see it for yourself! The 55 year-olds from around the year 120 were not very intelligent, and Person A, who after all was born in the year 120 and is a 55 year-old now—well, he must not be very intelligent either!

But of course that line of reasoning is completely wrongheaded. An individual's intelligence characteristics are not determined by reading up the chart from the year of his birth, they are determined instead by reading diagonally across the chart. Person A's intelligence characteristics (and by extension, the characteristics of his entire generation) are more accurately portrayed as follows:

Age	Raw Intelligence Scores by Age and Year
	95	40.8	41.6	42.0	42.5	43.3	44.2	45.1	46.0	46.9	47.9	48.8	49.8
	85	45.6	46.5	47.0	47.5	48.4	49.4	50.4	51.4	52.4	53.5	54.6	55.7
	75	50.4	51.4	51.9	52.5	53.5	54.6	55.7	56.8	58.0	59.1	60.3	61.5
	65	54.0	55.1	55.6	56.2	57.3	58.5	59.7	60.9	62.1	63.4	64.6	65.9
	55	55.8	56.9	57.5	58.1	59.2	60.4	61.7	62.9	64.2	65.5	66.8	68.1
	45	57.6	58.8	59.3	59.9	61.2	62.4	63.7	64.9	66.2	67.6	68.9	70.3
	35	58.8	60.0	60.6	61.2	62.4	63.7	65.0	66.3	67.6	69.0	70.4	71.8
	25	60.0	61.2	61.8	62.4	63.7	65.0	66.3	67.6	69.0	70.4	71.8	73.3
	15	51.0	52.0	52.5	53.1	54.2	55.2	56.4	57.5	58.7	59.8	61.0	62.3
	5	15.0	15.3	15.4	15.6	15.9	16.2	16.6	16.9	17.3	17.6	18.0	18.3
		105	115	120	125	135	145	155	165	175	185	195	205
	Year

Reading Person A's actual intelligence history makes it more clear why Person A is not generationally disadvantaged at time 175. The Flynn effect, which has not gone anywhere, that very same Flynn effect that has been helping produce an increased level of intelligence in the 15 year-olds at time 175, it has also been advancing the intelligence characteristics of Person A across the entire period of his life. By the time Person A reaches the year 175, he has had 55 years worth of Flynn effect actively pushing his intelligence ability ever higher, and thus he has no difficulty holding his relative intelligence position against the younger generations. Across the entire time period of the chart, the Flynn effect works with equal magnitude upon every person in the population, so that by any particular point in time, the only difference that can emerge is the age-based difference.


This concept is so important that it deserves to be examined in greater detail. We can begin by comparing side-by-side Person A's intelligence characteristics under and not under the influence of a Flynn effect. Without a Flynn effect, Person A's intelligence history would turn out to be exactly the same as what we saw by reading up the chart from the year 120. With a Flynn effect, as we have seen, Person A's intelligence characteristics are determined by reading diagonally across the chart. The contrast is most revealing:

Age	Without Flynn Effect	With Flynn Effect	Difference
85	47.0	55.7	8.7
75	51.9	60.3	8.4
65	55.6	63.4	7.8
55	57.5	64.2	6.7
45	59.3	64.9	5.6
35	60.6	65.0	4.4
25	61.8	65.0	3.2
15	52.5	54.2	1.7
5	15.4	15.6	0.2

What should immediately leap out from this side-by-side comparison is the primary characteristic of the Flynn effect itself, a characteristic which unfortunately almost never receives the attention of cognitive scientists. The primary characteristic of the Flynn effect, so apparent here, is that it is continuous—and relentlessly so. This side-by-side comparison of Person A's intelligence history, under and not under the influence of a Flynn effect, makes it abundantly clear that the Flynn effect does not have just sudden impact at Person A's birth, nor does it create an overly strong influence during Person A's childhood education, nor does it produce momentous occasion in Person A's adulthood or at any other time during Person A's life. Instead, the Flynn effect works smoothly, consistently and relentlessly throughout Person A's entire lifetime, all the way from its beginning to its end, with no apparent discontinuities. If one were to describe the impact of the Flynn effect on Person A's life, one would have to refer not to the discrete influences on Person A's existence, but one could instead more profitably rely upon differential equations.

Person A is of course just one individual, but when we examine the entire chart of idealized data, we realize that what must be said about Person A's intelligence history must also be said about everyone else, for there is nothing in the chart to suggest otherwise. Taking the diagonal intellectual course of any person's life who falls under the range of the chart of data, comparing it side-by-side to that person's intelligence characteristics minus the Flynn effect, the same continuous, persistent phenomenon predictably emerges. No matter what person is under study, no matter what place that person may find himself, no matter what time period is being given consideration, the Flynn effect unveils itself as ubiquitous, continuous and relentless.

This is why I remain highly skeptical of nearly every proposed cause for the Flynn effect. Heterosis, advanced education, better nutrition, social multipliers, video games, so many others—all these proposed causations for the Flynn effect have been put forth as discrete influences on the members of the human population, intended to produce a distinct impact upon the human brain. But if such a discrete influence were actually true it would have to produce a more noticeable disturbance in the temporal pattern of raw intelligence scores, and yet as far as I can tell, in the real world, there has been not the slightest hint of evidence for any such disturbance.

Thus it is that I think better sense might be made of the Flynn effect by giving the problem not to a cognitive scientist, but instead by giving it to a physicist. For instance, let's try the following scenario: let's give our idealized chart of data to a physicist but hide from him the fact that the numbers represent intelligence data and that the subject of study is human behavior. Instead, we simply put the matter to him as follows:

“Here's a chart of data from an experiment we've been conducting. We are measuring a characteristic for some objects and these are the results. The vertical axis represents the age of the objects since the start of their existence, and the horizontal axis represents the time periods during which measurements were taken. The data captured is the raw magnitude of the characteristic under study. We've run the experiment several times and the results are consistent, but we've had a hard time figuring out what's going on. Can you make any sense out of this data?”

It would not be all that unthinkable to conceive of our physicist friend as inspecting the data for a few moments, then nodding his head slowly and finally saying yes, he believes he can shed some light on what is going on.

“I don't recognize what objects these are or what characteristic you're studying,” he might begin, “but it appears to me that your objects are under the influence of a dynamic field, and the characteristic you're studying is essentially defined by that field. The objects themselves do have variable responsiveness to the field—less so in their early career, more in middle life, tapering off near the top of the chart—that behavior is evident in each object and would be an inherent part of the object itself. But the characteristic you're studying is something quite different, it seems to be determined primarily by the traits of the field. Look at how the field becomes more uniformly intense over time, increasing the raw measurement on each object. I can't say what this characteristic might be, but I'm fairly sure it would be best described as a field—we see this sort of thing all the time in physics.”

Field theory was first introduced into the world of physics by James Maxwell in the 1860s, when he published a series of differential equations that described the essential characteristics of an electromagnetic field. Up until that time, physics had been dominated by purely mechanical descriptions of all known physical phenomena, a lingering outcome from Isaac Newton's successful ideas. Some phenomena, however, such as electricity, were beginning to feel the strain of the mechanistic point of view. As more and more experimental data began to accumulate, a multitude of theories attempting to describe charged objects as being constituted out of electrical fluids or out of electric pieces arranged in a variety of shapes and sizes began to suffer the dubious distinction of being inadequate for explaining the observable data or of being so convoluted as to defy common sense.

Maxwell dispelled the confusion by essentially changing the paradigm of the problem. Instead of attributing electricity to the charged objects themselves, Maxwell moved the essential traits of electricity (magnetism and light too) into the spatial-temporal field in which charged objects existed. Charged objects no longer contained electricity so much as they responded to it—some more so, and some less, each according to its physical traits. But the structural characteristics of electricity itself belonged instead to the field, a field whose dynamic features and explanatory power had been elegantly captured inside Maxwell's differential equations.

As with most ideas that run against the grain of the prevailing view, Maxwell's innovations were a bit slow at first to catch on, but by the end of the nineteenth century they had entirely revolutionized physics, ushering in an unprecedented era of growth and discovery. Einstein, for example, would make extensive use of field theory in the development of his general theory of relativity. Use of field theory has become commonplace in physics today, it is the lingua franca for a large variety of physical phenomena, and a modern physicist would have little difficulty recognizing a field's distinctive signature.

But that of course is physics. What about intelligence? Can intelligence also be profitably described through the means of field theory?

Well, our physicist friend certainly seems to think so. Note that although he remains entirely unaware that the objects under study are humans and that the characteristic being measured is intelligence, such knowledge is inessential to his judgment. The physicist recognizes a dynamic field at work through the patterns in the numbers and through a familiarity with the theme, and the fact that the objects actually are humans and that the characteristic being measured actually is intelligence does not alter the reality of the patterns, nor does it disqualify the application of the theme.

The world of intelligence research is currently dominated by a mechanistic/biological point of view. In particular, nearly every scientific theory regarding human intelligence either explicitly or implicitly assumes that human intelligence is directly attributable to the material properties of neurons, is directly derivable from the substance of the human brain. This view has become so dominant that it almost seems superfluous to state it. But unfortunately, dominance is not the same thing as completion. The work that is still to be done by brain-based proponents of human intelligence remains notably conspicuous by its glaring absence. What would really compel us to accept a brain-based notion of human intelligence is a convincing description of the form the brain's material substance must take in order to accurately and informatively explain the known intelligence data, including the data contained in the chart at the beginning of this essay. Yet no such description has even been placed on the table. In its absence, what has unfolded is a crowded cacophony of competing ideas, replete with a host of unstated presumptions and a mass of garbled-up data (see all of neuroscience, for instance). And nowhere is this chaotic scene on more obvious display than in the many-fangled attempts to explain the Flynn effect. The competing biological and socio-environmental hypotheses (all with an underlying assumption of ultimate impact on the human brain) have grown so broad-ranging and to such great multitude that by logical necessity alone, the grand majority must be suffering from the dubious distinction of being unable to account for the observable data or of being so convoluted as to defy common sense (see for instance the Dickens-Flynn model and Woodley's theory of fast and slow life). The intelligence research community has become utterly convinced that intelligence must be a brain-based phenomenon, and yet it seems utterly uncertain about everything else.

It is my contention that the confusion can be dispelled by changing the paradigm of the problem. Instead of ascribing intelligence to the material substances found inside the human head, it would be far more enlightening—and far more fitting to the data—to ascribe intelligence to the structural traits of a surrounding field, a field which would encompass the entire spatial-temporal range of human existence. Humans would be seen not so much as carrying intelligence as they would be seen as responding to it—some more so and some less, each according to his biological capacity (for instance, we see one such biologically driven variation in responsiveness in the table above, in the age-related patterns of human intelligence, and another might be the racial-ethnic differences sometimes evidenced, and of course at a deep enough level, each individual produces a unique responsiveness to the intelligence field). But responsiveness to a characteristic is not the same thing as the characteristic itself. Intelligence, properly speaking, is not to be defined as a material property of human biology, it is instead to be defined by the structural traits of an intelligence field.

From our idealized chart of data, it is an easy matter to describe the dynamic temporal traits of the chart's intelligence field: that field has the characteristic defined roughly by the differential equation

dy / dt = .002 y,

where y is the raw score and t is in years. This is the simple mathematical form of the Flynn effect. In the real world of course, we might anticipate a little more messiness, as certain types of fluctuations and perturbations are likely to occur, and yet based on what we do know about the Flynn effect, we can still state with some confidence that its apparently linear influence will almost certainly dominate any real-world equation.

This way of describing intelligence, by reference to the structural traits of a dynamic field, must no doubt seem foreign to the reader, but it has an obvious advantage. First of all, the technique directly addresses the data, something that a neuron-based description will almost certainly never do, and furthermore the technique addresses that data with conciseness, simplicity and elegance—the expected consequence of any successful application of field theory.


I know that by placing the structural characteristics of intelligence firmly outside the human brain I am running full tilt against the prevailing view. But my challenge to the many who now hold to a brain-based, mechanistic, biological model of human intelligence is that they must explain the chart of data with which this essay began. In particular, how does any brain-centered, discrete influence not leave a massive ripple in that enormous pond? Just look at the pattern of that data! So continuous, so persistent, so ubiquitous—like a slowly building wave, like a relentlessly rising tide. It should give one pause. But for those who must defend the brain-based models of human intelligence, my prediction is that confusion and convolution will continue to dog their steps. Me, I much prefer elegance. I prefer intelligence as field.

References

Dickens, W. T. & Flynn, J. R. (2001). Heritability estimates versus large environmental effects: The IQ paradox resolved. Psychological Review, 108: 346–369.
Flynn, J. R. (2007). What is intelligence? Beyond the Flynn effect. New York: Cambridge University Press.
Woodley, M. A. (2012). A life history model of the Lynn-Flynn effect. Personality and Individual Differences, 53: 2. 152–156.

Repetition

As the Pharisees and scribes were to Jesus, as the Catholic Church was to Galileo, so is modern science to the genius of today.

Definition of a Modern Scientist

One who buries his logic errors under a mound of data.

Courage in Action

A modern scientist thinks he's being revolutionary when he adds an additional decimal point to the data's mean.

The Serpent Swallowing His Tail

It seems that the most productive job in neuroscience right now is to statistically demonstrate how neuroscience has gone statistically wrong.

Consequences

Attempting to get an autistic child to develop and learn through a greater and greater emphasis on acquiring social skills is like trying to get a blind person to develop and learn through a greater and greater emphasis on gaining eyesight, the tragedy made all the more poignant by the fact there are alternatives which serve everyone better. Attack autism and it attacks you back. Embrace autism and it embraces you back. Every parent, every advocate, every researcher who joins in the battle against autism does his or her part in contributing to the overall misery.

The Dickens-Flynn Model

[Edit 02/11/2017: The final version of this essay can be found here.]

The Dickens-Flynn model is an attempt by William Dickens and James Flynn to explain how environment and genes can interact to account both for environmentally-driven increases in raw intelligence scores over time (the Flynn effect) and for the persistent influence of genetically-driven individual intelligence differences (Spearman's g). Admittedly, there is much that is appealing about the Dickens-Flynn model, for it does directly address the observable tension between the Flynn effect and g (something that many explanations for the Flynn effect fail to do). And yet the model is also troubling in a rather conspicuous way, for it appears to sport the overall mechanics of a Rube Goldberg machine.

The original description of the Dickens-Flynn model is provided in Dickens & Flynn (2001)—unfortunately that paper seems not to be freely available. The authors do however provide an accessible summary at this location. Another source for the details constituting the Dickens-Flynn model is Flynn's book What is Intelligence? (Flynn 2007), which at various points outlines both the workings of the model and its explanatory power vis-a-vis the Flynn effect. There have also been several published criticisms of the Dickens-Flynn model, including this one from Linda Gottfredson, and Dickens has responded to some of these criticisms in outline form here.

Leverage plays a key role within the Dickens-Flynn model. For instance, small genetic advantages are described as being amplified through the means of a natural attraction to environmental surroundings that serve to enhance those advantages. Environmental influences on population-wide intelligence characteristics are said to be boosted through such factors as social multipliers and rolling triggers. And finally, genetic and environmental forces are depicted as mutually propelling each other by means of reciprocal feedback mechanisms. Indeed, there is something particularly mathematical about the use of such concepts as multipliers, triggers and feedbacks—instead of defining these terms through real world descriptions and empirical examples, the model more frequently casts them in the role of free variable, serving to balance out and drive the equations where required. This tendency towards calculational necessity can also be witnessed to some extent in Dicken's clipped responses to the model's criticisms, where arguments against social multipliers and circular feedback mechanisms are answered through an appeal to simultaneous equations, estimated network effects and functional form.

None of this is to say that the Dickens-Flynn model is inherently wrong or essentially inconsistent. It remains perfectly conceivable that the model's many mechanisms and various factors can be fitted (with a little more mathematical tweaking perhaps) to the observable characteristics of human intelligence behavior. But even if this is so, in parsing through such ideas as social multipliers, rolling triggers and reciprocal feedback mechanisms, one is left with the distinct impression that the Dickens-Flynn model can in no way be described as straightforward—and this of course is in contrast to the phenomenon which the model is intended to explain, a phenomenon which can be captured in very few words (human raw intelligence scores increase consistently and universally over time). A charitable way of depicting the Dickens-Flynn model would be to say that it is intricate and complex; a less charitable depiction would be to say that it is convoluted.

Which raises a question: why would Dickens and Flynn insist on providing a model of such intricacy and complexity? Why did they not gravitate instead to something more direct, with fewer moving parts? Flynn, ever the paragon of candor, provides a thorough and revealing answer in the pages of What is Intelligence?. There Flynn notes that during the height of the intelligence race debates, Richard Lewontin had offered a compelling description for how an environmental influence could uniformly impact an entire population while still leaving undisturbed any genetically-driven individual characteristics:

Lewontin tried to solve the paradox. He distinguished the role of genes within groups from the role of genes between groups. He imagined a sack of seedcorn with plenty of genetic variation randomly divided into two batches, each of which would therefore be equal for overall genetic quality. Batch A is grown in a uniform and optimal environment, so within that group all height differences at maturity are due to genetic variation; batch B is grown in a uniform environment which lacks enough nitrates, so within that group all height differences are also genetic. However, the difference in average height between the two groups will, of course, be due entirely to the unequal quality of their two environments.

Although Lewontin's description proved mostly untenable within the context of the intelligence race debates, Flynn later recognized that it seemed to form an ideal explanation for the Flynn effect:

So now we seemed to have a solution. The present generation has some potent environmental advantage absent from the last generation that explains its higher average IQ. Let us call it Factor X. Factor X will simply not register in twin studies. After all, the two members of a twin pair are by definition of the same generation. Since Factor X was completely missing within the last generation, no one benefited from it at all and, therefore, it can hardly explain any IQ differences within the last generation. It will not dilute the dominance of genes. Since Factor X is completely uniform within the present generation, everyone benefits from it to the same degree and it cannot explain IQ differences within the present generation. Once again, the dominance of genes will be unchallenged. Therefore, twin studies could show that genes explain 100 percent of IQ differences within generations, and yet, environment might explain 100 percent of the average IQ difference between generations.

And yet as Flynn attempted to apply Lewontin's idea specifically to the Flynn effect, he quickly found himself frustrated:

However, Lewontin offers us a poisoned apple. History has not experimented with the last two generations as we might experiment with plants in a laboratory. Consider the kind of factors that might explain massive IQ gains, such as better nutrition, more education, more liberal parenting, and the slow spread of the scientific ethos. It is quite unreal to imagine any of these affecting two generations with uniformity. Certainly, everyone was not badly nourished in the last generation, everyone well nourished at present; everyone without secondary school in the last generation, everyone a graduate at present; everyone raised traditionally in the last generation, everyone raised liberally at present; everyone bereft of the scientific ethos in the last generation, everyone permeated with it at present. If the only solution to our paradox is to posit a Factor X or a collection of such, it seems even more baffling than before. We should shut this particular door as follows: a solution is plausible only if it does not posit a Factor X.

It is in this manner that Flynn places a strange restriction around what he might accept as a plausible explanation for the Flynn effect: the explanation would have to carry all the desirable characteristics of a Factor X, but without actually being a Factor X. And it is into the space of that restriction that the Dickens-Flynn model comes to be. Flynn is quite forthcoming about what he perceives to be the model's most valuable characteristic:

Best of all, our solution posits no Factor X.

It is out of this historical context that we can get a better sense for why the Dickens-Flynn model must be necessarily complex and intricate, and not straightforward. It is as though Flynn has set forth the task of building a device designed to capture mice, and yet the device must in no way resemble a mousetrap. Therefore it's not all that surprising that soon we find ourselves inundated with the equivalent of levers, pulleys, waterwheels, whistles and bells, and if the objection is made that in the end it's still just a mousetrap, the architects can point to the enormous amounts of clever machinery and insist no one would mistake it for any such thing. And if the objection is raised that the mice are after all scurrying away, the assemblers can suggest that perhaps a few more pieces of duct tape and chicken wire should take care of the matter. Whether or not the many parameters of the Dickens-Flynn model can ultimately be fitted to the characteristics of human intelligence would be difficult for me to judge, but that does not take away from the observation that the model is a massively entangled contraption.

I'll be blunt: Flynn's mistake is in shutting the door on a Factor X.

Flynn's reasoning is perfectly correct when he notes that not only is every iterated item on his Factor X list inadequate for explaining the Flynn effect, but that indeed no particular item or subset of items could conceivably possess the requisite power or ubiquitous reach to account for population-wide, generational increases in raw intelligence scores. But instead of giving up—instead of shutting the door—what is needed at this point is some thinking outside the box. Or as I like to put it, some thinking outside the human skull.

In my essay The Flynn Effect's Unseen Hand, I describe a model for human intelligence that defines the term environmental intelligence as the total amount of non-biological pattern, structure and form tangibly contained within the human environment. Environmental intelligence serves not only as the palpable location of human intelligence, but its consistent increase over the course of human history serves also as the direct driver of the Flynn effect. It might not be perfectly correct to label environmental intelligence as a Factor X, since it's not really a factor, not a thing—instead it captures the impact of the environment as a whole. But nomenclature aside, environmental intelligence carries all the necessary characteristics of a Factor X. Environmental intelligence is ubiquitous. Environmental intelligence is constantly increasing. Environmental intelligence remains independent of the genetic influences driving individual intelligence differences.

The common objection that might be raised against a model of environmental intelligence is that it places the material locus of human intelligence firmly outside the human skull, firmly outside the human brain. But in exchange for jettisoning the widespread assumption that intelligence is a product strictly of human neurons, in exchange for accepting the total amount of non-biological pattern, structure and form contained within the human environment as the ideal Factor X, what is regained thereby is a great deal of simplicity. Gone is the need for social multipliers. Gone is the need for triggers. Gone is the need for feedback loops. Indeed gone is the need for any type of genetic/environmental interaction at all, especially of the complex, intricate kind. What one regains by jettisoning the Dickens-Flynn model and accepting instead a model of environmental intelligence is all the simplicity and straightforwardness that Lewontin must have originally had in mind.

I may be alone in this opinion, but I believe simplicity and straightforwardness should still count for something. I like to catch my mice with a mousetrap.

Dickens, W. T. & Flynn, J. R. (2001). Heritability estimates versus large environmental effects: The IQ paradox resolved. Psychological Review, 108: 346–369.

Flynn, J. R. (2007). What is intelligence? Beyond the Flynn effect. New York: Cambridge University Press.

Progress for the Masses

It's a funny thing about science these days. Brilliant science is as difficult to do as it always was, and bad science still requires a certain amount of (immoral) finesse. But good science is as easy as firing up the statistics package and networking with a few dozen co-authors on the iPhone.

Science Interrupted

At the heart of nearly every scientific achievement made over the past many centuries you will find two significant activities:

1. The collection of an accurate set of data.
2. The development of a cogent description consistent with that data.

Data mining doesn't fall under either of those headings.

The Queen of Data Mining

Many years ago a weekly lottery was started in my home state, and for awhile the newspapers would print a front-page article announcing that Mr. So-and-So from Anytown had won that week's million-dollar prize. My grandmother would read those articles with her eyes wide as saucers. “Can you just imagine? Mr. So-and-So won a million dollars last night.” She said it as if some kind of miracle had taken place. I never could quite explain to her that in publishing the results after the lottery was held, the news was not quite as significant as it seemed, that only if it had been specifically predicted ahead of time that Mr. So-and-So from Anytown was going to win the lottery would the result be more akin to a miracle, and not just a meaningless certainty.

That anecdote might not seem relevant at first to autism, but it's the key to understanding Irva Hertz-Picciotto's approach to science. In fact these days, it's the key to understanding just about everyone's approach to science.

Diminished Capacity

I'm surprised no one has pointed out yet the obvious weakness in the latest Nature-published data mining attempts. Clocking in at a mere 59, 23 and 30 authors respectively, these studies clearly must be trivial compared to earlier findings. Heck, Geraldine Dawson didn't even bother to get herself placed on any of the authorship lists, the chances of which must have been something like 0.0001%. (I had Matthew State confirm the statistics on that last figure.)

More Modern Phrenology

Here is the latest instance of the type of logic being applied in cognitive science—the feeling around for bumps inside the human skull.

To be honest, I'm not sure it's all that fair to compare phrenology to neuroscience. The insights are the same, but the phrenologists accomplish them at a far cheaper cost.

The Flynn Effect's Unseen Hand

[Edit 02/11/2017: The final version of this essay can be found here.]

The Flynn Effect's Unseen Hand

An Environmental Description of Human Intelligence

Abstract. The Flynn effect is a well known but inadequately explained phenomenon. Many different causes have been suggested for the population-wide generational increases in raw intelligence scores—including heterosis, better nutrition, more abundant education, environmental complexity and various combinations of the above—but none of these explanations have proven to be scientifically or logically compelling. This lack of progress could be pointing to a misunderstanding of human intelligence itself, which is depicted these days almost entirely in terms of brain-based functioning alone. This brain-based focus, however, has been precisely the bedevilment in the many explanations of the Flynn effect, for it has been difficult to reconcile neuronal mechanisms producing individual intelligence differences with neuronal mechanisms producing widespread intelligence gains. Accordingly, this paper proposes an alternative model of human intelligence, one not centered on the human brain. This model will highlight two complementary aspects of human intelligence: 1. environmental intelligence, defined as the total amount of non-biological pattern, structure and form contained within the human environment, and 2. individual intelligence, defined as an individual’s capacity to absorb and respond to environmental intelligence. It can be shown that it is environmental intelligence that serves as the sole driver of the Flynn effect, independently of individual intelligence, and along the way it will be demonstrated how environmental intelligence is similar to but far more comprehensive than the concept known as environmental complexity. It will also be demonstrated that this dual-aspect model of human intelligence effectively answers several of the Flynn effect paradoxes enumerated by James Flynn himself.

Introduction. Generational gains in raw intelligence scores were first noticed by several individuals—including Reed Tuddenham and Richard Lynn—but it was James Flynn in the 1980s who most clearly demonstrated the ubiquitous nature of what has come to be known as the Flynn effect. In the quarter century since, the Flynn effect has attracted a good deal of study and ink—in part because the phenomenon has been regarded as surprising, and in part because the phenomenon has continued to defy adequate explanation.

This situation stands in contrast to many other areas of intelligence research, including investigations into the source and impact of individual and group intelligence differences. Using factor analysis, identical twin studies and many other tools of modern cognitive science, researchers have been able to demonstrate consistently that individual intelligence differences produce significant impact in such areas as academics and career, and that these individual differences are driven mostly by genetics and are almost certainly neuronally based. These discoveries and achievements have led to a nearly unanimous consensus that intelligence is to be regarded exclusively as a brain-produced activity—in short, greater intelligence is spawned by a more effective brain.

The Flynn effect, however, throws something of a monkey wrench into this widely accepted view. To accept the conclusion that intelligence is exclusively a brain-produced activity—an activity determined primarily by genetics—one also has to anticipate that overall human intelligence will remain relatively stable across time, in accordance with standard biological and evolutionary principles. That is why the Flynn effect has been regarded as so surprising. The sizable raw intelligence gains recorded across the entire twentieth century far outstrip any plausible, brain-based advancement that might be anticipated under a biological, neuronal, or evolutionary framework.

One response to this dilemma would be to search for an orthogonal influence underlying the Flynn effect, and James Flynn himself (1999) has uttered that very reaction in almost those exact same terms (“it is as if some unseen hand is propelling scores upward”). In addition, Richard Lewontin’s (1976) parable of the seed corn provides a nearly precise description of how such an orthogonal influence would actually work. And yet no one has managed to follow this line of reasoning to its ultimate conclusion. What prevents further advancement is the widespread certainty that intelligence is strictly a brain-produced event. All the offered explanations for the Flynn effect—be they heterosis, better nutrition, advancing education, environmental complexity, or any combination or alternative to the above—all explanations are brought back eventually to human neurology, all are ultimately depicted as inducing material impact upon the human brain. This depiction is of course made mandatory by the dogma that intelligence arises exclusively out of human neurons. Vigor, nutrients, schooling, video games—all must somehow change the human brain, must make it more effective, make it more intelligent. Unfortunately, this circling back to neurology serves only to re-create the original tension: now there are neural mechanisms explaining individual intelligence differences and there are other neural mechanisms explaining population-wide intelligence gains, and yet somehow these mechanisms are supposed to co-exist within the same human brain and not interfere with the intelligence-producing impact of the other. Plausibility once again rears its ugly head.

A more decisive alternative would be to drop the dogma altogether. Nothing actually compels acceptance of the idea that intelligence is strictly a brain-produced phenomenon. Despite the consensus, no one has yet to demonstrate an actual neural mechanism producing an actual intelligence effect. Neural activity certainly accompanies intelligence behavior—there is plenty of evidence for that—but as scientists are wont to caution, correlation is not the same thing as causation. Furthermore, with the Flynn effect still a puzzle and a mystery, bumping against many of the fundamental assumptions regarding intelligence, it would seem there is adequate motivation for casting the cognitive net a little wider.

This paper will describe a model of human intelligence that removes the center of intelligence away from the human brain and places it more firmly within the human environment, a concept dubbed as environmental intelligence. Thus freed from the constraints of biology, neurology and evolution, human intelligence can be seen as able to change and accumulate at a significant rate, which indeed it must if it is going to produce the phenomenon known as the Flynn effect. The human brain still gets to play a role within this new model—under a concept dubbed as individual intelligence—but this role will be described as secondary. Instead of producing human intelligence, the human brain will be depicted as responding to the intelligence contained within the surrounding environment—responsiveness after all being the activity traditionally reserved for neural systems.

Environmental Intelligence and Individual Intelligence. A fresh perspective can be gained on human intelligence by considering two orthogonal aspects—environmental intelligence and individual intelligence.

Environmental intelligence is defined as the total amount of non-biological pattern, structure and form tangibly contained within the human environment. This definition immediately ties human intelligence to the advancement of the human species. Prior to the human great leap forward, there would have been essentially no environmental intelligence to be found within the human surroundings, only natural settings, similar to all the other animals. But beginning with ostrich shell beads, bone awls, rudimentary clothing and cave paintings, and proceeding straight through to domesticated crops, mud-plastered abodes and towering pyramids, and crescendoing today in highways, skyscrapers, televisions and rockets to the moon, the one indisputable observation that can be made throughout that entire course of human progress is that no matter where man has found himself, he has found himself among an accumulating amount of non-biological pattern, structure, symmetry, repetition and form. Over the past fifty thousand years, man has been increasingly surrounded with environmental intelligence.

To actually measure environmental intelligence would be admittedly a pragmatic nightmare—the sheer enormity of pattern and structure contained within the modern world would alone overwhelm any genuine effort to size it, and furthermore, there could be no easy agreement on how to quantify structure contained within, for instance, an automobile or a library book. But these practical difficulties do not nullify the material certainty of measurement—environmental intelligence tangibly exists, one can touch it, hear it, talk about it, it exists right before one’s very eyes. Plus there is no need to actually measure environmental intelligence in order to attest to its ever increasing presence. Think of the North American continent alone. Only a few hundred years ago, man dwelled in but a handful of places there and the amount of non-biological environmental complexity would have been quite modest. But by one hundred years ago, man had taken up residence from nearly coast to coast and had augmented an entire patchwork of fields, houses and roads. Today one glance at the skylines of such cities as Chicago and Toronto would be more than sufficient to convince even the most dire skeptic that by almost any reasonable means of measurement, it would have to be calculated that the total amount of human environmental intelligence has been consistently on the rise.

The second, more familiar aspect of human intelligence, individual intelligence, is in many respects nothing at all like the first. Individual intelligence is defined as an individual’s capacity to absorb and respond to environmental intelligence, making it clear that individual intelligence is a secondary (a responding) construct. Nonetheless, the great value in this definition of individual intelligence is that it corresponds exactly to what gets measured on a modern intelligence test—IQ is an ideal quantification of individual intelligence. To see this, consider that the questions, the content, of an intelligence exam—language, arithmetic, geometrical patterns, and so on—these are constructed as proxies for environmental intelligence, and a person demonstrates his comparative capacity for mastering environmental intelligence by answering the questions correctly. Intelligence exams do not assess an individual’s abilities to scavenge food, ward off predators or procreate; instead everything that appears on an intelligence test emanates from the non-biological material artifacts that have been introduced into the human world over just the last several thousand years (and in some cases, over just the last tens of years). Therefore it is in no way surprising, just as psychometric analysis confirms, that those who are more successful in answering the proxies for environmental intelligence on an IQ exam are by and large the same individuals who are more successful at navigating a constructive path through the day-to-day surroundings of the modern world.

It is important to emphasize the orthogonal relationship of environmental intelligence and individual intelligence. Individual intelligence is a biological capacity, a human behavioral ability, and thus there is no objection to associating individual intelligence with neural and genetic causes. But environmental intelligence is not biological at all, it is instead a physical artifact, quantifiable within the material world, and it remains independent of any neurological or evolutionary constraint. Environmental intelligence and individual intelligence are each a crucial aspect upon human intelligence, but each delivers its influence in an entirely separate domain.

The Model. Armed with these definitions and descriptions of environmental intelligence and individual intelligence, it is a straightforward task to develop an illustrative example showing how these two aspects dynamically relate. All that is required from the reader is agreement that the practical difficulties in measuring environmental intelligence can be theoretically overcome, and consistent with observations from human history, environmental intelligence can be assumed to increase over any significant interval of time.

In the example to be developed, EIU (environmental intelligence unit) will be called upon as the arbitrary unit of measure, and the amount of non-biological pattern, structure and form in the hypothesized environment will be assumed to double from 200 EIU at time 1 to 400 EIU at time 2 (time 2 occurring several generations after time 1). All that then remains is to assess the intelligence scores of individuals at time 1 and compare these with the intelligence scores of individuals at time 2.

At time 1, the cognitive characteristics of three individuals—call them A1, B1 and C1—are assessed by means of a standard battery of intelligence tests. Since the content of these tests can be regarded as proxies for the environmental intelligence that exists at time 1, it is reasonable (although not strictly necessary) to state the results of these tests as percentages of environmental intelligence successfully mastered. For instance, when it is discovered that A1 can correctly answer 80% of the test questions, the result can be stated as follows: A1 has demonstrated the capacity to master roughly 80% of the environmental intelligence to be found around him. In a similar vein, when B1 and C1 respectively answer 70% and 60% of the test questions correctly, it can be said they are demonstrating the capacity to master corresponding percentages of environmental intelligence.

As is done with real world intelligence exams, the raw test scores of A1, B1 and C1 are normed and compared to the remainder of the test-taking population. From this procedure, it is determined that A1 falls within the comparative range of high intelligence for the general population, B1 falls within the range of average intelligence, and C1 falls within the range of low intelligence. The results are summarized in the following chart:

Time 1 (Environmental Intelligence: 200 EIU)

	Test Scores	Population Rank
A1	80%	High Intelligence
B1	70%	Medium Intelligence
C1	60%	Low Intelligence

At this point, all the standard types of analysis regarding individual intelligence differences can be performed quite adequately. Using relative intelligence rankings, and employing factor analysis and incorporating an assortment of statistical and biological information gathered from the population at large, scientists would be able to show with considerable confidence that, all other things being equal, A1 can expect greater success than his B1 and C1 peers in academics and career, and that the individual intelligence differences between A1, B1 and C1 can be attributed in large part to biological and genetic forces. The relative intelligence scores at time 1 (or at any given time) provide a wealth of information into the characteristics of individual intelligence.

An absolute measure of intelligence for A1, B1 and C1 has not yet been determined, but it is a simple matter to do so. With a measure of 200 EIU having been assigned to time 1’s environmental intelligence, and test results having been stated as a percentage of environmental intelligence effectively mastered, a quick multiplication reveals that A1’s absolute level of intelligence is 160 EIU (200 EIU x 80%), B1’s is 140 EIU, and C1’s is 120 EIU. The comparative chart can be updated to reflect these figures:

Time 1 (Environmental Intelligence: 200 EIU)

	Test Scores	Population Rank	Absolute Intelligence
A1	80%	High Intelligence	160 EIU
B1	70%	Medium Intelligence	140 EIU
C1	60%	Low Intelligence	120 EIU

This additional calculation of absolute intelligence scores does not aid at all in the understanding of individual intelligence; as far as intelligence differences are concerned, the inclusion of absolute scores is nothing but a superfluous addendum. The information, however, will prove to be invaluable. It will be essential in the comparison of intelligence characteristics between time 1 and time 2.

Time 2 occurs several generations after time 1, and so A1, B1 and C1 no longer exist. Nonetheless, it is a simple matter to summon their descendants—call them A2, B2 and C2—all of whom can be essentially regarded as biological and genetic equivalents to their ancestors. Indeed, when A2, B2 and C2 take the standard tests offered at time 2, they score in a familiar pattern. A2 answers 80% of the questions correctly, which is said to reflect an 80% mastery of time 2’s environmental intelligence, and B2 and C2, as to be expected, score 70% and 60% respectively. Once again these raw scores are normed and compared against the general population, and as with their ancestors, A2 falls within the range of high intelligence, B2 falls within the range of average intelligence, and C2 falls within the range of low intelligence:

Time 2 (Environmental Intelligence: 400 EIU)

	Test Scores	Population Rank
A2	80%	High Intelligence
B2	70%	Medium Intelligence
C2	60%	Low Intelligence

Factor analysis and population statistics reveal the same pattern of individual intelligence differences as were seen at time 1, and this is consistent with real world experience. Science has repeatedly shown that patterns of individual and group intelligence differences remain relatively stable across time—just as to be expected of individual intelligence, a characteristic determined in large measure by biological and genetic causes.

Nonetheless, at time 2, there has been a significant anomaly.

The first intelligence tests offered to A2, B2 and C2 were the very same tests offered to their ancestors at time 1, but as it turns out, A2, B2 and C2 have found those tests to be laughably easy. So easy in fact that they are no longer useful for the purpose of distinguishing individual intelligence abilities. In order to make the tests suitable again for comparative purposes, the test producers have had to beef them up, make the questions more difficult, and it is only after these modifications have been made that the tests can be effectively administered, with the resulting scores as shown.

In one sense, the reason that the tests have to be re-set is clear from the parameters of the illustrative example: since the content of an intelligence exam stands as a proxy for environmental intelligence, and since environmental intelligence has dramatically changed from time 1 to time 2—doubled in fact—the tests must be reconstituted in order to reflect this fact. But in another sense, the reason that tests have to be re-set is made clear from an entirely different source, namely that this is precisely what has been taking place in the real world throughout the previous century. Each successive generation has been scoring progressively better on older intelligence exams, to the point that test makers find they must modify the exams in order to keep them useful. These modifications generally take the form of more difficult questions, questions reflecting a greater amount of pattern, structure and form. In both the illustrative example and in the real world, intelligence tests have to be regularly strengthened in order to counteract the creeping influence of the Flynn effect.

And it is not just the tests that need to be reconsidered. Despite demonstrating equivalent levels of individual intelligence to their ancestors, indicating an equal capacity to absorb and respond to environmental intelligence, A2, B2 and C2 have nonetheless seen their absolute levels of intelligence take a quantum leap. With the time 2 environmental intelligence assessed at 400 EIU, A2’s test results reflect an absolute level of intelligence of 320 EIU (400 EIU x 80%). B2 weighs in at 280 EIU, and C2 scores at 240 EIU—in each case a doubling over A1, B1 and C1:

Time 2 (Environmental Intelligence: 400 EIU)

	Test Scores	Population Rank	Absolute Intelligence
A2	80%	High Intelligence	320 EIU
B2	70%	Medium Intelligence	280 EIU
C2	60%	Low Intelligence	240 EIU

It takes barely a moment’s reflection to recognize the sole cause for this quantum leap. It has nothing to do with individual intelligence. It has nothing to do with neurology or genetics. The sole driver of raw intelligence gains is the increasing amount of environmental intelligence, the increasing amount of pattern, structure and form tangibly contained within the human environment. Individual intelligence, the biological capacity to absorb and respond to environmental intelligence, that capacity remains constant over time, but that capacity encounters an ever expanding target.

Flynn's Paradoxes. In his book What is Intelligence?, Flynn (2007) describes four paradoxes he associates with the Flynn effect. The first paradox deals with g factor analysis, which is only tangentially related to the present discussion, but the remaining paradoxes can be resolved directly using this paper’s dual-aspect model of human intelligence.

Two of the paradoxes, labeled as the intelligence paradox and the mental retardation paradox, state the apparent incongruity that if the Flynn effect were literally true, then humans from one generation would be too implausibly dumb or too implausibly smart compared to humans from a different generation. In Flynn’s words:

If huge IQ gains are intelligence gains, why are we not struck by the extraordinary subtlety of our children’s conversation? Why do we not have to make allowances for the limitations of our parents? A difference of some 18 points in Full Scale IQ over two generations ought to be highly visible.

If we project IQ gains back to 1900, the average IQ scored against current norms was somewhere between 50 and 70. If IQ gains are in any sense real, we are driven to the absurd conclusion that a majority of our ancestors were mentally retarded. (p. 9–10)

The resolution to these two paradoxes is to recognize that Flynn is confusing the two different aspects of intelligence; he is confusing environmental intelligence with individual intelligence. In particular, he is using the changed levels in one aspect (environmental intelligence) to infer a corresponding change in the other aspect (individual intelligence). That inference is unwarranted.

In the illustrative example, consider the individual named A1. At time 1, A1 is assessed to be highly intelligent. He demonstrates his above-average ability to absorb and respond to environmental intelligence by correctly answering 80% of the test questions presented to him, and as A1 navigates through his time 1 world, it can be anticipated he will experience relatively greater achievement, compared for instance to his B1 and C1 peers.

But when A1’s absolute (raw) intelligence score of 160 EIU is compared to the population of time 2, A1 suddenly appears much less smart. 160 EIU scores far below the 240 EIU of C2, a person assessed to be of low intelligence at time 2. If 240 EIU is considered to be of low intelligence at time 2, then A1’s score of 160 EIU seems to mark him as a borderline imbecile.

So which is it? Is A1 highly intelligent or an imbecile? This paradox is resolved by recognizing that A1’s individual intelligence is not subject to change. A1’s absolute intelligence score of 160 EIU has as much to do with the time period during which it was registered as it has to do with A1’s biological capacity. If A1 could be magically transported forward in time and raised in the time 2 world, he would absorb and respond to about 80% of the time 2 environmental intelligence and would score correspondingly on a time 2 intelligence exam, making it clear once again that he is a highly intelligent individual. A1’s apparently low score of 160 EIU has nothing to do with A1’s intelligence abilities; it has everything to do with the change in environmental intelligence from time 1 to time 2.

This works exactly the same way going backwards in time. Consider C2, who is assessed at time 2 to be of low intelligence. But when C2’s absolute (raw) intelligence score of 240 EIU is compared to the time 1 population, where a score of 160 EIU is considered to be highly intelligent, C2 suddenly comes across as a Mensa candidate, and one wonders if C2 simply had the misfortune of being born too late.

So which is it? Is C2 of low intelligence or a Mensa candidate? Once again, the resolution is to recognize that C2’s individual intelligence is not subject to change. If C2 could be magically transported back in time and raised in the time 1 world, he would absorb only about 60% of the time 1 environmental intelligence and would score relatively poorly on the time 1 intelligence exam. The timing of one’s birth does not alter one’s personal intellectual ability.

In addition to these illustrative examples, Flynn provides a real world scenario that brings out the principle quite nicely. After noting that the average raw intelligence score from about 1900 would translate to an IQ of about 50 to 70 on today’s scale, Flynn raises the specter of the following tableau:

Jensen relates an interview with a young man with a Wechsler IQ of 75. Despite the fact that he attended baseball games frequently, he was vague about the rules, did not know how many players were on a team, could not name the teams his home team played, and could not name any of the most famous players.

When Americans attended baseball games a century ago, were almost half of them too dull to follow the game or use a scorecard? My father who was born in 1885 taught me to keep score and spoke as if this was something virtually everyone did when he was a boy. How did Englishmen play cricket in 1900? Taking their mean IQ at face value, most of them would need a minder to position them in the field, tell them when to bat, and tell them when the innings was over. (p. 23–24)

This is a quintessential example of mistaking a change in raw intelligence scores as evidence for a change in individual intelligence, when in fact it is evidence for a change in environmental intelligence.

Think about putting questions dealing with baseball rules on an intelligence test. If such questions had appeared on an intelligence exam in say 1800, no one at all, including the smartest people who then lived, would have been able to answer the questions correctly (other than by random luck). By contrast, if such questions were to appear on today’s intelligence exams, many individuals, including those of average intelligence, would be able to answer the questions correctly—baseball and its rules have become an established part of the human environment. As Flynn indicates, it would be only those with an IQ around 75 or under who would have limited potential to answer such questions correctly.

So does this mean that the smartest people from 1800 had the same intellectual capacity as Jensen’s young man? It does not mean that at all.

The crucial moment in time would have been around 1900. If intelligence questions regarding baseball rules had appeared on intelligence exams at that time, the results would have been mixed. Some people would have been able to answer the questions correctly, but others would not, including those of otherwise average-to-high intelligence, and this because baseball had not yet become widely entrenched within the human environment (it was just then catching on). But after the exam, if one of those baseball-ignorant persons of average-to-high intelligence had been taken to the ballpark, bought a ticket, sat with in the grandstands, explained the rules, given a scorecard and a pencil, a perfectly capable behavior would have swiftly emerged. After all, this is a person of average-to-high intelligence, he can absorb and respond to baseball rules just fine, they will give him not the slightest bit of trouble. And around 1900, this scene would have actually taken place—again and again and again.

The increase in raw intelligence scores from 1900 to 2000 has everything to do with the increasing amount of environmental intelligence (including the addition of baseball rules). It has nothing to do with individual intellectual abilities.

Flynn names his final paradox the identical twins paradox. Flynn’s words again:

There is no doubt that twins separated at birth, and raised apart, have very similar IQs, presumably because of their identical genes. Indeed a wide range of studies show that genes dominate individual differences in IQ and that environment is feeble. And yet, IQ gains are so great as to signal the existence of environmental factors of enormous potency. How can environment be both so feeble and so potent? (p. 10)

In considering Lewontin’s attempt to resolve this paradox, Flynn (p. 37) sorts through a list of candidates for an environmental Factor X—nutrition, education, liberal parenting, the scientific ethos—and dismisses each as not being widespread or potent enough to produce the requisite intelligence gains, concluding finally that one must “shut the door” on any further discussions of an environmental Factor X. But Flynn’s search is not nearly wide enough and is being hampered by a presumed environmental influence on human intellectual capacity—like nearly everyone else, Flynn does not countenance human intelligence freed of human neurology.

Environmental intelligence—the total amount of non-biological pattern, structure and form tangibly contained within the human environment, the countless structural, patterned artifacts humans live among and navigate day by day—that serves as the ideal Factor X. Environmental intelligence is ubiquitous. Environmental intelligence is constantly increasing. Environmental intelligence remains unfettered by the constraints of human biology, remains independent of the workings of individual intelligence.

An analogy to describe the relationship between environmental intelligence and individual intelligence would be to think of ships in a harbor, including lightweight ships such as pleasure craft and heavy duty ships such as battle cruisers. Were a measurement to be taken of the bottommost part of each ship (relative to a fixed vertical point on land), it would be discovered there are significant differences. Some ships will sit higher, some will sit lower. Further analysis of characteristics such as geometric structure and material density (equivalent to psychometric analysis) would reveal ship-based factors that determine relative vertical positions, exactly as scientists determine genetic and neurological factors that drive individual intelligence differences.

At a later point in time, measurements might reveal all the same relative differences in bottommost positions of the ships (with the same factors determining those differences), and yet it is also discovered that the absolute position of each ship (relative to the fixed vertical point on land) has risen by a significant amount. If the mistake is made of trying to explain this increase by appealing to the ships’ characteristics or by insisting that the ships’ environment must be somehow changing the ships’ characteristics, confusion will reign. The ships’ characteristics can explain relative vertical positions, but do nothing to explain absolute changes in position. For that, the context—the environment—of these ships must be taken fully and independently into account, leading eventually to the aphoristic conclusion that it is a rising tide that raises all ships.

In the orthogonal relationship between environmental intelligence and individual intelligence, environment is both feeble and potent, as is genetics—it all depends on the domain. Individual intelligence works through the auspices of human neurology, driven primarily by genetics. Environmental intelligence grows within the context of the physical, non-biological world, free of neurological constraint. Their influences seem paradoxical only when they become mistakenly intertwined (for instance, within the human brain), but when their impacts are kept properly separate, Flynn’s identical twins paradox swiftly disappears.

Environmental Complexity. Of the many offered explanations for the Flynn effect, the one most similar to the model proposed here is the notion of environmental complexity. Schooler (1998) and Greenfield (1998) offer perhaps the most comprehensive introductions to environmental complexity, and Wai & Putallaz (2011) provide a recent instance of the idea being invoked. But it can be seen in these and other discussions highlighting environmental complexity, that in several crucial respects, the explanation falls short.

The first problem with environmental complexity is that its proponents appeal to certain things within the human environment, instead of grasping the environment as a whole. Two commonly cited instances of environmental complexity are the increased use of video games and the complex and multivariate nature of television shows and movie plots; others note the expanded presence of visual puzzles and games. But no matter what thing or set of things is being considered, it becomes immediately clear that by itself it cannot account for the ubiquitous, relentless nature of the Flynn effect. The Flynn effect was working its magic long before there even were video games and television sets, and the Flynn effect remains prominent in locations where video games and sophisticated dramas have yet to take much hold. Any alternative candidate would fall victim to the same dilemma.

It is only by appealing to complete environmental complexity that traction can be gained. One way to envision complete environmental complexity would be to imagine the Earth’s surface as it stood nearly fifty thousand years ago and contrast that with how differently the Earth’s surface stands today. Every material artifact that has been introduced—every house, every road, every sign, every book, the whole entire lot—all of it forms the complete and expanding sum of environmental complexity. The types and examples of environmental complexity will differ from time to time and from place to place, but the one reliable constant is that the total amount of environmental complexity will be nearly always on the rise.

The second problem is that the proponents of environmental complexity—like nearly everyone else—attempt to tie their explanation back to human neurology. Playing video games, for instance, is seen as expanding the capacity of working memory. Modern movie plots perhaps form a larger number of connections within the logical neural circuitry. It would seem that environmental complexity by itself is deemed to be useless; its only purpose is to prompt a major restructuring within the neurons, a massive rewiring between the ears—a proposal that lacks both parsimoniousness and plausibility. The human brain does not need to be changed by environmental complexity, the human brain needs merely to absorb and respond—the simple and common basis of neurological behavior.

The Flynn effect is independent of individual intelligence differences, and thus an explanation for the Flynn effect should be independent of the human brain. If the proponents of environmental complexity are to hit the mark, they must come to accept environmental complexity as the material form of human intelligence itself; they must stop looking for expanding intelligence inside the human skull.

Conclusion. One immediate consequence of this paper’s dual-aspect model of human intelligence is that the Flynn effect cannot be regarded as a twentieth-century anomaly. Tracking the increase in environmental intelligence, the Flynn effect would have begun at the time of the human great leap forward and has been shadowing human existence ever since, and there is no reason to expect it will end anytime soon.

Environmental intelligence, the increasing amount of pattern, structure and form tangibly contained within the human environment—that is the unseen hand propelling intelligence scores ever upward. That it has remained unseen for so long should not be that surprising; it is a frequent human experience that the thing most difficult to perceive is the thing that exists right before one’s very eyes.

References

Flynn, J. R. (1999). Searching for justice: The discovery of IQ gains over time. American Psychologist, 54, 5–20.

Flynn, J. R. (2007). What is intelligence? Beyond the Flynn effect. New York: Cambridge University Press.

Greenfield, P. (1998). The cultural evolution of IQ. In U. Neisser (Ed.), The rising curve. Washington, DC: American Psychological Association.

Lewontin, R. C. (1976). Further remarks on race and the genetics of intelligence; Race and intelligence. In N. J. Block & G. Dworkin (eds.), The IQ controversy. New York: Pantheon Books.

Schooler, C. (1998). Environmental complexity and the Flynn effect. In U. Neisser (Ed.), The rising curve. Washington, DC: American Psychological Association.

Wai, J., & Putallaz, M. (2011). The Flynn effect puzzle: A 30-year examination from the right tail of the ability distribution provides some missing pieces. Intelligence, 39, 443–455.

Introduction to a Paper

Tomorrow I'm going to post a paper here, and I think it might need a little explanation.

The paper's content will be familiar. Last fall, I scrambled together a quick series of posts on the Flynn effect (Flynn Effect 101, Flynn Effect 102, Flynn Effect 103, and Flynn Effect 104) and I've had a hankering ever since to reorganize that material into a single, more cohesive essay. Last month I finally got the opportunity to do so and tomorrow's post is the result.

For me, this paper is extremely formal. The reason for the formality is that I felt I should submit the paper at least once for publication, which I did, to the journal Intelligence. (No surprise, the paper was deemed “not suitable for a scientific journal.”) I didn't struggle with the rigidity of the format as much as I thought I would (although admittedly I bent a few of the rules), but the experience left me wondering why so many people would voluntarily submit to such confinement—the academic community never ceases to amaze me.

At any rate, for the few who might be interested in a different approach to describing human intelligence, tomorrow's essay is my semi-formal attempt to fit the bill.