Prototypical Autism Is Transformatively Atypical

1. Introduction

Laurent Mottron, an autism researcher based in Montreal, Canada, along with various colleagues and co-authors (hereafter referred to as the Mottron team), has of late introduced and promoted a concept called prototypical autism. Although this concept was hinted at in earlier writings, its main presentation has come in the form of two recent papers. The first paper, A radical change in our autism research strategy is needed: Back to prototypes (Mottron, 2021a), addresses the motivation for delineating prototypical autism from other instances of autism diagnosis, a motivation triggered mostly by concerns over the statistical noise produced by too much heterogeneity within autism research cohorts. The second paper, Prototypical autism: New diagnostic criteria and asymmetrical bifurcation model (Mottron & Gagnon, 2023), outlines in some detail the Mottron team's description of how to recognize prototypical autism among the population and how to distinguish such cases from other forms of autism diagnosis.

There is much to appreciate about this initiative. The highlight of the Mottron team's effort is to be found in the team's general description of how prototypical autism presents, especially during the critical age range of around two to five years. The behavioral characteristics outlined in this description are more accurate, more comprehensive and more vivid than can be found in any of the official diagnostic guidelines, painting an informative picture of what autism tends to look like close at hand. To this can be added the Mottron team's twofold awareness of the potential to be found in the unusual characteristics of autistic individuals, first with an emphasis on the fact that autistic children can often make significant gains by leaning into their unusual interests, as opposed to being forced to suppress them, and second, by reconceptualizing autism as a non-defective and viable branch of human development, including an explicit acknowledgement that many autistic children go on to live relatively normal and even exceptional lives after the age of five. Over the years, the Mottron team has been one of the few autism research teams (perhaps the only autism research team) willing to contemplate and to discuss the potential value arising out of autistic atypicality, and that willingness remains on display here.

Nonetheless, there are some shortcomings in the Mottron team's effort, two of which stand out in particular. First, although the Mottron team does recognize the critical importance of perception in distinguishing autistic individuals from their non-autistic counterparts, the team's characterization of these two different forms of perception is confusing at best, with a vague reference to the "social bias" of non-autistic children and an incongruous reference to the "enhanced perceptual functioning" of autistic children. These phrases fail to distinguish clearly the two perceptual types, but more importantly, they fail to explain why there is a perceptual difference between non-autistic and autistic children. This essay will discuss a more specific and more informative approach to making that distinction, employing the concept of conspecific perception—the innate tendency to perceive first and foremost the other members of one's own species—as the primary means both for delineating the non-autistic and autistic perceptual traits, as well as for explaining the contrasting genesis of each perceptual type.

The second shortcoming of the Mottron team's initiative is its complete silence regarding prototypical autism's impact upon the entire human species. The stated motivation for developing the concept of prototypical autism—to improve the statistical power of current autism research—discounts the possibility that there is a much bigger picture to consider here, one of importance to autistic individuals themselves. This essay will discuss humanity's turn towards behavioral modernity, including the perceptual and behavioral transformations that stand at the foundation of that turn, and will demonstrate that these transformations are mirrored precisely in the Mottron team's contrasting description of the non-autistic and autistic perceptual and behavioral traits. That is to say, the atypicality of autistic individuals within the human population explains much about the atypicality of the human species itself, now perceptually and behaviorally removed from the remainder of the animal kingdom, and perceptually and behaviorally removed from humanity's own not-so-distant animal past. If autistic individuals are going to be understood for who they actually are, and if their unusual characteristics are going to be valued for the impact they actually bring, then autistic contribution to human transformative history needs to be recognized. And although the Mottron team has all the information it requires to make that connection, it chooses not to consider the topic at all.

It might be stated in the Mottron team's defense that contemplating the conceptual leap from autistic traits to the transformative characteristics of the human behavioral turn goes beyond the jurisdiction of normal autism science. But the problem with this defense is that it dooms autism science to a self-imposed tunnel vision, a kind of group myopia that has been producing endless decades of null results (Myers et al., 2020; Parellada et al., 2023; Whitehouse et al., 2021). Accordingly, this essay will conclude with some thoughts on the stultifying consequences of modern scientific practice, where focus has shifted entirely towards cultivating professional and collaborative craft—standards and guidelines, grants and funding, credentials and citations, etc.—and has abandoned the type of individualistic and revolutionary effort that used to produce breakthroughs of understanding. Thus, it can be seen that the most significant problem with autism research is not one of statistical noise. The most significant problem with autism research is its perfunctory application of normal autism science.

2. Prototypical Autism

A rough summary of the Mottron team's description of a prototypically autistic child would include the following features:

1. The autistic child is generally indistinguishable from non-autistic children until sometime during the second year of life. Around the second birthday, the difference from typical development becomes prominent and remains prominent for the next two to three years.

2. During this period of around two to five years of age, the autistic child will display a significantly low degree of orientation towards social stimuli. This includes a diminished attention to human faces and to human voices, and also includes a noticeable lack of joint-attentive activities and human-mimicking behavior.

3. During this period, the autistic child will also display a high degree of orientation towards structural and environmental stimuli. This includes a focused attention on such things as patterned movement, geometrical objects, repetitive and/or musical sounds, the shapes of numbers and letters, etc.

4. During this period, language skills plateau, or even regress, resulting in a limited vocabulary and extremely limited sentence formation. Language peculiarities, such as echolalia and pronoun reversal, are also apparent in many instances.

5. During this period, certain unusual and telltale behaviors are more common, such as lateral gaze, hand flapping, food selectivity, resistance to change, etc.

6. After this period, there is usually some degree of developmental catch up, both in social orientation and language ability. This developmental catch up can vary greatly, resulting in outcomes that range all the way from non-verbalness in adulthood and a lifetime need for assistance and care, to promising prospects of advanced education, career, family, etc., with many prototypically autistic individuals experiencing outcomes that fall somewhere on the interval between.

In advocating for its concept of prototypical autism, the Mottron team notes there are a significant number of individuals who will receive an autism diagnosis under the current official diagnostic guidelines but who will tend not to have a presentation that follows the pattern as outlined above. There appear to be three major sub-categories of these individuals who could be described as being non-prototypical:

1. Individuals with specifiable neuro-genetic conditions. This would include such known instances as fragile X syndrome, Rett syndrome, identifiable de novo mutations, or a medical history giving evidence of neurological trauma. Such individuals will often display similarities to autistic-like behavior, but will also tend to deviate significantly from a course of prototypicality, either in intensity, timing or both.

2. "Quirky" or behaviorally challenged individuals who possess only a smattering of autistic-like traits. Many children are referred to specialists because of their developmental and/or behavioral challenges, and due to the current latitude in the official autism diagnostic guidelines, such individuals will often receive a diagnosis of being on the autism spectrum. But a large number of these individuals will not follow the prototypical course—that is, they will display a reasonable degree of social orientation, or will have limited structural and environmental engagement, or will give evidence of language skills that are progressing in the usual way. The Mottron team argues that such individuals are better excluded from certain types of autism research (Mottron & Bzdok, 2022).

3. Asperger-like individuals. This forms a less clearcut case. Until around a decade ago, Asperger Syndrome was an official diagnostic category, intended to delineate children with pronounced autistic-like characteristics but who also possessed notable language skills. This diagnostic distinction proved to be unworkable in practice, and the classification was dropped for the current diagnostic manual (Gamlin, 2017). Nonetheless, there are a significant number of children who appear to fall within this category, and their relationship to the remainder of the autism spectrum remains unclear. The Mottron team suggests these individuals could be formed into a second "prototypical" group (Mottron, 2021b), with characteristics similar to those of the first prototypical group but without the language plateau or regression. However, it could be argued that Asperger-like children are simply choosing linguistic structures as one of their preferred circumscribed interests—that is, instead of an intense focus on something like ceiling fans or calendar calculation, Asperger-like children choose to concentrate on spoken and/or written words. This is evidenced to some degree by the fact that Asperger-like language skills are usually not typical (Saalasti et al., 2008); that is, Asperger-like children do not employ language primarily for social purposes but instead make use of language in idiosyncratic ways (for example, perseveration). If this is an accurate depiction of what is actually taking place, then it would appear these two prototypical groups are far more similar than dissimilar, and the Mottron team's separation of Asperger-like children from the team's main prototypical definition is perhaps just a case of splitting hairs.

What does finally emerge from the Mottron team's lucid description of prototypical autism is a class of individuals remarkably similar to one another and yet identifiably distinct from the biologically typical population. Furthermore, this is a class of individuals who have been giving no evidence of possessing any underlying defect—not genetic, not neurological, not environmentally caused—and this despite the fact the autism research community has been assiduously searching for these defects for dozens of years. The Mottron team highlights this apparent biological benignity of prototypical autism by suggesting the condition would be more effectively understood as a minority but otherwise normal bifurcation of human development, analogous to similar asymmetrical bifurcations, such as left-handedness or twin pregnancy. This opens the door to embracing autistic characteristics for their potential constructive value, including making full use of these characteristics to support developmental progress. Such a viewpoint would stand in stark contrast to the standard approach taken towards autistic traits, where these traits are routinely suppressed instead of being productively employed, suppressed through an assortment of disruptive interventions that would appear to be no more effective than attempting to turn left-handedness into right-handedness (Brignell et al., 2018; Sandbank et al., 2020).

3. Atypical Autistic Perception

In attempting to explain the source of the bifurcation between non-autistic and autistic individuals, the Mottron team highlights the differential targets of interest and attention during information processing, assigning a label of "socially biased processing" for the preferential interests of non-autistic children and a label of "non-socially biased processing" for the preferential interests of autistic children. The Mottron team also tends to reserve use of the words perception and perceptual for the latter type of preferential interest, and this leads in turn to frequent employment of the phrase "enhanced perceptual functioning" to describe autistic cognitive traits. This approach seems confusing in several respects. First, it implies that non-autistic children lack perceptual characteristics, or at the very least are experiencing diminished perceptual functioning. It also suggests that autistic children possess a kind of fortuitous brain capacity that gives them perceptual skills beyond those of ordinary experience (Poulin-Lord et al., 2014), and yet somehow this fortuitous brain capacity proves disruptive to developmental progress. Although the Mottron team's highlighting of information processing and perceptual characteristics is very much on target, the team's odd labeling works to derail the discussion. Under any commonsense use of the words perception and enhanced, it would be difficult to reconcile the phrase "enhanced perceptual functioning" to the developmental pathways of autistic individuals.

For the purpose of this discussion, perception is to be understood as the filtering, foregrounding, and organization of the manifold of impressions arising from the sensory field. Perception creates targets of cognitive attention and provides the potential for a directed and productive reaction to environmental stimulus. Consider the example of three men sitting together in the grandstands at a football game. One man is intently following the plays on the field, scarcely aware of the crowd—he can accurately predict the play that is coming next. The second man is mesmerized by the workings of the scoreboard—he is counting down in his head the seconds until the yardage and downs are updated. The third man is flitting a gaze from person to person—cheerleader, then referee, then that cute snuggling couple three rows down—and he would be unable to tell you the score of the game if his life depended on it. Each individual has access to the exact same sensory stimuli, but each individual perceives something entirely different, foregrounding certain aspects of the sensory experience and backgrounding everything else. This is a commonsense approach to the word perception, and by its means, it should be abundantly clear that both non-autistic and autistic individuals possess perceptual characteristics, with neither of those perceptual types being ultimately enhanced or diminished relative to the other. Given the same sensory environment, non-autistic and autistic individuals simply tend to perceive different classes of things.

The genesis of each perceptual type begins in earnest by the second year of life. When human newborns enter this world, they must soon achieve a sensory grounding, because otherwise the manifold of sensory impressions would remain chaotic and unorganized, thwarting all effort towards productive action and developmental progress. The emerging components of this sensory grounding are what determine the perceptual type. For both non-autistic and autistic individuals, biological demand will bring certain environmental features to the fore—that is, the need for food and water, a fear of danger, and eventually the desire for sex will bring into cognitive attention certain ecologically critical aspects of the surrounding world, providing some of the means by which sensory experience can be differentiated and organized. As the Mottron team notes, prototypically autistic individuals give little to no evidence of having a diminished capacity in these basic biological domains, even when life circumstances cause the expression of these capacities to be manifested in alternative ways.

What does turn out to be the distinguishing characteristic between the non-autistic and autistic types of perception is that non-autistic perception is fundamentally human centric, and autistic perception is not. From out of the manifold of sensory impressions, what tends to foreground naturally and frequently for non-autistic individuals are human faces, human voices, human touch, human smells, human laughter, human activities, etc. Non-autistic children provide abundant evidence of their human-forward attentive awareness, responding with consistent delight to human interaction, joint-attentive sharing, and people-mimicking behavior. Even when their attention is drawn to the non-human aspects of the surrounding environment, it is usually done so through the means of human prompting and human encouragement. Thus, in addition to its basic biological components, non-autistic perception can be characterized by its human-forward content, meaning that non-autistic children tend to organize their sensory experience primarily around the species itself, and around the species' shared and natural interest in all things human.

There is of course nothing unusual about this non-autistic perceptual tendency. The foregrounding of species-specific sensory experience is not just typical within the human population, it is typical across the entire animal kingdom (Lickliter, 1991; Nunes et al., 2020). Lions tend to perceive first and foremost other lions, honeybees tend to perceive first and foremost other honeybees, etc. This widespread tendency can be given the label of conspecific perception, and it can be defined as the innate tendency to perceive first and foremost the other members of one's own species. Conspecific perception's ubiquitous appearance throughout nature can be attributed to its biological and evolutionary necessity. If mates are going to be able to recognize and discover mates, if parents are going to be able to keep track of their offspring, if members of a pack are going to be able to follow one another, then a foregrounded perceptual attention for the other members of one's own species is nothing short of essential. Conspecific perception has evolutionary roots that reach very far back in time, and conspecific perception is one of the more prominent carryovers from humanity's not-so-distant purely animal past.

With this as backdrop, autistic perception can be characterized as a significant diminution of conspecific perception. Autistic children—by the Mottron team's own definition of prototypical autism—do not have a natural and favored interest for human faces, human voices, human touch, etc., and autistic children do not frequently engage in human interaction, joint-attentive sharing, or people-mimicking behavior. There are two different approaches to depicting this autistic diminution of conspecific perception. As the Mottron team would have it, the characteristics of autistic perception arise from a strong and positive interest in the non-socially biased and raw informational aspects of various environmental features (Mottron et al., 2006). This "enhanced perceptual" interest is the result of a presumed alternative neural-cognitive mechanism (Kéïta et al., 2011; Mottron et al., 2014; Soulières et al., 2009), and its effects are powerful enough to eclipse the usual people-focused foregrounding of conspecific perception. Although this depiction is not an unreasonable hypothesis, it does appear to lack for parsimoniousness. Not only must this explanation postulate a special and mostly unspecified neural-cognitive mechanism, that mechanism must also be capable of producing for each autistic individual a particular and distinctive set of interests chosen from an extremely broad range of perceptual targets. Some autistic children are focused primarily on the auditory domain, others on the tactile domain, and still others on the visual domain. Some autistic children concentrate on geometric objects, such as ceiling fans and lined-up toys, while others concentrate on the repetitions of music and television scenes, while still others hone in on the properties of numbers, letters and words. What brain mechanism, special within the species, could produce such a selectively targeted set of interests across such a motley range of potential targets? And furthermore, why should it be expected that this "enhanced" brain mechanism would drown out the usual conspecific attachment to the other members of the species? If a population were almost entirely right-handed, but a portion of that population had special neural abilities to make extra use of the left hand, why should these special abilities result in exclusive left-handedness, why not instead ambidextrousness? If autistic children have a special neural ability to engage with the non-socially biased aspects of the surrounding environment, why should this special ability preclude their willingness to engage in the usual ways with other people?

The alternative approach to depicting autistic diminution of conspecific perception would be to accept this diminution as a definitive and fundamental fact, and then work out the consequences from there. To begin, since autistic children do not possess as strong a sense of conspecific perception as non-autistic children do, autistic children are more in danger of experiencing an ongoing sensory chaos. For autistic children, human-centric features do not emerge prominently from the manifold of sensory impressions, and this means that, other than some basic biological components, sensory experience for autistic children has the potential of remaining unorganized and ungrounded, a near jumble of undifferentiated sensory noise. The potential for this sensory chaos is evidenced by the frequent reporting of sensory issues in autistic children (Hazen et al., 2014; Kern et al., 2006)—hypersensitivity, hyposensitivity, synesthesia—with the wide variety of these sensory symptoms suggesting they are not the result of a particular physical defect so much as they are the result of a generalized difficulty in organizing sensory experience. And indeed, it can be surmised that the most troubling cases of autism, those in which developmental progress remains minimal, are those cases in which the attainment of a sensory grounding is insufficient to support timely developmental gains.

Nonetheless, most autistic children do not become stuck inside a sensory chaos and most autistic children do go on to make significant developmental progress. Since conspecific perception is not providing the primary means by which sensory experience can be organized, autistic sensory grounding must be getting attained by some other means. Chaos as a term denotes a lack of structure, and chaos can be dispelled by the foregrounded presence of structural properties—symmetry, repetition, pattern, number, form. Needing a sensory grounding to dispel their potential sensory chaos, and lacking a natural human-forward attentive focus, autistic children begin to latch onto those structural features that inherently stand out from the surrounding environment, features that serve to break the background sensory noise. Note the symmetry of ceiling fans and lined-up toys, the repetition of flapping, humming and predictable routines, the patterned and formal properties of calendars and television shows, the shapes and sequences of numbers and letters. Autistic children provide abundant evidence of a structure-forward attentive focus, responding with consistent delight to artifact interaction, pattern-oriented exploration, and form-mimicking behavior. Each instance of an autistic child's so-called restricted and repetitive behavior is an instance thoroughly suffused with structural underpinning, and autistic children do not just prefer these mostly non-human structural experiences, autistic children require them—they are what serve to organize the autistic child's sensory world.

Thus, a special or enhanced neuro-cognitive mechanism is not needed to explain autistic perceptual characteristics—all that is needed is the diminution of conspecific perception, the requirement of a sensory grounding, and the presence of inherently structural features within the surrounding environment. The artificially constructed modern world contains an abundance of these structural targets, and it can be surmised that an autistic child latches onto his or her particular subset of these potential targets through a combination of personal proclivity and random exposure to particular environmental elements. For some it will first be ceiling fans and spinning wheels that emerge from the sensory field, for others it will be rhythmic and musical sounds, and for still others it will be numbers, letters and words. Any circumstance that an autistic child happens upon that boosts that child's sensory grounding will become a circumstance likely to be returned to again and again. And to increase the range of an autistic child's perceptual domain, frequent exposure to a wide variety of structural features, along with encouragement to explore freely, can only be beneficial (Jacques et al., 2018). This is the strongest argument that can be made for aiding the developmental progress of autistic children by leaning into their autistic characteristics, instead of mistakenly suppressing them.

In summary, the significant presence or diminution of conspecific perception determines the non-autistic and autistic perceptual types. Non-autistic perception has deep biological and evolutionary roots, continuing the species-specific perceptual focus evident throughout the entire animal kingdom and accounting for the non-autistic child's natural affinity for human interaction and human engagement. In contrast, autistic perception, lacking this influence of conspecific perception, produces little natural affinity for human interaction and human engagement, but in compensation nudges the autistic child to hone in on those structural features that inherently stand out from the surrounding environment, leading to a structure-forward perceptual focus. This distinction is most apparent during the critical age range of around two to five years. Sometime during this period for non-autistic children, and by the end of this period for autistic children, each perceptual type will begin to overlap with the other. Following the encouragement and instruction of the humans that fascinate them so much, non-autistic children will begin to explore a world of non-human structural features, thereby expanding their perceptual horizons and furthering their developmental course. At the same time, and with their sensory grounding now more firmly established, autistic children soon discover that many of the structural features they have taken such interest in also have human connections and human origins, and this discovery will eventually prompt a secondary interest in the workings of the species itself, including the leveraging powers of language and personal interaction. Given enough time and opportunity, both types of perception can become broadly effective.

Nonetheless, the difference in the genesis of each perceptual type is not to be ignored. There is great significance to the fact that one of these types of perception is biologically typical, and the other type of perception is thoroughly atypical.

4. The Autistic Influence on Behavioral Modernity

The fascinating and stubborn question facing humanity is how did this species transform from being pure animal not more than a few hundred thousand years ago to being the modern creature observed today—talking, writing, calculating, constructing, innovating, driving, flying, and so on. What launched human behavioral modernity, and what sustains its operations today? Many vague suggestions centered around the concepts of evolution and brain intelligence mechanics are frequently tossed around (Klein, 2002; Pinker, 1994), but these suggestions clearly lack for specificity, seldom reaching the level of detailed hypothesis. Furthermore, there is an obvious problem with the timeline. For sake of argument, assume that the beginning of the human behavioral turn happened around two hundred thousand years ago. By fifty thousand years ago, although the evidence of this turn was now unmistakeable—control of fire, structured tools and weapons, cave paintings, etc.—human life was still extraordinarily primitive, a hunter-gatherer's bare subsistence, with virtually nothing of modern culture to be found anywhere within the human environment (Christian, 2018). By ten thousand years ago, agriculture and civilizations were only on the verge of getting started, and by a mere five hundred years ago, the revolutionary impact of modern science had yet to be seen. Almost everything that humans experience today—electricity, fast transportation, effective medicines, vast stores of readily available information—nearly all this has appeared within only the last century or two. Thus, the human transformation has been continuous but it has never been uniform. The human transformation has instead been accelerating, and it continues to accelerate through the present day, its ongoing effects now experienced almost immediately population wide. Vague suggestions centered around the concepts of evolution and brain intelligence mechanics will never fit the dynamics of this unprecedented scenario.

A more effective answer is to be found in the extraordinary expansion of human perception. When humans were still in the state of being pure animals—a period of time lasting for millions of years—their perceptual characteristics would have been the same as those of all the other animal species. Responding to the pressing demands of biological and evolutionary need, human attentive focus would have been directed exclusively to those environmental features crucial for survival and procreation—food, water, danger, sex, etc. Within this biologically driven attentive focus would have been found also the workings of conspecific perception, allowing humans to foreground naturally and frequently the other members of their own species, a trait essential for the various activities promoting survival and procreation. For these ancient humans—as is the case for all the wild animal species—this powerful combination of biological and conspecific perception helped foster the continuation of the lineage, directing all attentive awareness and all resulting behavior towards the essential requirements of evolutionary demand.

However, there is a significant limitation that accompanies this type of perception. As humans have come to realize and to take advantage of in recent years, the surrounding environment contains a plenitude of inherent structure that can provide benefit to a species when used in the right way—for example, the linear forces of gravity, the patterned repetitions of celestial objects, the framework of numerical and symmetrical groupings, and so on. Yet despite these available benefits, no other animal species has ever displayed a perceptual awareness for any of these underlying structural features, and neither did humans for a very long time (Klein, 2009). The powerful combination of biological and conspecific perception is such that it locks each organism into a perceptual and behavioral stasis, leaving the organism fixated entirely on the immediate needs of survival and procreation, and utterly oblivious to everything else. This is why the perceptual and behavioral characteristics of all the wild animal species are so remarkably similar, both across species and across time. With each organism bound to the exact same way of perceiving its environment, each organism is bound also to the exact same set of responsive behaviors—eating and drinking, fighting and fleeing, mating and rearing. Each organism within the species, and each species within the animal kingdom, lives out essentially the same biologically driven existence, again and again and again. It is an existence determined primarily by the restricted attentive focus imposed by biological and conspecific perception.

Therefore, to explain the human turn towards behavioral modernity, it is necessary to explain how this perceptual and behavioral stasis has been broken within the species, and how this stasis has been replaced with the types of expanded perception and resulting behavior that can be observed broadly within the human population today. Vague suggestions centered around the concepts of evolution and brain intelligence mechanics do not even go to the heart of the matter—they specify nothing about the recent dynamics of human perceptual properties. Instead, the question to be asked is as follows: are there any observable characteristics, significantly present within the human population, that can account for a diminishment in the restrictive power of biological and conspecific perception, while at the same time introducing an expanded awareness for the underlying structural properties that humans now take advantage of in overwhelming abundance? The answer to this question is yes. There are such observable characteristics, and they have already been identified earlier in this essay. They are the same perceptual and behavioral characteristics that the Mottron team has outlined in exquisite detail in defining the distinctive nature of prototypical autism.

It remains unclear how and when the size of the autistic population became significant within the human species, but once that significance was reached, its impact would have been persistent and predictable. Not bound by the combined restrictive power of biological and conspecific perception, and driven by sensory need to an awareness of the structural features to be found in the surrounding environment, autistic individuals would have begun to bring these structural features to the perceptual fore, mostly through engagement in the so-called restricted and repetitive behaviors, behaviors that mirror and reconstruct the underlying structural properties autistic individuals naturally perceive. In turn, the non-autistic population, previously locked inside the restrictions of biological and conspecific perception, and yet keenly attuned to what other humans do, would have begun to notice these atypical autistic behaviors and the artificial constructions they engender, eventually adopting these behaviors and constructions for themselves.

This symbiotic process would have been slow and halting at first, but because it results in a permanent and artificial reconstruction of various aspects of the human environment, its impact becomes accretive. The increasing amount of artificial construction accruing within the environment gives autistic individuals an ever-growing array of perceptual targets to latch onto, and the survival-and-procreative efficacy of many of these artificial features—for instance, structured tools and weapons—gives non-autistic individuals an ever-growing incentive to adopt these atypical constructions for themselves. This symbiotic and accelerating process defines the historical pattern of the human behavioral turn, a pattern of increasing environmental reconstruction, built upon an increasing and autistically originated perceptual awareness of the environment's underlying structural properties.

This pattern continues unabated through the present day. It can be seen in the developmental course of non-autistic children, a course established first through the powerful and species-connecting consequence of conspecific perception, and furthered through a species-forward introduction into a broader world of artificial construction, a world valued precisely for the advantages it continues to bring to the species. And the pattern can be seen also in the ongoing discovery of previously unseen underlying structural attributes, a process notably and remarkably dominated by individuals possessing an abundance of autistic-like traits—Newton, Darwin, Einstein, Gauss, Dostoyevsky, Beethoven, Wittgenstein, Turing, to name just a few (James, 2003; Snyder, 2004). The human behavioral turn is still ongoing, and any search for its causal mechanism inside a genetic sequence or a neural signature would be nothing short of folly. Much easier would be to observe the process as it unfolds right before one's very eyes, unfolds in the symbiotic and productive relationship between the non-autistic and autistic types of perception.

5. Normal Autism Science

It was Thomas Kuhn who coined the phrase normal science to denote those stable periods of scientific practice during which revolutionary ideas are seldom considered or explored (Kuhn, 1962). As Kuhn describes it, the work of science during such periods tends to be more technical and incremental in nature, directed towards a shoring up and a promulgation of the prevailing paradigm. In Kuhn's world of the 1950s and 1960s, normal science was embodied in its textbooks, journals, conferences, academic associations, and so on, with these routine proceedings balanced to some extent by the fresh memories of recent upheavals, such as relativity and quantum mechanics. Thus, an equilibrium between normal science and scientific revolution seemed to have been established, and Kuhn was eloquently capturing its outline.

But what Kuhn failed to anticipate was that this particular form of normal science would soon grow into a cancer. Heavily influenced by the twentieth-century surge in governmental and commercial interests, the scientific community had been rapidly transforming from a relatively isolated domain of individuals into a mass operation gainfully employing many millions (Agar, 2012). And to keep this burgeoning crowd under paradigmatic control, science quickly transitioned into a system of professional and collaborative craft. Individuals stopped being individuals and became members of ever-enlarging teams. Scientific method morphed into countless codified standards of practice. Intricate networks of funding were established and soon became a primary and necessary goal. And credentials and citations began to form into a currency of status, the price of admission to the more elite corners of the field. Trampled in this march towards professional and collaborative craft was any interest directed towards individualistic and iconoclastic innovation, the kind of innovation that used to spawn scientific revolutions. By the beginning of the twenty-first century, for all intents and purposes, science had turned into nothing but normal science.

Nowhere is this circumstance more apparent than in the field of autism research. Having established early on a paradigm of autism as a dire medical condition, the autism research community has been leveraging this framework to grow by leaps and bounds (Jiang et al., 2023). The size and number of research teams, the catalogs of practice guidelines, university and government grants, citations and self-congratulatory awards—all have expanded exponentially over the last fifty plus years. And to keep this expansion under professional and collaborative control, autism projects and hypotheses are restricted to an acceptable domain: the search for the genetic markers of autism (Wiśniowiecka-Kowalnik & Nowakowska, 2019), the quest for the neural signatures of autism (Hernandez et al., 2015), the hunt for the metabolic insults of autism (Cheng et al., 2017), and of course the development of treatments and cures (DeFilippis & Wagner, 2016). Perhaps with just a few more research teams, perhaps with just one more set of practice guidelines, perhaps with the essential increase in government grant funding, or perhaps with some additional journal opportunities for self-citation, a breakthrough in an understanding of autism will appear around the corner just about any day. And so goes fifty plus years of normal autism science.

In the meantime, the plight of autistic individuals remains unchanged. Misunderstood and mistreated, autistic individuals continue to be subjected to a broad range of corrective activities: applied behavioral analysis (Gitimoghaddam et al., 2022), depressive drug therapies (LeClerc & Easley, 2015), stem cell experimentation (Siniscalco et al., 2018), and so on—each treatment costing a pretty penny and each treatment designed to suppress autistic characteristics instead of making productive use of them. Normal autism scientists benefit greatly at the hands of normal autism science; autistic individuals suffer.

The Mottron team might be seen as pushing against the boundaries of normal autism science, and to a certain extent this characterization is valid. The Mottron team has been the one autism research team consistently arguing for the potential value of autistic characteristics, and the Mottron team has been the one autism research team willing to offer new theoretical approaches to the condition. But over the years, these efforts have amounted to little more than a chipping at the edges, a token stab at the idea of being revolutionary, with the team ultimately unwilling to venture far from modern science's career-protective walls. So when it comes to embracing a truly atypical conception of autism, and when it comes to considering and exploring autism's monumental impact upon the human species, the Mottron team maintains a comfortable silence. For autistic individuals, such reticence is a tragedy. Because for autistic individuals, of what value is a description of prototypical autism, if its primary purpose is to boost the statistical power of normal autism science?

References

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A Field Theory of Human Intelligence

1. Introduction

The brain-centric depiction of human intelligence is so widely accepted it has become in essence the primary, and usually unstated, assumption backing nearly all intelligence research. In the standard model of intelligence, the human brain is described as producing intelligence behavior, and the brain is typically portrayed as hosting intelligence within the material confines of its assorted lobes (Barbey, 2018; Colom et al., 2010). That is to say, the human brain and its mechanisms embody the substance of human intelligence. This deep adherence to a brain-specific model of human intelligence is evidenced these days at the very frontiers of intelligence research, where there are now many ardent attempts being made to record intelligence in action, through a broad assortment of increasingly sophisticated neuroimaging techniques (deBettencourt et al., 2023; Kristanto et al., 2023; Zacharopoulos et al., 2023). For nearly every intelligence researcher practicing his or her craft today, there is no questioning that the human brain forms the locus of human intelligence.

Nonetheless, despite this nearly universal acceptance of a brain-centric depiction of human intelligence, the standard model does face some serious challenges. In particular, there are two major challenges, that if left unresolved, could be seen as casting significant doubt on the validity of any brain-specific model of human intelligence. The first major challenge is the lack of specificity. Although it is widely presumed that somewhere within the cerebral mesh of neurons, synapses and biochemical activity there must exist a describable set of structures and dynamics that correspond and link directly to actual intelligence behavior, to date essentially no element of this set of structures and dynamics has been detailed in any degree (Goriounova & Mansvelder, 2019). The current situation regarding specificity for brain intelligence mechanics can be likened to that of someone having inventoried the many parts composing a clock or watch, but then being unable to say anything definitive about how those parts actually come together to represent time.

The second major challenge to a brain-centric depiction of human intelligence is the Flynn effect. The Flynn effect is the phenomenon first observed in the twentieth century—and observed nearly universally—that each generation has been scoring significantly better than previous generations on intelligence exams (Pietschnig & Voracek, 2015; Trahan et al., 2014). In other words, measurable human intelligence, as represented by the raw scores on intelligence tests, has been steadily increasing over time. This persistent and sizable increase has been so puzzling and so unexpected that many intelligence researchers have taken to insisting that the Flynn effect must be little more than a twentieth-century aberration, a temporary circumstance soon to disappear or even reverse (Dutton et al., 2016). But in fact, it can be easily demonstrated that an increase in measurable intelligence has likely been with humanity for a very long time, ever since the species' turn towards behavioral modernity, and in consequence, there is no reason to expect that the Flynn effect will end anytime soon (Griswold, 2023b). And if this is indeed the case, it poses a deep challenge to any brain-specific model of human intelligence. For if the human brain is to be described as physically producing and hosting intelligence, and if the level of that intelligence has been consistently and significantly increasing over time, what biological agency could account for such rapid and population-wide improvement? Taken at its face value, the Flynn effect would appear to defy almost every known biological and evolutionary principle.

Given the existence of these major challenges, it is not unreasonable to consider alternative models of human intelligence. In particular, any model that could provide greater specificity regarding the material structure of human intelligence, and could untangle the enigma of the Flynn effect, would be a model worth some serious consideration. One pointer to outlining such an alternative model can be found in the above statement regarding the brain and its mechanisms embodying the substance of human intelligence. By example and by analogy from the domain of physics, it can be noted there was a period of time, following the publication of Newton's Principia, when mechanistic, substance-based models of natural phenomena were the standard approach—indeed, the only approach—to explaining observed events of the physical world. Heat, for instance, was generally conceived of as a caloric substance, materially transferable from body to body. Magnetism and electricity too were similarly hypothesized as consisting of different kinds of fluid, fluid tangibly housed within the entities producing and experiencing the corresponding effect. Eventually, however, these substance-based models began running up against a series of disquieting challenges, with scientists ultimately unable to describe in detail how the proposed fluids and substances could account for observed outcomes in a broad range of experimental trials (Einstein & Infeld, 1938).

This impasse was resolved beginning in the nineteenth century, first through the work of Michael Faraday and James Maxwell, who proposed that phenomena such as magnetism and electricity could be better described not as fluids or substances, but instead as dynamic properties of the contextual environment, as dynamic properties of a spatial-temporal field (Forbes & Mahon, 2014). This alternative approach to describing physical phenomena became known as field theory, and it broke the logjam that was holding up a deeper understanding of the material world. Among the many milestones that field theory has produced are Maxwell's differential equations detailing the characteristics and propagation of electromagnetic waves (Maxwell, 1865) and Einstein's gravity-solving formulas underlying general relativity (Einstein, 1916). Indeed, field theory has proven to be so effective within the domain of physics, that today almost no physical phenomena are studied as substance or material, but instead are studied almost entirely as characteristics of a corresponding field (Wit & Smith, 1986).

Human intelligence too can be modeled as a field. In a field theory of human intelligence, intelligence is identified with the structural properties of the human spatial-temporal environment, and in particular, with the structural properties of the artificial aspects of that environment. The symmetry, pattern, repetition, logic, form and so on that undergirds buildings, roadways, books, tools, etc., all this can be seen as constituting the properties of a surrounding intelligence field. The human neural system, including the brain, now released from any presumed need to produce and to host intelligence, can be restored to its customary biological role of being a stimulus/response system, responsive in this case to the stimulus of a surrounding artificial environment, to the stimulus of a surrounding intelligence field. Furthermore, this field is dynamic, it has undergone, and continues to undergo, an intensification. Several hundred thousand years ago, humans lived in an entirely natural setting, free of all artificial influence, which could be described as the equivalent of living in a zero-strength intelligence field. But today, as can be experienced at the heart of any modern city, humans find themselves literally surrounded by an ocean of artificiality, with the structural aspects of that artificial environment forming an extremely strong—and ever strengthening—intelligence field.

A field theory of human intelligence clearly runs counter to the standard brain-centric model, but a field theory of human intelligence does have some distinct advantages. For one, field theory provides a specified description of the material structure of human intelligence. Since intelligence is now being identified directly with the structural aspects of the surrounding artificial environment, describing those structural aspects is no more difficult than detailing the characteristics of the constructed world, characteristics that are entirely open to observation and readily enumerated. This is in sharp contrast to brain intelligence mechanics, which to date remain almost entirely unobserved and unspecified. Also, a field theory of human intelligence untangles the enigma of the Flynn effect. Because intelligence is now being identified directly with the structural aspects of the surrounding artificial environment, and because throughout human history—ever since the turn towards behavioral modernity—the amount, type and complexity of these structural aspects has been continuously increasing with time, this ongoing intensification of the surrounding intelligence field provides for an extremely straightforward and observable explanation of the Flynn effect.

 

2. Challenges to a Brain-Centric Depiction of Human Intelligence

Lack of Specificity

Picture if you will a modern computer on a table in the office of a Chief Financial Officer (CFO). On a daily basis, this computer performs the following set of tasks: it reads documents from the company's network containing recent billings, receipts, payroll, investment income, etc., then it updates the company's ledger with this new information, and finally it prints out a summary of current assets, liabilities, revenue, costs and profit. The CFO recognizes that this computer is displaying a type of accounting intelligence, an intelligence that the CFO could also display if needed. The CFO is curious about how this machine works, and one day asks a specialist from the technology department to explain the computer's underlying operations. "It seems like magic to me," the CFO says.

"Oh, it's not magic at all," the specialist replies. "There are very specific technologies underlying each step of the process. Here, let me demonstrate." The specialist then brings in some extremely sophisticated imaging equipment and arranges it around the computer. Then as the computer performs its daily set of tasks, the imaging equipment makes recordings of all the activity it can detect. Finally, the specialist provides an explanation of the computer's operations with the help of the pictures the imaging equipment has produced: "You see here, when the computer is performing payroll, this area gets much brighter, over near the fan, and there are some streaks of red color by the hard drive. Those are the operations of the payroll module. Now here, in contrast, when the computer is summarizing liabilities, the pattern of activity changes: it's darker near the fan but much brighter over there by the network card, and those red streaks of color have turned blue. That's the liabilities circuit running under the guidance of the balance sheet module."

The CFO looks quizzically at the specialist. "I appreciate what you've done, but that's not exactly what I meant. I still don't know how the computer works."

The specialist grins back at the CFO. "I know. I was just pulling your leg."

In way of apology for the joke, the specialist then goes on to explain and to demonstrate, in great detail, the actual operations of the computer. It is not an easy or a quick task. To give a thorough explanation of how a modern computer performs something like an accounting task requires a multi-leveled and painstakingly intricate description of many particulars: NAND gates, system-level caches, encodings, machine language, voltage sources—to name just a few of the technologies involved. Nonetheless, despite all this hierarchical complexity, the task of explication can still be sufficiently performed. There is not a single element of a computer's operation or architecture that cannot be outlined and explained in adequate detail (Hennessy & Patterson, 2012).

Now recall what was said of the CFO, that the CFO could also display accounting intelligence if needed. Here too, one could inquire about the CFO's underlying operations, how is it that the CFO can turn receipts and investment statements into an organized and meaningful financial summary? Where does this intelligence come from? If you ask intelligence researchers to explain how the CFO manages to perform these activities, here is what they would do. They would bring in some extremely sophisticated neuroimaging equipment and arrange it around the CFO. Then as the CFO performs accounting tasks, the neuroimaging equipment would make recordings of the CFO's cerebral activity. And finally, the intelligence researchers would explain the CFO's accounting intelligence with the help of the pictures and data the neuroimaging equipment has produced, including descriptions full of references to brain modules and neural pathways. But this time, unlike with the joke played by the technology specialist, everyone will be satisfied and impressed (Haier, 2021).

It might be argued that this comparison is not quite fair, that intelligence researchers do not have the luxury of tearing down a human brain and examining its parts and connections while searching for the intelligence inside—especially while the brain is in operation. But in fact we do already know a great deal about how the human neural system works, knowledge that comes both from post-mortem analyses and from experiments conducted on a wide range of other animal species. And what we do know is this: in general, the human neural system, just as is the case with the neural systems of other animal species, is primarily a stimulus/response mechanism (Simmons & Young, 2010). Certain aspects of the neural system are associated with receiving environmental stimulus, such as those nerve pathways connected to the eyes. Other aspects are associated with giving response, such as those nerve pathways that provoke muscle movement. And some aspects of the neural system connect and coordinate stimulus and response, allowing the organism to act productively as a biologically cohesive whole. It is true that we do not yet know in complete and perfect detail every component of this stimulus/response mechanism, but as an evolutionary artifact that is shared in common across nearly the entire animal kingdom, neural systems, including brains, are not magical or mysterious. They are, by and large, stimulus/response mechanisms that have been finely tuned to support survival and procreative demands.

Intelligence, however, seems to be something quite different, an augmentation beyond just stimulus and response. Indeed, if we are talking about language production, arithmetic problem solving, logical reasoning, etc.—abilities that can be assessed via an intelligence exam—then we are no longer talking about a system shared across the entire animal kingdom. Even among hominins, measurable intelligence is an activity—historically and evolutionarily speaking—that is really quite new (Klein, 2002). So the question is, exactly what could it be inside the human brain, an organ originally and biologically designed to be part of a stimulus/response mechanism, that would allow it to assume this supplemental role of producing and hosting intelligence? The standard model of intelligence assumes that these supplemental operations must exist. But without tangible evidence and without specificity, how is it that we can be so sure? No matter how convinced intelligence researchers have become that somewhere inside the human brain-and somewhere inside those neuroimaging pictures-there is to be found the material source of human intelligence, could it not be just as likely that the opposite is true, that these brain-based, neuroimaging-driven assumptions are just the latest form of an old practice, are just the twenty-first century version of phrenology (Uttal, 2001)?

There is a further problem for the standard model. Recall the comparison to a modern computer, for which every aspect of its operations can be described and explained in adequate detail. That comparison also suggests that even if we were to understand every intelligence operation within the human brain, that knowledge alone would not be enough for explaining intelligence. As any computer scientist could tell you, understanding every component and every procedure of a modern computer is not by itself sufficient to explain fully the computer's overall behavior. On its own, a modern computer will not display intelligence at all—be it accounting intelligence or otherwise. To perform tasks that can be seen as the equivalent of intelligence tasks, a computer must be primed with additional structure, additional structure that comes not from the machine itself but instead comes from the outside. This additional structure might be in the form of a program uploaded into the computer's memory, or nowadays, this additional structure might come in the form of machine learning, in which the computer is trained to perform various tasks via the influence of large amounts of ambient data (Mohan et al., 2021). But either way, in order for a computer to display something that could be likened to intelligence, it must first be organized into a structural system, a structural system that is not derived from the machine itself but is instead derived from the external environment. This raises the question of whether a computer's intelligence should be attributed to the machine or instead to the machine's contextual surroundings. And if this question is pertinent for a modern computer, why would it not be pertinent for a human brain?

 

The Flynn Effect

The first iterations of the modern IQ exam began to appear early in the twentieth century, and as that century progressed a curious artifact began to emerge from the growing collection of IQ exam results: the average raw scores on these exams were getting consistently and significantly better over time. Several researchers had made note of this phenomenon, but it was James Flynn in the 1980s who demonstrated convincingly, with large amounts of data, that the phenomenon was essentially universal, and shortly thereafter it would be dubbed the Flynn effect (Flynn, 1984, 1987). The Flynn effect remains surprising and perplexing to this day.

Because raw IQ scores have been increasing since they first began to be measured, the question arises as to whether this increase would have been apparent during earlier times, had IQ exams been available prior to the twentieth century. In other words, for humans, when did this increase in measurable intelligence begin? Oddly, it seems the general consensus from the intelligence research community is that the Flynn effect began sometime near the start of the twentieth century, the coincidental timing with the invention of IQ exams apparently notwithstanding. A few researchers, including James Flynn, have suggested that the Flynn effect could trace its origin back to somewhat earlier, to around the time of the Industrial and Scientific Revolutions (Flynn, 2007; van der Linden & Borsboom, 2019). But no researcher it seems is willing to entertain the possibility that the Flynn effect has been operative for a much longer period of time. And coupled with these suggestions of a recent start for the Flynn effect are further suggestions that the Flynn effect soon must end—if indeed it has not ended already. One of the latest trends in intelligence research has been the diligent hunt for evidence that the Flynn effect has plateaued or even reversed (Dworak et al., 2023).

What is driving this insistence that the Flynn effect must have a recent origin and an imminent demise is the standard model of intelligence. In order for the standard model to continue to make biological sense, the Flynn effect must be temporary. If the Flynn effect were not temporary, if it were instead to be seen as operative over an extremely long period of time, then any brain-based depiction of human intelligence could be seen as violating biological and evolutionary principles and boundaries. For instance, the type of raw intelligence gains that were apparent throughout the twentieth century, when extrapolated over a much longer period of time, would be akin to the average human body doubling in weight every century or two, a biological and evolutionary implausibility. If the human brain is to be depicted as producing and hosting intelligence, then in some sense intelligence must be biological and organic, and thus must also adhere to biological and evolutionary principles. This means that, according to the standard model, intelligence cannot grow indefinitely, and population wide, by leaps and bounds.

The need for the Flynn effect to be temporary is evident also in the many hypotheses that have been offered in way of explanation for the phenomenon. Better education, better nutrition, increased exposure to video games and puzzles, increased exposure to science, etc.—all these suggestions, explicitly or implicitly, are intended as recent and short-term boosts to brain productivity, boosts that ultimately have a limited shelf life. Nutrition and education cannot be improved forever, exposure to video games and science eventually becomes routine, and thus intelligence inevitably returns to something more steady. The apotheosis of these attempts to explain the Flynn effect as a fleeting phenomenon on top of a long-term trend towards intelligence stability can be seen in both the Dickens-Flynn model (Dickens & Flynn, 2001) and in Woodley's theory of fast and slow life (Woodley, 2012). These are parametrically complex models that attempt to reconcile a broad assortment of environmental influences—such as education, family size, nutrition, pathogen stress, social motivators, etc., influences that purportedly can account for short-term surges and pullbacks in measurable intelligence—reconcile these to genetic and physical factors, factors critical for determining the biological basis of intelligence and for ensuring the long-term stability demanded by the standard model. Thus, the labyrinthine complexities of the Dickens-Flynn model and the Woodley theory are motivated ultimately by the presumptive need for the Flynn effect to be temporary.

But in fact, there is no conclusive evidence and no compelling reason to assume that the Flynn effect is temporary. IQ scores prior to the twentieth century do not exist, so we cannot know for certain what the characteristics of measurable intelligence were before that time, and as for recent studies suggesting that the Flynn effect is ending, the data remains preliminary and is contradicted by continuing gains in various countries (Colom et al., 2023; Liu & Lynn, 2013; Nijenhuis et al., 2012). Perhaps more importantly, a straightforward analysis of human history indicates the opposite of what researchers apparently expect, indicates that far from being temporary, the Flynn effect has actually been operative within the human population for quite some time, ever since the turn towards behavioral modernity (Griswold, 2017, 2023a). The easiest way to see this is to consider what the species would have been like at the moment of that turn, somewhere around a few hundred thousand years ago. Humans were still in the state of being pure animals, focused solely on survival and procreation, and were not in possession of a single characteristic that could be measured by a modern IQ exam: no language, no arithmetic, no abstract reasoning, no construction (Klein, 2009). Administering an IQ exam to a human of that time would have been no more successful than administering an IQ exam to a wild animal today, and this means that measurable intelligence for humans a few hundred thousand years ago would have been quantifiable as absolute zero, the same as measurable intelligence for wild animals today. And since measurable intelligence has clearly progressed for humans to something more substantive right now, that overall increase, by definition, is a Flynn effect. It is in fact a massive Flynn effect, one that has been operative over an extremely long period of time.

What is also notable about this analysis of human history is that it points to an alternative source of human intelligence, one that is consistent with a growth in intelligence over the course of that history. A few hundred thousand years ago there was no artificial construction in the human environment, humans lived in an entirely natural setting. But as humans advanced towards behavioral modernity, the amount, type and complexity of the artificial construction contained within the human environment continued to accumulate over time. From simple tools, animal skin clothing and makeshift shelters to highways, electricity and towering skyscrapers, humans have found themselves increasingly surrounded by the ubiquitous influence of artificial construction. And this artificial construction must have something to do with human intelligence, because the content of an IQ exam is composed itself entirely out of artificial construction—words, numbers, puzzles, matrices, etc. (Wechsler, 1997). When one takes an IQ exam, one is in essence demonstrating one's dexterity with artificial construction.

Thus, if intelligence could be associated to the characteristics of the artificial construction contained within the human environment—instead of to the biological characteristics of the human brain—then explaining the Flynn effect would be no more difficult than explaining the historical increase in artificial construction. But the reason no one considers associating human intelligence to the human environment is that the standard model of intelligence insists otherwise, insists that human intelligence is to be associated directly and solely to the human brain (Jung & Haier, 2007). But is this insistence justified, does the standard model actually capture the true nature of human intelligence? Is there a reasonable and effective alternative available, a means to model human intelligence that associates intelligence not to the human brain, but instead to the structural impact of the artificial aspects of the human environment?

 

3. Field Theory

It is important to begin by noting that a field theory of human intelligence is not the same thing as other field theories that have been proposed in the domains of psychology and sociology (for example, those of Lewin and Bourdieu), theories that appear to have closer relationships to Gestalt philosophies and socio-political doctrines (Fernández & Puente, 2009; Lewin, 1951). Instead, a field theory of human intelligence is more akin to its physical science counterparts, such as those describing the phenomena of electricity and magnetism. Of the different ways to characterize this type of field theory, perhaps the most straightforward is to focus upon the reactions of responsive objects to the presence of a relevant field. For example, different kinds of metallic shavings are moved and aligned by the presence of a magnetic field, with some types of metals more responsive to that field than others. Nonetheless, the dynamic properties of magnetism are not determined by the characteristics of the metals themselves, which remain essentially constant over time, but are instead determined by the dynamic properties of the surrounding magnetic field. In a weak magnetic field, every metal will display proportionally less reactivity, and in a strong magnetic field, every metal will display proportionally more reactivity, even though the metals themselves remain unchanged. Thus, the overall intensity of the magnetic effect is determined by the strength of the magnetic field (Black & Davis, 1913).

In a field theory of human intelligence, the strength of the intelligence field is determined by the amount, type and complexity of artificial construction contained within the human environment. In other words, the more artificial construction there is, the greater the intensity of the intelligence field and the greater the amount of intelligence that can be measured (for instance, via an IQ exam). The responsive object in this scenario is the human neural system—or more particularly, the human brain—and just as some metals are more responsive to a magnetic field than are others, some human brains are more responsive to an intelligence field than are others. But the dynamic properties of human intelligence are not determined by the characteristics of these brains—characteristics that remain essentially stable over time. Instead, the dynamic properties of human intelligence are determined by the changing strength of the surrounding intelligence field, by the changing amount, type and complexity of artificial construction contained within the human environment.

A few hundred thousand years ago, when humans were still pure animals and there was no artificial construction to be found in the human environment, the strength of the intelligence field would have been essentially zero. Human brains of that time, despite being as capable of responding to an intelligence field as are the human brains of today, would have found no artificial stimulus with which to engage, meaning there would have been no corresponding response and thus no measurable intelligence. By around twenty-five thousand years ago, instances of artificial construction had begun to make frequent appearance within the human surroundings—structured tools, ornamental jewelry, cave paintings, abstract sounds, etc.—and the human brains of that era, responding to the stimulus of this newfound artificial construction, would have thereby been capable of displaying intelligence behavior (Christian, 2018). Administering an IQ exam to that population would have been conceivable, even though the exam would have needed to be crude and simple by modern standards, because of limited vocabulary, primitive numeracy, etc. Indeed, a corollary of field theory for human intelligence is that an IQ exam, in order to be an effective and accurate measure of the intelligence of a given population, would need to reflect and to serve as a proxy for the amount, type and complexity of artificial construction to be found in that population's particular environment. A modern IQ exam such as Stanford-Binet or Wechsler would overwhelm an ancient population, but an appropriately simpler exam would be able to assess that population's intelligence characteristics.

By the later era of the Mesopotamian, Egyptian and Greco-Roman empires, the artificial construction in the human environment had swelled to an even greater magnitude—permanent abodes, irrigation techniques, written words, advanced numeracy, etc.—and the human brains of that era, still biologically the same as human brains of previous eras, would have been responding to this increased stimulus of artificial construction by displaying still greater degrees of measurable intelligence. And today, in the twenty-first century, in a world now thoroughly suffused with buildings, roadways, computers, streams of structured data, etc., human brains find themselves responding ever more continuously to a growing and fast-paced array of artificially constructed stimulus, so much so that today's human brain—still biologically the same as previous human brains—can now easily handle the increased and increasing complexities of modern IQ exams.

Because the intelligence field is an observable and structured feature of the human environment, this field is in theory quantifiable. Unfortunately, there are some practical difficulties to actually making such a quantification. For one, the quantification process would be self-referencing, since quantification and measurement are themselves instances of artificial construction. Perhaps even more challenging is the fact that in the modern era, the depth, breadth and hierarchy of artificial construction contained within the human environment has reached such expansive proportions as to make the quantification task nearly overwhelming—on an order perhaps of cataloging and numbering all the organic and inorganic molecules contained within the oceans. Nonetheless, despite these practical difficulties, it is still possible to make accurate and meaningful statements about the dynamic properties of the human intelligence field. For instance, it should be clear from human history that the strength of the intelligence field has been continuously and significantly increasing over time, ever since the human turn towards behavioral modernity. The number and type of constructed artifacts contained within the human environment, as well as their underlying complexity, has been continuously on the rise, something that was quite observable across the course of the twentieth century, with the advent of airplanes, automobiles, electronic communication, computers, and the like, a torrent of additional environmental construction coming at the same time evidence was first appearing that measurable intelligence was significantly increasing within the population.

The simplest assumption that can be made regarding the dynamic properties of the human intelligence field would be to say that growth in artificial construction is proportional to the amount of artificial construction existing at any given time. This assumption is captured in the differential equation di/dt=ki, where i is the intensity of the intelligence field, t is time, and k is a positive constant of proportionality. This differential equation has a solution, i=e^kt, indicating that the intelligence field strengthens exponentially (Trench, 2013). This assumption is perhaps not unreasonable in the modern era, when the deep interconnectedness of the entire human environment allows for innovation and new construction to spread rapidly and uniformly around the globe. Nonetheless, a longer look over the course of human history indicates that growth in the human intelligence field has generally been less regular, with localized surges and intermittent plateaus. And given that there are biological aspects to human intelligence, it cannot be expected that its underlying formulas will display the same mathematical exactitude as do physical phenomena—the true differential equations describing the human intelligence field will likely be somewhat messy. This does not, however, invalidate the overall message of the theory, namely that the dynamic properties of human intelligence can be derived from the artificial aspects of the human environment.

While a field theory of human intelligence clearly runs counter to the standard brain-centric model, field theory does have several advantages that speak in its favor:

1. A field theory of human intelligence does not require extraordinary biological and evolutionary assumptions regarding the functionality of the human brain. In a field theory of human intelligence, the human neural system retains its traditional biological role of being a stimulus/response mechanism, and what changes is not the brain itself, but instead the environmental stimulus to which the brain responds. This means that the brain does not need to take on the supplemental and biologically extraordinary role of producing and hosting intelligence, and this further implies that the human neural system has not needed to transform biologically in any significant way since the beginning of the human behavioral transformation, an implication more consistent with the principles of evolution. Also, since what changes is the environmental stimulus, and not the brain itself, there is no biological restriction on the rate of intelligence gain, no organic hindrance to having intelligence grow indefinitely, and population wide, by leaps and bounds.

2. A field theory of human intelligence provides a specified and observable description of the material structure of human intelligence. Because intelligence is now being identified with the structural aspects of the human artificial environment—and not with the neurons in the human brain—the material structure of intelligence is entirely open to observation. The symmetry, pattern, repetition, form and so on that underlies most types of intelligence behavior—language, arithmetic, problem solving, and the like—these characteristics exist right before our very eyes, there in the human environment. Indeed, most of these characteristics have already been cataloged and explained, using the tools of mathematics, logic and science. Precise descriptions of the structure of the artificial aspects of the material world are in essence the same thing as precise descriptions of the material structure of human intelligence. This means that any attempt to uncover a neuronal structure for human intelligence would be to engage in nothing but a redundancy, an attempt to find something that we have already perceived. In a field theory of human intelligence, the locus of intelligence is not to be searched for inside the human skull; instead, the locus of intelligence can be found within the expanding artificial structure of the human environment.

3. A field theory of human intelligence offers a straightforward and elegant explanation of the Flynn effect. Measurable human intelligence, represented by the raw scores on intelligence exams, is the result of the orthogonal combination of two different factors. One of these factors is general intelligence ability, the strength of an individual's intelligence scores across an assortment of correlated intelligence tasks (Spearman, 1904). Effectively, an individual's general intelligence ability is a measure of that individual's general responsiveness to the presence of an intelligence field, an ability that differs from person to person, the difference being driven mostly by genetic factors (Gottfredson, 1998). General intelligence ability is the biological component of intelligence, and as such, it can be assumed that the average general intelligence ability within the human species has remained nearly constant over time, as would be expected for a biological trait. But the same cannot be said of the second factor contributing to measurable intelligence. The second factor is the total amount, type and complexity of artificial construction contained within the human environment, the target towards which general intelligence ability is applied. The amount, type and complexity of artificial construction has been significantly and consistently increasing ever since the beginning of the human turn. And because measurable intelligence is the result of the orthogonal combination of both general intelligence ability (stable over time) and the amount, type and complexity of artificial construction (increasing over time), measurable intelligence also increases over time. This is a precise description of the Flynn effect, and it marks the increasing amount of artificial construction contained within the human environment—that is to say, the growing strength of the intelligence field—as the sole driver and the sole explanation of the Flynn effect.

In addition, a field theory of human intelligence gives rise to certain predictions about the future course of human intelligence:

1. Field theory indicates that there is no reason to expect that the Flynn effect is ending or reversing. Since field theory suggests that the Flynn effect has been operative within the human species for many millennia—ever since the turn towards behavioral modernity—it would be too much of a coincidence to have the phenomenon come to a screeching halt right at the very moment of its discovery. More importantly, barring a human catastrophe (such as civilization collapse), it can be expected that the amount, type and complexity of artificial construction will continue to accumulate within the human environment, and future generations, responding to this increased level of artificial construction, will thereby go on to demonstrate greater levels of intelligence performance on future intelligence exams. Therefore, it can be predicted that the average level of measurable intelligence at the end of the twenty-first century will exceed by a significant amount the average level of measurable intelligence from the beginning of the twenty-first century.

2. Field theory indicates that the content of intelligence exams will need to undergo significant alteration as time goes by. The content of an IQ exam is a proxy for the artificial construction contained within the human environment. The structure underlying questions regarding vocabulary, arithmetic, puzzles, matrices, etc., this structure mirrors the artificial structure that humans navigate and master in their everyday lives. Thus, an individual's performance on an IQ exam is an indirect measure of that individual's ability to navigate and to master ambient artificial construction, and since the amount, type and complexity of that ambient artificial construction continues to increase over time, the content of IQ exams must be similarly altered in order to remain effective. In general, future questions must take on greater variety and greater complexity, because if IQ exams were not altered in this fashion, they would gradually begin to fail in their purpose, becoming less able over time to detect individual intelligence differences and to predict accurately the life circumstances impacted by intelligence ability. Therefore, it can be predicted that the content of IQ exams at the end of the twenty-first century will differ significantly from the content of IQ exams at the beginning of the twenty-first century, mostly through the incorporation of greater variety and greater complexity, in an attempt to mirror and to proxy the increasing amount, type and complexity of artificial construction to be found within the human environment.

It is perhaps not out of place to mention that both of these predictions could have been made at the beginning of the twentieth century, and would have been verified by the end of the twentieth century. And unless one is convinced that the Flynn effect must be temporary, there is no reason to expect that the current century, or future centuries, will turn out to be any different.

 

4. Conclusion

The standard model of human intelligence is a brain-centric depiction of intelligence, and it enjoys nearly universal acceptance within the intelligence research community. Nonetheless, the standard model does have some serious shortcomings, including a lack of specificity and an inability to account for the Flynn effect, other than to assume that the Flynn effect must be a temporary aberration.

What has been presented here is an alternative model for human intelligence, one that identifies intelligence with the growing artificial structure contained within the human environment. Although this field theory approach to human intelligence runs counter to the widely accepted standard model, field theory does offer some advantages, including an eschewal of any extraordinary biological or evolutionary assumptions regarding the functioning of the human brain, a specific and observable description of the material structure of human intelligence, and a straightforward and elegant explanation of the Flynn effect. For these reasons, a field theory of human intelligence merits serious consideration.

 

 

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