How Autism Drives Human Invention: But Is It Just Autism?

In The Pattern Seekers: How Autism Drives Human Invention (2020), Baron-Cohen proposes the Systemizing Mechanism as an explanation for human progress through invention, from the first tools to the digital revolution. Autistic people tend to be hyper-systemizers, due to their repetitive behavior and obsessive interest. With their talent at spotting novel patterns which produce a potentially groundbreaking result, they have potential to be inventors. They are those who can’t help focusing on precision and detail and figure out how a system works, how to improve a system.

If-and-then pattern as the driving force behind human evolution

Seen through one lens, a child’s “obsessions” are a symptom of a “disorder” or a “disease” and associated with disability. Seen through another lens, a child’s relentless experimenting and detailed observations are the product of a mind whose pattern-seeking engine operates in overdrive and can lead them to invent, and sometimes to become great inventors.

The if-and-then pattern, which is the central idea behind the Systemizing Mechanism, explains how the huge stones at Stonehenge in England were transported to their eventual location 5,019 years ago. Back then, someone must have looked at a heavy stone, and then looked at their ox in a new way for its potential use: if a stone is hugely heavy, and I harness it to my ox, then the huge stone moves. The ox was no longer just an ox but now was seen as a causal operation in an if-and-then algorithm.

Hyper-systemizing is characterized by two key features: repetitive behavior and obsessive interest. There are three ways we systemize; by observation, experimentation, modeling. Observation is a powerful way to spot a change and start to hypothesize about an if-and-then pattern. In China, a book called Zhou Shu found in a tomb in AD 280 described how the moon changes in color when a lunar eclipse occurs, which we call today a lunar eclipse: “if I observe the white moon, and it lies in a straight line with the sun and the Earth, then the moon will seem red.” Systemizing via experimenting is part of how humans practice sports. Consider skateboarders shifting their weight to turn corners, driving the skateboard upwards to jump up onto a park bench. These stunts require systemizing each move, spotting and performing the repeating patterns. Experimenting within a model can be described using Fleming’s accidental discovery of penicillin’s antibiotic properties. One day, he discovered one of his culture was infected with a fungus but only the colonies of the bacteria close to the fungus have been destroyed. He hypothesized that “mold juice” must have killed the bacteria: if there is a living bacterial colony, and it is close to mold juice, then the bacteria will die.

The human progress just wasn’t characterized by the full Systemizing Mechanism alone, which drives the curiosity to ask questions and to experiment with variations in patterns, but also by the full Empathy Circuit, which includes a theory of mind, which provides the ability to imagine another’s beliefs. Together, these represent Baron-Cohen’s Empathizing-Systemizing (E-S) hypothesis, which has been validated in the general population (Svedholm-Häkkinen & Lindeman, 2016).

The theory of mind introduces a set of skills. First, a theory of mind allows for flexible deception, planting false beliefs in the mind of their victim, which assumes an awareness that others have beliefs in the first place. For humans, such capacity was a huge advantage in terms of natural selection. Neanderthals lacked this capability and Homo sapiens alone showed the capacity for generative, flexible deception (doing it in many different contexts) as opposed to the one-off forms of deception we see in some animals. Second, a theory of mind allows for flexible teaching, because this involves keeping track of what another person knows or needs to know. Humans alone seem to engage in teaching, especially adaptive teaching strategies. Third, a theory of mind allows flexible referential communication because this involves understanding that your listener or viewer knows that words refer to something out there in the world. One of the earliest signs is when a toddler uses the pointing gesture to refer to something, which highlights the understanding that another person has a mind with which we can communicate.

Baron-Cohen argues there are 5 different brain types: balanced, empathizers, extreme empathizers, systemizers, extreme systemizers. The last category, extreme S, is twice as common in men as in women and is often characterized by savantism, the prevalence of which is much higher among autistic persons (Treffert, 2009). The key feature of these brain types is that the tuning of the Empathy Circuit and the Systemizing Mechanism is a zero-sum game. The higher one of these is tuned, the lower the other is tuned. An interesting study using Latent Class Analysis and Factor Mixture Model, which I described earlier, confirmed a two factor three-class model as the best fit to the data (Grove et al., 2015). The two factors were identified as empathy and systemizing, while the three classes were identified as strong systemizing, strong empathizing, and balanced in terms of empathy and systemizing. There is evidence that empathizing and systemizing differentiate the male female brain types (Goldenfeld et al., 2007).

Only Homo sapiens seem to have the capacity for generative experimentation and invention. Homo habilis kept on making the same tool with the same functions (smash, cut, and scrape), showing no evidence of a capacity for invention. Homo erectus became almost terrestrial and were bipedal. They made a new stone hand ax, known as an Acheulian tool, but the same clear reward drove their tool-making behavior, namely, getting a food source. Creating a one-off novelty is insufficient to count as generative invention, as it can arise by chance or associative learning. Homo neanderthalensis used the same limited simple tools for smashing, cutting and scraping. There were several speculations, based on archaeological findings, that Neanderthals are able to invent things like boats, adhesives, spears, but such views have been challenged as well.

Baron-Cohen hypothesized that the cognitive revolution leading to the shift from simple to complex tool-making 70,000-100,000 years ago is explained by the idea that the Systemizing Mechanism had evolved, leading to inventions such as necklace and bow. Although there are cases of invention that predate modern humans they do not meet the criterion for being genuine inventions, as explained above. Engraving is a clear sign of if-and-then thinking: “if I take a smooth stone, and use a tool with a fine blade, then I can engrave patterns on the stone.” Around the same time, Homo sapiens reached Australian islands, which shows evidence they had invented boats. The author insists obviously that even this cognitive revolution does not entail abrupt changes but rather a gradual change (Heyes, 2012). Baron-Cohen mentioned several inventions, each characterized by if-and-then pattern. The 40,000-year-old bone flute being a perfect example of this system: “if I make a hole in the bone, and I cover the hole with my finger and blow, then I make a different sound.” These humans tested to see if this if-and-then pattern held true by repeating it over and over again. The 43,000-year-old Lebombo bone with twenty-nine tally marks was considered the earliest counting device. Other inventions around this period include adorned graves from 42,000 years ago and hand-printing on cave walls in Indonesia from 40,000 years ago. The agricultural revolution about 12,000 years ago entailed great leaps of inventions and showed the sign that humans had systemized nature: “if I sow my wheat seeds, and I use a hoe to plant them more deeply, then I will get a better crop.” or “if I take my ox, and castrate him, then he will be more obedient.”

The Systemizing Mechanism lead to societal transformations and gave rise to the capacity for invention and experimentation, while the Empathy Circuit gave rise to the capacity for thinking about another person’s thoughts and about one’s own thoughts and allowed for flexible communication. Together, these two new cognitive modules constituted the cognitive revolution.

Several language theories were proposed as the basis for human invention. Baron-Cohen argues first that invention does not require language as some can lose language due to stroke yet become remarkable musicians because they do not lose the ability to invent or perceive rhythm. Recursion, which revolves around the nesting of ideas, is easily accommodated by the if-and-then pattern of the systemizing mechanism: “if I take the phrase ‘Alex has a red car,’ and nest within it ‘whom you know very well,’ then the phrase becomes ‘Alex, whom you know very well, has a red car.’” Linguistic syntax, which breaks the phrase down into its individual units before changing the sequence, is also a property of the systemizing mechanism: if the phrase is “dog bites man,” and the first and third words are swapped around, then the phrase becomes “man bites dog.” The very fact that some of the autistic savants have minimal language but are hyper-systemizers suggests that systemizing and language are independent of one another.

Other theories put forth to explain the capacity for invention can be accommodated by the systemizing mechanism. One such theory argues we can integrate two ideas into one new one which is an operation within the systemizing mechanism. Thus: “if I take (the idea of) the top half of a lion, and I attach it to the bottom half of (the idea of) a man, then I have (the idea of) a lion-man.” Another theory is symbolic thinking, which is the capacity to let one thing stand for another, but this is equivalent to the if element of the if-and-then system. Unlike other proposed theories, the Systemizing Mechanism explains how and why humans invent. What drives invention is curiosity, to see what happens and what’s possible.

Autism research: Their important findings

Although Baron-Cohen cited many studies on autism, he also focused a bit too heavily on anecdotal evidence. It is certainly interesting to know that Isaac Newton, Thomas Edison, Albert Einstein, Henry Cavendish, Hans Christian Andersen, Andy Warhol, Bill Gates, Elon Musk were all autistic people. This is not to say this kind of evidence is unimportant, but here I’ll rather focus on data and review additional studies.

Hyper-systemizers are supposed to be faster and better at pattern recognition test. Consistent with this hypothesis, adults with autism perform faster on the Embedded Figures Test (Horlin et al., 2016; Jolliffe & Baron-Cohen 1997; Muth et al., 2014) but there seems to be a large variability unaccounted for. Consistent with many studies reporting impaired verbal IQ but not performance IQ on the Wechsler in autism, a study found that  normal persons had equivalent score levels on the Raven and Wechsler whereas autistic persons had better scores on the Raven than on the Wechsler (Dawson et al., 2007). Importantly, studies employing fMRI methodologies found enhanced activity in brain areas involved in visuospatial processing in autism during EFT (Damarla et al., 2010; Manjaly et al., 2007) or during n-back task (Koshino et al., 2005) or when solving Raven matrices (Soulières et al., 2009). It was theorized that the autism-associated advantage in visual search is related to stronger pupillary response. A meta-analysis confirmed a robust group difference in the latency of pupillary response but no group difference in amplitude and baseline of the pupillary response, however they could not investigate potential moderators such as age and the study designs varied widely across studies (de Vries et al., 2021).

Some studies compared the prevalence of ASD for children having an older sibling with ASD and those without, and the prevalence (using adjusted hazard ratio) was much higher among full-siblings than among half-siblings (Grønborg et al., 2013; Sandin et al., 2014). Another study found that baby siblings were two times more likely to develop autism when they have an older sibling affected by autism, and males were three times more likely than females to develop autism (Messinger et al., 2015; Ozonoff et al., 2011). The male bias is seemingly due to fetal testosterone (Baron-Cohen et al., 2011) which is known to correlate with autistic traits (Auyeung et al., 2010) and systemizing quotient (Auyeung et al., 2006). Few studies examined the link between prenatal testosterone and autism during adolescence, but one small sample found that the size and direction of this relationship depended on pubertal stage with correlation becoming stronger the later the pubertal stage (Dooley et al., 2022). A strong biological basis for autism is still established when one considers that prenatal testosterone is negatively correlated with brain regions involved with language but positively correlated with regions involved with audiovisual integration (Lombardo et al., 2012).

Fathers and grandfathers of children with autism were twice as likely to have an occupation in engineering compared to control groups (Baron-Cohen et al., 1997). This finding was confirmed in a study reporting that individuals working in a STEM career had higher scores in the autism-spectrum quotient (Ruzich et al., 2015). Engineering and mathematics are clear examples of systemizing. A study confirmed that autism is more prevalent among mathematics undergraduates than students in a control group composed of disciplines such as medicine, law and social science (Baron-Cohen et al., 2007). More generally, students in science (i.e., mathematics, physical and biological sciences, computer, engineering, medicine) scored higher on AQ than students in humanities (i.e., classics, languages, law, architecture, philosophy, theology, history, music) and social sciences (i.e., geography, economics, social-political sciences, archaeology and anthropology, management) (Baron-Cohen et al., 2001; Billington et al., 2007; Pisula et al., 2013); however it should be noted that the subscales of the AQ test had low reliability in Pisula’s study compared to Cohen’s. A study conducted in the Netherlands found that schools in Eindhoven (being the hub for IT) had much higher prevalence of autism compared to schools in control regions such as Utrecht and Haarlem (Roelfsema et al., 2012). It is therefore unsurprising that among the 330 wealthiest families in the United States, 27 (8%) have an autistic child, compared to 1% in the general population.

While the research on autism prevalence among high achieving individuals in engineering are congruent, the research on the autism-math relationship do not support the systemizing hypothesis. An analysis based on fMRI scan revealed that autistic children performed better than normal children on numerical problem-solving abilities but not mathematical reasoning when matched on IQ (Iuculano et al., 2014). But other samples showed that autistic children had lower scores than IQ-matched typically developing children on numerosity (Aagten-Murphy et al., 2015), numerical operations and mathematical reasoning (Chen et al., 2019; Hiniker et al., 2016), the Math Problem Solving subtest of the WIAT-III (Oswald et al., 2016), or no group difference in numerical operations after adjusting for verbal IQ (Kljajevic, 2023).

Another issue concerns the lack of study about the validity of systemizing quotient (SQ) and autism quotient (AQ) tests. One study found that SQ moderately predicts (r=0.31) math ability assessed by the arithmetic subtest of the WAIS-R (Bressan, 2018) while another study found that SQ did not correlate with AQ despite SQ correlating moderately with mechanical reasoning and mental rotation (Morsanyi et al., 2012) and another found that SQ weakly correlated with the WJ-III Applied Problems subtest (Escovar et al., 2016). It is possible that empathizing-systemizing (E-S) score difference is a more valid variable than SQ score, as one study emphasized (Pan et al., 2022) in accordance with Baron-Cohen’s theory that there is a trade-off between empathy and systemizing. This study analyzed a larger sample of chinese children, composed of three groups (normal, autistic with and without intellectual disability) in China and found that E-S difference, unlike SQ, predicted all autistic traits (social-communicative functioning difficulties and restricted repetitive behavior) in all groups, and that E-S difference is larger among autistic children without disability than with disability, which in turn showed larger difference than normal children, although IQ partially mediated the group differences. Consistent with this pattern, an interesting study showed that systemizing bias (at the expense of empathizing) becomes stronger as IQ increases among autistic adults (Larson et al., 2015). Another method is to derive a factor score from occupation-specific measures of the importance of a set of skills and abilities. A study using this technique found a systemizing factor that captures systems and ordering skills and reported that children of hyper systemizing parents have 35% higher likelihood of autism diagnosis compared to children whose parents both have balanced brain types (Daysal et al., 2021).

A meta-analysis of twin studies which used a liability threshold model found heritability estimates ranged between 64% and 91%, depending on whether the threshold for prevalence of autism risk is set at 1% or 5%, respectively, and after accounting for ascertainment bias (Tick et al., 2016). A sibling study employing ACDE decomposition model reported that heritability is lower for people with intellectual disability (33% versus 65%), which indicates that low-IQ people would have lower heritability in autism (Xie et al., 2020). GWAS established the high heritability of autism, with common variants explaining most of the heritability estimates, despite very low-IQ autistic people showing lower heritability (Gaugler et al., 2014; Grove et al., 2019). GWAS also established a modest genetic overlap (i.e., correlation) between autism and IQ (Grove et al., 2019). A study found a small relationship between polygenic risk for autism and g factor score and FSIQ in three large datasets (Clarke et al., 2016). Assortative mating plays a large role in the population prevalence of rare disorders with high heritability, including autism (Peyrot et al., 2016).

Not just autism

Now comes the crucial question. How does this affect IQ research? Crespi (2016) proposed an interesting theory. What appears paradoxical given that autism is characterized by below-average IQ can be resolved under the hypothesis that autism involves enhanced, but imbalanced, components of intelligence. Crespi reviews several studies suggesting that increased local brain connectivity in autism is linked with specific enhanced abilities such as hyper-sensitivities and attention to detail but that comes at the cost of reduced long-range brain connectivity which could contribute to such imbalances by reducing general intelligence. Autism is indeed the only psychiatric condition characterized by notable rates of savant skills (Treffert, 2009), which account for their highly limited range of enhancements.

Another study consilient with IQ research found that autistic people had higher SD in IQ. They are are 12 times more likely to score within the intellectual disability range but also 1.5 times within the superior range (Billeiter & Froiland, 2022).

We know that it is the smart fraction of the IQ distribution which drives nations’ wealth (Kirkegaard & Carl, 2022). Autism research does not contradict this conclusion and in fact validate it. Autistic people tend to gravitate towards STEM, so do high IQ people.

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