the hyper-systemizing assortative mating web viewthe hyper-systemizing theory posits that common...

SYSTEMIZING OCCUPATIONS AND AUTISM SPECTRUM DISORDER

PREVALENCE IN SEVENTEEN GEOGRAPHIC AREAS

_______________________________________________

A Thesis

Presented to

The Graduate Faculty

Central Washington University

_______________________________________________

In Partial Fulfillment

of the Requirements for the Degree

Educational Specialist

School Psychology

___________________________________

by

Michael David Walton

May 2014

CENTRAL WASHINGTON UNIVERSITY

Graduate Studies

We hereby approve the thesis of


Candidate for the degree of Educational Specialist

APPROVED FOR THE GRADUATE FACULTY ______________ _________________________________________ Dr. Suzanne Little, Committee Chair ______________ _________________________________________ Dr. Heath Marrs ______________ _________________________________________ Dr. Terry DeVietti ______________ _________________________________________ Dean of Graduate Studies

ii

ABSTRACT

SYSTEMIZING OCCUPATIONS AND AUTISM SPECTRUM DISORDER

PREVALENCE IN SEVENTEEN GEOGRAPHIC AREAS

by


May 2014

The relationship between autism prevalence and occupations that require a high

level of systemizing ability was studied. Special education data from 220 Washington

State School Districts was compared to “systemizing quotients” across fifteen eligible

metropolitan statistical areas. The hypothesis of the present study was that there should

be a significant positive correlation between female-only ASD prevalence and

systemizing quotients across seventeen geographically-defined metropolitan statistical

areas in Washington State. Systemizing quotients represented the prevalence of computer

and mathematical employment in each area. A second analysis compared area

systemizing quotients with autism prevalence calculated from sixty median-size school

districts. No correlation was found between autism prevalence and systemizing

occupations, rejecting this study’s hypothesis. This finding conflicts with at least two

other similarly-designed autism prevalence studies. Recommendations for future research

are discussed.

TABLE OF CONTENTSiii

Chapter Page

I INTRODUCTION .......................................................................................... 5

Autism Prevalence..................................................................................... 5 Assortative Mating..................................................................................... 6 Hyper-Systemizing..................................................................................... 7

II LITERATURE REVIEW ............................................................................... 9

Behavioral Description .............................................................................. 9 Natural History of ASD............................................................................ 10 Cognitive Models ..................................................................................... 14The Hyper-Systemizing Assortative Mating Hypothesis......................... 22

III METHODOLOGY........................................................................................ 28

Participants................................................................................................ 28 Measures................................................................................................... 28 Research Design ……….................................................................... ...... 31Methods………......................................................................................... 31

IV RESULTS ..................................................................................................... 33

General Analysis....................................................................................... 33 Size-Matched Analysis............................................................................. 35

V DISCUSSION ............................................................................................... 37

Study Limitations...................................................................................... 37 Conclusion and Recommendations for Future Research…...................... 38

REFERENCES ............................................................................................. 39

iv

CHAPTER I

INTRODUCTION

Autism Prevalence

Current prevalence estimates suggest that one in 54 boys and one in 252 girls are

identified with autism spectrum disorder (ASD) in the United States (Baio, 2012). The

number of students in the United States receiving special education services under the

autism disability category quadrupled from 93,000 in 2000-01 to 378,000 in 2009-10.

This population now accounts for about 5.8% of the U.S. students receiving special

education services (Scull & Winkler, 2011). Although increasing awareness and

broadening diagnostic criteria have certainly contributed to this increase of reported

ASD, an actual increase of ASD incidence cannot be ruled out (Baio, 2012; Golden,

2013; Newschaffer et al., 2007). About 76% of students with ASD require intensive

special education services, costing an average of over $8500 more each year than the

average student without ASD. Total societal cost for caring for children with ASD in the

United States was estimated to be over $9 billion in 2011 (Lavelle et al., 2014).

The specific causes of ASD have only been identified in 10-20% of cases and

include fragile X syndrome (Abrahams & Geschwind, 2008; Coplan, 2010), tuberous

sclerosis (Curatolo, Napolioni, & Moavero, 2010; Tye & Bolton, 2013) and variations in

genomic structure (Abrahams & Geschwind, 2008; Auerbach, Osterweil, & Bear, 2011).

The remaining 80% of ASD cases have been explained by several unproven and

competing hypotheses. Twin studies suggest a heritability estimate of around 90%, with

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HYPER SYSTEMIZING AND ASD 6

monozygotic twin concordance rates of around 60-95% and dizygotic twin concordance

rates of 0-23% (Gerdts, Bernier, Dawson, & Estes, 2013; Nordenbæk, Jørgensen, Kyvik,

& Bilenberg, 2014; Ritvo, Freeman, Mason-Brothers, Mo, & Ritvo, 1985; Steffenburg et

al., 1989). Taken together, these studies suggest that the majority of the risk of

developing ASD is due to variations in genetic structure.

If ASD is caused by randomly combined heritable variations in genetic structure,

the rapidly rising reported prevalence of ASD is difficult to explain. This has led

researchers to suggest that rates of ASD are possibly not actually increasing (Coo et al.,

2008) or that environmental risk factors may be responsible for this increase (Dietert,

Dietert, & DeWitt, 2011; Hertz-Picciotto et al., 2006; Volk, Hertz-Picciotto, Delwiche,

Lurmann, & McConnell, 2011). A third and most interesting possibility is that ASD

incidence is increasing and is primarily caused by genetic variability, but the way these

genes combine in a population is not entirely random (Baron-Cohen, 2006; Golden,

2013).

Assortative Mating

Assortative mating may suggest a causal relationship between sexual selection

and rising ASD prevalence. Positive assortative mating is the tendency for individuals to

mate with partners more phenotypically similar to themselves than if by chance (like

mates with like). This mating pattern has been observed across species to increase the

proportion of homozygous offspring and consequentially the percentage of individuals

found at the distribution extremes of any sexually-relevant trait. Societal changes that

deepen the mating pool and increase the ease of assortative mating include urban


expansion, increased college attendance rates and the prevalence of social media

(Golden, 2013).

The hyper-systemizing assortative mating theory of increasing ASD incidence

suggests that one such sexually selectable phenotype is level of systemizing ability

(Baron-Cohen, 2006). Systemizing (S) is defined as the drive to analyze, explore,

understand and construct systems based on discrete and causal relationships. This search

for change-governing laws is necessary to advance mathematics, physics, chemistry,

engineering, computer science, information technology, and all other disciplines that

discover and manipulate predictable systems (Baron-Cohen, 2006).

Hyper-Systemizing

While S is generally adaptive as a powerful way to predict system changes, the

hyper-systemizing theory of ASD posits that individuals with extremely high S levels

focus exclusively on systems with minimal variance (Baron-Cohen, 2003). Focus on only

entirely predictable systems may manifest as restricted and repetitive behaviors and

interests, and ignoring the unlawful systems of social interaction may result in the

interpersonal communication impairments of ASD (Baron-Cohen, 2009). Studies that use

questionnaires to quantify S levels within individuals have found approximally normal

distribution of S scores in the general population, with men tending to score higher than

women on average (Auyeung et al., 2009; Wright & Skagerberg, 2012). The hyper-

systemizing theory posits that common genetic variants and other biological factors are

responsible for setting the activation level of a biological mechanism dedicated to

systemizing (Baron-Cohen, 2006; Knickmeyer, Baron-Cohen, Raggatt, & Taylor, 2005).


If this S activation level is at least partially heritable and ASD is a result of hyper-

systemizing, increased assortative mating could partially explain rising prevalence of

ASD (Golden, 2013; Roelfsema et al., 2012).

The hypothesized link between S and ASD makes at least one testable prediction:

Populations employed in mathematical and computer-related occupations should have

higher rates of offspring identified with ASD than populations employed in non-technical

fields (Golden, 2013; Roelfsema et al., 2012). This study will compare the percentage of

students receiving special education services under the autism category with the

popularity of computer and mathematical occupations across seventeen geographical

areas in Washington State.


CHAPTER II

LITERATURE REVIEW

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder identified by

two core diagnostic features: Persistent deficits in social communication and interaction,

and restricted, repetitive patterns of behavior, interests, or activities (Baio, 2012;

Newschaffer et al., 2007). This behavioral method of identifying a neurobiological

condition does not identify the underlying etiology, so ASD is defined by different

professionals as a sensory processing disorder, a language disorder, a social disorder, a

behavioral disorder, a learning disorder, and a neurological syndrome (Coplan, 2010;

Happé & Ronald, 2008). Each of these perspectives highlights just one aspect of ASD,

but the typically shared behavioral symptoms suggest a core physiological cause.

Behavioral Description

The best way to get a sense for how a child with ASD behaves is to read excerpts

from the original description of 11 children with autism by Leo Kanner (1943):

“There was a marked limitation of spontaneous activity. He wandered

about smiling, making stereotyped movements with his fingers, crossing

them about in the air” (p. 219).

“Most of his actions were repetitions carried out in exactly the same way

in which they had been performed originally” (p. 219).

“But he was never angry at the interfering person. He angrily shoved

away the hand that was in his way or the foot that stepped on one of his

block” (p. 220).


“Many of his replies were metaphorical or otherwise peculiar. When asked

to subtract 4 from 10, he answered: “I’ll draw a hexagon” (p. 222).

“There was a marked contrast between his relations to people and to

objects. Upon entering the room, he instantly went after objects and used

them correctly” (p. 227).

“A pin prick resulted in withdrawal of her arm, a fearful glance at the pin

(not the examiner), and utterance of the word “Hurt!” not addressed to

anyone in particular” (p. 229).

“He did not respond to the simplest commands, except that his parents

with much difficulty elicited bye-bye, pat-a-cake, and peek-a-boo

gestures, performed clumsily. His typical attitude toward objects was to

throw them on the floor” (p. 238).

The behavioral components of ASD must be present in the early developmental

period and cause clinically significant impairment in social, occupational, or other

important areas of current functioning (Newschaffer et al., 2007).

The Natural History of ASD

Behavioral descriptions communicate the impact of ASD but are not sufficient to

describe or predict its course through time. The natural progression of any untreated

medical condition is referred to as the condition’s natural history. For example, the

natural history of Alzheimer’s disease begins with very minor changes in memory or

cognition. As the disease progresses it becomes more difficult for the individual to recall

recent events and they may become angry or withdrawn. The final stages of Alzheimer’s


include sleep disturbances, significant lapses in memory, difficulty in eating,

incontinence, lack of awareness, loss of speech and major personality and behavioral

changes (Wilson et al., 2012).

As with many neurodevelopmental disorders, ASD is not static and its

presentation evolves throughout an individual’s lifespan. When compared to a

degenerative disease like Alzheimers, however, the natural history of ASD is more

encouraging: In nearly all cases, the natural history of ASD is toward a higher level of

independent functioning and an overall reduction in behavioral symptomatology through

time (Coplan, 2010). While some children make very slow or almost undetectable

progress, others may eventually no longer exhibit the level of impairment required for

ASD identification. It is important to note that there is a significant difference between no

longer meeting diagnostic criteria and no longer experiencing impairment, but the fact

remains that a percentage of individuals diagnosed with ASD show a remarkable

reduction of ASD symptomology through time.

The speed at which these symptoms fade is highly variable and dependent on

several factors (Newschaffer et al., 2007). Dr. James Coplan created a three dimensional

model that uses the individual’s initial degree of atypicality and general level of

intelligence to predict the speed of this progression (Coplan, 2010). Understanding this

model requires us to understand the three dimensions used: atypicality, intelligence and

time.


Dimension 1: Atypicality

The dimension of atypicality is behaviorally defined and simply labels the degree

of atypicality in comparison to same-age neurotypical peers. An individual with mild

atypicality may have behavioral presentations similar to the historical description of

Asperger syndrome, while an individual with severe atypicality may present with

behaviors typical of classic autism (Coplan, 2010). This behavioral manifestation of ASD

defines the current level of atypicality and these behaviors may be objectively observed

and compared to the neurotypical population. The DSM-V even requires ASD to be

labeled with severity from Level 1 (requiring support) to Level 3 (requiring very

substantial support).

Dimension 2: General Intelligence

General intelligence is likely the single best predictor of ASD’s natural history

(Coplan, 2010; Newschaffer et al., 2007). Intelligence has been defined as a “very

general mental capability that, among other things, involves the ability to reason, plan,

solve problems, think abstractly, comprehend complex ideas, learn quickly and learn

from experience” (Gottfredson, 1997). Because children with ASD often show significant

variance in the specific abilities defined collectively as intelligence, it is important to

understand the difference between at least two of the broad abilities intelligence tests are

designed to measure.

Fluid intelligence. Fluid intelligence is the ability to reason with logic, find

solutions to novel problems, and identify relationships or patterns that can be

extrapolated with logic to new situations (Geary, 2005). Individuals with high levels of


fluid intelligence are skilled in recognizing patterns and making meaning out of

confusion. Fluid intelligence typically peaks in early adolescence then begins to decline

with age. Damage to the prefrontal cortex severely disrupts fluid intelligence (Geary,

2005).

Crystalized intelligence. Crystalized intelligence is the ability to store and

readily retrieve learned information from long term memory (Coplan, 2010). This

includes the retrieval of vocabulary definitions or math facts, the ability to reason with

words or numbers and the individual’s general fund of stored information. Although

crystalized intelligence remains relatively unaffected by prefrontal brain damage, the

individual’s ability to use their crystalized knowledge in novel situations is frequently

impaired (Geary, 2005). Crystalized intelligence tends to increase with age as we absorb

factual data and use our fluid intelligence to learn from our environment.

Dimension 3: Time

While the nature of time may make for fascinating dinner conversation among

theoretical physicists, Coplan’s model simply uses time as a dimension in which events

can be ordered from the past through the present and into the future. Coplan’s natural

history model summarizes what ASD intervention research (Ben-Itzchak & Zachor,

2007) has consistently shown: The higher the individual’s nonverbal IQ, the more

quickly and completely the individual’s atypicality will decrease through time. The

following is paraphrased from Coplan’s (2010) descriptive metaphor: The degree of

atypicality is the size of a block of ice in water. IQ is the temperature of the water that


may melt the ice (p. 132). The natural history model is useful to describe and predict the

behavioral course of ASD through time.

Cognitive Models

While the natural history model is descriptively useful to predict behavior change

through time, it does not suggest possible biological differences responsible or correlated

with these behavior changes. Efforts to link behavior to biology include cognitive models

that describe fundamental cognitive processes required or associated with specific

behavior patterns. Cognitive models seek to help us understand the underlying

differences among individuals with ASD by attributing the many observed behavior

differences to one or more core differences in underlying mental processes. Three

popular models used to describe cognitive differences among individuals with ASD

include the Weak Central Coherence (WCC) theory, Theory of Mind (ToM) theory, and

the Empathizing-Systemizing (E-S) theory (Baron-Cohen, 2009).

Weak Central Coherence

The weak central coherence (WCC) theory predicts that while individuals with

ASD may perform better than controls on tasks requiring detail-focused processing (Shah

&Frith, 1993), they also show delays on tasks that require global processing: “He was

extremely upset upon seeing anything broken or incomplete” (Kanner, 1943, p. 238). In

other words, while most neurotypical individuals effortlessly integrate details into a

generalized concept, those with ASD tend to focus on the details and may consequently

miss the “big picture” (Frith, 1989).


Because it is not necessarily true that a person with strong local processing skills

must also have weak global processing skills, the WCC theory has recently emphasized

the observed strength or tendency of local processing among individuals with ASD

(Baron-Cohen, 2009) rather than a possible deficit in global processing (Happé & Frith,

2006). Several studies have found a preference for local processing but no delays in

global processing when instructed to report global properties of stimuli (Mottron, Burack,

Iarocci, Belleville, & Enns, 2003). This may suggest that individuals with ASD do not

have an inability to process globally, but prefer to process information locally during

free-choice tasks (Koldewyn, Jiang, Weigelt, & Kanwisher, 2013).

The WCC theory is useful because it offers an explanation for the attention to

detail, narrow focus of knowledge or even exceptional specific abilities often found in the

ASD population. This extreme focus on details may even be the origin of many of the

sensory hypersensitivity issues found in the ASD population. Several researchers (Baron-

Cohen, Happe, and Frith) have proposed that the WCC theory may be modified to link

itself with the neurological connectivity theory of ASD. The connectivity theory suggests

that ASD is the result of an increased number and density of connections between local

cell groups and a decreased number of connections between more distant brain areas

(Baron-Cohen, 2008).

Theory of Mind

The WCC theory may offer an explanation for narrow or obsessive interests but it

does not explain deficits in communication and social reciprocity: “He never looked up at

people’s faces. When he had any dealing with persons at all, he treated them, or rather


parts of them, as if they were objects” (Kanner, 1943, p. 228). Uta Frith, Alan Leslie and

Simon Baron-Cohen proposed that the social and communication deficits central to ASD

may be the result of an impaired Theory of Mind (ToM) (Baron-Cohen, Leslie, & Frith,

1985).

Theory of Mind (ToM) is defined as the ability to attribute mental states to

oneself and others while understanding that others can have different desires, beliefs,

intentions, or knowledge than the self does. This includes the ability to infer motives,

predict behavior, and empathize with the hypothesized mental state of others (Baron-

Cohen, 1995). Stephen Pinker demonstrated how we use this “mindreading” or “folk

psychology” mechanism with the following example: “Woman: I’m leaving you. Man:

Who is he?” (1994, p. 227). The reader can only make sense of this dialogue if the

thought “She must be leaving me for another man” is attributed to the man participating

in the conversation. If no inference is made about the man’s mental state before his

response, this dialogue appears disjointed and meaningless: “He seemed unable to

generalize, to transfer an expression to another object or situation” (Kanner, 1943, p.

219). Children with ASD have been shown to fail tasks that require them to predict the

beliefs of others (e.g., the Sally-Anne test) at an older mental age (as measured by IQ)

and at a significantly higher rate than both neurotypical controls and children with Down

syndrome (Baron-Cohen, 1995).

Theory of mind mechanism. Just as most people use a color-vision system to

interpret wavelengths of light into the mentalistic attribute of specific colors, the Theory

of Mind Mechanism (ToMM) is hypothesized to be a specialized brain module that


interprets observed behaviors and eye movements of self-propelled organisms into

predictions of the observed agent’s knowledge, mental state, belief or intention (Baron-

Cohen, 1995). Functional neuroimaging studies have identified “social brain” areas

(medial prefrontal cortex, temporal parietal junction, anterior cingulate, insula, and

amygdala) that are active during “mind-reading” tasks but less active in the autistic brain

when compared to controls (Baron-Cohen, 2009). This may suggest possible locations in

the brain for ToM to exist.

Empathizing-Systemizing Theory

The empathizing-systemizing theory explains social and communication deficits

of ASD as a result of impaired empathizing (E), while narrow interests, repetitive

behavior and need for sameness are explained as superior systemizing (S). This two-

factor model posits that E and S are adaptive cognitive abilities but the discrepancy

between these factors predicts the likelihood of developing ASD (Baron-Cohen, 2008;

Baron-Cohen, 2009).

Empathizing. Empathizing (E) refers to understanding and appropriately reacting

to the emotions of others. Empathizing includes ToM but differs in that E is not just a

probabilistic calculation of another’s mental state; it is also an emotional reaction

triggered by another person’s emotion. This reaction can help the empathizer understand

the other person, predict their behavior, or connect with the person emotionally (Baron-

Cohen, 2003). A person with strong E would effortlessly read the emotional environment

through vocal tone, eye contact and body language, responding with appropriate emotion

in each context. A strong empathizer socially interacts from the perspective that his or her


own view may not be the only or the correct view and that understanding another

person’s perspective is important. People with strong E constantly perceive subtle shifts

in mood and react to these emotions because they connect with the other person. E differs

from ToM in that E is not just a cold calculation of an observed agent’s internal state: It

includes the emotional response commonly called affective empathy (Baron-Cohen,

2003; Baron-Cohen, 2008).

Mirror neurons. The construct of E is closely related to the function of the

proposed human mirror neuron system (MNS). Mirror neurons are proposed to be

neurons similarly activated when performing a behavior and when watching someone

else perform the same behavior. Neurons specialized to recognize human faces often

overlap with these MNS pathways, providing a physiological explanation for how

observing facial expressions may trigger similar internal states in the observer (Dapretto

et al., 2006). This affective change in internal state may assist a person in making

accurate assumptions about the cognitive and emotional state of an agent they are

observing (Oberman & Ramachandran, 2007).

If the MNS facilitates understanding other agents’ internal states, individuals who

have difficulty understanding and connecting with the internal states of others would be

predicted to have lower levels of MNS activity. Electroencephalographic mu rhythm

recordings have found that women have higher levels of proposed MNS activation than

men when observing hand movements (Yawei et al., 2008) and pictures of humans

engaged in simple actions (Proverbio, Riva, & Zani, 2010), on average. Because no

differences were observed in MNS activation when the participants observed a moving


dot, it is possible that women only have a greater MNS response to stimuli with a human

interaction component (Cheng et al., 2008). At least one study showed significantly lower

MNS activation (via electromyography readings) among individuals with ASD compared

to control subjects when watching video clips of a static hand or hand movement

(Enticott et al., 2012), and a fMRI study showed no MNS activation in the inferior frontal

gyrus among a group of children with ASD when imitating and observing emotional

facial expressions (compared to high MNS activation in this area in the control group

(Dapretto et al., 2006). Taken together, these studies suggest that typical males have a

lower MNS activation than typical females and individuals with ASD have an even lower

MNS activation than typical males when watching goal directed object related

movement.

Systemizing. While empathizing is most effectively used in the interactive

process between agents capable of self-directed movement (e.g. humans, animals),

systemizing (S) is the process of the mind most applicable to rule-governed aspects of the

environment (e.g. technology, social hierarchies, math). Systemizing is defined as the

biologically-based drive to identify the lawful relationships governing predictable

systems. Observation of input-operation-output relationships is used to detect structure in

data, predict change and construct new rule-governed systems. Major kinds of systems

include numerical systems, mechanical systems, natural systems, social systems, and

motoric systems (Baron-Cohen, 2003). The common theme of all of these systems is that

they all operate on inputs and outputs using “if-then” correlational rules that allow the

behavior of these inanimate systems to be predicted. The goal of a person with high S is


to discover the underlying rules that govern a system in order to understand, predict, and

possibly discover or create a new one: “When he was 1½ years old, he could discriminate

between eighteen symphonies. He recognized the composer as soon as the first

movement started. He would say ‘Beethoven’” (Kanner, 1943, p. 236).

The action of systemizing first involves analyzing each part of a system that can

vary, followed by systematic observation of what happens when each feature is varied.

Repeated observation allows the systemizer to discover rules governing each part of a

system and eventually understand the system as a whole. Understanding each component

of a lawful system allows the systemizer to predict and control behavior of all other

systems that use the same laws. This understanding may eventually allow the systemizer

to construct new systems through novel application and combination of these highly

predictable system components (Baron-Cohen, 2006; Baron-Cohen, 2008; Baron-Cohen,

2009).

ASD as hyper-systemizing. The hyper-systemizing theory posits that the narrow

interests, restrictive interests and repetitive behaviors associated with ASD are a direct

result of an over-reliance or over-activation of a biologically determined systemizing

mechanism. This mechanism is activated at different levels across individuals, driving the

brain to look for lawful input-operation-output relationships (Baron-Cohen, 2006). In

support of the idea that individuals with ASD may be hyper-systemizers, a large group of

individuals with ASD have been shown to outperform a comparison group of typically-

developing individuals on a variety of physics tests (Paganini & Gaido, 2013). When

using a questionnaire designed to measure S level (Systemizing Quotient) people with


ASD consistently obtain higher scores (stronger drive to systemize) than the general

population (Auyeung et al., 2009; Baron-Cohen, Richler, Bisarya, et al., 2003), and one

study found 34% of students in college with ASD chose Science, Technology,

Engineering and Mathematics (STEM) majors, significantly higher than the 23% of

general education students who choose STEM majors (Wei, Yu, Shattuck, McCracken, &

Blackorby, 2013). While human interaction may be anxiety-provoking or simply

uninteresting to individuals with ASD, these same individuals are often drawn to rule-

governed systems like mathematics and computers. In fact, sixteen percent of college

students with ASD chose computer science as their major compared with seven percent

of the general population (Wei et al., 2013). High functioning adults with ASD usually

spend their time learning as much as they can about a very specific interest, preferring to

read factual books than novels and watching documentaries rather than interpersonal

dramas. According to Cohen, the focused islets of ability often seen among individuals

with ASD can always be related back to a specific type of predictable system (Baron-

Cohen, 2009). This exclusive focus on entirely predictable systems is the hyper-

systemizing theory’s explanation of the “anxiously obsessive desire for the maintenance

of sameness” described in Kanner’s original descriptions of autism (1943, pp. 245) and

Baron-Cohen even suggests that the “self-stimming” behavior seen in many low-

functioning individuals with ASD is an observable example of deriving pleasure from a

predictable world (Baron-Cohen, 2006; Baron-Cohen, 2008).

Like conducting a well-controlled experiment, the hyper-systemizing theory

posits that individuals with ASD try to make sense of their world by observing and


modifying only one variable at a time. Unpredictable events are confounding variables

that impair the individual’s ability to find lawful and predictable patterns in their world.

The hyper-systemizing theory is more encouraging than the WCC theory because it does

not assume the individual will always miss the “big picture.” Given the opportunity to

observe and control all of a system’s variables, the hyper-systemizing theory suggests

that an individual with ASD may eventually achieve an excellent understanding of a

whole system (Baron-Cohen, 2008).

The Hyper-Systemizing Assortative Mating Hypothesis

The hyper-systemizing assortative mating hypothesis provides an explanation for

how ASD could be both genetically-influenced and increasing in incidence through time

(Golden, 2013). This relatively new and controversial theory is based on the following

assumptions:

Assortative mating on S level increases S level variance in the population.

Hyper-systemizing results in ASD.

Assortative Mating

Assortative mating occurs when individuals with similar genotypes or phenotypes

mate more frequently with each other than would be expected from chance (Zietsch,

Verweij, Heath, & Martin, 2011). This nonrandom mating pattern increases the

proportion of homozygous offspring and the percentage of the population at the tail-end

extremes of any sexually-relevant and partially heritable trait (Keller et al., 2013). For

example, individuals tend to mate with partners of a similar height and there is now a

larger percentage of very short and very tall people in the U.S. compared to fifty years


ago. While improved nutrition may explain the mean increase in population height over

time, increased variance on both tail end extremes of the height distribution is better

explained by the increased ease of assortative mating. Higher college attendance rates,

especially among women, have given individuals a greater chance of meeting a more

similar mate. The population shift toward urban residence deepens the mating pool and

the societal trend to delay childbirth increases the available number of mating partners to

choose from (Golden, 2013). Social media and dating websites even use algorithms to

connect individuals with similar interests and personalities, making assortative mating

more specific and potentially impactful than ever before. Assortative mating in humans

has been shown for age, religiosity, intelligence, physical traits, attitudes and level of

education (Zietsch, Verweij, Heath, & Martin, 2011).

The hyper-systemizing assortative mating hypothesis posits that one such

sexually-relevant trait is level of S ability. Social and nonsocial behavioral differences

typical of ASD have been consistently noted in the extended families of individuals with

ASD, providing evidence of a heritable broader autism phenotype (Gerdts & Bernier,

2011; Gerdts, Bernier, Dawson, & Estes, 2013; Hoekstra, Bartels, Verweij, & Boomsma,

2007; Klei et al., 2012; Nishiyama, Notohara, Sumi, Takami, & Kishino, 2009). To

examine if assortative mating is happening for this phenotype, a study compared ratings

of twin pairs and their parents with a well-researched quantitative measure of social

impairments related to ASD (Social Responsiveness Scale [SRS]) and found a correlation

of .38 between parent SRS scores (Constantino & Todd, 2005). Although this scale’s

primary focus concerns social impairments, not S strengths, this may suggest assortative


mating for social interaction styles typical of high-systemizers. In addition, the children

of parents with the highest quartile of SRS scores had mean SRS scores about 1.5

standard deviations higher than the children of the remaining parent groups. This

suggests that the genetic contribution from both sides of the family may combine to result

in offspring with even more pronounced behaviors associated with ASD (Constantino &

Todd, 2005). Further research needs to be conducted in this area, but S does not even

have to be an overtly selected phenotype for assortative mating to increase: A greater

percentage of women are obtaining advanced degrees and employed in high-S science,

technology, engineering and math (STEM) fields than ever before, and this increases the

interaction probability between potential mating partners with similarly high S levels.

About ten percent of men and six percent of women between 30 and 35 in the US have a

college degree in one of the hard sciences, and three percent of married couples in this

age range both have hard science degrees (Golden, 2013). Because systemizing careers

are generally more lucrative than non-systemizing occupations, there may also be a

reproductive advantage for high systemizers.

Hyper-Systemizing Results in ASD

The hyper-systemizing assortative mating theory suggests high S runs in families

and that there are genes associated with this tendency to systemize. It also assumes that

ASD is in some cases an extreme presentation of normally distributed S variation (Baron-

Cohen, 2006). The genetic contribution from two parents with high-S levels may result in

a hyper-systemizing profile and a clinical diagnosis of ASD. In support of this theory,

Simon Baron-Cohen and co-authors have found a higher prevalence of ASD among


Cambridge University math majors and their relatives when compared to sex-controlled

students (Baron-Cohen, Wheelwright, Burtenshaw, & Hobson, 2007). Another of Baron-

Cohen’s studies found that the fathers of children with ASD are over-represented in

engineering fields, and that the grandfathers of children with ASD are over-represented in

the field of engineering on both the paternal and maternal side (Wheelwright & Baron-

Cohen, 2001). Although most of Baron-Cohen’s studies focus exclusively on high-

functioning ASD (Previously called Asperger syndrome), this theory makes a testable

prediction about ASD prevalence in general: Areas that employ a higher percentage of

STEM employees should have higher rates of ASD.

Systemizing Occupations and ASD Prevalence

The first population-based study to test the connection between high-S

occupations and ASD compared ASD prevalence in three regions in the Netherlands:

Eindhoven, Haarlem, and Uttecht. Schools in the area that employed the greatest

percentage of employees in the information technology field (Eindhoven, 30%) reported

a significantly higher ASD prevalence than the other two regions (16% and 17%

employed in IT, respectively), possibly supporting the hyper-systemizing theory

(Roelfsema et al., 2012). A second population-based study examined the correlation

between systemizing occupations and ASD prevalence across 1337 census block groups

in a five county area of Metro Atlanta. This study found a significant positive correlation

(0.289) between “average math importance” and ASD prevalence across census tracts

(Golden, 2013).


Correlational prevalence studies cannot prove what causes ASD but they may

suggest potential risk factors. Modifying these hypothesized risk factors through other

design types may lead researchers to find causal and modifiable factors associated with

ASD risk, the logical first step toward ASD prevention. Suggesting possible risk factors

is done by identifying specific population variables that correlate with increased ASD

prevalence or by comparing the predictions of a causal theory to available prevalence

data.

The present study is designed to test a prediction suggested by the hyper-

systemizing assortative mating theory: Populations with higher rates of employment in

mathematical and computer-related occupations should have higher rates of offspring

identified with ASD than populations employed in non-technical fields (Golden, 2013;

Roelfsema et al., 2012). While the first population-based study of this kind (Roelfsema et

al., 2012) was only able to obtain data from about 56% of schools in three geographic

areas, the present study utilizes data from nearly every Washington State public school

district. The only other large-scale study of this type examined 2098 cases of ASD across

1337 area categories (Golden, 2013) but did not differentiate between male and female

ASD prevalence. Because most females with ASD are lower-functioning and lower-

functioning ASD is more easily identified (Golden, 2013), examining ASD prevalence in

females only should reduce variability associated with the quality of local diagnostic

services. Reducing this diagnostic variability should result in a more reliable comparison

of ASD prevalence across geographic areas. The hypothesis of the present study was that

there should be a significant positive correlation between female-only ASD prevalence


and computer/mathematical occupation employment ratios across seventeen

geographically-defined metropolitan statistical areas in Washington State.


CHAPTER III

METHODOLOGY

Participants

Washington State is divided into seventeen metropolitan and micropolitan

statistical areas (MSAs) by the U.S. Office of Management and Budget (OMB) (“May

2013,” 2013). Each geographic area was considered as a possible “participant” in this

study. MSA boundaries are determined by county lines but may include more than one

county per area. Because this study focused on Washington State ASD prevalence, MSAs

that included counties outside of Washington State were removed from this analysis.

Fifteen MSAs were entirely contained within Washington State and included in the

following analysis.

Measures

For each calculated correlation, two quantitative independent variables were

assigned to each eligible MSA. The first independent variable was each MSA’s

designated location quotient for the computer and mathematical major occupational

group. This “Systemizing Quotient” was used as an estimate of high-S occupation

prevalence within each MSA. The second independent variable was the percentage of

female, male, or total students in each MSA receiving special education services under

the autism eligibility category. Each MSA’s ASD prevalence was first calculated using

all available district data. A “size-matched” analysis calculated ASD prevalence using

only data from sixty school districts clustered around the Washington State total student

enrollment mean.


Location Quotient

A location quotient is the MSA’s ratio of a certain occupation’s share of

employment relative to the national average. For example, a location quotient of 2 would

indicate that the MSA contains twice the national percentage of individuals in the

specified occupation. A location quotient of 0.5 would indicate that the occupation is

underrepresented by a factor of two. Location quotients are calculated by the United

States Department of Labor’s Occupational Employment Statistics (OES) survey, a

cooperative program between the Federal Bureau of Labor Statistics (BLS) and State

Workforce Agencies (SWAs). This survey requests data from a sample of 200,000

employers every six months to estimate the percentage of the population employed in 22

major occupational groups for each MSA (“Occupational employment statistics,” 2014).

Location quotients for the computer and mathematical major occupational group

were obtained for each Washington State MSA using the most current available online

data from the OES survey (“Location quotients,” 2013). This major occupational group

was selected to estimate high-S employment because mathematical ability is a good

estimate of S ability (Golden, 2013) and the most prevalent STEM occupations that

require S are related to computers (Cover, Jones & Watson, 2011). As shown in Table 1,

this systemizing quotient (SQ) varied from 0.24 to 2.87 across MSAs (“Location

quotients,” 2013).


ASD Prevalence Estimates

Washington State requires every public school district to submit a detailed count

of student enrollment to the Washington State Office of the Superintendent of Public

Instruction (OSPI) on the first of each month. The total number of male and female

students enrolled in each Washington State School District as of November 1st, 2012 was

Table 1.

Popularity of high-systemizing occupations across Washington State MSAs.

MSA SQ Boundary Delineation (county or counties)1 0.24 Cowlitz2 0.26 Grays Harbor; Lewis; Mason; Pacific; Wahkiakum3 0.27 Yakima4 0.3 Adams; Grant; Kittitas; Klickitat; Okanogan5 0.33 Columbia Ferry; Garfield; Lincoln; Pend; Stevens;

Walla- Walla; Whitman6 0.36 Chelan; Douglas7 0.4 Skagit8 0.46 Clallam; Island; Jefferson; San Juan9 0.49 Pierce10 0.61 Whatcom11 0.67 Benton; Franklin12 0.69 Kitsap13 0.75 Spokane14 1.75 Thurston15 2.87 King; Snohomish

Note. Systemizing Quotient (SQ) = Computer and mathematical major

occupational group location quotient. Location quotients and metropolitan

statistical area (MSA) geographic boundary delineations as of May 2013

were retrieved from http://www.bls.gov/oes/current/map_changer.htm and

http://www.bls.gov/oes/2013/may/msa_def.htm


obtained from OSPI via special request, along with the number of male and female

students in each district receiving special education services under the autism disability

category. All relevant district data sets were grouped into geographically corresponding

MSAs. Male-only, female-only, and total ASD prevalence estimates were calculated by

dividing the total number of students receiving special education services under the

autism disability category in each MSA by the total number of students in each MSA.

This study did not use data from school districts that had schools located in more than

one MSA.

Research Design

A non-experimental correlational design was used to determine the Pearson

correlation between MSA systemizing quotients and each of three ASD prevalence

estimates (male, female, and total ASD prevalence). Each MSA was graphed where: X =

location quotient for the computer and mathematical major occupational group; Y1 = the

percentage of female students receiving special education services under the autism

category; Y2= the percentage of male students receiving special education services under

the autism category; Y3 = the percentage of total students receiving special education

services under the autism category.

Size-Matched Analysis

Differences in district-reported ASD prevalence may only suggest actual

differences in ASD incidence if equivalent identification and service delivery practices

are assumed. Because district resources are highly dependent on student enrollment

counts, a subset of sixty similarly-sized school districts was analyzed through a size-


matched analysis. This selection of similarly-sized school districts was made to reduce

confounds associated with differential diagnostic resources in very small and very large

school districts. Size-matched school districts were selected by first ranking all school

districts by total student enrollment count and calculating the mean number of students

per district. The thirty districts directly ranked on either side of the total student mean

were grouped into their respective MSAs and average male, female and total ASD

prevalence was calculated for each area.

A non-experimental correlational design was used to determine the Pearson

correlation between MSA systemizing quotients and size-matched ASD prevalence

(male, female, and total). Female-only ASD prevalence should be the least influenced by

differential access to diagnostic resources because females with ASD are typically lower-

functioning and easier to identify: Females with ASD are more likely than males to have

a recorded intellectual disability (46% vs. 37%) and female prevalence estimates are less

variable across U.S. geographic areas (Baio, 2012). For these reasons, the correlation

between size-matched female ASD prevalence and MSA systemizing quotients is

considered the most reliable indicator of the relationship between high-systemizing

occupations and ASD prevalence.


CHAPTER IV

RESULTS

District data obtained from OSPI reported 768,235 students enrolled in 303

Washington State Public School Districts as of November 1st, 2012. Student counts for

districts with fewer than ten students (28 school districts) were reported as N=<10 by

OSPI and removed from the district data set before analysis. Also removed from analysis

were six school districts that did not report special education enrollment data (a total of

526 students were enrolled in these districts) and 37 school districts that contained

schools in more than one MSA. A total of 220 Washington State school districts met the

criteria to be included in the first correlational analysis.

One final modification was made to the data before analysis. Data obtained from

OSPI indicated that Tacoma Public Schools served 253 students with autism during the

2012-2013 school year but had only two general education students enrolled. This error

was corrected by retrieving the 2012-2013 Tacoma Public Schools enrollment data from

the Tacoma Public Schools website. Total enrollment for this district was reported to be

28,909 students as of October 2012 (“October 3, 2012-Enrollment,” 2013).

General Analysis

The fifteen Washington State MSAs in the first correlational analysis included a

total of 691,883 students ages 3-21 as of November 1st, 2012. The total percentage of

students receiving special education services under the autism disability category in these

220 districts was 1.28 percent. A total of 2.09 percent of male students and 0.42 percent

of female students were identified as receiving special education services under the


autism disability category (5.37 males per female). A total of 8,873 students with

reported ASD were included in this analysis.

The percentage of students receiving special education services under the autism

category in these 220 Washington State school districts was higher than the 2009-10 U.S.

average (7.96 percent vs. 5.8 percent). These districts also reported a higher percentage of

total students enrolled in special education (16.1 percent vs. 13.8 percent) (Scull &

Winkler, 2011). As shown in Figure 1, the correlation between MSA systemizing

quotients and male ASD prevalence was not significant, r(14) = 0.363, p = .092. The

correlation was also not significant when using female-only ASD prevalence, r(14) =

0.183, p = .257 or total ASD prevalence, r(14) = 0.327, p = .117.

0 0.5 1 1.5 2 2.5 30.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

R² = 0.106825004458608

R² = 0.132007970208057

R² = 0.0334392130417031

FemaleLinear (Female)MaleLinear (Male)Total

Systemizing Quotient

ASD

Pre

vale

nce

Figure 1. ASD prevalence and systemizing quotients in fifteen Washington State

metropolitan statistical areas (MSAs). ASD Prevalence = percentage of students

receiving special education services under the autism disability category.

Systemizing Quotient = MSA location quotient for the computer and


mathematical major occupational group (as per OES survey data;

www.bls.gov/oes/).

Size-Matched Analysis

The sixty school districts clustered around the 220-district student enrollment

mean ranged from 1478 students to 7907 students per district (mean = 3,145 students).

Only fourteen MSAs were included in this analysis because the MSA composed of

Franklin and Benton County did not have a school district in this size range. These 60

districts represented a total of 219,458 total students and about 29 percent of the total

Washington State student population as of November 1st, 2012.

The total percentage of students receiving special education services under the

autism disability category in these 60 districts was the same as in the 220 districts

analyzed in the general analysis (1.28 percent). The male/female ASD ratio was also

similar (5.09 males per female). A total of 2,350 males and 462 females were identified

with ASD in these sixty districts (7.13 percent of total special education counts).

As shown in Figure 2, the size-matched correlation between MSA systemizing

quotients and male ASD prevalence was not significant, r(13) = 0.198, p = .249 . The

correlation was also not significant when using female-only ASD prevalence, r(13) =

0.065, p = .413 or total ASD prevalence, r(13) = 0.170, p = .281.


0 0.5 1 1.5 2 2.5 30.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

R² = 0.0288313243049533

R² = 0.039169945278853

R² = 0.00420466044818801

FemaleLinear (Female)MaleLinear (Male)Total

Systemizing Quotient

Mat

ched

-siz

e A

SD P

reva

lenc

e

Figure 2. Matched-size ASD prevalence and systemizing quotients in fourteen

Washington State metropolitan statistical areas (MSAs). Matched-size ASD

Prevalence = percentage of students in similarly-sized school districts receiving

special education services under the autism disability category (60 districts total).

Systemizing Quotient = MSA location quotient for the computer and

mathematical major occupational group (as per OES survey data;

www.bls.gov/oes/).


CHAPTER V

DISCUSSION

The correlation between ASD prevalence and the computer and

mathematical location quotient assigned to each MSA was not significant (p < .05) in any

of the six total conditions. This effectively means that there was no evidence of a

correlation between computer and mathematical occupational employment ratios and

male, female, or total ASD prevalence at the MSA level in Washington State. This result

effectively rejected the hypothesis of this study.

This finding conflicts with two other population-based studies that found

significant correlations between the prevalence of high-S occupations and reported ASD

(Golden, 2013; Roelfsema, et al., 2012). While the first population-based study of this

kind (Roelfsema et al., 2012) was only able to obtain data from about 56% of schools in

three geographic areas, the three areas selected for analysis were chosen because it was

already known that one of the areas had a significantly higher proportion of the

population employed in information technology occupations. The only other large-scale

population-based study of this type examined 2098 cases of ASD across 1337 area

categories (Golden, 2013), allowing a comparison of much smaller areas than the MSAs

used in the present study.

Study Limitations

A possible limitation of this study was that computer and mathematical

employment only represented a small percentage of total employment in each MSA (0.7

to 8 percent of total employment). Although the computer and mathematical location


quotient was theorized to estimate high-S employment in general, it is possible that other

occupation types that require a high level of S ability were distributed with differing

prevalence ratios across MSAs. If verified, this possibility could call into question the

sole use of computer and mathematical location quotients as high-S employment

approximations.

A second limitation of this study was the lack of geographic specificity that

resulted from using such large MSAs. By combining a large number of district data sets

within each MSA it is possible that localized differences in ASD prevalence were

statistically eliminated during analysis. Because this study only used one systemizing

quotient for each MSA, any variability in high-S employment prevalence within MSAs

was also eliminated.

Recommendations for Future Research

Future research could correlate the obtained ASD prevalence data with more

precise measures of high-S occupation prevalence. A possible confound was that

computer and mathematical occupation prevalence is not necessarily identical to the

prevalence of all high-S occupations. Future research could account for this by using

additional occupation groups assumed to require a high level of S ability (e.g.,

engineering) to derive a more comprehensive estimate of high-S employment in each

area. Because it is also possible that there was significant variability in high-S

employment concentrations within MSAs, future research could compare district ASD

prevalence with high-S occupations across more geographically precise county, city, or

census tract areas.


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