Autism at the behavioural level

Autism: A New Introduction to Psychological Theory and Current Debate - Tiffany Watt Smith 2019


Autism at the behavioural level

ALTHOUGH WE KNOW autism has a genetic foundation, leading to neurobiological differences (see Chapter 4), it is diagnosed on the basis of a set of behaviours. The key reason for this is that, despite attempts, no reliable biological marker has been found. A reliable marker must show adequate sensitivity and specificity: meaning that it must be found in virtually all members of a group, and it must be exclusive to that group. At the moment, the best biomarker candidates we have for autism resemble attempts to identify which UK region you are from based on hair colour. Yes, there are group differences in prevalence of red hair between Scotland and other UK nations, but red hair is not found in a large enough percentage of the Scottish population to be sensitive and is found too widely elsewhere to be specific.

Our reliance on behaviour to identify autism leads to challenges for the field, as we will see in this chapter and beyond. Interpreting changing prevalence estimates or identifying meaningful sub-groups is extremely hard given the variability in how behavioural diagnostic features have been defined over time, and the potential differences in how they are applied in different settings.

1. Current and changing diagnostic criteria

The current diagnostic criteria, in both the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; APA, 2013 — see Table 3.1) and the forthcoming 11th edition of the International Classification of Diseases (ICD-11; WHO, 2018) specify only one category for autism. This is termed “Autism Spectrum Disorder” (ASD) in recognition of the variability of presentation. The use of the word ’disorder’ as part of the diagnostic terminology is rejected by many autistic people, who emphasise that autism is a natural part of variation in the human population. For this reason, we refer to autism, rather than ASD, in this book. Nonetheless, for a diagnosis, the current pattern of traits or symptoms must be significantly impairing for the individual in at least some important aspects of life. It is important to recognise that when we say a trait is “impairing”, we mean that in the context of a world largely designed by and for neurotypical people (for more on this, see Chapters 9 and 10).

Table 3.1 DSM-5 criteria for Autism Spectrum Disorder

Autism Spectrum Disorder 299.00 (F84.0)

Diagnostic Criteria

A. Persistent deficits in social communication and social interaction across multiple contexts, as manifested by the following, currently or by history (examples are illustrative, not exhaustive, see text):

1. Deficits in social-emotional reciprocity, ranging, for example, from abnormal social approach and failure of normal back-and-forth conversation; to reduced sharing of interests, emotions or affect; to failure to initiate or respond to social interactions.

2. Deficits in non-verbal communicative behaviours used for social interaction, ranging, for example, from poorly integrated verbal and non-verbal communication; to abnormalities in eye contact and body language or deficits in understanding and use of gestures; to a total lack of facial expressions and non-verbal communication.

3. Deficits in developing, maintaining and understanding relationships, ranging, for example, from difficulties adjusting behaviour to suit various social contexts; to difficulties in sharing imaginative play or in making friends; to absence of interest in peers.

B. Restricted, repetitive patterns of behaviour, interests, or activities, as manifested by at least two of the following, currently or by history (examples are illustrative, not exhaustive; see text):

1. Stereotyped or repetitive motor movements, use of objects, or speech (e.g. simple motor stereotypies, lining up toys or flipping objects, echolalia, idiosyncratic phrases).

2. Insistence on sameness, inflexible adherence to routines, or ritualised patterns or verbal non-verbal behaviour (e.g. extreme distress at small changes, difficulties with transitions, rigid thinking patterns, greeting rituals, need to take same route or eat food every day).

3. Highly restricted, fixated interests that are abnormal in intensity or focus (e.g. strong attachment to or preoccupation with unusual objects, excessively circumscribed or perseverative interest).

4. Hyper- or hyporeactivity to sensory input or unusual interests in sensory aspects of the environment (e.g. apparent indifference to pain/temperature, adverse response to specific sounds or textures, excessive smelling or touching of objects, visual fascination with lights or movement).

C. Symptoms must be present in the early developmental period (but may not become fully manifest until social demands exceed limited capacities, or may be masked by learnt strategies in later life).

D. Symptoms cause clinically significant impairment in social, occupational or other important areas of current functioning.

E. These disturbances are not better explained by intellectual disability (intellectual developmental disorder) or global developmental delay. Intellectual disability and Autism Spectrum Disorder frequently co-occur; to make comorbid diagnoses of Autism Spectrum Disorder and intellectual disability, social communication should be below that expected for general developmental level.

Specify if:

With or without accompanying intellectual impairment

With or without accompanying language impairment

Associated with a known medical or genetic condition or environmental factor

Associated with another neurodevelopmental, mental or behavioural disorder With catatonia

Reproduced with permission of the APA

Diagnosis, according to both the DSM-5 and ICD-11, requires evidence of features in two domains: atypicalities in social and communication behaviours, and the presence of restricted and repetitive behaviours. Features should be present from the early years, though diagnosis can happen much later, and frequently does. Both manuals highlight sensory sensitivities — both hypo and hyper — and potential for concurrent intellectual and/or language disability. ICD-11 provides more detail on the differentiation of autism with and without intellectual disability and also explicitly mentions the fact that some autistic people may mask their symptoms to fit in. Finally, both systems also allow autism to be diagnosed in the presence of other conditions — such as Attention Deficit Hyperactivity Disorder (ADHD) or anxiety — when previously clinicians were instructed to select only a single diagnosis, denying the possibility of co-occurrence.

Previous versions of both diagnostic manuals (DSM-IV and ICD-10 — APA, 1994; WHO, 1992) specified a series of sub-classifications of autism, such as autistic disorder, Asperger’s syndrome, atypical autism or pervasive developmental disorder — not otherwise specified (PDD-NOS). The current and former diagnostic classification systems represent different approaches to handling the variability between people who share underlying features of the same condition. We can identify the condition as a spectrum, with inherent variability, or attempt to sub-divide based on intensity of features or specific markers (e.g. Asperger’s syndrome was distinct because it was not associated with a delayed onset of language).

Why do we see these changes in diagnostic criteria and concepts? Each iteration of the diagnostic manuals attempts to respond to the growing body of research, as well as trying to improve diagnostic processes. An example of the former is the decision to collapse the distinction between Asperger’s disorder and autistic disorder. A large body of work suggested few meaningful differences between these groups when current intellectual ability was equivalent (e.g. Eisenmajer et al., 1996; Macintosh & Dissanayake, 2004). Indeed, an influential study of diagnoses given across a range of expert clinics in America showed that the best predictor of which diagnosis was given (Asperger’s, Autism, PDD-NOS) was not any characteristic of the individual being diagnosed, but which clinic they went to! (Lord et al., 2012a). An example of a change in criteria reflecting ease of clinical use is the collapsing of social and communication criteria; it is hard to think of any piece of social behaviour a clinician might look for/ask about that does not involve communication, and vice versa. The overall aim of the changes in DSM-5 was to move to a system where a broad category diagnosis was accompanied by a detailed description of the individual’s strengths and needs, rather than trying to squeeze people into specific categories they didn’t necessarily fit.

Because the DSM tends to be more influential in research and therefore on psychological theory, which is our focus in this book, we will use this as the framework for subsequent discussions.

2. Diagnostic criteria in practice

The variability in diagnostic manuals is nothing compared to the variability of presentation in the autistic population. When identifying autism, clinicians must be alert to the fact that the same feature may be manifest in dramatically different forms between individuals. For example, communication difficulties could mean that an individual is entirely non-speaking, speaking a great deal but mainly by echoing, or speaking fluently but with an atypical approach to conversational rules or understanding of non-literal language (e.g. irony, metaphor). Socially, a young autistic child may seem oblivious to others, while another individual on the spectrum may be keen to make friends but be unsure how to do so, making approaches that seem odd to neurotypical peers. Likewise, restricted and repetitive behaviours could mean lining up toys, spinning and flapping, a very ’black and white’ thinking style, or might be evident as an immersive and impressively detailed interest in organic chemistry. One relevant dimension is clearly the presence or absence of intellectual disability (technically defined as an IQ less than 70 on a standardised assessment, accompanied by difficulties with daily living skills), but it is simplistic to suggest that IQ level alone determines how features are manifest.

As an example, let us consider the communication delays which are so prevalent in autism (Tager-Flusberg et al., 2005). A non-speaking autistic child may present with a significant intellectual disability which has contributed to their difficulties acquiring speech. Another non-speaking child may have no such intellectual barrier, but instead, their communication could be associated with anxiety-related mutism. Another factor which plays into this characterisation is the extent to which a feature presents obstacles in daily life. If either child can learn to communicate, for example via independent use of a text-to-speech device using visual symbols or Makaton signs, then this apparently profound difference might be minimally disabling (at least in environments where those communication modes are understood). Likewise, a specialist and all-consuming interest in geology could be something of a hindrance when trying to chat up a potential romantic partner (unless they are also a geologist!), but a boon when seeking employment in the mining industry.

Figure 3.1 The autistic constellation

In this three-dimensional space, we illustrate IQ scores, spoken language and a sensory feature often experienced by autistic people, as orthogonal dimensions. All data are hypothetical only. Autistic people may be located in every available point in the resulting three-dimensional space, but there is the potential, if we measure the right things, to identify clusters where features often overlap.

One attempt to visualise this complexity is shown above, where we try to illustrate autism as a constellation, rather than a spectrum.1 Here we show how a specific feature of autism (in this case, sensory hyper-sensitivity) might be plotted with intellectual ability and language profile. Autistic people, with a diagnosis, may locate themselves anywhere in the resulting three-dimensional space. Their exact location would further vary with context and across the lifespan. This space can be reproduced with different measures on each axis, using features which have relevance to theory, features more important for everyday life, or both in combination. For example, we might plot satisfaction with social relationships and against the number of social relationships and level of anxiety. Importantly, in this case, having few social relationships might cluster together with high relationship satisfaction and low anxiety. This group of people might be characterised as happy with a small number of friends, reflected in low anxiety scores. Another group might have few friends, but high anxiety and low satisfaction with their relationships. Could helping them achieve more social contact lower their anxiety? A third group might have high satisfaction, large amounts of social contact and high anxiety. Perhaps having a big social circle is both rewarding and stressful — could a better balance be achieved between the two?

1Thanks to Caroline Hearst who inspired our use of this term: www.autangel.org.uk/autism-constellation.html.

These examples illustrate that it is important to contextualise any such measurement within an individual’s own priorities — working with allies to identify these where the person themselves may find it hard to self-advocate. In the previous example, provided an individual is not at risk as a result of their relative isolation, a small amount of social contact should not be characterised as a sign of impairment, as in the first cluster described. The key take home message is that autism, both conceptually and in terms of lived experience, is manifest in complex interacting domains. To discuss the ’autistic spectrum’ in a linear fashion, or to attempt to measure ’severity’ vastly over-simplifies and misrepresents the reality. Where a measure of support need is relevant we propose using exactly that terminology — as it is in the DSM-5: e.g. Level 3: requiring very substantial support.

3. Making a diagnosis

Reliance on loosely defined diagnostic criteria presents challenges to differential diagnosis, risks of mis-diagnosis and difficulty matching a diagnosis with a relevant support package. Measures have been put in place to limit these problems. In the UK, the National Institute of Clinical Excellence (NICE) and the Scottish Intercollegiate Guidelines Network (SIGN) have both published criteria for a robust diagnostic process for autism. This should include multi- disciplinary assessment, direct observation of the individual across settings (e.g. clinic, home, school) and the use of standardised assessment tools. The widely used Autism Diagnostic Observation Schedule (now ADOS-2, Lord et al., 2012b) and Autism Diagnostic Interview (ADI-R, LeCouteur et al., 2003) are two such standardised diagnostic measures using direct observation and clinical history, respectively. Nevertheless, concerns remain about diagnostic practices. These tools are lengthy and costly to obtain and train in, making them impractical for low resource settings. Open-access diagnostic tools that are brief and can be administered by a range of people, are urgently needed, especially when one remembers that the vast majority of autistic people live in low- and middle-income countries. The utility of existing diagnostic tools for diagnosis in adulthood has also been questioned, as has the influence of such measures on the gender balance of diagnosed individuals (more on this later in the chapter). Despite flaws in the process, it should be emphasised that one function of the clinical diagnostic process is not only to offer an autism diagnosis when appropriate but also to take the opportunity to get to know the individual and their family. An in-depth diagnostic process has advantages, placing the clinician in a strong and informed position to make a diagnosis and, ideally, signpost relevant information and access to services for the future. Although we note that this is an ideal, rather than a reality, for many people, at the same time preliminary evidence indicates that having a diagnosis may yield benefits in terms of improved quality of life and reduced stress for the families of autistic people (McKechanie et al., 2017).

At present, a reliable, clinical diagnosis of autism is rare before the age of 3 years, though some argue it is possible as young as 18 months. This is primarily because the types of social behaviours that are characteristically different in autism (according to the diagnostic criteria noted earlier) do not emerge reliably in typical children until around three years. Moreover, restricted and repetitive behaviours are common in all children at about two years old (Leekam et al., 2007a). However, there is substantial interest in the possibility of pinpointing earlier indicators of autism. The search for very early signs that would allow one to predict which children would turn out to have autism, has been prompted by two different concerns. Practical considerations have pressed for earlier diagnosis in the hope that very early intervention might produce benefits on early stage outcomes like onset of language. Theoretical considerations urge the early identification of autism in order to explore the causal direction in development — for example, do differences in processing of faces lead to or result from difficulties in social interaction? The emergent findings on early signs will be reviewed in Chapters 4 and 7.

A relatively recent phenomenon is that people have begun to self-identify as autistic, often later in life and sometimes prompted by the diagnosis of a child in the family. There is little or no research evidence so far on the validity or consequences of this choice. However, it is certainly easy to understand why many people might recognise autism in themselves but either not feel the need for an external confirmation by a medical professional, or actively refuse to undergo a formal diagnostic evaluation that ends in being labelled with a ’disorder’ (Kapp et al., 2013). At the same time, self-identification does raise concerns. Are there individuals who would not be independently identified as autistic, merely seeking to align themselves with a group that seems to be getting a certain amount of public attention? Are people who would benefit from mental health support mis-identifying the causes of their feelings? These cases are hopefully in a small minority. Nevertheless, the phenomenon of self-identification presents challenges to academic research, not least in efforts to estimate prevalence.

4. Prevalence estimates

How common is autism? Understanding this is crucial for service provision planning, including budgeting for the economic impact of autism, which is estimated to be very high in the UK (Knapp et al., 2009) and USA (Buescher et al., 2014; Lavelle et al., 2014). Kanner and Asperger both described the condition as rare. However, current prevalence estimates in Western countries tend to hover around 1% of the population. There is considerable global variation — a meta-analysis cited rates from 0.3% to 1.2% with a median rate of 0.6% (Elsabbagh et al., 2012) and called for more epidemiological work in low and middle-income countries. It is virtually impossible to trace the precise roots of this variability, but it is likely that differences in diagnostic procedures account for much of the range. In particular, we can see that those countries with less developed healthcare systems consistently produce the lowest prevalence estimates. Cultural differences likewise impact decisions post-diagnosis (Mandell & Novak, 2005) and should be taken into account within, as well as between, countries.

Accounting for the dramatic changes in autism prevalence estimates over time has been the focus of considerable media attention and academic effort (Fombonne, 2005). A range of headline-grabbing accounts have been put forward including the effects of pollution, changes to diet and, famously and tragically, vaccines. While it is known that there are environmental factors that contribute to autism — the condition is not 100% heritable — there is no robust evidence to support any of these accounts. In the case of the role of vaccines, this hypothetical causal factor has been thoroughly, rigorously and conclusively disproven (Jain et al., 2015; Taylor et al., 1999, 2014). Instead, variation in prevalence estimates of autism can probably be attributed to a combination of the following factors.

First, the diagnostic criteria for autism have changed dramatically since autism was first enshrined in diagnostic manuals (as ’infantile autism’ in ICD-8, 1967; ’childhood schizophrenia’ in DSM-I and II, and then ’infantile autism in DSM-III in 1980). Specifically, they have broadened to admit a much wider variety of individuals under the diagnostic umbrella. Alongside this change, there has been a dramatic rise in awareness of autism, not just in the public but among medical professionals. If you visit your family doctor now to discuss concerns about your child’s development, autism will be a potential explanation on everyone’s radar (at least if your child is a boy!) in a way that was not the case 30 or even 20 years ago. This growing awareness, combined with broadened diagnostic criteria, may have given rise to a degree of diagnostic substitution as well. As autism diagnoses have increased, diagnoses of global developmental delay or intellectual impairment, have declined. This would imply that there is no absolute increase in the numbers of affected individuals, merely in the way they are being categorised. A fourth facet of this general process of raising awareness and widening categories, has been an increase in identification of autism in populations where that diagnosis was previously not considered. Diagnostic rates in adults are rising sharply, as adults with intellectual disability are re-assessed for autism as well, and as others start to recognise autism in themselves. Anecdotally, there seems to be a specific phenomenon of parents, and sometimes grandparents, seeking a professional opinion for themselves, following their child’s diagnosis.

Finally, in some cases differences in prevalence may result from differences in methodology. The Centers for Disease Control and Prevention in the USA recently published an estimate of 1 in 68, far exceeding any previous figure. However, the methods employed in this study have been criticised. There was no direct assessment involved — instead the estimate was based on prevalence of what appeared to be autism-linked features in the case notes of children and young people referred for evaluation by education or clinical service providers.

Is autism prevalence still increasing? There are a couple of UK studies that suggest that prevalence has reached something of a plateau over the last two decades, stabilising after the introduction of new diagnostic criteria in the early 1990s (Baxter et al., 2015; Taylor et al., 2013). Whether the recent changes in DSM-5 and ICD-11 will affect prevalence again remains to be seen. In addition, adult diagnosis and diagnosis among women and girls seem to be undergoing a recent sharp increase, which may not yet be represented in the latest epidemiological data.

5. Sensory symptoms and associated features

As we have seen, diagnosis is based on the presence of features in both of two, ’core’ domains: social and communication behaviours, and restricted and repetitive behaviours. However, a range of other features are mentioned in diagnostic manuals and are certainly ’core’ to the autistic experience, if not strictly required for diagnosis.

Chief among these is an array of sensory symptoms, normally in the form of marked hyper- or hypo-sensitivity to sensory input that non-autistic people would take in their stride (Ben-Sasson et al., 2009; Leekam et al., 2007b). These sensitivities can occur in every sensory domain, including disruptions to ’internal’ senses, such as interoception, proprioception and kinaesthesia (Schauder et al., 2015) — though these may be related to conditions commonly associated with autism (e.g. alexithymia — difficulty identifying one’s own emotions) rather than to autism itself (Shah et al., 2016). The same person may experience both hyper-sensitivity — such as an aversion to the sound of a vacuum cleaner — and hypo-sensitivity — such as a preference for strong, immersive squeezing sensations or apparent insensitivity to cold. This mix of over and under sensitivity can also be apparent within a single sensory domain, and of course experiences and responses may change with context and across the lifespan.

Sensory sensitivities present significant obstacles to daily life for many autistic people. A need to avoid aversive sensory input can result in autistic people becoming reluctant to leave their homes. Sensory-seeking behaviour can also cause problems, as in the case of a young man who would pinch strangers in the supermarket in order to hear the high-pitched squeal that inevitably followed. Sensory sensitivities can be beneficial as well, and many autistic people describe the intense beauty and pleasure of their heightened responses. Enhanced discrimination of pitch, smell or touch sensations are useful in some careers or hobbies. Synaesthesia appears to be prevalent above usual rates among autistic people, and some have used this to inspire visual art, create music or support memory and learning (e.g. Tammet, 2007).

Autistic people also receive other diagnoses at higher rates than the general population. Of particular note are high rates of anxiety, depression and epilepsy. These conditions are in turn linked to early mortality in autism; in addition to deaths related to seizures, suicide is more common than previously realised (Hirvikoski et al., 2016). Research is only just beginning to get to grips with the general and mental health of autistic people, in order to address this tragic pattern (Cassidy & Rodgers, 2017). The work is challenging. One example comes from investigations into anxiety in autism. Meta-analysis indicates that up to 40% of autistic children meet criteria for multiple anxiety disorders (van Steensel & Bogels, 2011). However, disentangling these data is a challenge — to what extent is the apparent co-occurrence a superficial consequence of overlap in self-report measures used both to capture autistic traits and features of anxiety? On the other hand, experiencing anxiety or depression should not be considered merely a component of autism, dismissed without proper investigation or treatment. One promising route to developing our understanding in this area has been the identification of an underlying psychology construct, intolerance of uncertainty, which is hypothesised to underpin both certain features of autism and aspects of anxiety (Wigham et al., 2015) — we will consider this in more detail in Chapter 5.

One thing is certain however: for many people, it’s not autism that is a problem, but all the baggage that goes with it. One of the major challenges for a psychological approach to the condition is to account for and address these difficulties. Treatments for co-occurring problems like anxiety are available, some of which already have autism-specific evidence showing potential for benefit (e.g. Guénolé et al., 2011). In other cases, further work is needed to explore whether and how ’mainstream’ psychiatric and psychological interventions should be adapted to autism (Spain et al., 2015).

6. The constellation and the autisms

The Autistic Spectrum was a term coined by Lorna Wing to describe autistic heterogeneity, but as we have seen, it now seems too linear to adequately capture the complex dimensions of variability between autistic people. When people write about some being “at one end of the spectrum”, it seems to suggest that we might theoretically be able to line up all the autistic people in the world in order of how autistic they are! Rather, variability between autistic people is more like variability between feminine people. Asked to sort a group of people according to “most to least feminine” you might consider a range of factors — body shape, clothing, hairstyle, career choice, personality and manner. But the chances you’d come to the same order as another person are surely very low. You might simply reject the notion that one person can be more or less feminine than another, but even if you didn’t, you’d probably give up trying. Autistic writers have sometimes referred to the autism constellation, which seems better: “It is more like a constellation than a spectrum. It does not move along one line going from low to high, it circles in many spheres” (Hearst, 2015). Whatever terms we choose to capture variability in the autistic population, there’s no doubt that it exists. Consequently, desire to parse autism into meaningful sub-groups is strong.

A good reason to identify sub-groups is to provide the right supports to the right people. For example, we might identify in childhood those who would be most likely to experience anxiety or depression in adolescence and put in place efforts to increase their resilience and coping strategies. However behavioural sub-types haven’t proved very successful, beyond a simple characterisation of autism plus or minus intellectual disability and/or language impairment. One reason is that the same individual may pass through different presentations as they age. For example, Asperger’s syndrome became problematic as a separate category as it was realised that often a ’classically’ autistic child grows into an ’Asperger-type’ adult. Another difficulty in the quest to identify sub-groups is that the sample sizes required are so huge. Capturing detailed genetic and phenotypic information, let alone relating this to developmental trajectories and adult outcomes, would require huge investments and sometimes international protocols. One such study is underway — the EU-AIMS (European Autism Interventions — A Multicentre Study for Developing new Medications) consortium is funded by the largest single grant for autism research worldwide and seeks to develop the knowledge and infrastructure to underpin new treatments for autism. Identifying biomarkers for personalised or precision medicine approaches is a key goal for this huge and ambitious project (Loth et al., 2017). The project also provides a salutary lesson in the importance of engagement from the outset; subsequent consultations with the autism community revealed significant gaps between community priorities and the original consortium goals (Russell et al., 2018).

As well as attempts to identify meaningful sub-groups within the spectrum, the notion of the broader autism phenotype (BAP) has also been extensively studied. This term is used to capture the pattern of autistic-like traits found in the general population. The BAP is generally measured via self- report of a range of behaviours such as, I would rather go to a library than a party, or I find it easy to “read between the lines” when someone is talking to me (Baron-Cohen et al., 2001). BAP traits are normally distributed in the general population and found in higher rates among the biological relatives of people with autism (e.g. Bishop et al., 2004). BAP traits have been correlated with diverse features, including sensory sensitivity (Robertson & Simmons, 2013), studying science and technology subjects (Stewart & Austin, 2009) and ability to read non-verbal communication cues (Ingersoll, 2010). However, it remains somewhat debated how much can be said about autism from studies exploring autistic-like traits in non-autistic people. This literature risks trivialising the autistic experience in the same way that relating a feeling of sadness to clinical depression (however well-intentioned) risks trivialising that diagnosis. Nonetheless, the evidence to date does suggest that similar genetic influences operate on autistic traits and at the subclinical and diagnosed levels (Constantino & Todd, 2003; Robinson et al., 2011).

7. The fractionated triad

While the notion of ’the autisms’ reflects our current belief that autism has different etiologies (reflected perhaps in different featural patterns) in different individuals on the spectrum, heterogeneity can also be understood in terms of the ’fractionated triad’ idea (Happé et al., 2006). This suggests that, even within one individual, different behavioural features of autism — whether we split those into a triad, dyad or any other number of clusters — may have multiple, distinct causes. This notion came from studies that showed that, in the general population, atypicalities in social skills or communication are often found without rigid and repetitive behaviour or interests. Furthermore, correlations between ratings of the three aspects of the traditional diagnostic triad are low or moderate (Happé & Ronald, 2008). In addition, twin studies of traits in the general population, or clinical-level features of autism, suggest distinct genetic influences on the three aspects of the triad (Robinson et al., 2012). These findings fit with evidence from family studies of the BAP in relatives of autistic people, where a great-aunt may be described as a loner who shunned company, while granddad is socially able but very rigid, eating the same food for lunch every day and working as a proofreader with a fantastic eye for detail. According to this account, autism is the result of a ’recipe’ of genetic and environmental ’ingredients’, with differences in social communication style and preference for routine deriving from different sources. In just the same way, we might recognise that we are each an amalgam of different aspects, with, for example, our mother’s curly hair and our father’s sticky-out ears!

Another contribution to this concept comes from the idea of resilience factors. Resilience is the capacity to recover from, or avoid the negative effects of something. For example, having good friends at school might provide resilience to the negative effects of parental divorce. In the case of autism, good executive functions are one candidate resilience factor that don’t make a person any less autistic, but might make them more resilient to some of the negative consequences of being autistic. An autistic person with good working memory and planning abilities might be able to learn rules to operate in the neurotypical world, monitoring and tracking social behaviours and working out how they are expected to respond. While we do not advocate for such an exhausting approach, but such phenomena may relate to compensation (Livingston & Happé, 2017) or ’camouflaging’ and the under-identification of autism outside the male gender. There is more on this next, and we’ll revisit the fractionated triad idea in Chapter 6 in relation to cognitive aspects of autism. The key point about this putative account is that it suggests autism is a composite of different and somewhat independent behavioural aspects, that have different origins at the biological and/or cognitive levels.

8. Autism and gender

All the epidemiological studies of autism show a significantly greater number of boys than girls in the population. Historically, a ratio of c.5:1 males to females was accepted, and the sex ratio was thought to vary with ability, reaching perhaps 10:1 among cognitively able individuals, and falling to 2:1 amongst those with intellectual disability. Thus, most girls diagnosed with autism also had an intellectual disability (Lord & Schopler, 1987), and it was thought that females required a “higher etiological load” to manifest autism. However, this picture is now changing. In the last five years, interest in the female profile of autism has risen sharply, accompanied by rising awareness that autism may manifest differently in women and girls. Recent meta-analysis suggests that when thorough epidemiological work is done, versus relying on known diagnosed cases in clinics or registries, the male preponderance falls to 3:1, with little difference across the ability range (Loomes et al., 2017). These figures still reflect the numbers meeting current diagnostic criteria; if our criteria or processes are male biased, we may be missing large numbers of females on the spectrum. Certainly, research has traditionally overlooked and even excluded females from autism research, leading to a vicious cycle of ignorance about possible gender differences.

A great deal of research investigating the female profile of autism has focused on qualitative descriptions of their experience, often revealing significant difficulties prior to receiving a diagnosis. Studies have highlighted that many autistic girls, lacking a diagnosis, experience significant mental health difficulties in adolescence and beyond (e.g. Duvekot et al., 2017). These findings are supported by data on high rates of previously undiagnosed autism in women presenting to eating disorders services (Mandy & Tchanturia, 2015). Why are these girls not picked up by clinical services? One likely explanation is that some autistic girls spend time and effort masking or camouflaging their autism (Dean et al., 2017; Lai et al., 2017). This in turn can lead to exhaustion and mental health difficulties. Another possible cause for this pattern is that, through long years of reiteration that autism is more prevalent in males, clinical services have become conditioned to diagnose boys and also more finely tuned to detecting their autistic features. For example, most clinicians would probably confidently identify the point at which an interest in trains starts to meet the definition of a ’restricted interest’. Would the decision be made so confidently if the interest was something more ’appropriate’ to female gender stereotypes (Sutherland et al., 2017) like a fascination with make-up or horses? Perhaps not. And if clinicians don’t think ’autism’ when they meet a girl with social difficulties, they may think social anxiety, eating disorder or depression: diagnostic overshadowing occurs when clinicians stop at one presenting problem and don’t go on to consider, for example, eating disorder and autism.

Many questions about the female presentation of autism remain to be answered, and there are some fundamental challenges for the field. For example, one concern is that the instruments used to standardise elements of the diagnostic process for autism may be skewed towards the expected male presentation. Do new assessments need to be constructed in order to capture the female profile? And if so, how do we go about creating such instruments at a time when every diagnosed female has been identified using the old ones?

A further challenge comes from the fact that a large proportion of autistic people identify outside the male/female gender binary (Cooper et al., 2018). The increasing focus on diagnosing and understanding autism in females risks overlooking the complex interplay of gender expectations and autistic features in those who don’t fall neatly into a binary gender category. For example, how might autism be manifest in natal males who later identify as non-binary, female or another gender identity? What modifications need to be made to diagnostic instruments to capture their autism accurately? And, conversely, how can we improve the process of transition for autistic transgender people? As yet, research on autism and gender identity is in its infancy but readers would do well to take this crucial consideration into account when considering research that invokes simplistic gender differences in autism.

9. What does autism look like in old age?

The short answer to this question is that nobody really knows. Of course, there are older autistic adults with personal experiences to share, but in terms of being able to make generalised, robust statements about ageing and autism, the data just aren’t in yet (Howlin & Magiati, 2017). Since the first children were described by Kanner and Asperger in the 1940s, and the diagnosis did not enter more general use until the 1960s and ’70s, the first cohort with recognised autism are only now growing old. Autism was considered rare and was diagnosed by far narrower criteria than today, so attempts to follow these children through to old age are tricky and won’t necessarily tell us about the future ageing of people diagnosed by today’s wider criteria. There are increasing numbers of people receiving a first diagnosis of autism in adulthood, but these may be unrepresentative by virtue of having managed so long without a correct or complete diagnosis.

As a result, knowledge about the links between autism and the typical ageing processes — cognitive decline, changing social support networks, physical illness — is in its infancy. Also important, and under-researched, are questions about the best way to provide care to an ageing autistic individual. While much remains to be done, awareness of autism in mainstream classrooms is now fairly good, and teachers are increasing in confidence about how to support autistic pupils. To our knowledge, there is no wide-spread effort in progress to similarly inform people working in elder care settings. Another obstacle to understanding ageing in autism is that the kind of support offered to autistic children five, six or seven decades ago was radically different from the kind of support being offered today. This impedes our understanding of the potential lifespan impact of intervention in childhood.

Is there anything we can say about ageing and autism? Pat Howlin has followed up one longitudinal cohort of autistic people diagnosed and recruited as children in the 1960s and ’70s at the Maudsely Hospital in London. In their 40s and 50s, their outcomes were generally poor in terms of independence, employment and quality of life (Howlin et al., 2014). A large-scale study of health records from more than 1,500 autistic adults found higher rates of almost every mental and physical health condition compared to non-autistic adults (Croen et al., 2015). Less than 10% of the sample were aged 50 plus, but the elevated rates of heart disease, diabetes and conditions associated with ageing (e.g. dementia, Parkinson’s) suggests that research on physical health in elderly autistic adults is urgently needed. Autistic adults also generally report lower quality of life than non-autistic people (van Heijst & Geurts, 2015) but a new autism-specific, validated measure for quality of life may provide more optimistic data in the future (McConachie et al., 2017). Anecdotal impressions suggest that many autistic adults find a niche for themselves as they grow older, with like-minded companions, professional fulfilment and passionate hobbies. What is unclear is how to accelerate this process and extend it to reach as many potential beneficiaries as possible.

10. Autistic behaviour and societal norms

Neurotypical syndrome is a neurobiological disorder characterized by preoccupation with social concerns, delusions of superiority, and obsession with conformity.2

2https://angryautie.wordpress.com/2013/06/24/the-institute-for-the-study-of-the-neurologically-typical/

It is essential when attempting to describe the behavioural profile associated with autism to recognise that autism is largely characterised against a backdrop of presumed normative standards. Probing this context reveals a number of assumptions which, while they may be constructive in permitting scientific investigation to move forward, deserve to be acknowledged at the very least, and possibly challenged.

One assumption, especially when discussing heterogeneity between autistic people, is that non-autistic people all fit neatly into an easily described, “neurotypical” box. This is clearly not the case, but it is easy to see why this notion took hold. First, many studies used standardised measures, such as IQ tests. These have usually been developed with data from very large samples. Such samples tend more and more towards a normal distribution, emphasising average performance and causing variability to be downplayed. The data are used to provide age-norms and it is easy to jump to the, clearly false, conclusion that any non-autistic individual would score right on the nose for their age.

Second, many studies that provide the foundation for our understanding of ’typical’ development and behaviour rely on very narrowly defined samples. Published data disproportionately draw on university undergraduates, or the children of university staff and their friends, often in the UK or USA, or other developed nations. These samples are often largely white and in middle to high socio-economic brackets. Control groups are often screened for any kind of mental health problem, resulting in ’super controls’, who are not at all population representative. In contrast, when recruiting an autistic group to a research project, because of the relative rarity of autism, researchers cast their net far and wide, often ending up with a more variable group than in the comparison data. That said, we should also note that white, middle-class participants are over-represented in research generally. Furthermore, in some kinds of studies — especially randomised controlled trials (RCTs) — inclusion criteria may be extremely strict, limiting external validity and clinical relevance (Jonsson et al., 2016).

Another way in which societal norms infiltrate supposedly objective autism research is by application of a normative lens to the study questions, design and especially interpretation of data. When the average performance of an autistic group differs from that of a comparison group, all too often, the immediate conclusion is that the autistic response pattern is inferior. This is particularly unjustified in the case when the developmental function or outcome of the behaviour is not well understood. For example, one recent study observed a reduced ’ownership effect’ in the toy choices of autistic relative to non-autistic children. Children with autism judged toys purely on their merits, showing no bias contingent on randomly assigned ownership (i.e. having been given the toy by an experimenter). Despite the autistic group showing more rational behaviour, the authors concluded that “deficits in self-understanding may diminish ownership effects in ASD” (Hartley & Fisher, 2018, p. 26).

In neuroimaging research, differences in blood flow during a task are interpreted to show problems in the autistic group, regardless of the direction of difference from control data. If the autism group shows greater brain activity they are ’having to work harder to solve the task’, but if they show reduced activation, they lack the expected neural specialisation of dedicated brain regions for the key computations! Researchers would do well to reflect on the normative perceptions they bring to their work and consider whether it is appropriate to take a more neutral stance when differences between autistic and non-autistic groups are uncovered. In both examples, of course, differences might truly be disadvantageous, even outside our laboratory settings. If so, it behoves the research team to demonstrate the process by which a disadvantage might arise from the original experimental difference. This has the additional benefit of identifying ways in which disadvantages could be eliminated, without automatically putting the onus on the autistic person to change.

11. Current debates

Summary

Autism is diagnosed on the basis of a pattern of behaviours, including essential criteria (social communication, restricted and repetitive behaviours) and additional common features (possible intellectual or language disability). These manifest in multiple ways, differing widely between individuals and also depending on their life stage and context. Reliance on behavioural markers presents challenges to interpreting changing prevalence estimates and to parsing heterogeneity within the autism constellation, and raises questions about recognition of autism in historically under-researched groups, such as women and the elderly.

Big questions

Investigating so-called core domains is the main focus of psychological research, but these may not present the biggest challenges for the autistic person. Psychologists focus on these, partly because of our beliefs about the causal pathways from a ’core’ challenge to a ’surface’ behaviour. Do these beliefs hold up under scrutiny, and are they data-driven? Is a focus on ’underlying’ features (e.g. executive functions) preventing us from investigating questions that are priorities for the community (e.g. potty training, seizure management, how to get a good night’s sleep)?

What is the role of diagnosis in the life of an autistic person? The experience of diagnosis can be positive for the individual and their family members — but why does it have this effect? Are positive experiences related to becoming part of a community, or simply increased self-knowledge? Are there cases where a diagnosis has a negative impact, and how should we respond to this?

Given the rise in self-identification by autistic people, will the clinical diagnosis of autism continue to be of value to the community in the future? After all, the diagnostic category of autism is ultimately no more than a social construct, used to try to describe a pattern of phenomena that may be very different when externally observed versus internally lived. If the diagnostic category were to disappear altogether, what would be the implications for research and service provision?

How can we understand heterogeneity within autism? Is autism a single thing, manifest differently depending on factors such as socialisation, environment, lived experience, and personal resilience? Or are there separate and different ’autisms’? How can we discover the answer while we are reliant on behavioural diagnosis?

How are our conceptualisations of autism shaped by cultural expectations? Is autism the same across cultures? What about in the technology- mediated social world — do autistic people behave in an identifiably ’autistic’ manner online and in other digital communication contexts?

COMMUNITY CONTRIBUTION: KABIE BROOK — AUTISTIC ACTIVIST, SPEAKER AND ADVOCATE

The change to specify only one category for autism in the most recent revision of diagnostic criteria was widely welcomed by many autistic people. From the point of view of autistic activists, those fighting for autistic people’s rights, we had always referred to autistic people in an inclusive way, fighting for the rights of all autistic people rather than just certain kinds of autistic people with the firm belief that we are all equal and that no-one should be left behind in the fight for equality.

Most autistic people recognise that the divisions were artificial, in practical terms of little use to us and quite often damaging. A person’s diagnostic label often had little to do with who they were and could often become a source of disablement.

For example, my own son was diagnosed at a young age; he went to a mainstream school where he was described as ’unteachable’. Teachers said that they had ’taught him all that they could’; they made predictions that he would leave school with no qualifications, that we should feel grateful that he was good at sports but give up on him ever learning to read. I found out that he was sitting at the back of the class, mostly ignored and given colouring sheets to pass time. At this point, as parents, we decided to shift our priorities; we decided that learning to read and be happy were the most important goals for our son. We wanted him to get through education with as little damage to his mental health as possible. As autistic parents, we had a deep understanding of living life as a minority group within a mostly neurotypical world. We knew that learning can be lifelong, but damage done by others can also last a lifetime.

My son now is at university studying a degree that suits his interests and getting good marks. His journey involved finding teachers who believed in him and understood him, including an autistic teacher who understood how to teach him, how to harness his ability and accept that his way of learning and speed of learning may differ from a neurotypical child.

I have heard similar stories to this from many autistic people, people who struggled to master basic self-care skills but went on to be successful academics or hold important positions within their chosen field of work but were still seen as failing neurotypicals rather than as fully rounded successful autistic people. Grading people, ignoring spiky profiles (including variability day to day, week to week, etc.), predicting what the future holds for people is extremely unreliable. Myself, my family and friends’ trajectories as well as those of autistic people in general aren’t based on ’off the peg’ stereotypes; we often don’t turn out as non-autistic people expect. Yet there seems to be an increasing drive, a need almost, to the extent of being obsessional to classify us, put us into neat categories, to view us in a similar way as bugs under a microscope and all of this is measured by the overwhelmingly neurotypical practitioner who will never fully understand what it is to be autistic but still wields so much power over our community: the autistic community.

As autistic people when we talk to the non-autistic practitioner it often feels as though we need an interpreter. We are easily misunderstood by people who do not share our disposition. Watching clinicians talking to my autistic children is always interesting and frustrating; what’s said, what’s meant and what meaning is attached by the listener is often very different. Even so, it is always the autistic person who is said to have the deficit, when of course in reality all communication is an active two-way process requiring shared understanding to guarantee success (see Damian Milton’s Double Empathy Problem, mentioned in Chapter 9, for more on this). Likewise with sociality: we need the others to be social, therefore deficits in social communication are subjective with the majority being labelled correct and the minority deviant: wrong, broken. ’Deficits’ in social communication are lessened when we are more alike to those we mix with. Many autistic people have said that they gain great benefit from meeting up with other autistic people. Indeed, Autscape, a retreat style conference run by and for autistic people (www.autscape.org) has been life changing for many, myself included. Being in autistic space, immersion in autistic community and culture is nurturing, self-affirming and a reminder of our unbrokenness rather than the day-to-day struggle that most experience in an almost alien non-autistic society.

The autistic population is a very varied one — just as the non-autistic population is. It often feels though that this is somehow forgotten by clinicians. Our individuality, our personalities don’t fit textbook definitions which lean heavily to very one-dimensional white males. This expectation of almost ’blandness’ and ’very male’ works against autistic people: women, transpeople, non-binary people, people of colour or from low-income backgrounds, even white quiet males are missed or passed over. I have heard more than once from autistic women about their fight to even get a GP referral for diagnosis because of the belief that autism is a male ’condition’. For me the current diagnostic problem needs to be solved for all autistic people and the conversations don’t seem to be going there — yet.

It feels as though where we are now is merely a ’tinkering’ when what we need is a revolution. Clinicians rarely truly understand autistic people; they don’t see us as friends, as a community, as equals. The deficit model often leads to a lack of access to services — for example health and education because the ’problem’ is always seen to be centred within the autistic person. This needs to be looked at differently, clearly seeing that autistic people, even if we are a minority group, we are full citizens and society should no longer accept our exclusion.

Recommended reading

Baird, G., Douglas, H. R., & Murphy, M. S. (2011). Recognising and diagnosing autism in children and young people: Summary of NICE guidance. British Medical Journal, 343(d6360), 10—1136.

Howlin, P., & Magiati, I. (2017). Autism spectrum disorder: Outcomes in adulthood. Current Opinion in Psychiatry, 30(2), 69—76.

James, L. (2017). Odd girl out: An autistic woman in a neurotypical world. London: Pan Macmillan.

Lawson, W. B. (2015). Older adults and autism spectrum conditions: An introduction and guide. London, UK: Jessica Kingsley Publishers.

Smith, P. A., Wadsworth, A. M., McMahon, W., Cottle, K., Farley, M., Coon, H., Gregg, C., Bakian, A., Grandin, T., Endow, J., & Baron, M. (2016). Autism spectrum disorder in mid and later life. London, UK: Jessica Kingsley Publishers.

Tammet, D. (2007). Born on a blue day: Inside the extraordinary mind of an autistic savant. New York, NY: Simon & Schuster.