Emily Rose South Bachelor of Science (Honours)

An investigation of the relationship between autistic traits and anxiety-linked

attentional bias to negative information

Emily Rose South

Bachelor of Science (Honours)

This thesis is presented for the degree of Doctor of Philosophy and Master of Clinical Psychology of The University of Western Australia

School of Psychological Science

October 2020

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Thesis Declaration

I, Emily South, certify that:

This thesis has been substantially accomplished during enrolment in this degree.

This thesis does not contain material which has been submitted for the award of any

other degree or diploma in my name, in any university or other tertiary institution.

In the future, no part of this thesis will be used in a submission in my name, for any

other degree or diploma in any university or other tertiary institution without the prior

approval of The University of Western Australia and where applicable, any partner

institution responsible for the joint-award of this degree.

This thesis does not contain any material previously published or written by another

person, except where due reference has been made in the text.

This thesis does not violate or infringe any copyright, trademark, patent, or other rights

whatsoever of any person.

The research involving human data reported in this thesis was assessed and approved

by The University of Western Australia Human Research Ethics Committee.

Approval number: RA/4/1/6140.

This research was supported by a University Postgraduate Award, UWA Safety Net

Top-up, and an Australian Government Research Training Program (RTP)

Scholarship.

This thesis does not contain work that I have published, nor work under review for

publication.

Signature:

Date: 30/10/2020

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Abstract

A wealth of literature has now established a link between elevated anxiety

vulnerability and the high end of the autism continuum. High rates of co-occurring

anxiety disorders have been well documented in Autism Spectrum Disorder, and

elevated anxiety symptomology has been noted in individuals high in autistic traits.

Despite this clear link, research investigated the cognitive mechanisms that underpin

anxiety vulnerability in the presence of heightened autistic traits has been lacking.

Cognitive models developed in populations unselected for levels of autistic traits

emphasise the role of selective attention to negative information in the aetiology and

maintenance of anxiety vulnerability. To date, however, only a handful of studies have

investigated anxiety-linked attentional bias in the high end of the autism continuum.

These studies have focussed primarily on clinically diagnosed autistic individuals and

have produced inconsistent findings. The absence of a clear anxiety-linked attentional

bias in autistic individuals in these studies has raised the hypothesis that heightened

levels of autistic traits are characterised by attenuated anxiety-linked selective attention

to negative information. However, the design of previous studies has limited adequate

assessment of this hypothesis, as autistic traits and anxiety vulnerability have been

confounded. The aim of the present research programme was to systematically evaluate

this hypothesis, while delineating autistic traits and anxiety vulnerability.

To accomplish this aim in the current research programme, participants were

recruited who varied in levels of trait anxiety and autistic traits and a series of

experiments was developed that included novel and established methodologies intended

to assess anxiety-linked attentional bias to negative information. In Experiment 1 an

unselected sample of adult participants completed an attentional probe task, with

findings indicating that autistic traits did not significantly moderate the relationship

between anxiety vulnerability and attentional bias to negative information. In

Experiment 2, four groups of participants were recruited in a 2 (high versus low levels

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of autistic traits) by 2 (high versus low levels of trait anxiety) factorial design and

administered the same attentional probe task. Contrary to expectations, participants high

in autistic traits displayed a significant anxiety-linked attentional bias to negative

information, whilst participants low in autistic traits did not. This finding stood in clear

contradiction to the hypothesis that high levels of autistic traits are characterised by

attenuated anxiety-linked attentional bias to negative information. However, it was not

immediately clear in this study why an anxiety-linked attentional bias to negative

information was not observed in participants low in autistic traits.

The aim of Experiments 3 and 4 was to evaluate candidate explanations for why

an anxiety-linked attentional bias to negative information was not observed in

participants low in autistic traits, while providing an opportunity for replication of the

Experiment 2 findings. Experiment 3 investigated the hypothesis that the stimuli used in

Experiment 2 were not sufficiently negative to detect an attentional bias in participants

low in autistic traits. To address this possibility, Experiment 3 incorporated a novel

image rating task that created personalised stimulus sets. Surprisingly, no anxiety-linked

attentional bias to negative information effects were observed in this study. Experiment

4 investigated the hypothesis that participants low in autistic traits demonstrate superior

attentional control, and this accounts for the lack of anxiety-linked attentional bias to

negative information observed in this participant group in Experiment 2. This

experiment retained the same attentional probe task as Experiment 2 while including a

measure of attentional control. Findings indicated an anxiety-linked attentional bias to

negative information approaching significance that was expressed across autistic trait

groups. Findings also indicated that participants low in autistic traits demonstrated

superior performance on one subtask of the attentional control task compared to

participants high in autistic traits.

Importantly, in the current research programme there was no evidence that high

levels of autistic traits were characterised by an attenuated anxiety-linked attentional

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bias to negative information. In the final chapter of this thesis, methodological and

theoretical implications arising from these findings are described. Avenues for future

research are also discussed, including alternative methods of assessing attentional bias,

and the potential for investigation of the components of attentional bias in participants

who vary in autistic traits.

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Table of Contents

Thesis Declaration .............................................................................................................. I

Abstract ........................................................................................................................... III

Table Of Contents ......................................................................................................... VII

Acknowledgements ......................................................................................................... XI

Chapter 1: Introduction ..................................................................................................... 1

Chapter 2: Experiment 1 ................................................................................................. 32

Method ............................................................................................................................ 36

Participants .......................................................................................................... 36

Materials .............................................................................................................. 37

Procedure ............................................................................................................ 39

Results ............................................................................................................................. 40

Calculation of attentional bias to negative information index (ABNII) ............. 41

Examination of moderating effects of autistic traits on the relationship between trait anxiety and attentional bias to negative information ................................... 42

Discussion ....................................................................................................................... 45


Method ............................................................................................................................ 50

Participants .......................................................................................................... 50

Materials .............................................................................................................. 52

Procedure ............................................................................................................ 53

Results ............................................................................................................................. 53


Analysis of attentional bias to negative information........................................... 55

Discussion ....................................................................................................................... 58

Prelude To Experiment 3 And Experiment 4 .................................................................. 64

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Method ............................................................................................................................ 70

Participants .......................................................................................................... 70

Materials.............................................................................................................. 72

Procedure ............................................................................................................ 76

Results ............................................................................................................................. 76

Examination of effects of autistic traits on image ratings ................................... 77


Analysis of attentional bias to negative information .......................................... 81

Discussion ....................................................................................................................... 82


Method ............................................................................................................................ 93

Participants .......................................................................................................... 93

Materials.............................................................................................................. 95

Procedure .......................................................................................................... 102

Results ........................................................................................................................... 102

Calculation of attentional bias to negative information index (ABNII) ........... 103

Calculation of attentional control indices ......................................................... 108

Analysis of selective attentional control ........................................................... 110

Analysis of inhibitory attentional control ......................................................... 111

Discussion ..................................................................................................................... 112

Chapter 6: General Discussion ...................................................................................... 118

Overview of Experimental Findings ................................................................. 118

Implications of the Current Findings ................................................................ 124

Limitations and Directions for Future Research ............................................... 134

Concluding Comments ...................................................................................... 149

References ..................................................................................................................... 152

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Appendix A ................................................................................................................... 200

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Acknowledgements

I must confess that I have found this section of my thesis one of the most

difficult to write, as I feel overwhelmed when reflecting on my PhD project by the

depth and breadth of support I have received. I doubt I will be able to properly convey

my gratitude to all of those involved over the years in words here, but I’m going to give

it my best shot.

I would firstly like to thank my wonderful supervisors for all of their support

and guidance over the years of this project. To Dr Ben Grafton, thank you for all of your

assistance and support, in particular with all those little annoying details like how to

extract BBC data or programme something in Inquisit. To Professor Colin MacLeod,

your incredible passion for research and remarkable conceptualisation of phenomena

has continuously challenged me to go further in my critical analysis and considerations,

and I am a better researcher for it. Your feedback throughout this project has been

invaluable. To Professor Murray Maybery, my principle supervisor, your consistently

thorough, patient, and wise supervision provided a strong, steadying force throughout

my candidature that I have appreciated immeasurably. I have thoroughly enjoyed my

time in the CANLab, and the bright, supportive group that you have fostered.

I must also express my deep gratitude to my brilliant companions who shared

this journey with me (presented alphabetically because you are wizards equally), Alice,

Caroline, Gemma, Georgina, Henry, Joanna, Kirsten, Natasha, and Rasangi. Your

unwavering support and confidence in me in my moments of greatest self-doubt

encouraged me to persevere, and I will always be grateful for our friendship. Together

we kept this experience not only tolerable but filled with warmth and laughter. I feel

truly honoured to have shared this journey with such talented clinicians and researchers.

To my treasured friends, Claire, Emma, Hannah, Mundawae, Nicholas, Paige,

and Zoie. You have kept me anchored to the real world through what I’m sure felt like

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an endless University career, and I sincerely appreciate you all. To my dear friend Kyle,

you have truly kept me sane – and you were right, I did finish it. Thank you.

Perhaps most important of all, my beautiful family. To my parents, Denise and

Peter, your unconditional love and support gave me the courage to always explore what

I was passionate about. You instilled in us a strong desire to always be curious and

critical, and to never take yourself to seriously – these turned out to be vital qualities,

thank you. My brother Callum, you are truly a superb human being and I look forward

to repaying your support and guidance in the coming years as you embark on your own

PhD journey. Thank you also to my beautiful mother- and father-in law, Paul and

Tammy, for your constant patience and love.

Finally, my husband, Patrick. Since my second year of my undergraduate

degree, you have been on this journey with me – an invaluable constant. Your

unwavering belief in me provided a well that I could draw from when my own tank was

running low. At every hurdle that tripped me up, you got me back on course. Thank you

for always keeping me afloat, whatever I needed - I love you. Our future feels bright,

and I’m excited for it.

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CHAPTER 1: INTRODUCTION 1

Chapter 1: Introduction

There is now substantial evidence to suggest that autistic traits are continuously

distributed across the population, with one end of the distribution represented by

extreme levels of autistic traits that manifest as Autism Spectrum Disorder (ASD;

Constantino & Todd, 2003; Lundström et al., 2012; Robinson et al., 2011; Ruzich et al.,

2015). Falling near this end of the continuum are individuals who endorse high levels of

autistic traits, but do not meet criteria for ASD. Thus, the high end of the autism

continuum can be considered to incorporate both individuals diagnosed with ASD, and

individuals who endorse high, but sub-clinical, levels of autistic traits. A wealth of

research has demonstrated that elevated rates of emotional difficulties are associated

with the high end of the autism continuum. In particular, considerable evidence suggests

that elevated anxiety vulnerability is characteristic of ASD (Hollocks, Lerh, Magiati,

Meiser-Stedman, & Brugha, 2019; van Steensel, Bogels, & Perrin, 2011), and is

similarly associated with high levels of autistic traits (Kanne, Christ and Reirsen, 2009;

Russell-Smith, Bayliss, Maybery, & Tomkinson, 2013). To better understand the

aetiology and maintenance of anxiety vulnerability in the general population,

researchers have studied the cognitive mechanisms thought to underpin this

vulnerability. One cognitive process that has attracted significant investigation is

attentional bias to negative information, which concerns the degree to which attention is

selectively allocated towards negative information in the environment (Bar-Haim,

Lamy, Pergamin, Bakermans-Kranenburg, & van IJzendoorn, 2007; Beck & Clark,

1997; Eysenck, Derakshan, Santos, & Calvo, 2007; Mogg & Bradley, 1998a; J.

Williams, Watts, Macleod, & Mathews, 1997). Despite the high levels of anxiety

vulnerability recognised to be associated with the high end of the autism continuum,

both in clinical and sub-clinical groups, to date research investigating the cognitive

processes that underpin anxiety vulnerability in groups high on the continuum has been


limited. As will be seen, the findings of previous research investigating anxiety-linked1

attentional bias to negative information in the high end of the autism continuum have

been inconsistent, with the majority of studies suggesting that anxious, autistic

individuals do not display a significant anxiety-linked attentional bias to negative

information. Based on these findings, one reasonable hypothesis advanced here is that

high levels of autistic traits are characterised by an attenuated anxiety-linked attentional

bias to negative information. The central purpose of the present research programme is

to provide an adequate test of this hypothesis. The present chapter will first provide

background information on ASD, followed by a review of the evidence that autistic

traits exist as a continuum. Second, it will review the evidence that high levels of

autistic traits are characterised by elevated anxiety vulnerability. Third, the evidence

that anxiety vulnerability is characterised by a heightened attentional bias to negative

information will be reviewed. Finally, the findings regarding anxiety-linked attentional

bias to negative information in the presence of high of levels of autistic traits will be

reviewed, followed by an outline of the current research programme.

The Phenomenology of Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a pervasive developmental disorder,

defined in the current Diagnostic and Statistical Manual of Mental Disorders (DSM-5)

as being characterised by marked difficulties with social communication, and restricted,

repetitive behaviours and interests (American Psychiatric Association, 2013). To meet

criteria for clinically diagnosed ASD, an individual must demonstrate persistent

difficulties in both of these domains (American Psychiatric Association, 2013). Within

the DSM-5, the previously discrete disorders under the DSM-IV of Autistic Disorder,

Asperger’s Disorder and Pervasive Developmental Disorder – Not Otherwise Specified

1 Throughout the current thesis, the term “anxiety-linked” will be used to refer to effects that are more

pronounced in the context of high levels of anxiety vulnerability relative to low levels of anxiety vulnerability. Thus, the phrase “anxiety-linked attentional bias to negative information” refers to an attentional bias that is more pronounced in individuals high in anxiety vulnerability relative to individuals low in anxiety vulnerability.


(PDDNOS) have been collapsed under the umbrella term ASD. This change was made

primarily due to evidence-based concerns that the previous categorical approach to ASD

created boundaries between the diagnoses that were unclear and lacked validity

(Macintosh & Dissanayake, 2004; Ozonoff, South, & Miller, 2000). Thus, the DSM-5

reorganised ASD as a dimensional diagnosis that reflects an elevation in the degree of

autistic symptoms or traits expressed.

At this point it is important to address terminology in relation to ASD, as the

terms used to describe autism vary widely in the community and in empirical research.

Increasingly, it has been recognised that the term ‘disorder’ is considered stigmatising

and does not necessarily reflect the terminology preferred by community groups (see

Kenny et al., 2016). However, for clarity in the current research programme, this thesis

will use the term ASD when referring to studies that have investigated populations with

this clinical diagnosis, and the term ‘autistic traits’ will be used to refer to studies that

investigated people with no clinical diagnosis who vary in these traits. The term ‘trait’

has previously been used to refer to the behavioural tendencies consistent with ASD that

vary in intensity but may not always be present at clinical levels, whilst the term

‘symptom’ has been used to refer to clinically significant atypical behaviours

represented in the diagnostic criteria (see Lai, Lombardo, Chakrabarti, & Baron-Cohen,

2013). The term autistic traits will be used in the current thesis to refer to behavioural

tendencies distributed in the general population, and symptoms will be incorporated

under autistic traits at their extreme.

Epidemiology studies estimate that, globally, approximately 60 in 10,000

individuals are diagnosed with ASD (Chakrabarti & Fombonne, 2005; Elsabbagh et al.,

2012). In Australia it is estimated that 0.74% of the population of children under seven

years old is currently diagnosed with ASD (Bent, Dissanayake, & Barbaro, 2015).

While estimates vary, ASD appears to be more common in males with a consensus ratio

of approximately 4:1 (Fombonne, 2009; Loomes, Hull, & Mandy, 2017; Werling &


Geschwind, 2013). It has been suggested that this gender difference may at least partly

reflect differences in the expression of the behaviours associated with ASD. Males tend

to exhibit more externalising behaviours such as hyperactivity and aggression, whereas

girls are more likely to demonstrate internalising symptoms such as anxiety and

depression, and have been suggested to exhibit better “camouflaging” abilities (Koenig

& Tsatsanis, 2005; Kreiser & White, 2014; Rivet & Matson, 2011; Szatmari et al.,

2006).

Behavioural characteristics of ASD. Difficulties with social interaction,

communication, and interpersonal relationships are considered hallmark features of

ASD. Frequently, an ‘oddness’ in social communication may be the first identifiable

sign of ASD. Typical presenting features include reduced social-emotional reciprocity

(i.e. the ability to share thoughts and feelings and engage with others), and atypical

nonverbal communicative behaviours (e.g. reduced eye contact, lack of facial

expressions; American Psychiatric Association, 2013). Such characteristics result in

difficulties in developing and maintaining close interpersonal relationships. The

restricted, repetitive behaviours that are also a core characteristic of ASD may manifest

differently based on age, ability, level of intervention and current supports. Within this

domain, the DSM-5 also includes the criterion of atypical responses to sensory stimuli,

recognising that autistic people may have hyper or hyporeactivity to sensory input or

sensory aspects of the environment (e.g. indifference to pain, adverse responses to

specific sounds or textures). Typically, restricted, repetitive behaviours include simple

motor movements (e.g. hand flapping, body rocking), repetitive speech (e.g. echololia,

stereotyped used of words or phrases), and/or repetitive use of objects (e.g. lining up

toys, flicking a rubber band; American Psychiatric Association, 2013). Often, there is an

insistence on sameness, with inflexible adherence to routines and rituals, and high levels

of distress if there are unexpected changes or rituals are impeded. Autistic individuals


also commonly show restricted, intensely focused interests (e.g. strong attachment to

unusual objects).

Cognitive characteristics of ASD. In an attempt to understand the behavioural

characteristics associated with ASD, a wealth of research has investigated cognitive

mechanisms that may be implicated in observed behaviours. Research examining the

cognitive profile associated with ASD has identified areas of typical strengths and

weakness, most notably in theory of mind, central coherence, and executive functions

(Anderson, Northam, & Wrennall, 2018). For instance, decades of research has

consistently demonstrated that autistic individuals show difficulties with theory of mind

(i.e. the ability to understand the intentions, desire, and beliefs of others; (Baron-Cohen,

Leslie, & Frith, 1985; F. G. E. Happé, 1995; Lind & Bowler, 2010; Sodian & Frith,

1992; D. Williams & Happé, 2010). Yet, it has also been noted that they often display

intact or superior performance in systemising (i.e. the ability to understand or construct

systems; Baron-Cohen, 2002, 2009; Lawson, Baron-Cohen, & Wheelwright, 2004).

It has been proposed that autistic individuals display a cognitive profile

characterised by weak central coherence. Central coherence refers to the tendency to

process incoming information within a context, such that information is pulled together

to extract higher level meaning. It has been suggested that autistic individuals have a

tendency towards local, detail focussed processing, at the expense of contextualised

meaning (Happé, 1997; Happé & Frith, 2006). Supporting this suggestion, research has

demonstrated that autistic individuals display superior performance on the embedded

figures test – a task requiring participants to locate simple target shapes within more

complex patterns as quickly as possible (see Muth, Hönekopp, & Falter, 2014 for a

review; Shah & Frith, 1983). Further, autistic individuals display superior block design

performance (Muth et al., 2014), which has been attributed to weak central coherence

(Shah & Frith, 1993). Related to these findings, it has been noted that autistic

individuals typically exhibit overly selective attention to limited portions of a stimulus


present in the environment, or ‘overselectivity’ (see Ploog, 2010 for a review).

However, it is important to note that enhanced local processing in ASD is a source of

considerable debate, with recent meta-analyses showing mixed or ambivalent support

(e.g.Muth et al., 2014; Van Der Hallen, Evers, Brewaeys, Van Den Noortgate, &

Wagemans, 2015).

Finally, a range of experimental findings have demonstrated atypical executive

functions in autistic individuals. While the definition of executive functions has been

debated (see Chan, Shum, Toulopoulou, & Chen, 2008; Jurado & Rosselli, 2007), it is

generally accepted that the term is an umbrella term for higher level cognitive

processes, such as planning, problem-solving, cognitive flexibility, and the ability to

direct and sustain attention. ASD weaknesses in executive functions have been

demonstrated in planning (Bennetto, Pennington, & Rogers, 1996; Ozonoff,

Pennington, & Rogers, 1991; Pellicano, 2007), cognitive flexibility (Ambery, Russell,

Perry, Morris, & Murphy, 2006; Ozonoff et al., 1991), and shifting attention (Burack,

1994a; Mann & Walker, 2003; Townsend, Harris, & Courchesne, 1996; Wainwright &

Bryson, 1996), however relative strengths have been demonstrated in sustaining

attention (Casey, Gordon, Mannheim, & Rumsey, 1993; Garretson, Fein, &

Waterhouse, 1990; Minshew, Goldstein, & Siegel, 1997). The findings of a recent

meta-analysis conducted by Demetriou and colleagues (2018), suggest a broad

executive functioning weakness in ASD, that is relatively stable across development.

Further, recent research findings indicate a link between atypical executive functioning

in ASD and elevated anxiety symptomology (see Hollocks et al., 2014; Lawson et al.,

2015; Ozsivadjian et al., 2020).

The findings reviewed in this section highlight the wealth of research that has

attempted to identify the core cognitive deficits and characteristics that underpin the

central features of ASD. However, despite this research attention, this approach has not

identified any universal cognitive deficits or capabilities (e.g. Pellicano, Murray,


Durkin, & Maley, 2006), and the focus on core deficits in developmental disorders has

recently been questioned (Astle & Fletcher-Watson, 2020). To date, relatively less

attention has been directed towards understand some of the major co-occurring

difficulties associated with autism, such as anxiety, depression. Further, even less

research has focussed on the cognitive mechanisms that may be implicated in the

aetiology and maintenance of these issues.

Diagnosis versus Continua: The Distribution of Autistic Traits in the Non-Clinical

Population

While previously thought of as a categorical diagnosis, the changes to ASD in

the DSM-5 reflect that, increasingly, a dimensional approach to autism is considered

more appropriate. Indeed, there is now substantial evidence to suggest that autistic traits

are continuously distributed across the population, with one end of the distribution

represented by extreme levels of autistic traits that manifest as clinically diagnosed

ASD (Constantino & Todd, 2003; Lundström et al., 2012; Robinson et al., 2011;

Ruzich, et al., 2015). Falling near this end of the continuum are also some individuals

who endorse high levels of autistic traits but do not meet criteria for ASD. The

following section will describe the evidence suggesting that autistic symptoms and traits

exist on a continuum that are distributed in the general population.

The suggestion that autistic traits extend outside clinically defined ASD first

emerged from research examining genetic contributions to autism. In his original paper,

Kanner (1943) noted that the parents of his autistic patients often shared subtle traits

with their children, such as social awkwardness, rigid behaviours and obsessive

tendencies. These observations, in conjunction with the growing work on attachment

theory (e.g. Harlow & Harlow 1962; Bowlby, 1958) led to the unfortunate interpretation

that the root of ASD lay in parenting style, and the “refrigerator mother” theory of

autism (Bettelheim, 1967; Kanner, 1949). While these early interpretations focused on

environmental factors, subsequent research noted that these shared characteristics may


point to the role of genetics in the aetiology of ASD. In a seminal study conducted by

Folstein and Rutter (1977), significantly higher concordance rates for ASD were

observed in monozygotic twins compared to same-sex dizygotic twins. This finding has

been replicated in numerous subsequent studies and a recent meta-analysis of twin

studies established that heritability estimates for ASD range between 64 and 91% (Tick,

Bolton, Happé, Rutter, & Rijsdijk, 2016). Since a genetic contribution to the aetiology

of ASD was recognised, research has identified that the first-degree relatives of autistic

individuals tend to show similar patterns of behavioural idiosyncrasies and social

difficulties to those observed in autism (for reviews see Bailey, Palferman, Heavey, &

Le Couteur, 1998; Pisula & Ziegart-Sadowska, 1993; Sucksmith, Roth, & Hoekstra,

2011). For instance, heightened social difficulties have been observed in the parents and

siblings of autistic individuals (Bolton et al., 1994; Narayan, Moyes, & Wolff, 1990;

Wolff, Narayan, & Moyes, 1988), and in second-degree relatives (e.g. Piven, Palmer,

Jacobi, Childress, & Arndt, 1997), compared to the relatives of children with other

neurodevelopmental conditions. Relatives of autistic individuals have also been noted to

have fewer or lower quality social relationships (e.g. Briskman, Happé, & Frith, 2001;

Losh, Childress, Lam, & Piven, 2008; Losh & Piven, 2007; Piven et al., 1997), greater

social pragmatic difficulties (e.g. Ben-Yizhak et al., 2011; Whitehouse, Barry, &

Bishop, 2007), and rigid or perfectionistic personality traits (Losh et al., 2008; Piven,

1997). Based on these findings, the concept of autistic traits emerged as a way to

characterise a profile of milder autistic features in individuals who do not have an ASD

diagnosis.

To further investigate how autistic traits extend into the general population,

several measures have been developed to assess the degree of autistic traits an

individual possesses. Early measures of autistic traits took the form of clinical

interviews, however these measures require considerable time on the part of the

participants, and so limit their use to assess broad general populations (e.g. Bolton,


1994). As such, brief surveys, that can be administered in person or online, have been

developed as more accessible measures of autistic traits in the general population. One

of the most common such scales is the Autism Spectrum Quotient (AQ; Baron-Cohen,

Wheelwright, Skinner, Martin, & Clubley, 2001), a self-report measure designed to

assess autistic traits in individuals without intellectual disability. The AQ consists of 50

items related to everyday preferences (i.e. “I find social situations easy”, “I tend to have

strong interests, which I get upset about if I can’t pursue”), which are rated on a simple

four-point Likert scale. While a number of factor structures for the AQ have been

proposed (e.g. Austin, 2005; Baron-Cohen et al., 2001; Lau et al., 2013) three factors

typically emerge from the AQ, which are suggested to tap into social difficulties,

communication difficulties, and attention to details and patterns (see English, Gignac,

Visser, Whitehouse, & Maybery, 2020; Russell-Smith, Maybery, & Bayliss, 2011). The

AQ has been demonstrated to have high test-retest reliability, and good internal

consistency (Baron-Cohen et al., 2001; Ruzich et al., 2015). As stated above, to meet

criteria for clinically diagnosed ASD an individual must demonstrate persistent

difficulties in both the social communication and restricted patterns of behaviour

domains (American Psychiatric Association, 2013). While an extreme score on the AQ

does not necessarily indicate an individual will meet these criteria, the AQ has been

demonstrated to have good discriminant validity, able to differentiate autistic and non-

autistic groups (Baron-Cohen et al., 2001; Hoekstra, Bartels, Cath, & Boomsma, 2008;

Woodbury-Smith, Robinson, Wheelwright, & Baron-Cohen, 2005). Given this

discriminate validity, the AQ is considered appropriate to use as a general measure of

autistic symptomatology (Landry & Chouinard, 2016; Ruzich, Allison, Smith, et al.,

2015). Previous research has shown that AQ scores are normally distributed in the

general population (Baron-Cohen et al., 2001; Hoekstra, Bartels, Verweij, & Boomsma,

2007; Hurst, Mitchell, Kimbrel, Kwapil, & Nelson-Gray, 2007), with autistic people


represented in the extreme end of the distribution. These findings support a continuum

of autistic traits in the general population.

Further supporting the notion that autistic traits exists on a continuum, research

using self-report measures in the general population, such as the AQ, have found that

high levels of autistic traits are associated with increased social and interpersonal

difficulties (e.g. Kanne, Christ, & Reiersen, 2009), and a tendency towards rigid

personality structures (e.g. Kunihira, Senju, Dairoku, Wakabayashi, & Hasegawa,

2006). Parents of autistic children also endorse higher levels of autistic traits on the AQ

than parents of non-autistic children (Bishop et al., 2004; Sally Wheelwright, Auyeung,

Allison, & Baron-Cohen, 2010; Woodbury-Smith et al., 2005). Further, twin-studies

suggest heritability estimates of autistic traits in children between 36 to 87%, similar to

the rates described in clinically diagnosed ASD, providing further support for the notion

of autism as a continuously distributed trait (see Ronald & Hoekstra, 2011, for a

review). Research has also demonstrated that individuals who endorse high levels of

autistic traits display patterns of performance on cognitive tasks consistent with those

observed in ASD. For example, compared to participants low in autistic traits,

participants high in autistic traits demonstrate superior performance on the adult

Embedded Figures Test (see Cribb et al., 2016 for a review) and block design task (e.g.

Stewart, Watson, Allcock, & Yaqoob, 2009), compromised global visual processing

(e.g. Grinter et al., 2009), reduced cognitive flexibility (e.g. Best, Moffat, Power,

Owens, & Johnstone, 2008; Christ, Kanne, & Reiersen, 2010), theory of mind

weaknesses (e.g. Best et al., 2008; Gökçen, Frederickson, & Petrides, 2016), stimulus

overselectivity (e.g. Reed, 2017), and general executive functioning weaknesses (e.g.

Best et al., 2008; Christ et al., 2010; Gökçen et al., 2016; Gökçen, Petrides, Hudry,

Frederickson, & Smillie, 2014).

The wide range of findings demonstrating that the behavioural and cognitive

characteristics of ASD are observed in both individuals with, and without, autistic


relatives, has provided convergent evidence that autistic traits exist on a continuum.

Further, these findings support the view that it is appropriate to consider autistic traits

dimensionally, with autistic traits approximately normally distributed in the general

population, and the high end of the autism continuum incorporating individuals who

endorse high but sub-clinical levels of autistic traits, as well as individuals with

clinically diagnosed ASD.

Evidence that Heightened Autistic Traits are Associated with Elevated Anxiety

Vulnerability

It has long been recognised that the ASD is associated with heightened rates of

co-occurring psychopathology, and this contributes to the broad variability in the ASD

presentation. In particular it has been noted that autistic individuals have a heightened

risk of developing emotional difficulties, with elevated depression and anxiety

symptomology especially common in autistic youth and adults (e.g. Leyfer et al., 2006).

While the rates of depressive symptomology are more inconsistent (e.g. Billstedt,

Gillberg, & Gillberg, 2005; Joshi et al., 2013), co-occurring anxiety disorders and

elevated anxiety vulnerability have been consistently demonstrated at the high end of

the autism continuum, both in autistic individuals, and in individuals who endorse high

levels of autistic traits. Anxiety vulnerability has also been noted to exacerbate the core

difficulties of ASD such as interpersonal difficulties and restricted, repetitive

behaviours (Wood & Gadow, 2010), and has been suggested to be impact the quality of

life of autistic children and their parents (Adams, Clark, & Simpson, 2020; den

Houting, Adams, Roberts, & Keen, 2020). The following section summarises the

research demonstrating that elevated autistic traits are associated with elevated anxiety

vulnerability.

Evidence of elevated anxiety vulnerability in ASD. Given that ASD has

previously been considered primarily a childhood disorder, a majority of the research

examining elevated anxiety vulnerability in ASD has focussed on autistic youth. It is


estimated that secondary psychopathology occurs in as many as 72% of autistic children

(Leyfer et al., 2006). Studies investigating co-occurring psychiatric disorders in autistic

youth note anxiety disorders to be among the most common co-occurring disorders,

with prevalence estimates ranging between 11% and 84% (De Bruin, Ferdinand,

Meester, De Nijs, & Verheij, 2007; Joshi et al., 2010; Leyfer et al., 2006; Simonoff et

al., 2008). Autistic children have also been demonstrated to show elevated subclinical

anxiety symptoms (Sukhodolsky et al., 2008; Weisbrot, Gadow, DeVincent, &

Pomeroy, 2005). Initial systematic reviews of the rates of anxiety disorders among

autistic youth noted significant heterogeneity across studies, and that assessment

methods varied greatly (MacNeil, Lopes, & Minnes, 2009; White, Oswald, Ollendick,

& Scahill, 2009). Most recently, van Steensel, Bogels and Perrin (2011) conducted a

meta-analysis examining the prevalence of anxiety disorders in autistic youth and

identified 31 studies involving 2,121 young people (<18 years old). This review

concluded that, across studies, 39.6% of autistic youth had a least one co-occurring

anxiety disorder. The most commonly identified disorder was specific phobia (29.8%),

followed by OCD (17.4%), social anxiety disorder (17.4%), agoraphobia (16.6%), and

generalised anxiety disorder (15.4%). In contrast, the prevalence rates of anxiety

disorders in non-autistic youth are estimated to be between 2.2% and 27% (Costello,

Egger, & Angold, 2005; Costello, Mustillo, Erkanli, Keeler, & Angold, 2003). In a

subsequent meta-analysis, van Steensel and Heeman (2017) demonstrated that anxiety

symptoms were significantly higher in autistic youth compared to neurotypical youth,

and that anxiety levels were elevated in autistic youth compared to youths clinically

referred for externalising or developmental issues. Further, the prevalence estimates of

specific anxiety disorders are approximately two times higher for autistic children than

those reported in neurotypical children (Costello et al., 2005), and higher than for

children seeking treatment for other neurodevelopmental disorders such as ADHD

(Shur-Fen Gau et al., 2010) and learning difficulties (Dekker & Koot, 2003).


In recent years, increasing attention has been given to understanding the social

and mental health needs of autistic individuals across the lifespan, including into

adulthood (Baxter et al., 2015). Increasingly, autistic adults are acknowledged to be at a

heightened risk of co-occurring psychological difficulties, with depressive and anxiety

disorders among the most common (Joshi et al., 2013). Given this, a recent meta-

analysis conducted by Hollocks and colleagues (2019) examined the rates of depression

and anxiety vulnerability in autistic adults. The pooled estimate of current and lifetime

presence of anxiety disorders for autistic adults was 42%, similar to the rate identified in

autistic youth. For specific anxiety disorders, the most commonly identified current

disorders were social anxiety (29%), followed by OCD (22%), and generalised anxiety

disorder (18%). These prevalence rates are again significantly higher than the rates

observed in the neurotypical population, where studies estimate rates of anxiety

disorders to be between 1% and 12% (Kessler, Petukhova, Sampson, Zaslavsky, &

Wittchen, 2012; Kessler et al., 2006). Considered together, these systematic reviews

provide strong evidence that anxiety vulnerability is significantly elevated in the clinical

ASD profile.

Evidence of elevated anxiety vulnerability in individuals with high levels of

autistic traits. Elevated anxiety vulnerability has also been demonstrated in both first-

degree relatives of autistic individuals, and in individuals drawn from the general

population who endorse high levels of autistic traits. Several studies have noted that the

relatives of autistic people show elevated rates of anxiety disorders compared to the

general population, in particular for social anxiety disorder (Mazefsky, Folstein, &

Lainhart, 2008; Piven et al., 1991; Piven & Palmer, 1999). Relatives of autistic

individuals have also been shown to report higher rates of trait anxiety than the general

population (Murphy et al., 2000). Research has also reliably demonstrated that

individuals in the general population who endorse high levels of autistic traits, show

similar elevated rates of anxiety vulnerability. In a study conducted by Kanne, Christ


and Reirsen (2009), undergraduate students with high and low levels of autistic traits

were administered a self-report measure of psychiatric and personality characteristics.

The participants high in autistic traits endorsed significantly more anxiety symptoms

than did participants low in autistic traits. Similar findings were obtained in an

Australian study conducted by Rosbrook and Whittingham (2010), in which

undergraduate university students were administered the AQ and a self-report measure

of depression, anxiety and stress. Findings indicated a significant, positive correlation

between autistic traits and anxiety symptoms (r = .31), indicating that higher levels of

autistic traits were associated with elevated anxiety vulnerability. In addition, Freeth,

Bullock, and Milne (2013) reported a moderate correlation between AQ scores and

social anxiety scores (r =.51) in a UK sample of undergraduate students. A number of

studies have also demonstrated similar findings that high levels of autistic traits are

associated with elevated anxiety vulnerability (Austin, 2005; Kunihira et al., 2006; Liss,

Mailloux, & Erchull, 2008; Russell-Smith, Bayliss, Maybery, & Tomkinson, 2013;

Wainer, Ingersoll, & Hopwood, 2011).

The literature reviewed in this section provides strong evidence that elevated

autistic traits are associated with elevated anxiety vulnerability, in both clinical and

subclinical groups. The association between autistic traits and anxiety vulnerability is

such that concern has emerged regarding overlap between anxiety symptoms and the

core features of ASD. Specifically, there is some discussion as to whether the high rates

of anxiety vulnerability observed in ASD reflect a ‘true’ discreet co-morbidity,

operating phenotypically similar to anxiety symptomology in the general population,

alternatively if such symptoms are part of the ASD profile, or instead if they represent a

unique manifestation of anxiety (see Kerns & Kendall, 2012; Wood & Gadow, 2010).

Emerging evidence suggests that autistic individuals may experience anxiety symptoms

that are more idiosyncratic and related to autistic features. For example, a fear of loud

sounds, unusual phobias such as fear of beards or mechanical objects, and social anxiety


without the fear of negative evaluation (Connor Morrow Kerns et al., 2014; Leyfer et

al., 2006; Ozsivadjian, Knott, & Magiati, 2012). Further, there are difficulties

disentangling the symptoms of anxiety from the characteristics of ASD (Lecavalier et

al., 2014). This has been most frequently discussed in relation to OCD and social

anxiety disorder, two of the most common anxiety presentations in ASD (Connor

Morrow Kerns et al., 2014; Magiati et al., 2017; Ozsivadjian et al., 2012). For instance,

a similarity has been noted between the compulsive behaviours in OCD and the

restricted, repetitive behaviours in ASD (Wood & Gadow, 2010; Zandt, Prior, &

Kyrios, 2007), and there is recent evidence suggesting some neurobiological overlap

(Carlisi et al., 2017). The reduced social motivation and difficulties with social

communication and interpersonal relationships characteristic of ASD have also been

noted to appear behaviourally similar to the anxious avoidance and social difficulties

common in social anxiety disorder (see Wood & Gadow, 2010).

Compounded this issue, despite the substantial literature highlighting the link

between heightened autistic traits and anxiety vulnerability, experimental research

examining the cognitive mechanisms of anxiety in individuals at the high end of the

autism continuum has been limited. Some contemporary research has revealed links

between cognitive, emotional, and neurobiological characteristics that are thought to

underlie elevated anxiety vulnerability in ASD (see Rodgers & Ofield, 2018; South &

Rodgers, 2017 for reviews). For instance, Herrington and colleagues (2017) noted

decreased amygdala volume (a region of the brain known to be associated with anxiety)

in autistic individuals with elevated anxiety vulnerability, relative to non-anxious

autistic individuals and non-anxious, non-autistic individuals. These findings suggest

that the neurodevelopmental trajectories of autistic individuals who develop elevated

anxiety symptomology may be different to the trajectories of non-anxious autistic

individuals and non-autistic individuals. Related to these findings, recent research has

found a link between sensory abnormalities and elevated anxiety vulnerability in ASD


(Green & Ben-Sasson, 2010; Neil, Olsson, & Pellicano, 2016; Wigham, Rodgers,

South, McConachie, & Freeston, 2015). Alexithymia (i.e. difficulty identifying and

describing internal emotional states) has been identified as a common difficulty in ASD,

with 45-60% of autistic individuals reporting elevated alexithymia (Griffin, Lombardo,

& Auyeung, 2016; Hill, 2004; Lombardo, Barnes, Wheelwright, & Baron-Cohen,

2007). Alexithymia has been demonstrated to be associated with symptoms of GAD and

social anxiety in non-ASD populations (Grabe, Spitzer, & Freyberger, 2004; Mennin &

Fresco, 2010; O’Toole, Hougaard, & Mennin, 2013), and recent research has found a

link between increased alexithymia and elevated anxiety vulnerability in autistic

individuals (Maisel et al., 2016). A cognitive characteristic that is gaining increasing

attention in relation to anxiety vulnerability in ASD is intolerance of uncertainty, which

is defined as “the tendency to react negatively on an emotional, cognitive, and

behavioural level to uncertain situations and events” (Dugas, Gagnon, Ladouceur, &

Freeston, 1998). Several recent studies have found significant associations between

intolerance of uncertainty and elevated anxiety vulnerability in ASD (Boulter, Freeston,

South, & Rodgers, 2014; Neil et al., 2016; Wigham et al., 2015). While each of these

factors has been demonstrated to be associated with anxiety vulnerability in populations

high on the autism continuum, research on cognitive models of the aetiology and

maintenance of anxiety vulnerability in autism remains limited. South and Rodgers

(2017) have proposed a model of anxiety vulnerability in ASD that incorporates sensory

functioning, alexithymia, and intolerance of uncertainty. However, the authors note that

while evidence supporting these factors is emerging, further research is needed to

investigate the relevance of many different cognitive mechanisms that may underpin

anxiety in the high end of the autism continuum.

It has been suggested that to further understand the nature of anxiety

vulnerability associated with elevated autistic traits, it is important to examine the

cognitive mechanisms that may maintain it (Wood & Gadow, 2010). While


investigations of cognitive models of anxiety vulnerability in the high end of the autism

continuum are to date currently limited, substantial research has investigated cognitive

models of anxiety vulnerability in the general population in individuals who have not

been selected for levels of autistic traits. Investigating the relevance of such models to

the anxiety vulnerability noted in populations with elevated autistic traits, may be

helpful in detangling the overlap between anxiety symptoms and the core features of

ASD, and have implications for the treatment of anxiety in individuals who exhibit

especially high levels of autistic traits. The following sections will first review literature

that has investigated the cognitive mechanisms underpinning anxiety vulnerability in

populations that have not been selected on the basis of autistic traits, followed by

research investigating these mechanisms in populations with heightened autistic traits.

Elevated Anxiety Vulnerability is Associated with Heightened Selective Attention

to Negative Information in Individuals Not Selected for Levels of Autistic Traits

Cognitive models of anxiety in the general population commonly posit that

selective attention towards negative information2 plays a role in the aetiology and

maintenance of elevated anxiety vulnerability (Bar-Haim et al., 2007; Beck & Clark,

1997; Eysenck et al., 2007; Mogg & Bradley, 1998a; J. Williams et al., 1997). It is

suggested that this attentional bias results in negative information becoming especially

salient in the environment compared to positive or neutral information (MacLeod,

Mathews, & Tata, 1986). In studies using a range of assessment approaches, such as the

variants of the visual search task, the emotional Stroop task, and attentional probe task,

this bias to negative information has consistently been demonstrated to be more

pronounced in individuals high in anxiety vulnerability, compared to individuals with

2 This attentional bias has also been commonly referred to as an attentional bias to threat (e.g. Bar-Haim

et al., 2007). However, it has been noted that most studies that have examined anxiety-linked attentional bias have used emotional negative stimuli (i.e. emotional faces or emotional words) that do not actually serve as threat cues by foreshadowing genuine future danger (e.g. Notebart, Tilbrook, Clarke & MacLeod, 2017). Thus, while these stimuli can be characterised as being negatively valanced, they are not necessarily “threatening” in the sense that they predict an aversive event. Thus, the current thesis will adopt the term “attentional bias to negative information” to refer to this phenomenon.


lower anxiety vulnerability (see Armstrong & Olatunji, 2012; Bar-Haim et al., 2007;

Dudeney, Sharpe, & Hunt, 2015; Price et al., 2016 for reviews). This range of

experimental findings will now be reviewed.

Early evidence of an anxiety-linked attentional bias to negative information

came from studies employing a dichotic listening task (see Yiend, 2010). In this task,

participants are simultaneously delivered two messages, one in each ear, with the

content of each message varying in emotional valence. Participants are required to

discriminate target words that appear in each message, and a greater ability to identify

target words that are emotionally negative, compared to target words that are

emotionally neutral, is suggested to indicate a selective attention towards negative

information. Using this task, early studies demonstrated that participants with high

levels of anxiety vulnerability detected significantly more emotionally negative target

words compared to participants who were low in anxiety vulnerability (Burgess, Jones,

Robertson, Radcliffe, & Emerson, 1981; Foa & McNally, 1986). However, tasks like

the dichotomous listening task attracted methodological criticism, as it is possible that

the results obtained by these tasks reflect a response bias for guessing and not a genuine

attentional bias (e.g. Macleod, 1991).

More recent methodologies include emotional variants of the visual search task.

In a typical emotional visual search task, participants are required to identify a

negatively valanced target stimuli in an array of neutral distractors, or a neutrally

valanced target in an array of negatively valanced distractors. The degree to which

participants are faster to detect negatively valanced targets, compared to neutral targets,

is taken as an index of selective attention to negative information. Using the visual

search task, a number of studies have demonstrated disproportionally speeded

performance at identifying negative targets in participants with a range of anxiety

disorders (e.g. Eastwood et al., 2005; Gilboa-Schechtman, Foa, & Amir, 2010; Ohman,

Flykt, Esteves, & Institute, 2001; Rinck, Becker, Kellermann, & Roth, 2003). While


findings from emotional visual search tasks provide convergent support for anxiety-

linked attentional bias to negative information, the paradigm is not as commonly used

as others. It has been suggested that this is because this methodology may be less

sensitive to effects in subclinical populations (see Yiend, 2010).

The most commonly used methods to assess anxiety-linked attentional bias to

negative information have overwhelmingly been the emotional Stroop task and the

attentional probe task (see Bar-Haim, 2010; Cisler & Koster, 2010; Yiend, 2010). The

evidence from these paradigms will now be considered in turn.

The emotional Stroop task is an adaption of the original Stroop paradigm

(Stroop, 1935). In the original Stroop task, participants are presented with a list of

colour names (e.g. ‘blue’, ‘green’) in different coloured ink. Participants are then

required to name the colour of the ink in which the words are presented as quickly as

possible and ignore the printed word. Findings from this task indicate that the printed

word content interferes with the speed of colour naming, such that participants are

consistently delayed in correctly naming the ink colour when the ink colour and word

content are incongruent (e.g. the word ‘blue’ displayed in green ink), compared to when

they are congruent (e.g. the word ‘green’ displayed in green ink; MacLeod, 1991). The

emotional Stroop modification of this task involves presenting differently valanced

words (i.e. negative and neutral) in different coloured ink, and assessing the degree to

which the semantic content of the words delays the speed of correctly naming the ink

colour. Demonstrating a greater delay in correctly identifying the ink colour of

negatively valanced words, compared to non-negatively valanced words, is taken to

indicate an attentional bias to negative information. A wealth of research has

demonstrated that individuals with elevated anxiety vulnerability show selective,

emotion-congruent interference effects on the emotional Stroop. For instance, in a study

conducted by Mathews and MacLeod (1985) clinically anxious individuals were

particularly slow at naming word colour for negatively valanced words relative to


positively valanced words, when compared to controls. Similar findings have been

reported in other studies that have demonstrated disproportionate slowing to name the

ink colour of negatively valanced words in participants with generalised anxiety

disorder (GAD; Martin, Williams, & Clark, 1991; Mathews, Mogg, Kentish, &

Eysenck, 1995), post-traumatic stress disorder (PTSD; Kaspi, McNally, & Amir, 1995;

McNally, English, & Lipke, 1993), panic disorder (McNally et al., 1994; McNally,

Riemann, & Kim, 1990; McNally, Riemann, Louro, Lukach, & Kim, 1992), obsessive-

compulsive disorder (OCD; Foa, Ilai, McCarthy, Shoyer, & Murdock, 1993; Lavy, Van

Oppen, & Van Den Hout, 1994), specific phobia (Lavy, van den Hout, & Arntz, 1993;

Martin et al., 1991; Watts, McKenna, Sharrock, & Trezise, 1986), and social anxiety

disorder (Hope, Rapee, Heimberg, & Dombeck, 1990; Mattia, Heimberg, & Hope,

1993). These findings in clinically anxious populations support the suggestion that

elevated anxiety vulnerability is characterised by a selective attention to negative

information (see Bar-Haim, 2010; Cisler & Koster, 2010; Dudeney et al., 2015; Yiend,

2010).

The finding that elevated anxiety vulnerability is associated with slowed

responding to negatively valanced words on the emotional Stroop is not limited to

clinically anxious populations. Mogg, Mathews, Bird and Macgregor-Morris (1990)

administered an emotional Stroop task containing negatively valanced and neutral

words to participants who varied in their level or trait anxiety. It was found that

participants who endorsed high levels of trait anxiety were disproportionally slower at

naming negatively valanced words relative to neutral words, compared to participants

who were low in trait anxiety. Similar findings have been reported across a range of

studies, demonstrating that high trait anxious participants display greater slowing for

negatively valanced words compared to low trait anxious participants (Dawkins &

Furnham, 1989; Fox, 1993b; MacLeod & Rutherford, 1992; Mogg, Kentish, & Bradley,

1993; Richards, French, Johnson, Naparstek, & Williams, 1992; Richards & Millwood,


1989). These findings provide further converging support that elevated anxiety

vulnerability is characterised by selective attention to negative information, and that this

characteristic extends from clinical to subclinical groups.

Despite the widespread use of the emotional Stroop paradigm, methodological

constraints associated with the task limit the interpretability of its findings. For instance,

it has been noted that the response slowing observed in the presence of negatively

valanced words may be due to selective attention to negative information, or may be

due to an overall delayed responding in the presence of negative information (Algom,

Chajut, & Lev, 2004; MacLeod et al., 1986). Specifically, it has been suggested that the

findings obtained using the emotional Stroop task could be due to the tendency to

“freeze” in response to negative information, and not necessarily because of an

enhanced selective attention to such stimuli (Mogg, Holmes, Garner, & Bradley, 2008).

Further, given that the semantic and ink colour content are presented within the same

stimulus, the emotional Stroop task is unable to discriminate whether response slowing

is due to attentional vigilance to the content of the negatively valanced words, or

attentional avoidance of negatively valanced stimuli (de Ruiter & Brosschot, 1994).

Thus, while the findings of the emotional Stroop paradigm are consistent with the

hypothesis that elevated anxiety vulnerability is associated with an attentional bias to

negative information, the interpretability issues associated with the task limit the

conclusions that can be drawn about whether this hypothesis is valid.

To address the limitations associated with the emotional Stroop paradigm,

MacLeod, Mathews and Tata (1986) developed the attentional probe task (also known

as the dot probe task). The attentional probe task involves presenting two competing

stimuli of differing emotional valence, typically one negative in valance and the other

non-negative, on either side of a central fixation point (MacLeod et al., 1986; Mathews

& MacLeod, 2002). These stimuli are presented briefly, followed by a probe in the

spatial position of one of the previous stimuli (MacLeod et al., 1986; Mathews &


MacLeod, 2002). When reaction times are speeded to discriminate the identity of probes

that appear in the spatial location recently vacated by negatively valanced stimuli,

relative to non-negative stimuli, this is taken to indicate an attentional bias to negative

information (MacLeod et al., 1986). With this methodology the attentional probe

paradigm avoids the limitations associated with the emotional Stroop paradigm. Firstly,

given that negative information is presented on every trial, any general slowing of

response in the presence of negative information is not confounded with speeded

reaction times to probes that appear in the location of negatively valanced stimuli

(Yiend, 2010). Secondly, the attentional probe task is better able to discriminate

whether response patterns indicate attentional vigilance, or attentional avoidance, of

negative information (Yiend, 2010). Specifically, if reaction times are speeded to

discriminate the identity of probes that appear in the location of negatively valanced

stimuli, this indicates attentional vigilance. Conversely if reaction times are speeded to

discriminate probes in the location of non-negative stimuli this would indicate

attentional avoidance.

Using the attentional probe task, MacLeod, Mathews and Tata (1986) assessed

attentional bias to negative information in clinically anxious and non-anxious

participants using negatively valanced and non-negatively valanced words. Results

demonstrated that clinically anxious participants showed significantly faster reaction

times to probes that appeared in the location of the recently vacated negatively valanced

words, compared to the non-negative words. This pattern of response latencies was not

demonstrated in the non-anxious participants. Mogg, Mathews and Eysenck (1992)

replicated these findings and demonstrated that clinically anxious individuals were

disproportionately speeded to discriminate probes that appeared in the location of

negatively valanced words relative to non-negative words, compared to non-anxious

individuals. Since this seminal work, a range of studies have demonstrated similar

response latency patterns in participants with GAD (see Mogg & Bradley, 2005), panic


disorder (Horenstein & Segui, 1997; Kroeze & Van Den Hout, 2000), OCD (Cohen,

Lachenmeyer, & Springer, 2003; Tata, Leibowitz, Prunty, Cameron, & Pickering,

1996), PTSD (Fani et al., 2012), SAD (Asmundson & Stein, 1994; LeMoult &

Joormann, 2012; Musa, Lépine, Clark, Mansell, & Ehlers, 2003) and specific phobia

(Mogg & Bradley, 2006).

Like the emotional Stroop paradigm, similar patterns of results using the

attentional probe task have been observed not only in clinically anxious individuals, but

also in subclinical populations reporting high levels of trait anxiety. Following from

their original study, MacLeod and Mathews (1986) administered an attentional probe

task to participants with high and low levels of trait anxiety, and found that participants

high in trait anxiety were significantly faster to discriminate probes that appeared in the

location of negatively valanced words compared to non-negatively valanced words.

Several other studies have confirmed that individuals high in trait anxiety display

significantly greater attentional biases to negative information than individuals low in

trait anxiety (Bradley, Mogg, & Lee, 1997; Broadbent & Broadbent, 1988; Fox, 1993a;

Mogg, Bradley, De Bono, & Painter, 1997).

Similar patterns of findings to those described above have been obtained using

the attentional probe paradigm whilst manipulating several parameters, such a stimulus

type and stimulus onset asynchrony (SOA; i.e. the interval between the presentation of

the valanced stimuli and the presentation of the probe). While much initial research

using the attentional probe task employed stimulus pairs of emotionally valanced words,

increasingly studies have used pairs of emotionally valanced images, such as faces (see

Bar-Haim et al., 2007). This is in part because it has been suggested that images, in

particular emotional faces, may have higher ecological validity than emotional words

(Bradley, Mogg, Millar, et al., 1997; Bradley, Mogg, White, Groom, & De Bono, 1999;

McNally et al., 1990). A number of studies have demonstrated significant anxiety-

linked attentional bias to negative information effects using emotional faces stimuli


(Bradley, Mogg, Millar, et al., 1997; Koster, Crombez, Verschuere, & De Houwer,

2006; Yiend & Mathews, 2001). Typically, researchers have employed an SOA of

500ms when using the attentional probe paradigm, however significant anxiety-linked

selective attention to negative information effects have been demonstrated at longer

SOAs such as 1000ms (see Bar-Haim et al., 2007). While automatic patterns of

selective attention will influence attentional bias observed at both 500ms and 1000ms

SOAs, the longer SOA permits greater opportunity for strategic patterns of selective

attention, such as avoidance of stimuli (Bradley, Mogg, Falla, & Hamilton, 1998;

Koster, Verschuere, Crombez, & Van Damme, 2005; Mogg, Bradley, Miles, & Dixon,

2004; Mogg et al., 1997). By comparing anxiety-linked attentional bias effects at the

two SOAs, it is possible to infer whether these anxiety-linked effects implicate

automatic or more strategic attentional processes. In summary, the literature reviewed in

this section provides strong convergent evidence that elevated anxiety vulnerability is

associated with an attentional bias to negative information.

Anxiety-Linked Attentional Bias to Negative Information in the Presence of High

Levels of Autistic Traits

Given the wealth of evidence that elevated anxiety vulnerability is characterised

by heightened attention to negative information, it is pertinent to investigate if this

mechanism underpins anxiety vulnerability in individuals at the high end of the autism

continuum, in both clinical and sub-clinical groups. Despite this, investigations of

anxiety-linked attentional bias to negative information in these populations has been

limited. Studies that have directly investigated anxiety-linked attentional bias to

negative information in clinical and subclinical autistic groups have produced

inconsistent findings, and the suggestion has emerged that the mechanisms

underpinning anxiety vulnerability in ASD may not be analogous to those in the general

population. The following section will review these findings, and the suggestion that the


high end of the autism continuum may be characterised by attenuated anxiety-linked

attentional bias to negative information.

To date, there have been a limited number of studies that have investigated

whether anxiety-linked attentional bias to negative information is attenuated at the high

end of the autism continuum. In a study conducted by Hollocks and colleagues (2013),

anxiety-linked attentional bias to negative information was assessed in autistic and non-

autistic youth with two versions of the attentional probe paradigm that used either

emotional faces or emotional words. Autistic participants displayed significantly higher

parent- and child-rated trait anxiety than non-autistic participants. However, results

from the attentional probe task demonstrated no significant group differences in

attentional bias to negative information, and no significant relationship between anxiety

symptoms and attentional bias scores for either the emotional faces or emotional words.

A similar finding was obtained by May, Cornish and Rinehart (2015). In their study,

autistic and non-autistic children were administered an attentional probe task with

emotional faces. Like the Hollocks et al. (2013) study, autistic participants had

significantly higher parent-reported trait anxiety levels than non-autistic participants.

Despite this, no significant group differences in attentional bias to negative information

were observed between the autistic and non-autistic participants. Further, there was no

significant relationship between anxiety symptoms and attentional bias.

Recent studies using other attentional paradigms, such as visual search, have

also failed to find a significant anxiety-linked attentional bias to negative information in

autistic individuals (Antezana, Mosner, Troiani, & Yerys, 2016; García-Blanco et al.,

2017; May, Cornish, & Rinehart, 2016). One study has investigated anxiety-linked

attentional bias to negative information in subclinical autistic groups. In a study

conducted by Milosavljevic and colleagues (2017), first-degree relatives of autistic

individuals (designated high-risk for ASD) and participants with no known autistic

relatives (designated low-risk for ASD), completed an attentional bias assessment task.


The high-risk participants were split into those that met criteria for autism (HR-ASD)

and those who did not (HR-non ASD). Similar to previous studies, the HR-ASD group

had the highest levels of parent-rated trait anxiety. Results of the attentional bias task

revealed that level of trait anxiety was associated with attentional bias, such that

increased anxiety symptoms were associated with increased attentional bias to negative

information. However, while the HR-non ASD group exhibited a significant attentional

bias to negative information, the HR-ASD group did not. In each of the studies that

have failed to demonstrate a significant anxiety-linked attentional bias to negative

information at the high end of the autism continuum, the authors have concluded that

these findings may indicate that the cognitive mechanisms of anxiety vulnerability in

ASD may not be analogous to those in the general population. Thus, it is possible that

the high end of the autism continuum is characterised by attenuated anxiety-linked

attentional bias to negative information. However, the design of these previous studies

has limited conclusions regarding this possibility.

When investigating an anxiety-linked attentional bias to negative information, it

is imperative to recruit groups that differ in levels of anxiety vulnerability, whilst

holding other group characteristics constant. Otherwise, a confound is introduced that

may mask the expression of a significant anxiety-linked attentional effect. Thus, in

investigating anxiety-linked attentional bias in individuals at the high end of the autism

continuum, it is important to compare two groups of participants who differ in levels of

anxiety vulnerability but who are matched in terms of elevated levels of autistic traits.

This comparison will reveal whether there is an anxiety-linked attentional bias to

negative information in these two high autistic trait groups. But critically, two

additional groups of individuals, both low in levels of autistic traits, and who differ in

levels of anxiety vulnerability also need to be included. These low autistic trait groups

should have the same separation in anxiety vulnerability as the two high autistic trait

groups. With this fully crossed design it can be established whether this anxiety-linked


attentional bias is attenuated by high levels of autistic traits. The separation of the

autistic trait and anxiety-vulnerability dimensions in assessing anxiety-linked attentional

bias to negative information is especially important given the entangled nature of

anxiety vulnerability in ASD as reviewed above. None of the previous studies that have

examined anxiety-linked selective attention at the high end of the autism continuum

have included the four groups necessary to compare the magnitude of anxiety-linked

attentional bias to negative information for groups high and low in autistic traits. The

previous studies conducted by Hollocks et al. (2013), May et al. (2015), and

Milosavljevic et al. (2017) compared a high trait anxious, autistic group to a non-

anxious, non-autistic group. Thus, these studies have not fully unconfounded autistic

traits and trait anxiety.

To date, only one study has demonstrated a significant anxiety-linked attentional

bias to negative information in the presence of high levels of autistic traits. In an

attempt to address the diagnostic overlap and atypical presentation of anxiety in the

autism spectrum, Hollocks, Pickles, Howlin and Simonoff (2016) assessed attentional

bias using probe tasks with emotional faces and emotional words, in autistic participants

with and without a co-occurring anxiety disorder (ASD-anx vs ASD-only), and also in

non-anxious, non-autistic participants. The ASD-anx group exhibited a significantly

greater attentional bias to faces exhibiting negative expressions than did both the ASD-

only, and the non-ASD group. However, this study did not include an anxious, non-

autistic group. Therefore, it was not possible to compare the difference in magnitude of

attentional bias between anxious and non-anxious ASD groups with the difference in

magnitude of attentional bias between anxious and non-anxious non-ASD groups to

establish whether the presence of high levels of autistic traits influenced the extent to

which anxiety-linked attentional bias to negative information is expressed. Thus, while

this study demonstrated a significant anxiety-linked attentional bias to negative

information effect in autistic individuals, the findings are not able to shed light on


whether the magnitude of the anxiety-linked attentional bias to negative information is

attenuated in high levels of autistic traits compared to low levels of autistic traits.

A second limitation of previous studies is that they have included only a 500ms

SOA, restricting the assessment of anxiety-linked attentional bias to negative

information to rapid effects. As discussed previously, while 500ms is the most

commonly used SOA, there is good evidence to suggest that significant anxiety-linked

attentional bias to negative information effects are demonstrated in the general

population at longer SOAs, like 1000ms (see Bar-Haim et al., 2007). Further, these

longer SOAs are thought to capture, slower more strategic attentional processing effects

(Mogg et al., 1997; Mogg et al., 2004). There is evidence to suggest that high levels of

autistic traits are associated with slowed processing of emotion in faces (Annaz,

Karmiloff-Smith, Johnson, & Thomas, 2009; Dalton et al., 2005; Miu, Panǎ, & Avram,

2012). For instance, Miu, Pana and Avram (2012) reported that while high autistic trait

and low autistic trait groups had comparable accuracy rates for identifying the emotion

in faces, the high autistic trait group had significantly longer latencies for these

judgements compared to the low autistic group. Given this, it is possible that that

including a longer SOA, such as 1000ms, that is able to capture slower attentional

effects may be useful in assessing differences in attentional biases as a function of

position along the autistic trait continuum.

In summary, the findings on anxiety-linked attentional bias to negative

information in the context of elevated autistic traits are inconsistent. Given these

heterogenous results, it is currently unclear what role, if any, attentional bias to negative

information may play in the aetiology and maintenance of anxiety vulnerability in

individuals with high levels of autistic traits. The majority of studies reviewed have

failed to find evidence of a significant anxiety-linked attentional bias to negative

information in participants who exhibit high levels of autistic traits. Thus, it is possible

that high levels of autistic traits may be characterised by attenuated anxiety-linked


attentional bias to negative information. This could have important implications for

treatment, in particular for the modifications that are made to anxiety interventions for

individuals at the high end of the autism continuum.

The Current Research Programme

Elevated levels of anxiety vulnerability are associated with more pronounced

autistic traits, in both clinical and subclinical groups (Hollocks, Lerh, Magiati, Meiser-

Stedman, & Brugha, 2019; van Steensel, Bogels, & Perrin, 2011). Despite this, the

cognitive mechanisms characterising anxiety vulnerability in these populations remain

poorly investigated. A heightened selective attention to negative information appears to

be a clear candidate underlying process, as there is strong evidence to suggest this

attentional bias underpins anxiety vulnerability in the general population. To date,

however, the research suggests that the role, if any, that selective attention to negative

information plays in the aetiology and maintenance of anxiety vulnerability in

individuals with elevated autistic traits is unclear. The few studies that have investigated

anxiety-linked attentional bias to negative information in autistic individuals have

inconsistent findings, with the majority of studies suggesting that anxious, autistic

individuals do not display a significant anxiety-linked attentional bias to negative

information (Antezana et al., 2016; García-Blanco et al., 2017; Hollocks et al., 2013;

May et al., 2015). Based on these findings, a reasonable hypothesis to advance is that

high levels of autistic traits are characterised by an attenuated anxiety-linked attentional

bias to negative information; however, the design of previous studies has limited the

assessment of this possibility. Firstly, previous studies have been limited to ASD, and

have not fully unconfounded autistic traits and anxiety vulnerability. Secondly, previous

studies have been limited in their use of a single SOA, limiting their ability to capture,

slower and more strategic patterns of selective attentional processing of negative

information. Thus, the focus of the present research programme is to provide an


adequate test of the hypothesis that high levels of autistic traits are characterised by

attenuated anxiety-linked attentional bias to negative information.

To assess the validity of this hypothesis in the current research programme,

several conditions must be met. Firstly, it is important that the separate dimensions of

anxiety vulnerability, autistic traits, and attentional bias to negative information are

adequately assessed. Within ASD, the high prevalence of anxiety vulnerability, and

entangled nature of anxiety symptomology and core autistic features, is such that the

ability to separate these dimensions is especially difficult. Given this, it is

understandable that only one previous clinical study has recruited a non-anxious,

autistic group (i.e. Hollocks et al., 2016). It is therefore beneficial to recruit participants

in the general population who vary in levels of autistic traits, and trait anxiety, to assess

anxiety-linked attentional bias to negative information. The variability of autistic traits

and trait anxiety in the general population allow for separation of these dimensions, and

better assessment of whether high levels of autistic traits attenuate anxiety-linked

attentional bias to negative information. As such, the studies in the current research

programme will investigate anxiety-linked selective attention to negative information in

undergraduate student participants who vary in their level of autistic traits, and level of

trait anxiety. Secondly, a task is required that has the proven capacity to detect anxiety-

linked attentional bias to negative information in participants who vary in their levels of

autistic traits. As reviewed above, the most commonly used tasks to assess selective

attention have been the emotional Stroop task, and the attentional probe task. Given that

methodological limitations of the emotional Stroop task limit the interpretability of

findings collected using it, the studies in the present research programme will adopt an

attentional probe methodology to assess anxiety-linked attentional bias to negative

information.


Experiment 1 will now be described, which represents the research programme’s

first investigation of whether high levels of autistic traits are characterised by attenuated

anxiety-linked attentional bias to negative information.

.

CHAPTER 2: EXPERIMENT 1 32

Chapter 2: Experiment 1

As reviewed in the previous chapter, it is well established that elevated levels of

autistic traits are associated with elevated anxiety vulnerability. Further, there is

considerable evidence to suggest that selective attention to negative information in the

environment plays a significant role in the aetiology and maintenance of elevated

anxiety vulnerability. However, as noted above, investigation of anxiety-linked

attentional bias to negative information in the context of high levels of autistic traits has

been limited in scope with mixed findings reported (e.g. Hollocks et al., 2013; Hollocks

et al., 2016; May et al., 2015). As discussed previously, a reasonable hypothesis based

on these findings is that high levels of autistic traits are characterised by attenuated

anxiety-linked attentional bias to negative information. However, as reviewed in the

preceding chapter, the methodology of previous studies has limited the ability to draw

conclusions regarding this hypothesis. The purpose of Experiment 1 was to conduct a

study that would test whether high levels of autistic traits attenuate anxiety-linked

attentional bias to negative information using a version of the attentional probe task

described in the preceding chapter, and an unselected sample of undergraduate

university students.

There are two broad methodological approaches that could be employed when

attempting to investigate whether high levels of autistic traits are characterised by an

attenuated anxiety-linked attentional bias to negative information – a “continuous”

design, or a “quantile” design. In a continuous design, an unselected pool of participants

would be recruited and administered questionnaires assessing autistic traits and trait

anxiety. Attenuation effects of high levels of autistic traits on anxiety-linked attentional

bias to negative information would then be assessed by performing a moderated

regression analysis. A quantile design would involve selecting groups of participants

that represent the factorial combination of high and low autistic traits, and high and low

trait anxiety, and then assessing for a significant interaction between autistic traits and


trait anxiety in the determination of attentional bias to negative information. A recent

meta-analysis has suggested that quantile designs can provide more power to detect

effects involving autistic traits than continuous designs (see Cribb et al., 2016).

However, unlike a continuous design, quantile designs that recruit participants that are

especially low or high on autistic traits may not properly represent relationships that

exist across the whole autistic trait continuum. Further, as an association between high

autistic traits and elevated anxiety vulnerability has been well established, it is possible

that a quantile design would encounter difficulty recruiting adequate numbers of high

autistic trait/low trait anxiety and low autistic trait/high trait anxiety participants. This

potential issue is circumvented with the use of a continuous design. Thus, Experiment 1

adopted a continuous design to investigate the possible influence of high levels of

autistic traits on anxiety-linked attentional bias to negative information.

In order to assess attentional bias to negative information in the current study, a

modified version of the attentional probe task (MacLeod et al., 1986; Mathews &

MacLeod, 2002) was developed. As described in the previous chapter, on each trial of

the attentional probe task, participants are presented with pairs of stimuli comprising

one member that is negative, and one member that is non-negative, in emotional tone,

followed by a probe in the spatial location of one of the previous stimuli (MacLeod et

al., 1986; Mathews & MacLeod, 2002). An attentional bias to negative information is

demonstrated when participants are disproportionally speeded to identify probes that

appear in the location of the negative, rather than the non-negative, members of

stimulus pairs (MacLeod et al., 1986). In developing the task used in the current study,

three issues were considered: (1) the nature and valence of the negative and non-

negative stimuli; (2) the duration of the stimulus presentation prior to presentation of


the probes; (3) the nature of the probes used in the task. Each of these issues will be

discussed below.

As discussed in the preceding chapter, two types of stimuli are commonly used

in the attentional probe task, emotional words and emotional faces. For the current study

emotional faces were selected as stimuli, as such stimuli have been demonstrated to

consistently detect anxiety-linked selective attention to negative information (see Bar-

Haim et al., 2007) and also have been used in previous studies assessing anxiety-linked

attentional bias to negative information in autistic children (Hollocks et al., 2013, 2016;

May et al., 2015). Further, as mentioned previously, it has been suggested that

emotional faces have higher ecological validity than emotionally negative words

(Bradley et al., 1997; Bradley et al., 1999; McNally, Riemann & Kim, 1990). In the

current study, angry faces were used as negative stimuli, and happy faces were used as

positive stimuli. As discussed in the preceding chapter, previous studies have

demonstrated significant anxiety-linked selective attention to angry faces, when these

are paired with neutral faces (see Bar-Haim et al., 2007). However, it has been

suggested that this may reflect a bias towards emotional information, rather than

specifically towards negative information. Studies that have included both positive and

negative stimuli have confirmed that anxious participants show disproportionally

speeded responses to identify probes in the location of negative stimuli when compared

to the positive stimuli (e.g. Bradley et al., 1997, Bradley et al., 1999; Basanovic &

MacLeod, 2017). Thus, in the current study, angry and happy faces were selected to

represent the negative and positive stimuli, respectively.

The second consideration in the development of the task for the current study

was the duration of stimulus onset asynchrony (SOA). Two SOAs were adopted: 500ms

and 1000ms. The first SOA, 500ms, is the most commonly used SOA in attentional

probe tasks and has consistently demonstrated attentional bias to negative information

in individuals high in trait anxiety (see Bar-Haim et al., 2007). Further this SOA was


used in previous studies investigating anxiety-linked attentional bias to negative

information in autistic participants (Hollocks et al., 2013; Hollocks et al., 2016; May et

al., 2015). As noted in the proceeding chapter, these studies have been limited in their

inclusion of only this single SOA, restricting inferences about whether potential

anxiety-linked effects implicate automatic or more strategic attentional processes. Thus,

the second SOA, 1000ms, was included as this longer SOA permits greater opportunity

for strategic attentional processing to operate (Bradley et al., 1998; Koster, Verschuere,

et al., 2005; Mogg et al., 2004, 1997).

The final issue for consideration in the current study was the nature of the

probes used in the attentional probe task. In order to assess selective attention, it is

important that the latency of the response to the probe is sensitive to the allocation of

attention to the region of space occupied by either the negative or the positive face at

the time of probe presentation. During the development of the task two alternative

methods of responding to the probe were considered. One method would have required

participants to respond to the probe by indicating the location of the probe on the screen

(i.e. pressing a key to indicate that the probe appeared on the left or right). However, it

is recognised that this method of responding does not require the participant to actually

assign attention to the location of the probe, as the participant could simply focus

attention on one side of the screen and ascertain if the probe appeared there or not. As

such, this method of probe response is not ideal for assessing attentional bias to

negative information. A second method requires responding to the probe by indicating

the identity of the probe based on a probe characteristic (e.g., the probe’s orientation).

Additionally, a foil designed to be of similar appearance is presented in the location

opposite the probe. Importantly, in this method the participant must attend to the probe

in order to respond correctly, and so this method was chosen as it ensured biased

monitoring of one side of the screen could be mitigated. Thus, when probes appeared on

the screen in each trial of the task, participants were required to discriminate the identity


of the probe as quickly as possible. In this methodology, variation in attentional bias to

negative information will be revealed by the degree to which participants are

disproportionally speeded to discriminate the identity of probes that appear in the

location of negative faces compared to positive faces.

In summary, the present study sought to investigate the relationship between

autistic traits and anxiety-linked attentional bias to negative information using an

attentional probe task and moderated regression analysis. The primary hypothesis under

investigation in the current study was that high levels of autistic traits would

significantly attenuate anxiety-linked attentional bias to negative information. This

hypothesis predicts that autistic traits will significantly moderate the relationship

between anxiety and attentional bias to negative information, such that as level of

autistic traits increased the association between trait anxiety and attentional bias to

negative information will decrease.

Method

Participants

Participants in the study were undergraduate students at the University of

Western Australia, who participated in the experiment in exchange for partial course

credit. An unselected subset of undergraduate students were invited to participate in the

experiment via email, and participants in the current study consisted of those that

accepted that invitation. Previous research investigating anxiety-linked attentional bias

to negative information in the general population has reported effect sizes in the

moderate-large range (see Bar-Haim et al., 2007). Assuming an effect size of similar

magnitude in the current study, a power analysis conducted using G*Power (Faul,

Erdfelder, Lang, & Buchner, 2007) indicated that a sample size of approximately 55

would be required to detect such an effect, with a two-tailed alpha error probability of

0.05, and a power (1- ) of 0.80. Recruitment occurred during a six-week period, and


the recruited sample consisted of 59 participants (34 female; Mage = 19.64 years, SDage

= 2.19 years).

Materials

Autism Spectrum Quotient (AQ; Baron-Cohen, Wheelwright, Skinner, Martin,

& Clubley, 2001). The AQ is a self-report measure designed to measure autistic traits in

the general population (Baron-Cohen et al., 2001). The questionnaire contains 50 items

related to everyday preferences (i.e. “I find social situations easy”), with each item

including four response options: “definitely agree”, “slightly agree”, “slightly disagree”,

“definitely disagree”. In the current study, the four-point (1-4) scoring method (Austin,

2005) was used. Scores on the AQ can range from 50 to 200, with higher scores indicate

a greater presence of autistic traits. The AQ has been demonstrated to have good

reliability and validity (Baron-Cohen et al., 2001, Ruzich et al., 2015), including in

student samples (Hoekstra, Bartels, Cath, & Boomsma, 2008).

State-Trait Anxiety Inventory (trait version) (STAI-T; Spielberger, Goruch,

Lushene, Vagg & Jacobs, 1983). The 20-item STAI-T is a self-report measure designed

to assess an individual’s tendency to experience a range of anxiety symptoms (i.e. “I

feel nervous and restless”). Items are rated on a four-point Likert scale, with four

response options; “almost never”, “sometimes”, “often”, “almost always”. Scores can

range between 20 and 80, with higher scores reflecting greater levels of trait anxiety.

The STAI-T has been shown to have good reliability, (Barnes, Harp, & Jung, 2002),

and validity (Grös, Antony, Simms, & McCabe, 2007), including in student samples

(Spielberger & Sydeman, 1994).

Attentional Bias Assessment Stimuli. A set of emotional face images was

created by drawing images from the NimStim (Tottenham, Borscheid, Ellersten,

Marcus, & Nelson, 2002), Radboud (Langner et al., 2010), FACES (Ebner, Riediger, &

Lindenberger, 2010), and KDEF (Lundqvist, Flykt, & Öhman, 1998) databases. The

image set consisted of 512 colour photographs of adults, depicting equal numbers of


males and females (128 males and 128 females), and with each individual displaying

both happy (positive) and angry (negative) expressions. Eight separate images were also

included for use in practice trials. The photographs saved at a resolution of 439 × 585,

and standardised for background colour (white), contrast and brightness.

Attentional Bias Assessment Task. A version of the emotional faces

attentional probe task (MacLeod et al., 1986) was used to assess attentional bias at two

SOAs. The sequence of events for a trial of the task is shown in Figure 1.1. Each trial

began with a fixation cross appearing in a central location for 1000ms. This was

immediately followed by the presentation of a pair of face stimuli, one showing an

angry expression and the other a happy expression, and the angry face appeared equally

often on the left or right side of the screen. The two images were presented side-by-side

on the screen, at a size of 80mm x 105mm, with a 75mm gap between them. On 25% of

the trials, image pairs were male-male, on 25% they were female-female, and on the

remaining 50% they were of mixed gender. When the trials were mixed gender the male

face appeared equally often on the left and right side of the screen. The faces remained

on the screen for either 500ms or 1000ms, with each SOA used with equal frequency.

After the presentation of the faces, two small stimuli (3mm in height), one a probe and

the other a foil, appeared immediately in the locations vacated by the face stimuli. The

probe was a single headed arrow (pointed up or down) and the foil was a double-headed

arrow. In half the trials, the probe appeared in the location of the happy face, and in the

other half it appeared in the location of the angry face, and half the time the probe was

presented on the left and half on the right. Participants were required to discriminate

whether the probe pointed up or down and respond by pressing the corresponding arrow

key on the keyboard. The probe and the foil remained on the screen until an arrow key

was pressed. A correct response progressed the task to the next trial, with an inter-trial

blank-screen interval of 1000ms. An incorrect response resulted in an on-screen

message presented for 3s (“ERROR TRIGGERED DELAY”) prior to the 1000ms of


blank screen. This provided participants time to recover from their error, regain

concentration, and reduce the chance of post error response slowing effects. Probe

discrimination latency (recorded from the onset of the probe), and accuracy of the

response were recorded. A probe discrimination accuracy criterion of discriminating at

least 75% of the probes correctly was applied. The 256 trials were presented in two

blocks of 128 trials, separated by a brief rest period.

Figure 1.1. An example of a trial in the attentional bias assessment task. The

target probe is indicated, as is the correct keyboard response.

Procedure

All participants were tested in accordance with UWA Human Research Ethics

guidelines and procedures (approval number: RA/4/1/6140). The experiment was

delivered online. Participants accessed the experiment with their personal computers

using the Inquisit Web software package (Millisecond Software, 2015). Participants

were required to use a system running Windows Vista or higher, or Mac OS X 10.9 or

higher. Devices such as tablets or smartphones could not be used. After receiving an

email invitation and loading the program, participants were asked to complete a screen

calibration. This required participants to measure and input, in millimetres, the length of

a red line presented in the centre of the screen. The task programming then used this

information to ensure that equivalent spatial characteristics (i.e. dimensions of the

stimuli and spaces between stimuli) would be maintained across different device screen


sizes and resolutions. Participants were then asked to provide consent and input

demographic information. Following this, participants completed the STAI-T followed

by the AQ. The questionnaire items were presented sequentially, arrayed vertically

down the device screen, with 10 items presented on each screen. Participants indicated

their response by selecting a response bubble that corresponded to one of the four

options described above for the relevant questionnaire. Participants were required to

enter a response for each item before they could progress through each screen for each

questionnaire. Participants then viewed the instructions for the attentional bias

assessment task, that emphasised the importance of accurately identifying the target

probe as quickly as possible. Ten practice trials were then delivered, followed by the

two blocks of test trials on the attentional bias assessment task. Participants were

debriefed following completion of the task.

Results

Probe identities were designed to be easily discriminable, thus participants were

required to demonstrate a probe discrimination accuracy rate of at least 75%. One

participant failed to meet this accuracy criterion and so was excluded from the

subsequent analyses. The accuracy of the remaining participants was reassuringly high

(minimum = 82.4%). Mean probe discrimination latencies are shown in Table 1.1.

Descriptive statistics for the STAI-T (M = 45.95, SD = 10.23, Range = 30-73) and the

AQ (M = 111.08, SD = 14.98, Range = 76-147) suggested a good range of scores on

both scales, suitable for testing the moderation hypothesis.

Table 1.1.

Discrimination latencies (in ms) to identify the target probe as a function of face

emotion (angry/happy) and stimulus onset asynchrony (SOA; 500ms/1000ms).

Face Emotion SOA Mean Std. Deviation

Angry Face


Calculation

of

attentional

bias to

negative

information index (ABNII)

To assess attentional bias to negative information, an index was computed using

the latency to discriminate the target probe. To compute this index of attentional bias to

negative information, probe discrimination latencies were initially filtered using an

exclusion approach commonly used in past research (e.g. Bradley et al., 1999; Clarke,

Browning, Hammond, Notebaert, & MacLeod, 2014; Mogg, Bradley, de Bono, &

Painter, 1997). Specifically, latencies to incorrect responses were excluded, as were

latencies that were > 2000ms or < 200ms, and latencies that fell more than 1.96

standard deviations from the participant’s mean latency. Following these exclusions,

mean probe discrimination latency was calculated for each participant in each

experimental condition (target probe appearing in place of happy/angry face x

500/1000ms SOA). These mean latencies were then used to calculate an attentional bias

to negative information index (ABNII). This index was created by subtracting the mean

latency for probes that appeared in the location of the angry face, from the mean latency

for probes that in the location of the happy face. Accordingly, positive ABNII values

indicate an attentional bias towards negative information (angry face), while negative

ABNII values indicate an attentional bias away from negative information.

As two SOAs were used in the current study (500ms and 1000ms), two ABNIIs

were calculated: ABNII500, ABNII1000. Prior to analysis, the data were screened for

normality, and for outliers and influential cases based on examination of deleted

studentised residuals, Cook’s distance, and leverage values (see Fox & Long, 1990).

500ms

871.69 112.94

1000ms

850.37 114.43

Happy Face

500ms

876.48 99.98

1000ms

855.89 110.22


One case was identified as being both an extreme outlier and an influential case, and so

was excluded from the analysis. Both ABNII variables were found to be approximately

normally distributed, with absolute skew and kurtosis values less than 2 and 4

respectively.

Examination of moderating effects of autistic traits on the relationship between

trait anxiety and attentional bias to negative information

Prior to conducting statistical analysis of the moderation model, the

relationships between trait anxiety scores (STAI-T), autistic trait scores (AQ) and

ABNII scores were examined through a series of Pearson correlations. Unsurprisingly,

the analysis revealed a significant, moderate correlation between autistic trait and trait

anxiety scores, r(59) = .43, p < .001, indicating that as autistic trait scores increased so

did trait anxiety scores. No significant correlation was found between trait anxiety and

ABNII scores at 500ms, r(59) = -.02, p = .86, or at 1000ms, r(59) = .16, p = .23.

Similarly, no significant correlation was found between autistic trait and ABNII scores

at 500ms, r(59) = -.13, p = .36, or at 1000ms, r(59) = -.14, p = .31. Following the

computation of these correlations, analysis turned to focus on the hypothesis under test

in the current study.

It was hypothesised that autistic trait level would influence the relationship

between trait anxiety and attentional bias to negative information, such that a high level

of autistic traits would attenuate the relationship between trait anxiety and attentional

bias to negative information. If this were the case, then autistic traits would significantly

moderate the relationship between trait anxiety and attentional bias to negative

information. To determine the validity of this prediction two moderated regression

analyses were computed using the approach detailed by Hayes (2018). Separate models

were calculated for the two ABNII scores (ABNII500, ABNII1000). Each model included

an ABNII score as the outcome variable, trait anxiety (STAI-T) scores as the predictor


variable, and autistic trait (AQ) scores as the moderator variable. This moderation

model is depicted in Figure 1.2.

Figure 1.2. A schematic of the hypothesised anxiety-linked attentional bias to negative

information moderation model.

The first model examined ABNII500 as the outcome variable. The analysis

revealed that the overall model was not significant, F(3,55) = 1.39, p = .25, R2 = .07. Of

greatest importance to the hypothesis under test, the analysis indicated that the inclusion

of the interaction between autistic traits and trait anxiety trended towards significance,

F(1,55) = 3.15, p = 0.08, R2 change = .05. This finding may indicate a trending

moderation of effect of autistic traits on the relationship between trait anxiety and

attentional bias to negative information, at 500ms SOA, however this effect did not

reach statistical significance in the current sample and so should be interpreted with

caution. The predictors of the model are presented in Table 1.2 below. As can be seen in

Table 1.2, the analysis indicated that trait anxiety scores were not a significant predictor

of attentional bias to negative information, nor were autistic trait scores.

Table 1.2

Moderated regression model of predictors of trait anxiety (STAI-T), autistic traits

(AQ), and the interaction between trait anxiety and autistic traits.

b SE B p

Constant 5.24 6.09 0.39

STAI-T Score 0.23 0.63 0.05 0.71

Attentional Bias to Negative information

Autistic Traits

Trait Anxiety


The second model examined ABNII1000 as the outcome variable. The analysis

again revealed that the overall model was not significant, F(3,55) = 1.67, p = .18, R2 =

.08. Of greatest importance to the hypothesis under test, the inclusion of the interaction

between autistic traits and trait anxiety did not explain a significant amount of variance

in the model, F(1,55) = .54, p = .46, , R2 change = .01, indicating that autistic traits did

not significantly moderate the relationship between trait anxiety and attentional bias to

negative information, at the 1000ms SOA. The predictors of the model are presented in

Table 1.3 below. As can be seen in Table 1.3, trait anxiety was approaching significance

as a predictor of attentional bias to negative information, with a positive beta-weight

indicating that this relationship was in the expected direction; as trait anxiety increased,

so did attentional bias to negative information. Additionally, autistic traits were trending

towards a significant predictor with a negative beta-weight indicating that as autistic

traits increased, attentional bias to negative information decreased. However, given that

these effects did not reach statistical significance in the current sample, they should be

interpreted with caution.

Table 1.3

Moderated regression model of predictors of trait anxiety (STAI-T), autistic traits

(AQ), and the interaction between trait anxiety and autistic traits.

b SE B p

Constant 1.01 5.63 0.86

STAI-T Score 1.09 0.58 0.27 0.06

AQ Score -0.69 0.39 -0.25 0.08

AQ Score -0.47 0.43 -0.16 0.28

Interaction (STAI x AQ) -0.06 0.03 -0.20 .08


Interaction (STAI- T x AQ) -0.02 0.03 -0.08 0.46

Discussion

The primary hypothesis under investigation in the current study was that high

levels of autistic traits would significantly attenuate anxiety-linked attentional bias to

negative information. This hypothesis generated the prediction that autistic traits would

significantly moderate the relationship between anxiety vulnerability and attentional

bias to negative information, such that as levels of autistic traits increased the

association between trait anxiety and attentional bias to negative information would

decrease.

The results of the current study did not support the primary hypothesis. In both

of the moderation analyses the overall model was not significant, and importantly, the

addition of the interaction between autistic traits and trait anxiety did not explain a

significant amount of variance in the model, indicating that autistic traits did not

significantly moderate the association between trait anxiety and attentional bias to

negative information. This suggests that in the current study high levels of autistic traits

did not significantly attenuate the relationship between trait anxiety and attentional bias

to negative information. It should be noted that at 500ms SOA there was trend-level

evidence of a moderation effect of autistic traits on the association between trait anxiety

and attentional bias to negative information. However, as this effect failed to reach

statistical significance, appropriate caution must be taken in interpreting this effect.

One interpretation of the current findings is that autistic traits do not

significantly moderate the expression of anxiety-linked attentional bias to negative

information. However, given that the current study also found no evidence of a positive

association between trait anxiety and attentional bias to negative information, and this

relationship has previously been robustly demonstrated (see Bar-Haim et al., 2007), it is

worth considering alternative explanations for the current findings. It is possible that the

lack of significant moderation effects in the current study could reflect the fact that a


continuous design, with unrestricted sampling and a modest sample size (n = 59) is not

sensitive enough to detect interaction effects between autistic traits and trait anxiety on

the attentional bias to negative information indices. As noted above, a recent meta-

analysis has suggested that quantile designs, where participants groups are selected to

be high and low on autistic traits, are more sensitive to picking up effects involving

autistic traits than continuous designs (Cribb et al., 2016). Using Monte-Carlo

simulations to augment the meta-analysis, Cribb and colleagues (2016) showed that

quantile designs more consistently demonstrated effects involving autistic traits, than

studies that employed continuous designs. Furthermore, the current study demonstrated

a moderately strong, statistically correlation between AQ scores and STAI-T scores.

Given this, a quantile study that systematically recruits participants to form a 2 (high

versus low levels of autistic traits) by 2 (high versus low levels of trait anxiety) factorial

design may be more sensitive to detecting a significant interaction between these factors

on attentional bias to negative information. The continuous design adopted in

Experiment 1 allows for investigation of effects across the whole trait dimension, and

recruiting an unselected sample is less resource-intensive than recruiting selected

groups of participants. However, in order to maximise sensitivity to potential interaction

effects between autistic traits and trait anxiety on attentional bias to negative

information indices, future studies in the research program will employ a quantile

design and recruit groups of participants based on the factorial combination of high/low

autistic traits, and high/low trait anxiety.

An unexpected finding of the current study was the absence of a significant

positive relationship of trait anxiety on ABNII scores. Thus, no overall anxiety-linked

attentional bias to negative information effect was observed in the current study. While

it is the case that a significant positive association between trait anxiety and attentional

bias to negative information has been demonstrated in continuous designs (e.g. Bradley,

Mogg, & Millar, 2000; Broadbent & Broadbent, 1988; Egloff & Hock, 2001, 2003;


Schofield, Johnson, Inhoff, & Coles, 2012), the majority of studies that have

demonstrated significant anxiety-linked differences in attentional bias to negative

information have used quantile designs to contrast participants especially high or low in

trait anxiety (see Armstrong & Olatunji, 2012; Bar-Haim et al.,2007). Further, Hollocks

et al. (2016) posited that a certain level of anxiety symptom severity may be required to

detect anxiety-linked attentional bias to negative information in ASD. The authors

hypothesised that this may explain the lack of anxiety-linked attentional bias to negative

information observed in Hollocks et al. (2013), as participants in the later study had

significantly higher levels of parent-reported anxiety. As such, it is possible that a

quantile design, that would allow for the recruitment of participants with more extreme

trait anxiety scores, would have greater sensitivity to detect anxiety-linked attentional

bias to negative information effects.

It is possible that variables which could not be controlled when using an online

delivery method for the experimental task may have impacted adversely on the power of

Experiment 1. This online method was selected for Experiment 1 because it is less

resource costly than testing participants in person. However, while accuracy on the task

was reassuringly high, the setting in which participants completed the experiment could

not be controlled. Previous investigators have noted that studies of attentional bias using

tasks delivered online cannot ensure consistency in variables such as the participants’

distance from the monitor, and cannot ensure freedom from disruptive distractions

(Boettcher, Berger, & Renneberg, 2012; Neubauer et al., 2013). Given these limitations

of online testing, future studies in this research program will require that participants

complete the experimental tasks in-person, where experimental conditions can be better

controlled.

As an initial investigation of the whether autistic traits moderate the relationship

between trait anxiety and attentional bias to negative information, the current study

reveals no significant evidence that high levels of autistic traits significantly attenuate


anxiety-linked attentional bias to negative information. However, it is possible that the

continuous design adopted in the current study may not have been sensitive enough to

detect effects of central interest. In addition, the online delivery method of the

experimental task may have reduced assessment sensitivity. Thus, in Experiment 2, four

groups of participants will be recruited representing the factorial combination of

high/low autistic trait levels and high/low trait anxiety scores, and the experimental task

used in Experiment 2 will be delivered with the laboratory to ensure a higher degree of

control over experimental conditions.



As will be recalled from the previous chapter, Experiment 1 did not demonstrate

that high levels of autistic traits significantly attenuate the relationship between trait

anxiety and attentional bias to negative information. As was noted when discussing this

experimental outcome, it is possible that the unrestricted sampling methodology used in

Experiment 1 may not have been sufficiently sensitive to effects that could be more

prominent in a design that incorporates groups selected to be especially high in levels of

autistic traits and trait anxiety. As discussed in the previous chapter, using meta-analysis

and Monte Carlo simulations, Cribb et al. (2016) demonstrated greater statistical power

for quantile (extreme group) designs compared to continuous (unrestricted sample)

designs. As such, in Experiment 2 participants were recruited to form a 2 (high versus

low levels of autistic traits) by 2 (high versus low levels of trait anxiety) factorial

design. Another benefit of this design is that contrasting such groups should allow for

greater separation of autistic trait and trait anxiety effects. This is important given the

moderate correlation between STAI-T scores and AQ scores demonstrated in

Experiment 1.

To permit the findings of Experiment 2 to be meaningfully compared to those of

Experiment 1, the attentional bias assessment task used in the current study was the

same as that used in the previous study. As noted in the discussion section of the

preceding chapter, it is possible that extraneous variables introduced by delivering the

task online (e.g. disruptions, engagement with the task, distance from the monitor) may

have impacted the results of Experiment 1. Thus, in Experiment 2, participants were

required to complete the experiment in-person, where experimental conditions could be

better controlled.

The current study recruited participants to form the factorial combination of

high/low in trait anxiety and high/low in autistic traits. As with Experiment 1, the

current study aimed to test the hypothesis that high levels of autistic traits are


characterised by an attenuated anxiety-linked attentional bias to negative information.

This hypothesis predicts that the degree to which attentional bias to negative

information is stronger in high compared to low trait anxiety participant groups will be

attenuated in the high autistic trait participant group compared to the low autistic trait

participant group.

Method

Participants

Participants were selected to form four groups representing the factorial

combination of high and low levels of autistic traits and trait anxiety: high autistic traits,

high trait anxiety (High AT/High TA); high autistic traits, low trait anxiety (High

AT/Low TA); low autistic traits, high trait anxiety (Low AT/High TA); and low autistic

traits, low trait anxiety (Low AT/Low TA). A total of 1455 undergraduate students at

the University of Western Australia were initially screened on the AQ and the trait

version of the STAI-T. The upper and lower 30% of the AQ and STAI-T distributions

defined high and low levels of each trait. As stated in the preceding chapter, previous

research investigating anxiety-linked attentional bias to negative information has

reported effect sizes in the moderate-large range (see Bar-Haim et al., 2007). Assuming

an effect size of similar magnitude in the current study, a power analysis was conducted

using MorePower 6.0 (Campbell & Thompson, 2012) on the 2 (autistic traits: high/low)

x 2 (trait anxiety: high/low) interaction effect central to the hypothesis under test in the

current study. The power analysis indicated that a sample size of approximately 80

participants (20 participants per group) would be required to detect such an effect, with

an alpha error probability of 0.05, and a power (1- ) of 0.80. Eighty-nine students who

met the criteria for any of the four groups agreed to participate in the study in exchange

for partial course credit.

Descriptive statistics for the four groups are displayed in Table 2.1. Separate 2 x

2 ANOVAs were conducted on AQ scores and STAI-T scores, each considering the


between group factors autistic trait group (high AT/low AT) and trait anxiety group

(high TA/low TA). The ANOVA conducted on the AQ measure revealed a significant

main effect of autistic trait group, F(1, 85) = 470.50, p < .001, p = .85, reflecting the

fact that AQ scores were significantly higher in the high autistic trait groups than in the

low autistic trait groups. Neither the main effect of anxiety group nor the interaction

between autistic trait group and trait anxiety group was significant in this analysis on

AQ scores (p > .05), confirming appropriate matching of AQ scores across the high and

low trait anxiety groups. The ANOVA conducted on STAI-T scores, revealed a

significant main effect of trait anxiety group, F(1, 85) = 813.25, p < 0.001, p = .91,

reflecting the fact that STAI scores were significantly higher in the high trait anxiety

groups compared to the low trait anxiety groups. There was no significant main effect of

autistic trait group and no significant interaction between autistic trait group and trait

anxiety group on this ANOVA, (p > .05), confirming appropriate matching of STAI-T

scores across the high and low autistic trait groups. These

results indicated that the four groups were appropriate for assessing whether anxiety-

linked attentional bias to negative information is attenuated by high levels of autistic

traits.


Materials

Autism Spectrum Quotient (AQ; Baron-Cohen, Wheelwright, Skinner, Martin,

& Clubley, 2001). Similar to Experiment 1, the AQ was used to assess levels of autistic

traits. Higher scores indicate a greater presence of autistic traits.

State-Trait Anxiety Inventory - Trait Form (STAI-T; Spielberger, Goruch,

Lushene, Vagg & Jacobs, 1983). As in Experiment 1, the STAI-T was used to assess

levels of trait anxiety. Higher scores indicate greater levels of trait anxiety.

Apparatus. An EDsys Desktop PC with an LG Flatron 22-inch LCD colour

monitor, and a standard 104-key keyboard was used to deliver the task.

Table 2.1.

Descriptive statistics (mean, SD, and range) for the four groups; M(SD).

High Autistic Traits Low Autistic Traits

High Trait

Anxiety

Low Trait

Anxiety

High Trait

Anxiety

Low Trait

Anxiety

n 23 25

20 21

n females 16 16 17 15

Age (years)

Mean (SD) 19.13 (1.87) 21.84 (10.96) 21.20 (8.45) 20.81 (7.65)

AQ

Mean (SD) 120.48 (6.16) 120.24 (7.14) 94.60 (4.11) 90.76 (5.81)

Range 111-131 111-141 87-102 78-100

STAI

Mean (SD) 51.78 (3.94) 32.16 (2.93) 50.25 (3.35) 31.05 (2.33)

Range 45-61 28-37 46-58 27-36


Attentional Bias Assessment Stimuli. The same set of emotional face images,

displaying both happy and angry expressions, used in Experiment 1 were again used in

the current experiment.

Attentional Bias Assessment Task. The attentional bias assessment task used

in the current experiment was identical to the task used in Experiment 1. As in

Experiment 1, a probe discrimination accuracy criterion of discriminating at least 75%

of the probes correctly was applied.

Procedure

All participants were tested in accordance with the protocol approved by UWA

Human Research Ethics Committees (approval number: RA/4/1/6140). Each participant

provided informed written consent to take part in the study and completed the

attentional bias assessment task in a quiet, individual testing room. The participant was

seated approximately 60cm from the computer screen. Instructions emphasised the

importance of accurately identifying the probe as quickly as possible. As in Experiment

1, 10 practice trials were delivered prior to commencing the test trials. The test trials

were then delivered in two blocks, separated by a rest period. The testing duration was

approximately 30 minutes.

Results

On the attentional bias assessment task, no participants failed to meet the

required accuracy criterion on the attentional bias assessment task of discriminating at

least 75% of the probes correctly. Probe discrimination accuracy across participants was

uniformly high (minimum accuracy = 94.14%). To assess for any group differences in

accuracy, the accuracy data were subjected to a 2 x 2 between-groups ANOVA that

considered the factors autistic trait group (high AT/low AT) and trait anxiety group

(high TA/low TA). The analysis revealed no significant main effects, and no significant

interaction between autistic trait group and anxiety group on accuracy (p > .05),


suggesting that accuracy rates did not significantly differ between groups. The mean

probe discrimination latencies in each condition are displayed in Table 2.2.

Calculation of attentional bias to negative information index (ABNII)

As in Experiment 1, to assess attentional bias to negative information an index

was computed using latency to discriminate the target probe. The same data screening

and pre-processing steps described in Experiment 1 were used in the current study to

derive an ABNII score for each participant for each SOA (500ms and 1000ms). As in

previous experiments, this index reflects speeding to discriminate probes that appeared

in the location of the angry faces stimuli, relative to probes that appeared in the location

of the happy faces. Therefore, positive ABNII values indicate an attentional bias

towards negative information (angry face), while negative ABNII values indicate an

attentional bias towards away from negative information. Prior to analysis, the ABNII

data were screened for normality, and outliers by examining boxplots. No participants

were identified as being extreme outliers (i.e. greater than three standard deviations

away from the mean). The ABNII scores were found to be approximately normally

distributed with absolute skew and kurtosis values less than 2 and 4 respectively. The

mean ABNII scores in each condition are displayed in Table 2.2.

Table 2.2.


Analysis of attentional bias to negative information

The ABNII data were subjected to a 2 x 2 x 2 mixed design ANOVA that

considered the between group factors autistic trait group (high AT/low AT) and trait

anxiety group (high TA/low TA), and the within group factor SOA (500ms/1000ms). If

the hypothesis that high levels of autistic traits attenuate anxiety-linked attentional bias

to negative information is valid, then this would give rise to a significant interaction

effect between autistic traits and trait anxiety in this analysis. Specifically, support for

this hypothesis would be demonstrated if the nature of such an interaction indicated that

ABNII was significantly higher for high trait anxiety groups compared to low trait

Discrimination latencies (in ms) to identify the target probe, and computed ABNII, as a

function of face emotion (angry/happy), autistic trait group (high/low), anxiety group

(high/low) and stimulus onset asynchrony (500ms/1000ms); M(SD).


High Trait

Anxiety

Low Trait

Anxiety

High Trait

Anxiety

Low Trait

Anxiety

Angry Face

500ms

671.83 (112.71) 613.80 (129.25) 814.47 (144.93) 809.14 (86.04)

1000ms

636.61 (105.34) 790.83 (125.87) 810.63 (142.99) 794.76 (90.77)

Happy Face

500ms

668.82 (108.64) 801.37 (132.27) 819.47 (139.36) 818.26 (90.20)

1000ms

638.16 (112.63) 783.28 (137.18) 817.00 (132.86) 799.08 (93.09)

ABNII

500ms 4.57 (20.88) 2.98 (27.07) 1.00 (25.16) 9.12 (22.18)

1000ms 19.84 (27.03) -7.55 (34.90) 6.36 (36.01) 4.33 (25.34)


anxiety groups, and the magnitude of this difference was reduced in the high autistic

trait groups relative to the low autistic trait groups.

The analysis revealed a significant interaction between between SOA and trait

anxiety group, F(1, 91) = 4.79, p = .031, p = .05. To assess the nature of this

interaction, the significance of the simple effect of trait anxiety group was examined at

each level of the SOA factor. The interaction was such that at the 500ms SOA, ABNII

score did not differ significantly between the high and low trait anxiety groups, F(1, 85)

= .41, p = .52, p = .00; whereas at the 1000ms SOA, ABNII score was significantly

higher for the high trait anxiety group than for the low trait anxiety group, F(1, 85) =

4.91, p = .029, p = .06 (Figure 2.1). This finding indicates that a significant anxiety-

linked attentional bias to negative information was observed at the 1000ms SOA, but

not at the 500ms SOA. To further examine this effect, the simple effect of whether

ABNII significantly differed from zero at each of level of the interaction was examined.

These analyses revealed that at 1000ms ABNII significantly differed from 0 for the high

trait anxiety group, t(42) = 2.79, p = .008, d = .43, suggesting that at 1000ms the high

trait anxiety group demonstrated significant attentional vigilance to negative

information. ABNII did not differ significantly from zero in any of the other conditions

(p < .05).

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

20

500ms 1000ms

Att

enti

onal

Bia

s to

Neg

ativ

e In

frorm

atio

n

Index

(A

BN

II)

Low Trait Anxiety High Trait Anxiety


Figure 2.1. Plot illustrates the significant interaction of trait anxiety (high/low) and

stimulus onset asynchrony (500ms/1000ms) on attentional bias to negative information

index (ABNII) scores. Positive scores indicate an attentional bias towards negative

stimuli, while negative scores indicate a bias away from negative stimuli, with larger

scores indicating a greater degree of bias. Error bars represent one standard error of the

mean.

Of greatest importance to the theoretical hypothesis under test, the analysis also

revealed a significant interaction between autistic trait group and trait anxiety group,

F(1, 85) = 4.21, p = .043, p = .05 (see Figure 2.2). To reveal the nature of this

interaction, the significance of the simple main effect of trait anxiety group was tested at

each level of the autistic trait factor. For the high autistic traits group, ABNII score was

significantly higher for the high trait anxiety group compared to the low trait anxiety

group, F(1, 85) = 6.24, p = .014, p = .07, indicating that, for participants with high

autistic traits, those high in trait anxiety participants displayed greater relative

attentional bias to the negative information compared to those low in trait anxiety. In

contrast, for the low autistic trait groups, ABNII score did not differ significantly across

trait anxiety groups, F(1, 85) = .24, p = .63, p = .00, suggesting that for participants

with low autistic traits attentional bias to negative information did not differ between

those high in trait anxiety and those low in trait anxiety. To further investigate the

interaction, the simple effect of whether ABNII differed significantly from zero was

examined at each level of the interaction. These analyses revealed that ABNII differed

significantly from zero in the High AT/High TA group, suggesting that this group

demonstrated significant attentional vigilance towards negative information. ABNII did

not differ significantly from zero in any of the other groups (p > .05). Thus, the nature

of this interaction directly contradicts the prediction generated from the hypothesis that

high levels of autistic traits would attenuate anxiety-linked attentional bias to negative

information. As stated above, support for this hypothesis would be demonstrated if the


nature of the interaction between autistic traits and trait anxiety indicated that

attentional bias to negative information was significantly higher for high trait anxiety

groups compared to low trait anxiety groups, and the magnitude of this difference was

reduced in the high autistic trait groups relative to the low autistic trait groups. Instead,

the observed pattern of the interaction is in the opposite direction to that predicted by

the hypothesis, and instead suggests that low levels of autistic traits may be

characterised by attenuated anxiety-linked attentional bias to negative information.

Figure 2.2. Plot illustrates the significant interaction between autistic traits (high/low)

and trait anxiety (high/low) on attentional bias to negative information index (ABNII)

scores. Positive scores indicate an attentional bias towards negative stimuli, while

negative scores indicate a bias away from negative stimuli, with larger scores indicating

a greater degree of bias. Error bars represent one standard error of the mean.

No other effects in the analysis were statistically significant (p > .05).

Discussion

As in Experiment 1, the primary hypothesis under investigation in the current

study was that high levels of autistic traits are characterised by attenuated anxiety-

linked attentional bias to negative information. This hypothesis predicted that the degree

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Low Austistic Traits High Austistic Traits

Att

enti

onal

Bia

s to

Neg

ativ

e In

form

atio

n I

ndex

(A

BN

II)



to which a greater attentional bias to negative information would be displayed by high

relative to low trait anxiety groups would be reduced for the high autistic traits groups,

compared to the low autistic trait groups.

The results of the current study were not consistent with the hypothesis. As

predicted, a significant interaction was observed between autistic trait group and trait

anxiety group, but the nature of this interaction was in the opposite direction to that

predicted by the attenuation hypothesis. For the high autistic trait participants,

significant anxiety-linked attentional bias to negative information was observed, with

the High AT/High TA group showing a significantly higher mean ABNII score than the

High AT/Low TA group. Further, the High AT/Low AT group demonstrated significant

attentional vigilance to negative information. In contrast, no such anxiety-linked

attentional bias to negative information was observed within the low autistic trait

participants, as there was no significant difference in ABNII scores between the Low

AT/High TA and Low AT/Low TA groups. Thus, an anxiety-linked attentional bias to

negative information was present only in the subset of participants with high levels of

autistic traits, and not in in the subset of participants with low levels of autistic traits.

This finding is in direct contradiction to the hypothesis under scrutiny in the current

study.

The outcomes of this study may appear inconsistent with those of prior studies

that have failed to demonstrate a significant attentional bias to negative information

effect among anxious autistic children (Hollocks et al., 2013, May et al., 2015).

However, it is important to note that these studies compared a high trait anxiety ASD

group to a low trait anxiety non-ASD group. As noted previously, given that these

studies did not include a low trait anxiety ASD group, or a high anxiety non-ASD

group, they were not able to adequately assess whether anxiety-linked attentional bias to

negative information was attenuated in autistic participants. The present finding

observed in the high autistic trait groups is consistent with the results of Hollocks and


colleagues (2016), who observed a significant anxiety-linked attentional bias to negative

information between ASD groups differing in levels of trait anxiety, with anxious

autistic participants showing a significantly greater attentional bias than non-anxious

autistic participants. However, it is important to note that this study also did not include

a high trait anxiety non-ASD group, and so was not capable of assessing whether this

anxiety-linked attentional bias to negative information was attenuated in autistic

participants compared to non-autistic participants. Having included these appropriate

comparison conditions, the present study has yielded findings that stand in contradiction

to the hypothesis that high levels of autistic traits attenuate anxiety-linked attentional

bias to negative information. Further, the findings of the current study underscore the

need when assessing anxiety-linked attentional bias to negative information to examine

groups that differ in levels of trait anxiety, with other group characteristics held

constant.

The primary hypothesis addressed by the research program concerns whether

anxiety-linked attentional bias to negative information is be attenuated by high levels of

autistic traits. The results of the current study suggest that high levels of autistic traits

do not attenuate anxiety-linked attentional bias to negative information. Further, the

findings suggest that the task used in the current study had the capacity to detect

anxiety-linked attentional bias effects in participants high in autistic traits. Surprisingly,

the findings of the current study suggest that anxiety-linked attentional bias to negative

information was attenuated in participants low in autistic traits. Given that anxiety-

linked attentional bias to negative information has been robustly demonstrated in

neurotypical individuals (Bar-Haim et al., 2007), it is unexpected that a significant

difference in ABNII was not presently observed for participants low in autistic traits.

These findings raise a new question for the research program, namely, why a significant

anxiety-linked attentional bias to negative information was not observed among the low

autistic trait participants in the current study.


There are two possible reasons why the current study may have failed to find a

significant anxiety-linked attentional bias to negative information in the low autistic

trait participants. The first is that the stimuli (i.e. emotional faces) may not have been

emotionally negative for individuals selected on the basis of being particularly low in

autistic traits. Given the nature of the AQ, individuals who score especially low on this

autistic trait measure tend to be highly sociable, enjoy and seek out social activities, and

experience low levels of social discomfort (Baron-Cohen et al., 2001; Baron-Cohen &

Wheelwright, 2003; Jobe & Williams White, 2007). Thus, it is possible that faces, even

when showing angry expressions, may not be considered especially negative by

individuals low in autistic traits. If such stimuli were not sufficiently negative to these

individuals to reveal an anxiety-linked attentional bias to negative information, it may

nevertheless be possible to demonstrate this bias for low autistic trait groups by using

stimuli considered by them to be especially negative. One way to address this

possibility would be to use stimuli selected on the basis of participant ratings, to ensure

that the intended negative stimuli are considered negative by each participant. Previous

studies have demonstrated more pronounced anxiety-linked attentional bias to negative

information effects towards stimuli that have been selected by participants to be

personally especially emotionally negative (e.g. Amir, Najmi, & Morrison, 2009;

McNally et al., 1994).

A second possibility is that participants who endorse especially low levels of

autistic traits may have unusually good attentional control, which may in turn mask the

expression of anxiety-linked attentional bias to negative information. It has been

demonstrated in previous research that the expression of anxiety-linked attentional bias

to negative information is moderated by top-down attentional control (Beck & Clark,

1997; Mathews & Mackintosh, 1998; Mogg & Bradley, 1998). Results from a growing

number of studies, that have assessed attentional control using both using self-report

measures (Derryberry & Reed, 2002; Taylor, Cross, & Amir, 2016) and performance-


based tasks (Gorlin & Teachman, 2015; Reinholdt-Dunne, Mogg, & Bradley, 2009),

suggest that high levels of attentional control can attenuate anxiety-linked attentional

bias to negative information. There is also persuasive evidence that autistic individuals

exhibit poor attentional control (Courchesne, Townsend, Akshoomoff, Saitoh, & et al,

1994; Greenaway & Plaisted, 2005; Maekawa et al., 2011; Pascualvaca, Fantie,

Papageorgiou, & Mirsky, 1998). Thus, it is possible that individuals who are

particularly low in autistic traits may demonstrate particularly good attentional control.

If so, then their heightened attentional control may result in an attenuation of the

anxiety-linked attentional bias to negative information. This possible account could be

tested by including a measure of attentional control within a future variant of the present

study.

Interestingly, in the current study a significant interaction was observed between

trait anxiety group and SOA on ABNII scores. At 500ms, no significant difference in

ABNII scores was observed between high and low trait anxiety participants, however at

1000ms, participants high in trait anxiety demonstrated a significantly higher ABNII

scores than participants low in trait anxiety. Thus, in the current study, a significant

anxiety-linked attentional bias to negative information was observed at a SOA of

1000ms, but not at 500ms. This is surprising, given that 500ms is the SOA most

commonly used for assessing attentional bias to negative information, and has

consistently demonstrated more pronounced attentional bias to negative information

among adult participants with high compared to low trait anxiety (see Bar-Haim et al.,

2007). As discussed in earlier chapters, the rationale for including SOAs of 500ms and

1000ms is that the inclusion of a rapid and slower SOA allows for greater sensitivity to

capture a mixture of both automatic and more strategic attentional processing effects.

The findings of the current study appear to suggest that at 500ms, the condition that

afforded the least opportunity for strategic attentional processing, neither participants

high or low in trait anxiety displayed attentional vigilance for negative information.


However, at 1000ms, a condition that afforded greater opportunity for strategic

attentional effects, a significant anxiety-linked attentional bias to negative information

was observed, with high trait anxiety participants now demonstrating significant

attentional vigilance for negative information.

In summary, the findings of the current study run counter to the hypothesis that

high levels of autistic traits attenuate anxiety-linked attentional bias to negative

information. In contrast, the present findings suggest that low levels of autistic traits,

not high levels, attenuated anxiety-linked attentional bias to negative information. This

raises the new question of why participants endorsing low levels of autistic traits did not

display a significant anxiety-linked attentional bias towards negative information. One

potential explanation for this finding is that participants who endorse especially low

levels of autistic traits may display high levels of attentional control abilities that

attenuate anxiety-linked attentional bias to negative information. A second possibility is

that the stimuli used in the current study (i.e. emotional faces) may not have been

sufficiently negative to participants to reveal a significant anxiety-linked attentional bias

to negative information. The focus of subsequent studies in the research programme will

be to test these alternative possible accounts of the unexpected observation that

participants with low levels of autistic traits fail to exhibit the anxiety-linked attentional

bias to negative information that is evident in participants with high levels of autistic

traits.

PRELUDE TO EXPERIMENT 3 & 4 64

Prelude to Experiment 3 and Experiment 4

The primary question for the research programme introduced in Chapter 1

concerned whether high levels of autistic traits are characterised by attenuated anxiety-

linked attentional bias to negative information. The findings of the first two experiments

in the research programme do not suggest this to be the case. The results of Experiment

1 suggested that level of autistic traits did not significantly moderate the relationship

between trait anxiety and attentional bias to negative information. However, as

discussed previously, the continuous design used in this study may not have been

sufficiently sensitive to effects that could potentially be evident only at especially high

levels of autistic traits. In Experiment 2, a 2 (high versus low autistic traits) by 2 (high

versus low trait anxiety) factorial design was used to separate and sample more extreme

levels of trait anxiety and autistic traits. As will be recalled from the previous chapter, a

significant anxiety-linked attentional bias to negative information was observed in the

high autistic trait groups, with participants high in trait anxiety and high in autistic traits

displaying ABNII scores significantly greater than the ABNII scores displayed by

participants equally high in autistic traits but low in trait anxiety. However, an

unanticipated finding of Experiment 2 was that a significant anxiety-linked attentional

bias to negative information was not observed in the low autistic trait groups. No

significant difference was observed in attentional bias to negative information index

scores between participants low in autistic traits and high in trait anxiety, and

participants low in autistic traits and low in trait anxiety. Thus, the findings of

Experiment 2 stand in clear contradiction to the hypothesis that high levels of autistic

traits attenuate anxiety-linked selective attention to negative information. Indeed, the

observed pattern of the interaction between autistic traits and trait anxiety in Experiment

2 was in the opposite direction to that predicted by this hypothesis, and instead suggests

that low levels of autistic traits may be characterised by attenuated anxiety-linked



This absence of anxiety-linked attentional bias to negative information, observed

in Experiment 2 for participants low in autistic traits, is surprising given that anxiety-

linked attentional bias to negative information has been robustly demonstrated in

neurotypical individuals (Bar-Haim et al., 2007). It is not immediately clear why a

significant difference in anxiety-linked selective attention to negative information was

not observed for participants low in autistic traits. As will be recalled from the previous

chapter, two potential explanations were put forward to potentially account for this

finding. The first is that the stimuli (i.e. emotional faces) may not have been sufficiently

emotionally negative to reveal a significant attentional bias to negative information in

individuals selected on the basis of being especially low in levels of autistic traits. If this

explanation is valid, then the use of stimuli chosen ideographically to ensure their high

negative emotional valence for each individual should lead to the finding that

participants who are either high or low in autistic traits would display an anxiety-linked

attentional bias to negative information. The second candidate explanation was that

participants low in autistic traits may have better attentional control than participants

high in autistic traits, which may mask the expression of anxiety-linked attentional bias

to negative information. The focus of the following chapters in the research programme

will therefore be to investigate the validity of the predictions generated by these two

potential explanations for the absence of an anxiety-linked attentional bias to negative

information observed for participants with low autistic traits in Experiment 2.

The benefits of further investigating the lack of anxiety-linked attentional bias to

negative information observed in Experiment 2 for participants low in autistic traits are

two-fold. First, in addition to testing the validity of the two proposed explanations,

conducting further studies that use the same 2 (high versus low autistic traits) by 2 (high

versus low trait anxiety) design, and a similar attentional bias assessment task, will

provide the opportunity to see if the anxiety-linked attentional bias to negative

information observed in participants high in autistic traits in Experiment 2 is a


replicable effect. The second benefit of investigating anxiety-linked attentional bias to

negative information in low autistic trait participants concerns the typical use of low

autistic trait participants as analogous to a neurotypical comparison group. Studies

employing a quantile design to investigate effects involving high levels of autistic traits

commonly recruit a group of participants high in autistic traits which is compared to a

group of participants low in autistic traits (e.g. Cribb et al., 2016; Grinter et al., 2009;

Kasai & Murohashi, 2013; Lassalle & Itier, 2015; Van Boxtel & Lu, 2013). In this way,

groups of participants low in autistic traits are treated as a neurotypical comparison

group. Indeed, this was part of the rationale in selecting low autistic trait groups for

comparison in the current research programme. However, the unexpected finding of

Experiment 2 raises the possibility that it may be inappropriate to consider participants

low in autistic traits to be representative of the neurotypical population, as they are also

selected on the basis of extreme score, and may exhibit their own atypical

characteristics and behaviour. Further investigation of why the low autistic trait groups

did not display the expected anxiety-linked attentional bias to negative information in

Experiment 2 may shed light on the cognitive characteristics of low autistic trait

participants.

The broad aim of Experiments 3 and 4 was to test the validity of predictions

generated by the two alternative proposed explanations for the lack of anxiety-linked

attentional bias to negative information demonstrated by low autistic trait participants in

Experiment 2. Experiment 3 tested the possibility that the emotional faces stimuli used

in Experiment 2 were not sufficiently negative to participants low in autistic traits to

elicit a significant anxiety-linked attentional bias to negative information. Experiment 4

tested the possibility that participants low in autistic traits display especially high

attentional control abilities that mask the expression of anxiety-linked bias to negative

information. As these possibilities are independent of each other, such that neither or

both could potentially be valid, it was intended to conduct both Experiments 3 and 4


without the execution of either being dependent on the outcomes of the other. Each

study will be separately reported, one in each of the subsequent two chapters.



The purpose of Experiment 3 was to assess the proposal that the lack of anxiety-

linked attentional bias to negative information observed among participants low in

autistic traits in Experiment 2 was due to the stimuli used not being sufficiently

emotionally negative to this participant group. Two possible methods of empirically

testing this account were considered when designing Experiment 3.

One possible means of testing this candidate explanation would be to use

negative and positive word stimuli instead of emotional faces. As will be recalled from

the previous chapter, it was suggested that emotional faces may not have been

sufficiently emotionally negative to participants low in autistic traits given that the

nature of the AQ means that low scorers tend to be highly social and experience low

levels of social discomfort. Thus, it is possible that faces, even when showing angry

expressions, may not be considered especially negative by individuals low in autistic

traits. As reviewed in Chapter 1, words varying in emotional tone have been commonly

used to demonstrate significant anxiety-linked selective attention to negative

information (see Bar-Haim et al., 2007). However, as previously discussed, it has been

suggested that emotional images have a higher ecological validity than emotional words

(Bradley et al., 1997; Bradley et al., 1999; McNally, et al., 1990). As such, it was

deemed appropriate to continue to use images in the current study.

A second possible method would be to create a stimulus set that is selected

individually for each participant. This can be achieved by first compiling a large

pictorial stimulus set that contains a diverse range of images, and then asking

participants to rate these images for their perceived emotional valence. The rated images

can then be used to create an individualised set of positive and negative stimuli for the

subsequent attentional bias assessment task for each participant. The benefits of this

method are two-fold. Firstly, an individualised image set for each participant will ensure

that the stimuli in the attentional probe task have the intended emotional valence for


each participant. Secondly, the inclusion of a rating task allows for investigation of

whether participants high and low in autistic traits rate the emotional valence of faces

compared to non-facial images differently. Thus, adding an image rating subtask to

create an individualised stimulus set for the subsequent attentional bias assessment task

was adopted as the method for Experiment 3.

The next consideration in the design of the current study was to decide what

images would be used in the candidate image set to be rated by participants. In order to

investigate the suggestion from Experiment 2 that emotional faces (i.e. faces displaying

angry expressions) may not be sufficiently negative to participants low in autistic traits,

it was considered valuable to include emotional faces (displaying both happy and angry

expressions) from the previous stimulus set in the image set for the rating task. This

would allow for analysis of whether the valence ratings of the emotional faces differed

between groups of participants low and high in autistic traits. For the rest of the image

set, images were sought that ranged in emotional valence (i.e. were both positive and

negative), and did not contain information that would be relevant to only one form of

anxiety (i.e. socially negative information that only has relevance to individuals with

heightened social anxiety). The International Affective Picture System (IAPS; Lang,

Bradley, & Cuthbert, 2008) image set met these criteria, so images were selected from

this database to form the rest of the image set used in the rating task.

Following Experiment 2, the aim of Experiment 3 was to assess the validity of

the hypothesis that the lack of anxiety-linked attentional bias to negative information

observed in participants low in autistic traits was due to emotional faces not being

sufficiently emotionally negative to these participants to reveal vigilance. To test this

hypothesis, the current study used individualised stimulus sets, and retained a 2 (high

versus low autistic traits) by 2 (high versus low trait anxiety) factorial design. The

hypothesis generated two predictions that were addressed in the statistical analysis.

Firstly, it was predicted that participants high in autistic traits would rate the emotional


faces (and in particular angry faces) as significantly more negative than participants low

in autistic traits. Secondly, it was predicted that, with personalised stimulus sets, there

would be a main effect of trait anxiety group on attentional bias to negative information

that was not moderated by autistic trait group. Specifically, it was expected that

participants high in trait anxiety would show a more pronounced attentional bias to

negative information compared to participants low in trait anxiety, irrespective of levels

of autistic traits.

Method

Participants

As in Experiment 2, participants for the study were selected to form four groups

representing the factorial combination of extreme levels of trait anxiety and autistic

traits: high autistic traits, high trait anxiety (High AT/High Anx); high autistic traits,

low trait anxiety (High AT/Low Anx); low autistic traits, high trait anxiety (Low

AT/High Anx); and low autistic traits, low trait anxiety (Low AT/Low Anx).

Recruitment occurred over two semesters at the University of Western Australia. In

both semesters undergraduate psychology students at the university (518 and 832

respectively) were initially screened on the AQ and the STAI-T. As in Experiment 2,

the upper and lower 30% of the AQ and STAI-T distributions were used to define high

and low levels. As stated previously, research investigating anxiety-linked attentional

bias to negative information has reported effect sizes in the moderate-large range (see

Bar-Haim et al., 2007). Assuming an effect size of similar magnitude in the current

study, a power analysis was conducted using MorePower 6.0 (Campbell & Thompson,

2012) on the 2 (autistic traits: high/low) x 2 (trait anxiety: high/low) interaction effect

central to the hypothesis under test in the current study. The power analysis indicated

that a sample size of approximately 80 participants (20 participants per group) would be

required to detect such an effect, with an alpha error probability of 0.05, and a power (1-


) of 0.80. In total across both time points, 92 students agreed to participate in the study

in exchange for partial course credit.

Descriptive statistics for the four groups are displayed in Table 3.1. As in

Experiment 2, separate 2 x 2 ANOVAs were conducted on AQ scores and STAI-T

scores, each considering the between group factors autistic trait group (high AT/low

AT) and trait anxiety group (high TA/low TA). The ANOVA conducted on the STAI-T

measure showed a significant main effect of trait anxiety group, F(1, 87) = 920.45, p <

.001, p = 0.91, reflecting that the STAI-T scores were significantly higher in the high

trait anxiety groups compared to the low trait anxiety groups. Neither the main effect of

autistic trait group, nor the interaction between autistic trait group and trait anxiety

group was significant for the STAI-T scores (p > .05), reflecting appropriate matching

of STAI-T scores across the high and low autistic trait groups. For the AQ scores, there

was a large main effect of autistic trait group, F(1, 87) = 738.67, p < .001,

p = indicating that the AQ scores were significantly higher in the high autistic

trait groups compared to the low autistic trait groups. Further, no significant interaction

effect was observed between autistic trait group and trait anxiety group (p > .05).

However, results showed a significant main effect of trait anxiety group on the AQ

measure, F(1, 87) = 29.89, p < .001, p = .26, with autistic traits being significantly

higher in the high trait anxiety groups compared to the low trait anxiety groups (see

Table 3.1). This confound effect means that a significant effect of autistic trait group

could be carried by trait anxiety group. Thus, if main effects are observed in the analysis

of the stimulus rating or attentional bias data, the confound will be parsed out by

supplementary analysis.


Materials

Questionnaires. As in previous experiments, the STAI-T (Spielberger, Goruch,

Lushene, Vagg & Jacobs, 1983) was used to assess trait anxiety and the AQ (Baron-

Cohen et al., 2001) was used to assess levels of autistic traits in the screened

participants.

Apparatus. A HP Compaq 8200 Elite PC with a 23-inch LCD colour monitor,

and a standard 104 keyboard and mouse were used to display and control both tasks.

Image Rating Subtask Stimuli. Stimuli for the image rating subtask included

facial stimuli used in the previous two experiments, together with a range of images

selected from the International Affective Pictures System (IAPS; Lang, Bradley, &

Cuthbert, 2008). For the facial stimuli, 96 colour photographs of adults, with an equal

Table 3.1

Descriptive statistics (mean, SD, and range) for each of the four groups.


High Trait

Anxiety

Low Trait

Anxiety

High Trait

Anxiety

Low Trait

Anxiety

n 22 20

30 19

n females 18 9 23 12

Age (years)

Mean (SD) 19.23 (2.16) 19.80 (2.59) 19.20 (3.36) 21.63 (7.40)

AQ

Mean (SD) 126.91 (5.43) 122.55 (5.28) 99.33 (4.41) 92.00 (5.18)

Range 119-137 117-136 86-104 83-101

STAI

Mean (SD) 54.56 (2.61) 33.70 (3.20) 54.43 (4.14) 31.11 (3.20)

Range 50-59 24-37 49-62 26-36


number of males and females, and an equal number of individuals displaying both

happy (positive) and angry (negative) expressions, were selected randomly from the

image set used in Experiment 2.

Additionally, 96 images were selected from the IAPS database. This database is

a normative set of emotional stimuli depicting people, objects, and animals in

naturalistic settings (Lang et al., 2008), and is commonly used in studies investigating

emotion and attention. Images within the database have been rated by a normative

sample on two primary dimensions: affective valence (ranging from unpleasant to

pleasant) and arousal (ranging from calm to excited), each on a scale from 1-9 (Lang et

al., 2008). Candidate images were initially compiled by selecting images with average

to low arousal ratings (i.e. arousal rating 6). Images with arousal ratings greater than 6

were excluded as these images tend to be especially graphic. Forty-eight images that

had been rated positively (i.e. a valence rating 7) and 48 images that had been rated

negatively (i.e. a valence rating 3) were then randomly selected from the IAPS

database. The average arousal rating of the images selected in the positive and negative

groups was then subjected to an independent samples t-test, which revealed no

significant difference in arousal rating between the negative and positive images (p >

.05).

Thus, in total 192 images (96 emotional faces and 96 IAPS images) were

included in the final image set. These images were standardised for size (resolution of

768 x 768 pixels), contrast and brightness.

Attentional Bias Assessment Task. In order to include personalised stimuli in

the attentional bias assessment, an initial image rating task was introduced before the

attentional probe task, which was similar in design to the attentional probe task used in

previous two experiments in the research programme, but now employed ideographic

stimuli for each participant based on their personal ratings of candidate stimulus items’

emotional valence.


Image Rating Task. For this rating task, each trial began with the presentation

of one of the images from the stimulus set, presented in the centre of the screen. The

image was presented in isolation for 1000ms, before a rating scale and prompt appeared

underneath the image. The prompt appeared in yellow text and stated, “Rate how this

image makes YOU feel emotionally”. It was presented below the image, but above the

rating scale. The rating scale was white, 410mm in length, and presented centred

underneath the image to be rated. The scale included 7 check-marks with descriptions at

equal distance along the scale which included (from left to right): “Very Negative”,

“Moderately Negative”, “Slightly Negative”, “Emotionally Neutral”, “Slightly

Positive”, “Moderately Positive”, “Very Positive”. Between each checkmark there were

15 evenly spaced, unmarked points at which a rating could be made, with ratings

ranging from -45 (“Very Negative”) to 45 (“Very Positive”). In order to prevent

careless responding, a rating could not be made in the middle exactly on “Emotionally

Neutral”, requiring the participant to move the mouse along the scale to make a rating.

An example of a trial from the rating task is shown in Figure 3.1. The rating task

consisted of 192 trials, in which the images were presented randomly, and each

participant rated each image in the stimulus set once. The images were presented in four

blocks of 48 images with a brief rest period separating successive blocks. Once all

images had been rated by a participant, the software was designed to select for that

participant the 32 images that they had rated the most positive, and the 32 images that

they had rated the most negative, and these were the stimulus images employed for that

participant in the subsequent attentional probe task (a total of 64 images).


Figure 3.1. Example of a trial from the rating task.

Attentional Probe Task. The attentional probe task used in this study was

identical in design to the task described in Experiment 2, except for the modification

whereby ideographically selected images, selected on the basis of the rating task, were

used for each participant. Each trial began with a fixation cross presented for 1000ms.

This was immediately followed by the presentation of a pair of stimuli, randomly

selected from the image sets derived from the rating task, on either side of the screen

(one negatively rated image and the other a positively rated image). Each image

appeared eight times, and images appeared with equal frequency on the left- and right-

hand sides of the screen. The images were presented 75mm apart and, as in previous

experiments, remained on the screen for either 500ms or 1000ms, with equal frequency.

After the presentation of the images, two small arrows, one a probe and one a foil,

identical to those used in previous experiments, appeared immediately in the locations

vacated by the stimuli. In half of the trials, the probe appeared in the location of the

positively rated image, and in the other half it appeared in the location of the negatively

rated image. As in previous experiments, participants were required to discriminate

whether the probe pointed up or down and respond by pressing the corresponding key

on the keyboard. A correct response progressed the task to the next trial, with an inter-

trial interval of 1000ms. An incorrect response resulted in an on-screen message

presented for three seconds (“ERROR TRIGGERED DELAY”). Probe discrimination

latency (recorded from the onset of the probe), and accuracy of the response were


recorded, with the arrows remaining on the screen until an arrow key was pressed. As in

previous studies in the research program, participants were required to discriminate the

probes with a 75% accuracy criterion or were excluded from the analysis. The 256 trials

were presented in two blocks of 128 trials, separated by a brief rest period.

Procedure


guidelines and procedures (approval number: RA/4/1/6140), including providing

informed written consent. After providing consent to participate in the experiment,

participants were shown a hard-copy array of eight of the images used in the rating task

(including examples of the most negatively valanced images used in the task).

Participants were then asked to provide verbal consent to continue with participation in

the experiment. For the image rating subtask, participants were instructed to rate the

images based on how each image made them personally feel. For the attentional probe

subtask, instructions emphasised the importance of accurately identifying the probe

while responding to the probe as quickly possible. Each participant then completed the

attentional bias assessment task in a quiet, individual testing room. The participant was

seated approximately 60cm from the computer screen. Prior to completing test trials,

participants completed four practice trials of the rating task, and eight practice trials of

the attentional probe task. Images used in the practice trials did not appear in test trials.

Testing duration was approximately 30 minutes.

Results

Statistical analyses were designed to first examine the data from the image

rating task to assess whether differences in the rating of the images would be revealed

between participants high and low in autistic traits. Analyses then examined data from

the attentional probe task to assess whether attentional bias to negative information was

observed across participants high and low in autistic traits, and high and low in trait

anxiety.


Examination of effects of autistic traits on image ratings

Descriptive statistics for the ratings for the autistic trait groups, and each image

type and valence, are shown in Table 3.2. The data files from the rating task for two

participants were unfortunately corrupted after collection, and so were not included in

the analysis. To assess for differences in the rating data when comparing participants

high and low in autistic traits, the image rating data were subjected to a 2 x 2 x 2 mixed

design ANOVA that considered the between groups factor autistic trait group (high

AT/low AT), and the within groups factors of image type (IAPS/faces) and image

valence (positive/negative). If it was the case that individuals low in autistic traits found

emotional faces, and in particular angry faces, to be less negative than participants high

in autistic traits, then we might expect a significant three-way interaction between

autistic trait group, image type, and image valence.

As would be expected, the analysis revealed a significant effect of image

valence, F(1, 92) = 675.89, p < .001, p = demonstrating that participants rated the

negative images as being significantly more emotionally negative than the positive

images. The analysis also revealed a significant main effect of image type, F(1, 92) =

85.28, p < .001, p = such that IAPS images were rated as significantly more

emotionally negative than the emotional face images. Further to this, the analysis

revealed a significant interaction between image type and image valence, F(1, 92) =

678.69, p <.001, p = .88. To follow up the nature of this interaction, the simple main

effect of image type was examined at each level of the image valence factor. The

interaction was such that for the negative images, the IAPS images were rated as

significantly more negative than the face images, F(1, 92) = 880.153, p < .001, p =

.91. Similarly, for the positive images, the IAPS images were rated as significantly

more positive than face images, F(1,92) = 259.01, p < .001, p = .74. This finding

suggests that across the rating task the IAPS images were rated as significantly more

evocative than the emotional face images.


No other effects in the analysis were statistically significant (p < .05). Of

importance to the hypothesis, no effects involving autistic trait group were significant,

including the predicted three-way interaction between autistic trait group, image type,

and image valence, F(1,92) = .76, p = .39, p = .008.

Given that the analysis indicated that the IAPS images were rated as eliciting

greater emotional responses than the face images, the proportions of IAPS and face

images selected from the image rating task to form the ideographic stimuli used in the

attentional probe task were examined. Overall, IAPS images were disproportionately

selected to form the stimuli in the attentional probe task, with IAPS images representing

97.60% of the selected negative images, and 86.28% of the selected positive images.


As in previous studies in the research programme, participants were required to

demonstrate a probe discrimination accuracy rate of at least 75% on the attentional

probe task for their data to be retained for analysis. No participants were excluded on

Table 3.2

Descriptive statistics for the positive-negative ratings of the images in the image

rating task for the autistic trait groups; M(SD)


IAPS

Negative Images -27.70 (8.88) -28.93 (7.42)

Positive Images 22.93 (10.01) 23.49 (7.43)

Faces

Negative (Angry) -7.44 (5.63) -7.44 (5.63)

Positive (Happy) 9.91 (9.38) 9.91 (9.38)


the basis of failing to achieve this accuracy rate (minimum accuracy = 91.40%). As in

the previous study, a 2 (high AT/low AT) x 2 (high TA/low TA) ANOVA was

conducted on the accuracy data. The analysis revealed no significant main effects, and

no significant interaction between autistic trait group and anxiety group on accuracy (p

> .05), indicated that accuracy rates did not significantly differ between groups. Mean

probe discrimination latencies each condition are displayed in Table 3.4. The data

screening and pre-processing steps used in Experiments 1 and 2 (described in

Experiment 1) were used to create an ABNII for each of the 500ms and 1000ms

conditions. As in previous experiments, positive ABNII values indicate an attentional

bias towards negative information, while negative ABNII values indicate an attentional

bias away from negative information. Prior to analysis, the ABNII data were screened

for normality, and screened for extreme outliers based on examination of box plots.

Four cases were identified as being extreme outliers on ABNII at both 500ms and

1000ms, and as they fell more than three standard deviations away from the mean, were

excluded from the analysis. Mean computed ABNII scores for each condition following

these exclusions are shown in Table 3.4. Most of the ABNII scores were found to be

approximately normally distributed with absolute skew and kurtosis values less than 2

and 4 respectively. ABNII scores for 1000ms SOA in the Low AT/Low Anx group

were positively kurtotic with a kurtosis value of 5.64. Given that ANOVA has been

demonstrated to be robust to deviations in normality (Glass, Peckham, & Sanders, 1972;

Harwell, Rubinstein, Hayes, & Olds, 1992; Schmider, Ziegler, Danay, Beyer, &

Bühner, 2010), no data transformations were performed in light of this kurtosis.


Table 3.4.

Discrimination latencies (in ms) to identify the target probe, and computed ABNII, as a function of autistic trait group (high AT/low AT), trait

anxiety group (high TA/low TA), image valence (negative/positive) and SOA (500ms/1000ms); M(SD).


High Trait Anxiety Low Trait Anxiety High Trait Anxiety Low Trait Anxiety

Negatively Rated Images

500ms

849.08 (94.48) 871.02 (88.04) 882.53 (91.95) 858.59 (189.21)

1000ms

826.23 (96.64) 851.47 (84.83) 859.08 (93.60) 839.88 (195.97)

Positively Rated Images

500ms

852.43 (101.37) 875.47 (92.15) 884.65 (95.23) 869.98 (171.82)

1000ms

818.04 (115.76) 851.17 (96.28) 860.29 (94.84) 823.34 (197.03)

ABNII

500ms 2.29 (54.26) 4.45 (47.53) 2.11 (49.10) 11.62 (59.93)

1000ms -7.59 (59.68) -0.30 (54.80) 1.22 (63.52) -16.34 (74.52)




considered the between group factors autistic trait group (high AT/low AT) and trait

anxiety group (high TA/low TA), and the within group factor SOA (500ms/1000ms). In

regard to the hypotheses, if it were the case that individualised stimuli were sensitive to

anxiety-linked attentional bias to negative information effects for low autistic trait as

well as high autistic trait groups, then we would expect to see a significant main effect

of trait anxiety group on the ABNII.

The analysis revealed a significant main effect of SOA, F(1, 84) = 4.24, p =

.043, p = .05, indicating that across groups, the ABNII was significantly higher in the

500ms SOA condition than in the 1000ms condition, suggesting that the degree to

which participants attended to negative stimuli decreased at the longer SOA compared

to the shorter SOA (see Figure 3.1). No other effects in the analysis were statistically

significant. Of relevance to the hypothesis, the analysis did not reveal a significant main

effect of trait anxiety group, F(1, 84) = .000, p = .99, p = <001. Also of interest, the

interaction effect between autistic trait group and trait anxiety group on ABNII

observed in Experiment 2 was not replicated in the current study, F(1, 84) = .15, p =

.70, p = <001.


Figure 3.1. Plot illustrating the significant main effect of stimulus onset

asynchrony (500ms/1000ms) on attentional bias to negative information (ABNII)

scores. Positive scores indicate an attentional bias towards negative information, while

negative scores indicate a bias away from negative information, with larger scores

indicating a greater degree of bias. Error bars represent one standard error of the mean.

Discussion

The aim of the current study was to assess the validity of the hypothesis that the

lack of anxiety-linked attentional bias to negative information observed in participants

low in autistic traits was due to emotional faces not being sufficiently emotionally

negative to these participants to reveal vigilance. The hypothesis generated two

predictions. Firstly, it was predicted that participants high in autistic traits would rate

the emotional faces (and in particular angry faces) as significantly more negative than

participants low in autistic traits. Secondly, it was predicted that, with personalised

stimulus sets, there would be a main effect of trait anxiety group on attentional bias to

negative information that was not moderated by autistic trait group. Specifically, it was

expected that participants high in trait anxiety would show a more pronounced

-10

-8

-6

-4

-2

0

2

4

6

8

10

500ms 1000ms

Att

enti

onal

Bia

s to

Neg

ativ

e In

form

atio

n

Index

(A

BN

II)


attentional bias to negative information compared to participants low in trait anxiety,

irrespective of levels of autistic traits.

The results of the current study did not support the first hypothesis. Participants

high in autistic traits did not rate emotional faces depicting angry expressions as

significantly more negative than participants low in autistic traits. There was also no

significant difference in the ratings of emotional faces depicting happy expressions

between participants high and low in autistic traits. Indeed, the analysis of the image

rating task did not reveal any significant effects involving either autistic trait group or

trait anxiety group, indicating that ratings of all image types did not differ between

groups. These findings fail to support the hypothesis that the autistic trait group by trait

anxiety group interaction on ABNII scores observed in Experiment 2 was a

consequence of the faces depicting angry expressions not being sufficiently negative to

participants low in autistic traits.

The findings also did not support the second prediction. No significant main

effect of trait anxiety group on attentional bias index scores was observed and there

were no significant interactions involving this factor. Thus, neither the low or high

autistic trait group showed evidence of anxiety-linked selective attention to negative

information. Taken together with the findings above, that there were no significant

differences in how participants high in autistic traits rated the emotional faces compared

to participants low in autistic traits, the findings of the current study fail to support to

hypothesis that the stimuli used in Experiment 2 contributed to the lack of anxiety-

linked attentional bias to negative information observed in participants low in autistic

traits.

The interaction between trait anxiety group and autistic trait group that was

observed in Experiment 2 did not replicate in the current study. Further, the anxiety-

linked attentional bias to negative information effect observed within the high autistic

trait groups in the previous experiment was not replicated. This is surprising, as the


modified method of employing individualised stimulus sets, aimed at revealing an

anxiety-linked attentional bias to negative information in low autistic trait participants,

was not expected to influence the expression of this bias in the high autistic trait groups.

It is unexpected that participants high in autistic traits did not display an anxiety-linked

selective attention to negative information in the current study, especially as the facial

stimuli from Experiment 2 were included in the current study. While this finding is

consistent with the overarching hypothesis of the thesis (i.e. that high levels of autistic

traits are characterised by attenuated anxiety-linked attentional bias to negative

information), it is inconsistent with the findings of the previous study that used the same

facial stimuli. One possible explanation for these findings is that the inclusion of an

image rating task, presented prior to the attentional probe task, reduced the sensitivity of

the stimuli subsequently presented in the attentional probe task. That is, as participants

had already been exposed to the stimuli used in the attentional probe task as part of the

rating task, participants may have been at least partially desensitised to the images, thus

reducing the sensitivity of the stimuli to attentional bias to negative information effects.

However, this possibility would be supported by an absence of any significant effects in

the current study, and as a main effect of SOA was observed, this explanation is

inconsistent with the broader findings of the experiment.

An interesting finding in the current study was the significant main effect of

SOA, such that greater attentional bias to negative information was demonstrated at

500ms compared to 1000ms. As discussed in previous chapters, the inclusion of shorter

and longer SOAs allows for greater sensitivity to capture a mixture of both automatic

and more strategic attentional processing effects. The findings of the current study

suggest that there was a significant decrease in degree to which participants attended to

the negative stimuli in the 1000ms SOA condition compared to the 500ms SOA

condition. The effect suggests that increased opportunity for strategic attentional control

(i.e. 1000ms) may have led to a decrease in the degree to which participants selectively


attended to negative information relative to positive information. Interestingly, this

pattern of results is inconsistent with the findings of the previous study. As will be

recalled from Experiment 2, an interaction was observed in that previous study between

trait anxiety group and SOA, with a significant anxiety-linked attentional bias to

negative information effect observed only at 1000ms, and not at 500ms. This interaction

was not observed in the present study. A possible explanation for this is that the set of

negative images selected for inclusion on the basis of each participants’ own rating

resented in these stimuli being so negative in nature that they automatically captured all

participants’ attention (i.e. at 500ms SOA), and were strategically avoided by all

participants (i.e. at 1000ms SOA). Supporting this suggestion, previous research has

demonstrated that participants high and low in trait anxiety show equivalent attentional

bias to negative information when the intensity of the negative stimuli is especially high

(Koster, Crombez, Verschuere, & De Houwer, 2006; Mogg, McNamara, et al., 2000;

Notebaert, Crombez, Van Damme, De Houwer, & Theeuwes, 2011; Wilson &

MacLeod, 2003). Thus, it is possible that the images that participants rated as being the

most emotionally negative were also of high emotional intensity, reducing the

sensitivity of the task to anxiety-linked attentional bias effects. This may account for

why the significant anxiety-linked attentional bias to negative information observed in

the high autistic trait groups in Experiment 2 was not replicated in the current study.

Specifically, it is possible that the relatively subtle social-affective stimuli (i.e. angry

faces) used in Experiment 2 were sensitive to anxiety-linked attentional bias effects in

high autistic trait groups, but such sensitivity was lost when employing less subtle

negative stimuli in the current study (i.e. IAPS images).

In summary, the current study did not support the candidate explanation for the

lack of anxiety-linked attentional bias to negative information observed in participants

low in autistic traits in Experiment 2, attributing this to the possibility that these

participants did not find angry faces to be emotionally negative. There were no


significant differences in the ratings of emotional faces between participants high and

low in autistic traits in the current study, and no significant anxiety-linked attentional

bias to negative information in either the high or low autistic trait group, despite the use

of ideographically chosen negative and positive stimuli.




The purpose of Experiment 4 was to investigate the possibility that participants

who endorse especially low levels of autistic traits may have unusually good attentional

control, as this could mask the expression of anxiety-linked attentional bias to negative

information. As will be recalled from the Prelude to Experiments 3 and 4, the two

studies were designed to test different hypotheses, and so the report of Experiment 4 in

this chapter is independent of the report of Experiment 3 in the previous chapter.

As discussed previously, there is evidence to suggest that individual differences

in top-down attentional control can moderate the expression of anxiety-linked

attentional bias to negative information (Beck & Clark, 1997; Mathews & Mackintosh,

1998; Mogg & Bradley, 1998). Recent studies that have included instructions requiring

participants to control their attentional responses to negative information (i.e. attend to,

or away from, negative information) have shown attenuation of anxiety-linked

attentional bias when such control is invoked, though this anxiety-linked bias is

otherwise exhibited when no such instruction is provided (Basanovic & MacLeod,

2017; Dodd, Vogt, Turkileri, & Notebaert, 2017). Further, as mentioned previously, a

number of individual difference studies have demonstrated that high levels of

attentional control can attenuate anxiety-linked attentional bias to negative information

(Derryberry & Reed, 2002; Gorlin & Teachman, 2015; Reinholdt-Dunne, Mogg, &

Bradley, 2009; Taylor, Cross, & Amir, 2016). For instance, in a study conducted by

Derryberry and Reed (2002), participants completed both the STAI and the Attentional

Control scale (Derryberry & Reed, 2001), a self-report measure of attentional control,

before completing a selective attention to negative information task. Findings of this

study indicated that participants high in trait anxiety and low in self-reported attentional

control displayed an attentional bias to negative information, whereas participants high

in trait anxiety who self-reported having high attentional control did not display this

attentional bias. Similarly, in a study conducted by Reinholdt-Dunne and colleagues


(2009), high and low trait anxious participants completed an task assessing attention to

negative information, and also the Attention Network Task (ANT; Fan et al., 2002), a

performance-based measure of attentional control that produces three indices; alerting,

orienting and attentional control. To assess attentional control, the ANT requires

participants to respond to target arrows in the presence of congruent and incongruent

flanker arrows. An attentional control index is then calculated by subtracting the mean

reaction time of incongruent flanker trials from the mean reaction time of congruent

flanker trials (Fan et al., 2002). Results of this study mimicked those reported by

Derryberry and Reed (2002) in that participants who were high in trait anxiety and low

in attentional control demonstrated selective attention to negative information, while

participants high in trait anxiety and high in attentional control did not (Reinholdt-

Dunne et al., 2009). Given these findings that high levels of attentional control can

attenuate the expression of anxiety-linked attentional bias to negative information, it is

possible that individuals low in autistic traits may have high levels of attentional

control, which may account for the lack of attentional bias to negative information

observed in these participants in Experiment 2.

Further support for this possibility is the evidence of reduced attentional control

capability in autistic individuals. Previous research has demonstrated that autistic people

have difficulties with attentional control using a range of methods including

neuroimaging techniques (e.g. Dichter & Belger, 2007; Maekawa et al., 2011), eye-

tracking (e.g. Loth, Gómez, & Happé, 2010; Mosconi et al., 2009; Neumann, Spezio,

Piven, & Adolphs, 2006), and behavioural measures (e.g. Burack, 1994; Greenaway &

Plaisted, 2005; Christ, Holt, White, & Green, 2007). For instance, using a modified

visual-search task, Burack (1994) found that autistic participants demonstrated a search-

advantage when attention was limited to a visual field constrained by a window, but

when distractors where presented within the window autistic participants displayed

greater detriment to reaction times compared to non-autistic controls. In a study


conducted by Mosconi and colleagues (2009) eye movements were recorded whilst

autistic and non-autistic individuals performed an anti-saccade task and a visually

guided saccade control task. During the the anti-saccade task participants were

instructed to inhibit the saccade towards a peripherally presented stimulus and instead

look in the opposite direction to this stimulus. In this study, autistic partcipants showed

increased saccadic error rates suggesting that their attentional control was inferior to

that of the non-autisic control group. There also is some evidence to suggest that

attentional control difficulties differ in severity as a function of variation in level of

autistic symptomatology in subclinical groups. For example, recent studies have

demonstrated that participants high in autistic traits show reduced distractor suppression

compared to participants low in autistic traits (Bayliss & Kritikos, 2011; Dunn, Freeth,

& Milne, 2016). Thus based on the available evidence, it is plausible that individuals

low in autistic traits may potentially display unusually good attentional control

compared to participants high in autistic traits. However, to date it has not been directly

assessed whether attentional control capabilities vary as a function of autistic trait level.

The aim of the current study was therefore to test whether reduced attentional

control accounts for the lack of anxiety-linked attentional bias to negative information

in participants low in autistic traits observed in Experiment 2. To assess this possibility,

the current study retained the same attentional bias assessment task used in Experiment

2, and additionally included a task designed to assess attentional control.

An important consideration in designing the current study was the selection of

the task used to assess attentional control. A number of previous studies have used self-

report measures to assess the association between attentional control and anxiety-linked

attentional bias to negative information (e.g. Derryberry & Reed, 2002; Taylor, et al.,

2016). While these measures have demonstrated a significant moderating effect of self-

reported attentional control on anxiety-linked attentional bias to negative information,

self-report measures of attentional control are have indeterminate validity. Confidence


in self-report measures of cognitive processes is compromised by the fact that

introspective assessment of these processes can be inaccurate (Nisbett & Wilson, 1977).

Further, self-report measures of attentional control have shown little to no association

with performance-based measures of attentional control (Reinholdt-Dunne et al., 2009;

Reinholdt-Dunne, Mogg, & Bradley, 2013). Given these limitations of self-report

measures, a performance-based measure of attentional control was chosen for use in the

current study.

The choice of this performance-based task was guided by consideration of the

prior experimental literature. Previous research has identified several different processes

that are thought to underpin attentional control. These include processes that reflect a

capacity to selectively direct attentional deployment to goal-relevant stimuli within a

competing stimulus set (Hopfinger, Buonocore, & Mangun, 2000; Lavie, Hirst, de

Fockert, & Viding, 2004; Smith & Jonides, 1999), and processes that function to inhibit

attentional deployment towards goal-irrelevant stimuli (Friedman & Miyake, 2004;

Smith & Jonides, 1999). Previous assessments of the relationship of attentional control

and anxiety-linked attentional bias to negative information have typically focussed on

one facet of attentional control, either control of selective attention (e.g. Derryberry &

Reed, 2002; Reinholdt-Dunne et al., 2009; Taylor et al., 2016) or inhibitory attentional

control (e.g. Gorlin & Teachman, 2015), not assessing both at the same time. To enable

assessment of both facets of attentional control, it was decided to employ an assessment

task designed by Basanovic et al. (2017). This included subtasks to assess both selective

attentional control capability and inhibitory attentional control capability, in both cases

using an attentional-probe methodology. The subtask designed to measure selective

attentional control capacity was adapted from a probe-based methodology commonly

used to assess the capacity to selectively attend to a target stimulus amongst competing

stimuli (Basanovic et al., 2017). In this subtask, participants are presented with two

shapes (a diamond and a circle) each containing either a probe (single-headed arrow) or


a foil (double-headed arrow). Participants are instructed to attend to one of the shapes

and discriminate the identity of the probe inside that shape (i.e. attend to diamonds and

ignore circles). The subtask designed to assess inhibitory attentional control is also a

probe-based task, and is similar to an anti-saccade task (Basanovic et al., 2017). In this

subtask, a shape (diamond or circle) appears on the screen, accompanied by a probe and

a foil, one presented within the shape and the other opposite the shape. In trials not

requiring inhibitory control, participants are instructed to attend to the shape and the

probe appears within this shape, whereas on trials requiring inhibition participants are

instructed to attend to the opposite side of the screen from the shape and the probe is

presented in this location opposite the shape. Using this task, Basanovic and colleagues

(2017) demonstrated that both facets of attentional control were positively associated

with the magnitude of change in attentional bias to negative information following a

bias modification procedure. Specifically, the findings of their study demonstrated that

both greater control of attentional selectivity, and greater inhibitory attentional control,

were associated with greater change in attentional bias to negative information. Given

the task designed by Basanovic and colleagues (2017) has the capacity to assess both

facets of attentional control, and employs a probe methodology similar to the attentional

bias assessment task used in the present research programme, this task was used to

assess attentional control in the current study.

The current study sought to investigate the hypothesis that participants low in

autistic traits display better attentional control than participants high in autistic traits,

and this accounts for the lack of anxiety-linked attentional bias to negative information

observed in Experiment 2. This hypothesis was predicated on the assumption that the

interaction between autistic trait group and trait anxiety group on attentional bias to

negative information observed in Experiment 2 will be replicated in the current study.

Given this assumption is met, the hypothesis generated two predictions. Firstly, it was

predicted that there would be a significant difference in attentional control between


participants high and low in autistic traits, such that participants low in autistic traits

would show superior attentional control compared to participants high in autistic traits.

The current study would also reveal whether this difference, if present, was observed

both for selective attentional control and inhibitory attentional control measures.

Secondly, it was predicted that that this difference in attentional control would account

for an interaction between autistic traits and trait anxiety on attentional bias to negative

information. This would be investigated using covariance analyses.

Method

Participants

As in previous studies in the present research programme, participants for this

experiment were selected to form four groups representing the factorial combination of

high and low levels of trait anxiety, and high and low levels of autistic traits. Thus the

four groups were: high autistic traits, high trait anxiety (High AT/High TA); high

autistic traits, low trait anxiety (High AT/Low TA); low autistic traits, high trait anxiety

(Low AT/High TA); and low autistic traits, low trait anxiety (Low AT/Low TA). A total

of 1019 undergraduate students at the University of Western Australia were initially

screened on the AQ and the STAI-T. As in previous experiments, the upper and lower

30% of the AQ and STAI-T distributions were used to define high and low levels. As

stated previously, research investigating anxiety-linked attentional bias to negative

information has reported effect sizes in the moderate-large range (see Bar-Haim et al.,

2007). Assuming an effect size of similar magnitude in the current study, a power

analysis was conducted using MorePower 6.0 (Campbell & Thompson, 2012) on the 2

(autistic traits: high/low) x 2 (trait anxiety: high/low) interaction effect central to the

hypotheses under test in the current study. The power analysis indicated that a sample

size of approximately 80 participants (20 participants per group) would be required to

detect such an effect, with an alpha error probability of 0.05, and a power (1- ) of 0.80.


In total, 104 students agreed to participate in the study in exchange for partial course

credit.

Descriptive statistics for the four groups are displayed in Table 4.1. As in

previous studies, separate 2 x 2 ANOVAs were conducted on AQ scores and STAI-T

scores, each considering the between group factors autistic trait group (high AT /low

AT) and trait anxiety group (high TA/low TA). The ANOVA conducted on the STAI-T

measure confirmed a significant main effect of trait anxiety group, F(1, 100) = 614.47,

p < .001, p = .86, reflecting the fact that the STAI-T scores were significantly higher

in the high trait anxiety groups than in the low trait anxiety groups, as required. Neither

the main effect of autistic trait group, nor the interaction between autistic trait group and

trait anxiety group was significant for the STAI-T scores (p > .05), confirming

appropriate matching on the STAI-T measure across the high and low autistic trait

groups. The ANOVA conducted on the AQ measure confirmed a significant main effect

of autistic trait group, F(1, 100) = 645.36, p < .001, p = .87, reflecting the fact that the

AQ scores were significantly higher in the high autistic trait groups than in the low

autistic trait groups, as required. There was no main effect of trait anxiety group on AQ

score, nor a significant interaction between trait anxiety group and autistic trait group in

this ANOVA (p > .05), confirming that the high and low trait anxiety groups were

appropriately matched on AQ score. These results indicated that the four groups were

appropriate for assessing whether anxiety-linked attentional bias to negative information

is attenuated by high levels of autistic traits.

Table 4.1

Descriptive statistics (mean, SD, and range) for each of the four groups; M(SD).



Materials

Questionnaires. As in previous experiments, the STAI-T (Spielberger, Goruch,

Lushene, Vagg & Jacobs, 1983) was used to assess trait anxiety and the AQ (Baron-

Cohen et al., 2001) was used to assess levels of autistic traits in the screened

participants.

Attentional Bias Assessment Stimuli. The set of emotional face images used in

Experiments 1 and Experiment 2 was also used in the current study. Likewise, the same

practice image set (described in Experiment 1) was also used in the current experiment.

Attentional Control Assessment Stimuli. The stimuli used in the attentional

control assessment consisted of two easily identifiable shapes, a diamond and a circle.

The shapes were shown as a 5 mm wide white outline on a black background, and each

was equivalent in size, covering a spatial areas of 34cm2.

Apparatus. A HP Compaq 8200 Elite PC with a 23-inch LCD colour monitor,

and a standard 104 keyboard and mouse were used to display and control both tasks.

High Trait

Anxiety

Low Trait

Anxiety

High Trait

Anxiety

Low Trait

Anxiety

n 31 21 20 32

n females 26 7 15 21

Age (years)

Mean (SD) 19.26 (1.48) 20.00 (2.90) 19.40 (2.52) 18.72 (1.69)

AQ

Mean (SD) 126.81 (5.84) 123.29 (5.62) 94.85 (7.13) 94.38 (5.49)

Range 118-138 117-138 82-102 81-102

STAI

Mean (SD) 56.94 (4.89) 33.19 (4.15) 56.70 (6.58) 31.31 (4.22)

Range 51-68 23-38 50-74 22-37


Attentional Bias Assessment Task. The attentional bias assessment task used

in the current experiment was identical to the task used in Experiments 1 and 2. As in

those experiments, a probe discrimination accuracy criterion of discriminating at least

75% of the probes correctly was applied.

Attentional Control Assessment Task. The attentional control task used in the

current experiment is described in Basanovic et al (2017). The task included two

subtasks, a an inhibitory attentional control subtask and an selective attentional control

subtask. The inhibitory attentional control subtask assessed a participant’s ability to

inhibit the allocation of attention toward a stimulus in order to discriminate the identity

of a probe presented in the opposite location. The selective attentional control subtask

assessed the participant’s ability to selectively allocate attention toward a relevant

stimulus, among competing alternatives, in order to discriminate the identity of a probe.

The time taken to make the attentional shifts in these tasks was inferred using a shared

baseline condition, which assessed probe discrimination latencies when no attentional

shift was required. As in the Attentional Bias Assessment Task, the probe in the

Attentional Control Assessment Task was a single-headed arrow that pointed up or

down, and the foil was a double-headed arrow. In each subtask participants were

required to discriminate the identity of the probe by pressing the corresponding key on

the keyboard. In each of the subtasks the factors of stimulus (i.e. diamond versus circle),

probe (i.e. up versus down pointing arrow), and location (i.e. shape or probe presented

on the left versus the right of fixation) were used equally often, and counterbalanced for

their combination with other factor options.

Baseline condition. Trials in the baseline condition required participants to

discriminate probes without the need to execute an attentional shift. The baseline

condition consisted of 64 trials with two trial types, each delivered in a dedicated block

of 32 trials. As some trials requiring attentional shifts (described below) presented a

probe within a surrounding stimulus, one type of baseline trial was designed to measure


the latency to discriminate a probe within a surrounding stimulus, but without the

requirement of an attentional shift; these trials were labelled probe within stimulus

baseline trials. As some trials requiring attentional shifts presented a probe without a

surrounding stimulus, the second type of baseline trial was designed to measure the

latency to discriminate a probe without a surrounding stimulus, and without the

requirement of an attentional shift; these trials were labelled probe without stimulus

baseline trials.

During probe within stimulus baseline trials, participants were required to

discriminate the identity of a probe presented within a shape stimulus (circle or

diamond). The surrounding stimulus and probe were presented centrally, in an already

attended location, requiring no attentional shift to discriminate the probe. The circle and

diamond shape appeared with equal frequency, as did up- and down-pointing probes,

with these two stimulus factors counterbalanced. During probe without stimulus

baseline trials, participants were required to discriminate the identity of a probe that

was presented alone, without shape stimuli. Half of the probes were up-pointing and

half down-pointing. Importantly, during these baseline trials the surrounding shape

stimulus, and the probe, matched those used in the selective attentional control subtask

and the inhibitory attentional control subtask trials (described below).

Each baseline trial began with a white fixation cross, presented in the centre of

the screen. Participants progressed the task by pressing the space-bar on the keyboard.

The cross was then removed after 500ms and a probe was presented within the central

12 mm square area of the screen. For both types of baseline trials, the target probe was a

small, grey, single-headed arrow (5 mm in height). Participants were required to

discriminate whether the target probe pointed up or down and respond by pressing the

+

Key Press

Probe response

time recorded

+

Key Press

Probe response

time recorded


corresponding arrow key on the keyboard. An example of each trial type is shown in

Figure 4.1. Correct responses progressed the task to the next trial with an inter-trial

interval of 500ms. Incorrect responses resulted in an on-screen message presented for

three seconds (“WRONG BUTTON, 3s TRIGGERED DELAY”). At the start of each

block, participants received instructions relevant to the upcoming trials. Specifically,

participants were instructed to attend to the centre of the screen and respond to probe

presented there. For each trial, probe discrimination latency (recorded from the onset of

the trial), and accuracy of the response was recorded, with the probe and the

surrounding stimulus remaining on the screen until an arrow key was pressed.

Figure 4.1. Example of a baseline-without-stimulus trial (left), and a baseline-within-

stimulus trial (right), delivered in the baseline subtask. Items in the figure are not to

scale.

Inhibitory attentional control subtask. Trials in this subtask required

participants to shift attention toward, or away from, a presented stimulus (diamond or

circle), and discriminate the identity of a probe presented in that location. This subtask

consisted of 64 trials, with two trial types presented in dedicated blocks of 32 trials

each. At the start of each block, participants received instructions relevant to the

upcoming trials. Specifically, for the block with inhibition trials participants were

instructed to attend away from the shape stimulus that would be presented. For the

block with non-inhibition trials, participants were instructed to attend towards the shape

stimulus that would be presented. For each trial type, the trial began with the

presentation of a centred white fixation cross and participants progressed the trial by

pressing the space-bar. The cross was then removed, and after 500ms a shape (either a

diamond or a circle) was presented 50mm to the right or the left side of screen centre.

Simultaneous to the presentation of the shape, a probe and a foil were also presented.

The location of the probe depended on the trial type. For inhibition trials, the probe was

presented in the location opposite the shape, and the foil was presented in the shape.


Thus, these trials required participants execute an attentional movement to the location

opposite the shape, whilst inhibiting a prepotent attentional movement towards the

shape. In non-inhibition trials, the probe was presented in the centre of the shape

stimulus, and the foil was presented in the location opposite the shape. Thus, for these

trials the participants were required to execute an attentional movement towards the

shape, without needing to inhibit attentional movement towards a salient stimulus.

Again, the foil was a double-headed arrow and the probe was a single-headed arrow that

pointed upward or downward with equal frequency. As in the baseline trials,

participants were required to respond to the probe by pressing the corresponding arrow

key on the keyboard. An example of each of the inhibition subtask trial types is shown

in Figure 4.2. As in the baseline trials, correct responses progressed the task to the next

trial with an inter-trial interval of 500ms, and incorrect responses resulted in the error

message described above. The presentation of the inhibition and non-inhibition blocks

was counterbalanced across participants. For each trial, probe discrimination latency

(recorded from the onset of the trial), and accuracy of the response was recorded, and

the probe and surrounding stimulus remained on screen until a response was made.

Figure 4.2. Example of an inhibition trial (left), and a non-inhibition trial (right),

delivered in the inhibitory attentional control assessment subtask. Items in the figure are

not to scale.

Inhibitory control index (ACInhibition). In order to assess the degree to which

participants were slower to execute attentional shifts during inhibition trials, an index of

attentional inhibitory control (ACInhibition) was calculated that corrected responses for the

time taken to identify the probe. For each participant, this was determined by


subtracting the mean probe discrimination latency recorded on probe without stimulus

baseline trials from the mean probe discrimination latency on inhibition trials, and

subtracting the mean probe discrimination latency recorded on probe within stimulus

baseline trials from the mean probe discrimination latency on non-inhibition trials.

Following this, an index of inhibitory attentional control (ACInhibition) was calculated by

computing the difference between these two indices, such that greater scores on this

index reflected a greater delay to execute an attentional shift during inhibition trials as

compared to non-inhibition trials. Thus higher ACInhibition scores reflect poorer control of

attentional inhibition. This index is expressed in the equation below:

Inhibitory Control Index (ACInhibition) =

[(inhibition trial latency) – (baseline without stimulus trial latency)] – [(non-

inhibition trial latency) – (baseline probe within stimulus trial latency)]

Selective attentional control subtask. Trials in this subtask required participants

to allocate attention toward a specified shape stimulus (circle or diamond), in the

presence of a competing stimulus. The task consisted of a total of 32 trials with one trial

type, labelled selection trials. These trials were delivered in two blocks containing 16

trials each. At the start of each block, participants were instructed that during the trials,

two shapes would be presented (diamond and circle), and they were required to attend

to one shape in order to identify the probe. In one block, they were instructed to attend

to the circle, and in the other block they were told to attend to the diamond. The probe

was always presented within the shape the participants were instructed to attend to. As

in the other subtasks, each trial began with the presentation of a centred white fixation

cross and pressing the space-bar key progressed the trial. The cross was then removed,

and after 500ms two shapes (a circle and a diamond) were presented 50mm to the right

and the left side of the screen. In 50% of the trials the circle appeared on the left, and in

50% of the trials the circle appeared on the right. Simultaneous to the presentation of

the shape, a probe was presented in the centre of one of the shapes, and a foil was


presented in the other shape. As in the other subtasks, the target probe was a single-

headed arrow pointed upwards or downwards, and the foil was a double-headed arrow.

The method of responding to the probes and the subsequent feedback for incorrect

responses was identical to the subtasks described above. An example trial is shown in

Figure 4.3. For each trial, probe discrimination latency (recorded from the onset of the

trial), and accuracy of the response was recorded, with probes and surrounding stimulus

remaining on the screen until an arrow key was pressed.

Figure 4.3. Example of selection trial, delivered in the selective attentional control

assessment subtask. Items in figure not to scale.

Selective attentional control index (ACSelectivity). In order to assess the degree to

which participants were slower to execute an attentional shift from fixation towards the

location of the probe in the shape they were instructed to attend to, an index of control

of selective attention (ACSelectivity) was calculated, with a correction for the time taken to

discriminate the probe. For each participant this index was determined by subtracting

the mean probe discrimination latency on probe within stimulus baseline trials from the

mean probe discrimination latency on selection trials, such that greater scores on this

index reflected reduced capacity to selectively attend to the instructed shape. Thus,

greater ACSelectivity scores reflected poorer control of selective attention. This index is

expressed in the equation below:

Selective Attentional Control Index (ACSelectivity) =

(selection trial latency) – (baseline within stimulus trial latency)


Block order counterbalancing. The order of the blocks in the task was rotated

across participants such that half of the participants started with blocks of the inhibitory

attentional control subtask (i.e. inhibition and non-inhibition trials), and half the

participants started with blocks related to the selective attentional control subtask and

baseline subtask (i.e. selection and within stimulus baseline trials). Within this, the

order of the blocks was also counterbalanced across participants, such that for the

participants beginning with the inhibitory attentional control subtask half began with

inhibition trials, and half began with non-inhibition trials. The block of without stimulus

baseline trials was always presented last, as this block contained the only trials without

a surrounding stimulus.

Procedure


guidelines and procedures (approval number: RA/4/1/6140). Each participant provided

informed written consent to take part in the study, and completed the tasks in a quiet

testing room seated approximately 60cm from the computer screen. Participants first

completed the attentional bias assessment task, followed by the attentional control

assessment task. During both tasks, instructions emphasised the importance of

accurately identifying the probe, while responding as quickly as possible without

compromising accuracy. Prior to completing the attentional bias task, participants

completed 10 practice trials. Testing duration was approximately 30 minutes.

Results

The statistical analyses were designed to first examine whether the attentional

bias assessment task revealed anxiety-linked attentional bias to negative information

across participants high and low in autistic traits. Specifically, the analyses examined

the assumption that the significant interaction effect between autistic traits and trait

anxiety on attentional bias to negative observed in Experiment 2 would be replicated in

the current study. The analyses then examined the predictions under test in the current


study. Specifically, whether the attentional control task revealed significant differences

in attentional control between participant high and low in autistic traits, and whether

differences in attentional control accounted for an interaction between autistic traits and

trait anxiety on attentional bias to negative information.


As in previous studies in the research programme, participants were required to

demonstrate a probe discrimination accuracy rate of at least 75%. No participants were

excluded on this criterion (minimum accuracy = 95.7%). As in previous studies, a 2

(high AT/low AT) x 2 (high TA/low TA) ANOVA was conducted on the accuracy data.

The analysis revealed no significant main effects, and no significant interaction between

autistic trait group and anxiety group on accuracy (p > .05), indicated that accuracy

rates did not significantly differ between groups. Mean probe discrimination latencies

are shown in Table 4.3.

The same data screening and pre-processing steps used in Experiments 1 and 2

(described in Experiment 1) were used to create an ABNII for each of the 500ms and

1000ms conditions. As in previous experiments, positive ABNII values indicate an

attentional bias towards negative information, while negative ABNII values indicate an

attentional bias away from negative information. Prior to the analysis, the ABNII data

were screened for normality, and screened for extreme outliers based on examination of

box plots. No participants were identified as being extreme outliers (i.e. index scores

greater than three standard deviations away from the mean). Mean computed ABNII

scores are shown in Table 4.3. Most ABNII scores were found to be approximately

normally distributed, with absolute skew and kurtosis values less than 2 and 4

respectively. ABNII scores for the 1000ms SOA in the Low AT/Low Anx group were

negatively skewed with a skew value of -2.65 and positively kurtotic with a kurtosis

value of 9.48. Given that ANOVA has been demonstrated to be robust to deviations in


normality (Glass et al., 1972; Harwell et al., 1992; Schmider et al., 2010) no data

transformations were performed in light of these normality deviations.


Table 4.3.

Discrimination latencies (in ms) to identify the target probe, and computed ABNII, as a function of face emotion (angry/happy), autistic trait group

(high/low), anxiety group (high/low) and stimulus onset asynchrony (500ms/1000ms); M(SD).



Angry Face

500ms

832.51 (111.63) 825.79 (81.68) 832.13 (120.03) 831.96 (80.64)

1000ms

815.59 (120.42) 811.44 (103.09) 826.03 (116.28) 812.85 (80.64)

Happy Face

500ms

852.97 (92.66) 852.10 (99.20) 842.60 (95.47) 869.98 (171.82)

1000ms

837.87 (96.85) 833.65 (105.32) 834.35 (98.70) 823.34 (197.03)

ABNII

500ms 3.84 (26.05) 4.62 (36.12) 2.49 (33.37) -10.56 (44.28)

1000ms 6.90 (33.49) -5.46 (41.06) 12.74 (33.79) -13.32 (47.14)




considered the between-groups factors autistic trait group (high AT/low AT) and trait

anxiety group (high TA/low TA), and the within group factor SOA (500ms/1000ms). If

it were the case that the findings of Experiment 2 were replicated in the current study,

then a significant interaction effect between autistic trait group and trait anxiety group

would be expected.

The analysis revealed a main effect approaching significance of trait anxiety

group, F(1, 100), 3.72, p = .056, p = .04, indicating that ABNII was higher in

participants high in trait anxiety then in participants low in trait anxiety (see Figure 4.1).

Specifically, this finding suggests that an anxiety-linked attentional bias to negative

information approaching significance was observed when averaging across autistic trait

groups.


Figure 4.4. Attentional bias to negative information scores (ABNII) as a function of

trait anxiety group. Positive scores indicate an attentional bias towards negative

information, while negative scores indicate a bias away from negative information, with

larger scores indicating a greater degree of bias. Error bars represent one standard error

of the mean.

No other effects in the analysis were statistically significant (p > .05), including

the interaction between autistic trait group and trait anxiety F(1, 100) = 1.10, p = .30,

p = .01. Importantly, this finding indicates that the pattern of interaction between

autistic trait group and trait anxiety group found in Experiment 2 was not replicated in

the present study. Given this, an assumption of the current study, that anxiety-linked

attentional bias to negative information would be more pronounced for participants high

in autistic traits compared to participants low in autistic traits, was not confirmed. Thus,

the validity of the proposed hypothesis in the current study cannot be adequately

investigated. However, given that to date it has not been directly assessed whether

attentional control differs as a function of autistic trait level, it was deemed worthwhile

to continue with analysis of the attentional control data.

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Calculation of attentional control indices

As with attentional bias data, the attentional control data were screened prior to

analysis and a 75% accuracy criterion was applied. No participants were excluded from

the analysis due to poor accuracy (minimum accuracy = 94.4%). Similar to the

attentional bias data, a 2 (high AT/low AT) x 2 (high TA/low TA) ANOVA was

conducted on the accuracy data. The analysis revealed no significant main effects, and

no significant interaction between autistic trait group and anxiety group on accuracy (p

> .05), indicated that accuracy rates did not significantly differ between groups for the

attentional control data. Descriptive statistics for the latency data for each of the

attentional control subtasks are shown in Table 4.4.

Prior to computation of the attentional control indices (described above), for

each participant probe discrimination latencies were filtered with the same procedure

applied to the attentional bias data (described in Experiment 1). As with the attentional

bias data, prior to analysis the attentional control indices were screened for normality

and screened for outliers by examining boxplots. No participants were identified as

being extreme outliers (i.e. having an index score greater than three standard deviations

away from the mean). Mean computed attentional control index scores for each

condition are shown in Table 4.4. The attentional control indices were found to be

approximately normally distributed with absolute skew and kurtosis values less than 2

and 4 respectively.


Table 4.4.

Discrimination latencies (in ms) to identify the target probe, and computed attentional control indices, as a function of attentional control subtask,

autistic trait group (high/low), and anxiety group (high/low); M(SD).



Baseline Subtask

Probe without shape trials 507.63 (41.86) 522.88 (59.54) 528.97 (67.06) 515.24 (48.43)

Probe within shape trials 530.40 (53.97) 530.82 (40.29) 559.22 (72.79) 552.13 (56.56)

Selective Attentional Control

Selection trials 715.77 (78.77) 731.34 (62.20) 731.12 (99.57) 721.56 (82.62)

ACSelectivity 185.37 (53.27) 200.51 (41.99) 171.90 (56.09) 169.44 (62.35)

Inhibitory Attentional Control

Inhibition trials 673.93 (85.02) 686.41 (71.59) 664.78 (86.95) 669.46 (84.46)

Non-Inhibition trials 645.69 (71.43) 668.34 (65.86) 663.73 (87.96) 648.41 (52.57)

ACInhibition 51.01 (73.34) 26.00 (74.93) 31.29 (31.60) 57.94 (68.50)


Analysis of selective attentional control

The hypothesis under test predicts that participants low in autistic traits will

show significant superior performance on the attentional control task than participants

high in autistic traits. To assess this prediction the ACSelectivity data were subjected to a 2

x 2 between-groups ANOVA, with the factors autistic trait group and trait anxiety

group. The analysis revealed a significant main effect of autistic trait group, F(1, 98) =

4.02, p = .048, p = .04, indicating that ACSelectivity was significantly higher for

participants high in autistic traits than for participants low in autistic traits (see Figure

4.5). This outcome is evidence that individuals low in autistic traits displayed

significant greater control of selective attention than individuals high in autistic traits.

Thus, this finding is consistent with the prediction that participants low in autistic traits

will show superior attentional control compared to participants high in autistic traits. No

other effects in this analysis were statistically significant (p > .05).

Figure 4.5. Selective attentional control index scores (ACSelectivity) as a function

of autistic trait group. Greater scores reflect poorer control of selective attention. Error

bars represent one standard error of the mean.

150

155

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165

170

175

180

185

190

195

200

Low Autistic Traits High Autistic Traits

Sel

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Att

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(A

CS

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Analysis of inhibitory attentional control

The prediction that participants low in autistic traits will show better attentional

control that those high in autistic traits was also tested using the measure of inhibitory

attentional control yielded by the attentional control assessment task. To assess this

prediction, the ACInhibition data were also subjected to a 2 x 2 between-groups ANOVA

with the autistic trait group and trait anxiety group factors. The main effect of autistic

trait group obtained in the analysis of selective attentional control was not evident on

this measure of inhibitory attentional control F(1, 98) = .21, , p = .65, p = .002.

However, this analysis revealed an interaction effect approaching significance between

autistic trait group and trait anxiety group, F(1, 98) = 3.67, p = .058, p = .04 (see

Figure 4.6). Visual inspection of this interaction suggests that in the high trait anxiety

groups, participants low in autistic traits exhibited better inhibitory control than

participants high in autistic traits. Conversely, in the low trait anxiety group,

participants low in autistic traits exhibited poorer inhibitory control than did participants

high in autistic traits. However, in following-up this interaction approaching

significance with simple-effects analyses, the simple effect of autistic trait group was

found to be not significant for either the low trait anxiety groups, F(1, 98) = 2.89, p =

.09, p = .03, or for the high trait anxiety groups, F(1, 98) = 1.04, p = .31, p

= . 01.


Figure 4.6. Inhibitory attentional control scores (ACInhibition) as a function of autistic trait

group (high AT/low AT) and trait anxiety group (high TA/low TA). Greater scores

reflect poorer inhibitory attentional control. Error bars represent one standard error of

the mean.

Given that the critical assumption for the current study was not met (i.e. a

significant interaction between autistic traits and trait anxiety on attentional bias to

negative information), no further analyses were conducted examining the relationship

between attentional control and anxiety-linked attentional bias to negative information.

Discussion

The aim of the current study was to investigate the hypothesis that participants

low in autistic traits display better attentional control than participants high in autistic

traits, and this accounts for the lack of anxiety-linked attentional bias to negative

information observed in Experiment 2. Thus, an assumption of the current study was

that the interaction between autistic trait group and trait anxiety group on attentional

bias to negative information would be replicated in the current study. If this assumption

was met, the hypothesis generated two predictions. Firstly, it was predicted that there

would be a significant difference in attentional control between participants high and

low in autistic traits, such that participants low in autistic traits would show superior

0

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70


Inhib

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Att

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Low Autistic Traits High Autistic Traits


attentional control compared to participants high in autistic traits. Secondly, it was

predicted that that this difference in attentional control would account for an interaction

between autistic traits and trait anxiety on attentional bias to negative information.

The assumption underpinning the hypothesis in the current study was not met.

The significant interaction between autistic trait group and trait anxiety group on

attentional bias to negative information observed in Experiment 2 was not replicated in

the current study. Thus, the hypothesis of the current study was not able to be assessed.

Instead, a main effect approaching significance of trait anxiety group was observed,

such that participants high in trait anxiety displayed a greater attentional bias to negative

information than participants low in trait anxiety. Interestingly, this trend towards an

anxiety-linked attentional bias to negative information effect was demonstrated

irrespective of autistic traits. This finding provides further evidence of participants high

in autistic traits displaying an attentional bias to negative information. However, the

current findings are inconsistent with the findings of Experiment 2, as low levels of

autistic traits did not attenuate anxiety-linked attentional bias to negative information in

the current study. It is not immediately clear why an anxiety-linked attentional bias to

negative information approaching significance would be observed across all participants

in the current study but was not observed for participants low in trait autism in

Experiment 2. The task used to assess attentional bias to negative information in the

current study was identical to the task used in Experiment 2. Further, the attentional bias

assessment task was administered prior to the attentional control assessment task, so it

is unlikely that the addition of this task impacted the results. Potential explanations for

these discrepant findings, and further discussion of the findings regarding high autistic

traits participants, will be considered in the General Discussion in the following chapter.

While the hypothesis under test in the current study was not able to be

adequately examined, it was deemed worthwhile to examine the predictions regarding

the attentional control data. The findings of the attentional control data provided partial


support for the prediction that participants low in autistic traits would demonstrate

superior attentional control compared to participants high in autistic traits. Participants

low in autistic traits demonstrated significantly superior control of selective attentional

control than participants low in autistic traits. This finding is consistent with previous

research that has suggested that autistic individuals display poorer attentional control

compared to non-autistic individuals (Dichter & Belger, 2007; Christ, Holt, White, &

Green, 2007; Greenaway & Plaisted, 2005; Maekawa et al., 2011; Mosconi et al., 2009).

Further, this finding is consistent with studies that have found that autistic individuals

(Burack, 1994; Greenaway & Plaisted, 2005; Mann & Walker, 2003) and individuals

high in autistic traits (Bayliss & Kritikos, 2011; Dunn et al., 2016) show difficulties in

selectively attending to targets in the presence of distractors, and adds to the evidence

that this characteristic extends across the broader autism spectrum. It has been

suggested that this difficulty reflects that individuals on the autism spectrum have an

“inefficient attentional lens” (Burack, 1994b; Mann & Walker, 2003), which results in a

reduced ability to focus on a target stimulus and filter distractors. This finding has been

linked to the sensory hypersensitivity and social processing issues common in autism,

as an inability to filter distracting information in the environment may lead to perceptual

overload (Burack, 1994; Dunn et al., 2016, Greenaway & Plaisted 2005).

The findings in the current study regarding inhibitory attentional control are

more complicated. The results are not consistent with the prediction that individuals

high in autistic traits would demonstrate poorer inhibitory attentional control than

participants low in autistic traits. Further, the results are not consistent with previous

research that has suggested that autistic individuals display impaired inhibitory

attentional control when compared with non-autistic individuals (Christ, Holt, White, &

Green, 2007; Mosconi et al., 2009). While the flanker and antisaccade tasks used in

these studies are similar in design to the task used in the current study, participants in

the study conducted by Christ et al. (2007) were children, as opposed to the adult


participants in the current study, and the anti-saccade task used in the study conducted

by Mosconi and colleagues (2009) employed eye-tracking. It is possible that these

methodological differences could account for the differences in findings between the

current study and previous research. Importantly, the current study also differed from

previous studies by examining neurotypical participants with high and low levels of

autistic traits. It is possible that the current findings indicate that reduced inhibitory

control does not extend from ASD into subclinical groups. Supporting this possibility, a

study conducted by Christ, Kanne and Reiersen (2010) found that participants high and

low in autistic traits did not differ significantly on inhibitory attentional control,

however the measure employed in their study was self-report. Given the issues

associated with using self-report measures for assessing attentional control (Nisbett &

Wilson, 1977; Reinholdt-Dunne, Mogg, & Bradley, 2009, 2013) further research

examining inhibitory attentional control in subclinical groups using performance-based

measures is needed.

The marginal interaction effect of trait anxiety group and autistic trait group on

inhibitory attentional control in the current study, though it fell just outside

conventionally accepted statistical significance, appears to suggest that within the high

trait anxiety groups, attentional control performance was better in participants low in

autistic traits compared to participants high in autistic traits, yet the opposite pattern was

observed in the low trait anxiety group. While these effects were not statistically

significant, this finding was also inconsistent with research that has demonstrated

anxiety-linked deficits in inhibitory attentional control (see Shi, Sharpe, & Abbott,

2019). It has been demonstrated that individuals high in trait anxiety typically show

poorer inhibitory attentional control than individuals low in trait anxiety (Shi, Sharpe, &

Abbott, 2019). However, in the current study participants low in autistic traits and low

in trait anxiety appeared to display the poorest inhibitory attentional control. Given that

the interaction effect observed in the current study was approaching significance, and


analysis of the simple main effects revealed non-significant differences, interpretation

of these effects is limited. Further research is needed to understand the possible

interaction of autistic traits and trait anxiety on inhibitory attentional control.

The primary aim of the current study was to investigate if superior attentional

control in participants low in autistic traits could account for the apparent attenuation of

anxiety-linked attentional bias to negative information observed in these participants in

Experiment 2. However, critically, a significant interaction between autistic trait group

and trait anxiety group on attentional bias to negative information was not observed in

the current study. Indeed, the findings indicated that participants high in trait anxiety

displayed an attentional bias toward negative information approaching significance

irrespective of autistic traits. Thus, while the study indicated some differences in

attentional control as a function of autistic traits, it could not be assessed if these

differences contribute to differences in the expression of anxiety-linked attentional bias

to negative information.

In summary, the current study did not replicate the significant interaction

between autistic trait group and trait anxiety group on attentional bias to negative

information observed in Experiment 2. Specifically, a low level of autistic traits did not

attenuate anxiety-linked attentional bias to negative information in the current study, as

a trend towards the anxiety-linked bias was observed irrespective of level of autistic

traits. The findings of the current study indicated that participants low in autistic traits

have superior selective attentional than participants high in autistic traits. However, it

was not able to be assessed if this variation is associated with variation in anxiety-linked

attentional bias to negative information. With regards to participants high in autistic

traits, the findings of the current study appear to be consistent with the findings of

Experiment 2. Specifically, there is no evidence in the current study that anxiety-linked

attentional bias to negative information is attenuated in participants high in autistic

traits. The potentially unreliable effects noted in participants low in autistic traits, and


the differences in outcomes across studies will be discussed in more depth in the

subsequent chapter.

CHAPTER 6: GENERAL DISCUSSION 118

Chapter 6: General Discussion

This final chapter in the thesis first provides a review of the experimental

findings. Next, conclusions regarding the central hypothesis that can be drawn from the

present research are described. Following this, the implications of these conclusions for

the understanding of the relationship between anxiety vulnerability and selective

attention to negative information in the high end of the autism continuum. Finally,

potential avenues for future research are discussed.

Overview of Experimental Findings

As will be recalled from the introductory chapter to this thesis, it is well

established that significantly elevated anxiety vulnerability is associated with the high

end of the autism continuum, both in clinical (Hollocks, Lerh, Magiati, Meiser-

Stedman, & Brugha, 2019; van Steensel, Bogels, & Perrin, 2011; van Steensel &

Heeman, 2017) and subclinical groups (Kanne et al., 2009; Kunihira et al., 2006;

Rosbrook & Whittingham, 2010; Russell-Smith et al., 2013). However, to date

cognitive factors that may be implicated in anxiety vulnerability in individuals high in

autistic traits remain poorly understood. A clear candidate mechanism for investigation

is anxiety-linked attentional bias to negative information. However, the majority of

previous studies have suggested that autistic individuals with elevated anxiety

vulnerability do not show significant anxiety-linked selective attention to negative

information (Antezana et al., 2016; García-Blanco et al., 2017; Hollocks et al., 2013;

May et al., 2015, 2016). One interpretation of these findings was that high levels of

autistic traits are characterised by an attenuated anxiety-linked attentional bias to

information; however the design of previous studies limited conclusions regarding this

possibility. Specifically, previous studies did not fully unconfound levels of autistic

traits and anxiety vulnerability, and used only a single SOA in their attentional probe

task, limiting the ability to capture potentially slower attentional effects. Thus, the

central focus of the current research program was to systematically evaluate the validity


of the hypothesis that high levels of autistic traits are characterised by an attenuated

anxiety-linked attentional bias to negative information. To assess the validity of this

hypothesis, the research programme comprised of two phases. The first phase consisted

of investigations of anxiety-linked attentional bias to negative information using two

alternative designs for separating anxiety vulnerability and autistic traits: continuous

and quantile. The second phase of the research programme consisted of experiments

designed to replicate the findings obtained in the first phase regarding participants high

in autistic traits, and test candidate explanations for a lack of anxiety-linked attentional

bias observed in participants low in autistic traits. The findings obtained from these

experiments will now be reviewed in turn.

Evidence that heightened anxiety vulnerability is characterised by

heightened selective attention to negative information. Of the four experiments

reported in the current research programme, Experiments 2 and 4 revealed evidence of

an anxiety-linked attentional bias to negative information.

Experiment 2 was the first experiment in the research programme to recruit

participants in a 2 (high versus low levels of autistic traits) by 2 (high versus low levels

of trait anxiety) factorial design. Attentional bias to negative information was assessed

using an attentional probe task that included emotional faces as negative and positive

stimuli and included two SOAs; 500ms and 1000ms. Analyses conducted in Experiment

2 demonstrated a significant interaction between autistic traits and trait anxiety on

attentional bias to negative information. Specifically, within the high autistic trait

groups, participants high in trait anxiety, compared to participants low in trait anxiety,

allocated disproportionally greater attention to the location of negative emotional faces

than to the location of positive emotional faces. This is consistent with the presence of

anxiety-linked attentional bias to negative information in the high autistic trait group.

Within the low autistic trait groups, no difference in attentional bias was observed

between participant high and low in trait anxiety.


Experiment 4 recruited participants in the same 2 (high versus low levels of autistic

traits) by 2 (high versus low levels of trait anxiety) factorial design, and assessed

selective attention to negative information with the same task used in Experiment 2. The

findings of Experiment 4 revealed a marginal main effect of trait anxiety, with no

interaction effects. Thus, Experiment 4 also provided evidence that participants high in

trait anxiety, compared to participants low in trait anxiety, allocated greater attention of

negative faces compared to the location of positive faces. This finding is again

consistent with the presence of anxiety-linked attentional bias to negative information.

While these experiments revealed evidence of anxiety-linked selective attention

to negative information, two experiments in the research programme did not obtain

evidence of this attentional bias. Experiment 1 assessed attentional bias using the same

task that was used in Experiment 2 and Experiment 4, yet this experiment did not

provide evidence of any anxiety-linked attentional bias to negative information effect.

However, as noted previously, the unselected sampling design employed in this

experiment may have limited its sensitivity to effects involving autistic traits and trait

anxiety. Experiment 3 recruited participants in the 2 (high versus low levels of autistic

traits) by 2 (high versus low levels of trait anxiety) factorial design, but developed a

novel methodology for assessing selective attention in which participants completed an

image rating task of candidate images that included a mixture of both IAPS images and

the emotional faces stimuli used in the other experiments in the research programme.

This created personalised stimulus sets for the attentional probe task for each

participant. Analyses in this experiment revealed no evidence of anxiety-linked

attentional bias to negative information across participants. However, as noted

previously, it is possible that the IAPS images included in this task may have been too

intense, which reduced sensitivity to attentional bias effects.

As can be seen, the current research programme did not consistently

demonstrate anxiety-linked attentional bias to negative information in all experiments in


the research programme. However, evidence of such effects was demonstrated in

Experiment 2 and Experiment 4, which employed the same factorial design of recruiting

participants, and the same attentional probe task. While the effects observed in

Experiment 4 were marginal, on balance across the research programme there was

reasonable evidence that heightened anxiety vulnerability was characterised by

heightened selective attention to negative information.

Evidence that selective attention to negative information effects are

automatic versus strategic in nature. Of the four experiments reported, Experiments 2

and 3 revealed significant effects suggesting differences in automatic versus strategic

attentional processing, however the operation of these effects was not consistent.

As will be recalled, the attentional bias assessment task used in all experiments

in the research programme included two SOAs, 500ms and 1000ms. The 500ms SOA

was included as this is the SOA that has been most commonly used in experimental

research and has consistently demonstrated anxiety-linked attentional bias to negative

information in the general population (Bar-Haim et al., 2007). Further, a 500ms SOA

was used in the previous studies investigating anxiety-linked attentional bias to negative

information in autistic children (Hollocks et al., 2013, 2016; May et al., 2015). A

1000ms SOA was also included as a longer SOA permits greater opportunity for slower,

more strategic attentional processing (Bradley et al., 1998; Koster, Verschuere, et al.,

2005; Mogg et al., 2004, 1997).

In Experiment 2, a significant interaction was found between SOA and trait

anxiety on anxiety-linked attentional bias to negative information. Specifically, the

analyses revealed that at 500ms, the condition that afforded the least opportunity for

strategic attentional processing, neither participants high or low in trait anxiety

displayed attentional vigilance for negative information. However, at 1000ms, a

condition that afforded greater opportunity for strategic attentional effects, a significant

anxiety-linked attentional bias to negative information was observed, with high trait


anxiety participants now demonstrating significant attentional vigilance for negative

information.

While Experiment 2 provided evidence of SOA moderating the anxiety-linked

attentional bias to negative information, the other three experiments in the research

programme did not obtain evidence of this effect. In Experiment 3, the analyses

revealed a significant effect of SOA that did not interact with either trait anxiety or

autistic traits. Specifically, this finding indicated that participants displayed greater

selective attention to negative information at 500ms, and greater attentional avoidance

of negative information at 1000ms. Thus, findings of this experiment did not provide

evidence that SOA moderated anxiety-linked attentional bias to negative information.

Additionally, no significant effects involving SOA were observed in Experiment 1 or

Experiment 4, again providing no evidence that SOA significantly moderated the

relationship between trait anxiety and selective attention to negative information.

As can be seen, evidence of SOA moderating effects of attentional bias to

negative information was inconsistent across the research programme. Importantly,

while some different patterns of effects involving SOA were observed, SOA did not

significantly interact with any trait anxiety by autistic trait group effects in any of the

experiments in the research programme. In both cases where an anxiety-linked

attentional bias to negative information was observed (i.e. Experiment 2 and

Experiment 4), these effects were not significantly influenced by SOA.

Evidence that low levels of autistic traits are characterised by superior

attentional control. One experiment in the research programme assessed attentional

control in participants varying in autistic traits, and found mixed evidence that low

levels of autistic traits are associated with superior attentional control.

Experiment 4 assessed attentional control using a task designed by Basanovic et

al. (2017) that used a probe-based methodology to assess two facets of attentional

control, selective attentional control and inhibitory attentional control. Analyses


revealed that participants low in autistic traits demonstrated significantly better selective

attentional control than participants high in autistic traits. Analyses also revealed a

marginal interaction between autistic traits and trait anxiety on inhibitory attentional

control, such that participants low in trait anxiety and low in autistic traits demonstrated

the poorest inhibitory attentional control. Thus, in the current research there was

inconsistent evidence that low levels of autistic traits are characterised by superior

attentional control, and this effect appeared to be constrained to control of selective

attention.

Evidence that autistic traits moderate anxiety-linked attentional bias to

negative information. Of the four experiments reported, only Experiment 2 revealed

significant evidence that autistic traits moderated the expression of anxiety-linked


Analyses in Experiment 1 revealed no significant moderation effects of autistic

traits on the relationship between trait anxiety and attentional bias to negative

information at either the 500ms or the 1000ms SOAs. However, as noted previously, it

is possible that the design of this experiment limited its sensitivity to moderation

effects.

In Experiment 2, the analyses revealed a significant interaction between autistic

traits and trait anxiety on attentional bias to negative information, however the pattern

of results was in the opposite direction to that anticipated by the hypothesis. The

findings indicated that participants high in autistic traits displayed a significant anxiety-

linked attentional bias to negative information, whilst participants low in autistic traits

did not. Thus, this experiment suggested some evidence that autistic traits moderated

anxiety-linked attentional bias to negative information. Crucially, however, the findings

of this experiment suggested that high levels of autistic traits were not characterised by

an attenuated anxiety-linked attentional bias to negative information. Indeed, the


findings supported the existence of an anxiety-linked attentional bias to negative

information in participants high in autistic traits.

Following the findings of Experiment 2, Experiments 3 and 4 aimed to

investigate the apparent attenuation of anxiety-linked attentional bias to negative

information in participants low in autistic traits. However, no significant evidence of

autistic traits moderating anxiety-linked attentional bias to negative information was

observed in Experiment 3 or Experiment 4. In Experiment 3, no significant anxiety-

linked attentional bias to negative information effects were observed. In Experiment 4, a

marginal effect of trait anxiety was observed that did not interact with autistic traits. The

findings of this experiment suggested that, on average, anxiety-linked attentional bias to

negative information was observed across all participants, irrespective of autistic traits.

In summary, the findings of the current research programme suggested

inconsistent evidence that autistic traits moderate the expression of anxiety-linked

attentional bias to negative information. A significant interaction between autistic traits

and trait anxiety was observed only in Experiment 2.

Conclusions concerning the validity of the proposed hypothesis of the

current research programme. As described earlier in this chapter, the central question

for the research programme was whether high levels of autistic traits are characterised

by attenuated anxiety-linked attentional bias to negative information. Of the four

experiments conducted in the current research programme, none showed significant

evidence to support this hypothesis. Indeed, the findings of Experiment 2 and

Experiment 4 provide evidence that high levels of autistic traits are associated with an

anxiety-linked attentional bias to negative information. With regard to low levels

autistic traits, the evidence of anxiety-linked attentional bias to negative information is

inconsistent. The implications associated with these findings will now be discussed.

Implications of the Current Findings


Methodological implications of the current findings. Foundational to the

current research programme was the assumption that heightened anxiety vulnerability is

characterised by heightened selective attention to negative information. With this in

mind, an objective of the current research stated in the introductory chapter was to

develop a methodological design that was sensitive to anxiety-linked differences in

attentional bias to negative information. Such a design would then have the capacity to

reveal whether autistic traits moderate the expression of anxiety-linked attentional bias.

While an anxiety-linked attentional bias to negative information was not

consistently observed in all experiments, on balance, there is reasonable evidence to

suggest that the attentional probe task used in the current research was sensitive to such

effects. Thus, given such sensitivity, there is encouraging evidence that the

methodological framework described in the current research programme has the

capacity to reveal the influence of autistic traits on anxiety-linked attentional bias to

negative information. However, it is important to acknowledge that within the current

research, anxiety-linked attentional bias to negative information effects were not

consistently expressed across all studies. Thus, it is important to consider how

methodological and design variations influence the sensitivity to anxiety-linked

attentional bias to negative information effects.

A methodological feature that was included in all studies within the current

programme of research was the use of two SOAs in the attentional probe task, 500ms

and 1000ms. As noted above, while some different patterns of findings involving the

two SOAs were observed in the current research programme, SOA did not significantly

interact with any trait anxiety by autistic trait group effects in any of the experiments

described above. In both cases where an anxiety-linked attentional bias to negative

information was observed in participants high in autistic traits (i.e. Experiments 2 and

4), these effects were not significantly influenced by SOA. This finding suggests that

the SOA of 500ms was sensitive to anxiety-linked selective attention effects in


participants high in autistic traits in the current research and including the additional

longer SOA did not significantly increase sensitivity. Some previous research has

suggested that participants high in autistic traits show increased latency to identify

emotions in faces compared to participants low in autistic traits (Miu et al., 2012). The

current findings suggest that while individuals high in autistic traits may be slower to

identify emotions in faces than participants low in autistic traits, this does not preclude

the assessment of attentional bias effects in participants high in autistic traits at shorter

SOAs.

Another methodological implication of the current findings regards the intensity

of the negative stimuli. As noted in Experiment 3, previous research has identified that

varying the intensity of the stimuli used in the assessment of selective attention,

modifies the expression of anxiety-linked attentional bias to negative information.

Specifically, it has been found that when that intensity of negative stimuli is especially

high, low and high trait individuals show an equivalent attentional bias to negative

information, and that anxiety-linked differences in attentional bias are most evident for

stimulus of moderate intensity (Koster, Crombez, Verschuere, & De Houwer, 2006;

Mogg et al., 2000; Notebaert, Crombez, Van Damme, De Houwer, & Theeuwes, 2011;

Wilson & MacLeod, 2003). In light of the findings that negative stimuli of high

intensity reduce sensitivity to the influence of anxiety on the attentional bias effect, the

lack of anxiety-linked differences in attentional bias to negative information observed in

Experiment 3 may indicate that some the candidate IAPS images used in the image

rating task included images that were of especially high intensity. Indeed, the IAPS

images did attract more negative ratings than the face images drawn from Experiments

1 and 2, and so the IAPS images were more likely to be included in the individualised

stimulus sets used in the attentional probe task, arguably reducing the sensitivity of this

task to anxiety-linked differences in attentional bias to negative information.

Furthermore, based on the stimulus ratings, faces depicting angry expressions appear to


be moderate in intensity of negative experience. This would account for the observation

of anxiety-linked attentional bias effects when using emotional faces in Experiments 2

and 4, and the loss of the effect in Experiment 3. Thus, given this possibility it is

important to ensure that candidate stimuli are moderate in intensity when assessing

anxiety-linked attentional bias to negative information in participants that vary in levels

of autistic traits.

A potential avenue for future research would be to replicate the method of

Experiment 3, but design the rating task such that stimulus images are selected that

attract moderate emotional ratings. Personalised stimuli sets could then be created that

are of moderate intensity, and images that are rated with especially high intensity could

be excluded. Such a task may be better able to assess anxiety-linked attentional bias to

negative information effects in participants low in autistic traits. Additionally, future

research could include personalised stimulus sets that are more relevant to each

participant’s worries or anxieties. Previous research has demonstrated that the

perceived relevance of negative information to an individual’s anxiety modifies the

observation of anxiety-linked selective attention to negative information (Amir et al.,

2009; Mathews & MacLeod, 1985; McNally et al., 1994). For example, Asmundson

and Stein (1994) demonstrated that an attentional bias to negative information in

participants high in social anxiety was observed only for stimuli related to social threats,

and not stimuli relevant to physical threats. Future research could therefore replicate the

design of Experiment 3, but within the rating task, ask participants to rate candidate

images on the relevance to their personal anxieties. Thus, in future research a rating

task could be developed that created personally relevant stimuli of moderate negative

emotional intensity. A task of this design may be more sensitive to anxiety-linked

attentional bias to negative information effects in participants low in autistic traits.

Theoretical implications of the current findings. Although evidence of

anxiety-linked selective attention to negative information was not consistent across the


four studies, the findings of anxiety-linked selective attention to negative information in

participants high in autistic traits are consistent with theoretical models of anxiety

vulnerability, and have implications for theories regarding the aetiology and

maintenance of anxiety vulnerability in autism. Separately, the findings regarding

reduced attentional control in participants high in autistic traits are consistent with some

theoretical models of autism. The theoretical implications of these two findings will

now be considered in turn.

The present findings supporting the existence of anxiety-linked attentional bias

to negative information in participants high in autistic traits are consistent with existing

theoretical models that implicate selective attention to negative information in the

aetiology and maintenance of anxiety vulnerability, while challenging the central

hypothesis under test. For example, schema theories propose that cognitive processing

is guided by schemas, and in anxious individuals these schemas are biased towards

negative information (Beck & Clark, 1997; Beck, Emery, & Greenberg, 1985; Bower,

1981, 1987). As a result, a bias towards negative information occurs at all stages of

processing, including early processes such as attention. Other theories assert that

elevated anxiety vulnerability is associated with elevated biases at specific stages of

processing. For example, an influential account presented by Williams, Watts, MacLeod

and Matthews (1988) argued that anxious individuals are sensitive to negative

information and tend to direct their attention towards negative information at early,

automatic stages of processing. Similar to this account, Mathew and Mackintosh (1998)

described anxiety vulnerability as being characterised by a cognitive evaluation system

that overrides focus on task demands to instead processes negative information. This

model further proposed that “strong” danger cues will attract biased selective attention,

regardless of trait anxiety levels, whereas “weak” danger cues will only do so for

individuals with elevated anxiety vulnerability. This suggestion is consistent with the


overall attentional bias to negative information, irrespective of trait anxiety level, that

was observed in Experiment 3.

Other accounts of anxiety vulnerability emphasise the time course of attentional

allocation in maintaining elevated levels of anxiety (Bradley et al., 1998; Clark &

Wells, 1995; Mathews & Mackintosh, 1998; Mogg & Bradley, 1998b; van der Doef,

1992). Specifically, it has been suggested that individuals with heightened anxiety

vulnerability direct their attention towards negative information in early, automatic

stages of processing, potentially followed by a tendency to direct their attention away

from negative information at more strategic stages of processing. Interestingly, while

different patterns of findings were observed at the two different SOAs in the current

programme of research, broadly the findings were not consistent with this suggestion.

The clearest finding concerning SOA and trait anxiety in the current research was

observed in Experiment 2, where a significant anxiety-linked attentional bias was

observed at the longer SOA (1000ms), but not at the shorter SOA (500ms). Thus, in

Experiment 2, it appeared that anxiety-linked selective attention to negative information

was demonstrated in the condition that afforded greater opportunity for strategic control

of attentional allocation. However, given that this finding did not replicate in

Experiment 4, conclusions regarding SOA and trait anxiety in the current research

programme are limited. Thus, the evidence supporting the existence of anxiety-linked

attentional bias to negative information in individuals high in autistic traits is broadly

consistent with existing models of anxiety vulnerability that implicate selective

attention to negative information. Further research is needed to understand the time

course of this attentional bias in individuals high in autistic traits, and the specific

cognitive mechanisms that underpin it. These avenues for future research will be

discussed in more depth below.

The findings of anxiety-linked selective attention to negative information in

participants high in autistic traits have implications for models of anxiety vulnerability


in the high end of the autism continuum. As will be recalled, South and Rogers recently

proposed a model of anxiety vulnerability in autism that incorporates constructs and

factors such as intolerance of uncertainty, alexithymia and amygdala activity. Both

intolerance of uncertainty and increased amygdala activation have been shown to be

associated with elevated anxiety-linked selective attention to negative information in

samples not selected on the basis of autistic traits. For instance, Monk and colleagues

(2008) found that increased right amygdala activation was positively correlated with

anxiety symptomology, and increased amygdala activation was positively correlated

with increased vigilance to negative information on an attentional probe task. Similar

findings of a positive association between amygdala activation and anxiety-linked

attentional bias to negative information has been demonstrated in a range of

experimental studies (see Cisler & Koster, 2010 for a review). Recent research has also

demonstrated a positive association between self-reported intolerance of uncertainty and

elevated attentional bias to negative information(Fergus, Bardeen, & Wu, 2013; Fergus

& Carleton, 2015). Given the evidence supporting the existence of anxiety-linked

attentional bias to negative information in individuals high in autistic traits in the

current research programme, it will be worthwhile for future research to investigate how

attentional biases may be implicated in models of anxiety vulnerability in the high of

the autism continuum. Specifically, future research could replicate the current

methodological framework, while including measures of intolerance of uncertainty,

and/or physiological measures of neural activity to further reveal links between

mechanisms underpinning anxiety vulnerability in ASD and individuals high in autistic

traits.

More recent cognitive models of anxiety propose that elevated anxiety

vulnerability is characterised by impaired attentional control processes, which in turn

can impact anxiety-linked selective attention to negative information. However, the

nature and direction of this association has been debated. For instance, Derryberry and


Reed (2001) suggest that attentional control moderates the expression of anxiety-linked

attentional bias to negative information, such that participants high in trait anxiety show

an elevated selective attention to negative information only when they also demonstrate

impaired attentional control. Thus, this model treats attentional control as a moderator

of the relationship between anxiety vulnerability and attentional bias to negative

information. Conversely, the model proposed by Eysenck, Derakshan, Santos and Calvo

(2007) asserts that elevated selective attention to negative information results in

impairments in attentional control, which in turn contributes to elevated anxiety

vulnerability. Thus, this model treats attentional control as a mediator of the relationship

between selective attention to negative information and anxiety vulnerability.

Regardless, both accounts contend that attentional bias to negative information, and

impairments in attentional control are characteristic of elevated anxiety vulnerability.

Interestingly, the findings of Experiment 4 were not consistent with these models. There

were no significant findings in this study to suggest anxiety-linked impairments in

attentional control. While there was some evidence that high levels of autistic traits

were associated with poor attentional control, this association was not moderated by

trait anxiety level. These findings are not consistent with previous models that have

implicated impairments in attentional control in heightened anxiety vulnerability (e.g.

Derryberry & Reed, 2001; Eysenck et al., 2007; Mathews, May, Mogg, & Eysenck,

1990). However, it should be noted that some previous studies examining anxiety-

linked impairments in attentional control (e.g. Derryberry and Reed, 2002) have

included self-report measures of attentional control, while the current research

employed a performance-based measure. Further, the task employed in the current

research differentiated discreet facets of attentional control, whilst previous research has

not. A valuable direction for future research would be to systematically evaluate the

relationship between anxiety vulnerability, selective attention to negative information

and attentional control in participants high in autistic traits. Such, research could


provide further insight into the cognitive mechanisms that underpin anxiety

vulnerability in individuals high in autistic traits.

The findings regarding attentional control in individuals high in autistic traits

also have implications for the broader autism literature. The finding in Experiment 4

that participants high in autistic traits demonstrated poorer control of attentional

selectivity is consistent with models that have implicated atypical executive functioning

in the behavioural features of ASD. While links between executive dysfunction and

social cognition have been observed (e.g. Leung, Vogan, Powell, Anagnostou, &

Taylor, 2016; Pellicano, 2007), predominately the executive functioning theory of

autism has been proposed to account for the non-social features of ASD (see Hill,

2004). In particular, difficulties observed in planning (e.g. Bennetto, Pennington, &

Rogers, 1996; Ozonoff & Jensen, 1999; Ozonoff, Pennington, & Rogers, 1991),

cognitive flexibility (Ozonoff & Jensen, 1999; Ozonoff & Mcevoy, 1994; Prior &

Hoffmann, 1990), and inhibition of a prepotent response (Hughes & Russell, 1993;

Russell, Hala, & Hill, 2003; Russell, Mauthner, Sharpe, & Tidswell, 1991) have been

suggested to underlie the restricted and repetitive behaviours, and perseveration

commonly observed in ASD (Hill, 2004). As noted in Experiment 4, it has also been

demonstrated that autistic individuals show difficulties in selectively attending to targets

in the presence of distractors (Burack, 1994; Greenaway & Plaisted, 2005; Mann &

Walker, 2003). Burack (1994) theorised that this difficulty may reflect an ‘inefficient

attentional lens’, and that this may contribute to the sensory hypersensitivity

characteristic of ASD. Specifically, it has been suggested that a reduced ability to

selectively attend to information may expose autistic individuals to a bombardment of

information from the environment, which may lead to perpetual overload (Burack,

1994; Dunn et al., 2016). The findings of reduced selective attentional control in

participants high in autistic traits in Experiment 4 are consistent with previous studies

that have demonstrated high levels of autistic traits are associated with reduced


distractor suppression (Bayliss & Kritikos, 2011; Dunn et al., 2016), and suggest that a

reduced ability to selectively attend to targets in the presence of distractors extends from

clinical to subclinical groups.

More broadly, these findings are also consistent with literature that has

identified genetic (Rommelse, Franke, Geurts, Hartman, & Buitelaar, 2010) and

cognitive (Taurines et al., 2012) overlaps between ASD and Attention Deficit

Hyperactivity Disorder (ADHD). ADHD and ASD are known to frequently co-occur

with 30-50% of individuals diagnosed with ASD also meeting criteria for ADHD

(Gadow, DeVincent, & Pomeroy, 2006; Goldstein & Schwebach, 2004; Hofvander et

al., 2009; Lee & Ousley, 2006). Recent research has demonstrated comparable

impairments in domains of attention across individuals diagnosed with ASD and

ADHD, including attentional control (Karalunas et al., 2018; Taurines et al., 2012).

Further, it has been suggested that shared difficulties in attentional control may underlie

the high association between ADHD traits and autistic traits in the general population

(Polderman et al., 2013). Thus, the findings in Experiment 4 are consistent with recent

evidence suggesting cognitive overlaps between autism and ADHD, and theoretical

accounts that suggest that atypical executive functions are characteristic of ASD. Given

the role attentional control potentially plays in the relationship between selective

attention to negative information and anxiety vulnerability, a worthwhile area of future

research may be to investigate how variations in other executive functions that have

been demonstrated to be atypical at the high end of the autism continuum (e.g.Cribb et

al., 2016; Demetriou et al., 2018; Gökçen et al., 2016; Grinter et al., 2009; Reed, 2017)

influence the relationship between attentional bias to negative information and anxiety

vulnerability in these populations.

As noted in Experiment 4, the findings regarding inhibitory attentional control

in the current research are more complex. Broadly, the findings were not consistent with

previous research that has demonstrated an autistic weakness in inhibitory attentional


control (Christ et al., 2007; Mosconi et al., 2009), as participants high in autistic traits

did not demonstrate significantly weaker inhibitory attentional control compared to

participants low in autistic traits. In fact, the findings of Experiment 4 suggested that

participants low in autistic traits, and low in trait anxiety, demonstrated the poorest

inhibitory attentional control. As noted previously, it is possible that methodological

differences between Experiment 4 and previous studies of inhibitory attentional control

in ASD account for the inconsistent findings. However, it is also possible that a

weakness in inhibitory attentional control is a domain that differentiates ASD and

individuals who endorse high levels of autistic traits.

Previous research suggests that the pattern of executive functioning weakness observed

in ASD is not consistently observed in individuals high in autistic traits, such as with

planning and cognitive flexibility (e.g. Christ, Kanne, & Reiersen, 2010; Ferraro,

Hansen, & Deling, 2018; Kunihira et al., 2006). Thus, it is possible that significantly

poorer inhibitory control does not extend from ASD to subclinical groups. Future

research is needed to investigate this possibility, both in autistic individuals and

individuals who endorse high levels of autistic traits. Such research could further shed

light on the relationships between attentional control, trait anxiety, and autistic traits.

Limitations and Directions for Future Research

The findings of the current research programme suggest a number of avenues

that could serve as the focus of future research, that have not been previously discussed.

The first includes utilising alternative methods of assessing selective attention to

negative information and incorporating these into the methodological framework

presented in the current research programme. Specifically, the use of eye tracking

paradigms as complementary to behavioural measures may provide further insight into

the time-course of anxiety-linked attentional bias in participants both high and low in

autistic traits. A second avenue for future research involves assessing the theorised

facets of selective attention to negative information. Investigation of the theorised


components of attentional bias, engagement and disengagement, could further

understanding of how anxiety-linked selective attention to negative information

operates at the high end of the autism continuum. A third avenue for future investigation

includes consideration of different dimensions, and clinical manifestations, of autistic

traits. Finally, future research could address limitations of the current research

programme (that have not been previously discussed), such as the impact of state

anxiety on attentional bias tasks, the use of a measure of anxiety vulnerability that was

not autism-specific, and the gender balance of the experiments reported in the thesis.

Each of these potential avenues for future research will now be considered in more

detail.

Alternative methods of assessing anxiety-linked attentional bias to negative

information. As noted above, adding alternative methods for assessing anxiety-linked

selective attention may augment the methodology used in the current research. One

alternative method is the assessment of eye-movements. These methods track the

movement of participants’ eye gaze while they view visual stimuli. It has previously

been demonstrated that eye movements are guided by shifts in selective attention

(Michael I. Posner, 1980; Shepherd, Findlay, & Hockey, 1986) so these methods are

able to provide a continuous measurement of attentional selection.

Numerous studies have demonstrated significant anxiety-linked attentional bias

to negative information through the assessment of eye-movements (see Armstrong &

Olatunji, 2012 for a review). For example, in a study conducted by Mogg, Millar and

Bradley (2000) participants with and without GAD completed an attentional probe task

while eye-movements were assessed. Consistent with an attentional bias to negative

information, when compared to participants without GAD, participants with GAD were

more likely to initially orient their attention towards threatening faces relative to neutral

faces, and also shifted their gaze more quickly towards threatening faces, rather than

away from them (Mogg, Millar, et al., 2000). Similarly, eye-movement measures taken


during visual search tasks have revealed that participants with elevated anxiety

vulnerability are disproportionately likely to orient attention towards threat distractors

(Miltner, Krieschel, Hecht, Trippe, & Weiss, 2004). Further, previous research has

shown that when freely viewing displays containing negative and benign stimuli,

participants high in anxiety vulnerability are more likely to look towards negative

stimuli than are participants low in anxiety vulnerability (Felmingham, Rennie, Manor,

& Bryant, 2011; Holas, Krejtz, Cypryanska, & Nezlek, 2014).

Eye-movement recording has also been used in the assessment of attention in

ASD, primarily to assess variations in social attention. For instance, previous research

has demonstrated decreased fixation time to social stimuli in autistic children, and an

increased latency to orient attention towards faces, relative to non-autistic children (M.

Freeth, Chapman, Ropar, & Mitchell, 2010; Riby & Hancock, 2009; C. E. Wilson,

Brock, & Palermo, 2010). Using eye-movement measures, previous research has also

demonstrated comparable levels of selective attention for negative information in

autistic and non-autistic groups. In a study conducted by Crawford, Moss, Anderson,

Oliver and McCleery (2015), autistic and non-autistic participants viewed pairs of

emotional faces depicting positive, neutral, and negative expressions while eye gaze

was tracked. Results indicated that the autistic and non-autistic participants did not

differ in that both groups showed a strong preference to fixate on negative relative to

neutral expressions, but no preference for happy relative to neutral expressions,

indicating an attentional bias to negative information. Other studies have reported

similar findings using eye-movement measures, demonstrating that autistic individuals,

like non-autistic individuals, preferentially execute eye-movements that allocate

attention to negative information relative to non-negative stimuli (Unruh, Bodfish, &

Gotham, 2020; White, Maddox, & Panneton, 2015). While these studies have not

assessed for anxiety-linked differences in attentional bias to negative information, they

provide evidence of the suitability of the use of eye-movement measures in the


assessment of selective attention to negative information at the high end of the autism

continuum.

The benefits of using measurements of eye-movement as complementary to

reaction time measures include the ability to more accurately assess the time-course of

attentional bias. As has been noted in previous reviews (Armstrong & Olatunji, 2012;

Bar-Haim et al., 2007; Weierich, Treat, & Hollingworth, 2008) a limitation of reaction

time tasks, such as in the attentional probe paradigm, is that they are only able to

provide “snapshots” of attention. Thus, inferences regarding dynamic attentional

processes are constrained to the SOAs employed in the attentional probe task. The

addition of eye-movement measures to the methodological framework used in the

current thesis, would allow for greater sensitivity in assessing potential differences in

the pattern of anxiety-linked selective attention to negative information in participants

varying in autistic traits. Further, eye-movement measures may serve to increase

sensitivity to attentional patterns in participants low in autistic traits that were not

captured by the SOAs used in the current thesis.

The incorporation of eye-movement measures within the current methodological

framework would allow future research to conduct a more robust assessment of the

hypothesis under test in the present programme of research. Specifically, a future

experiment could replicate the framework used in the current research, with participants

recruited to a 2 (high versus low autistic traits) by 2 (high versus low trait anxiety)

design and asked to complete an emotional faces attentional probe task, but additionally

including a measure of eye-movements. The hypothesis that high levels of autistic traits

are characterised by attenuated anxiety-linked attentional bias to negative information

could then be evaluated through two separate measures of selective attention. The

hypothesis under test in the current programme of research generates the prediction that

participants high in autistic traits show reduced anxiety-linked initial orienting to

negative information compared to participants low in autistic traits. A future experiment


that included a measure of eye-movements would allow for a more rigorous assessment

of this prediction, and provide convergent, or divergent, behavioural evidence for the

present findings. Further, such an experiment would allow for a more nuanced

assessment of the time-course of attentional bias in participants that vary in autistic

traits, and would allow for assessment of hypotheses regarding facilitated engagement

or delayed disengagement with negative information (discussed below).

Another potential method that has been occasionally used to assess anxiety-

linked attentional bias to negative information is the use of electroencephalogram

(EEG) measures. Using a behavioural measure and EEG, Bar-Haim, Lamy and

Glickman (2005) demonstrated that early components of the event-related potential

(ERP) had reduced latency and heightened amplitudes in participants high in trait

anxiety, relative to participants low in trait anxiety, when the participants viewed

negative stimuli, but not when they viewed neutral stimuli. The authors of this study

suggested that this finding may reflect that, compared to participants low in trait

anxiety, participants high in traits anxiety allocated increased attentional resources to

negative information relative to neutral information. Further studies have also reported

heightened ERP amplitudes in groups high in anxiety vulnerability, relative to groups

low in anxiety vulnerability, when viewing negative stimuli relative to non-negative

stimuli (Angelidis, Hagenaars, van Son, van der Does, & Putman, 2018; Felmingham et

al., 2011; Li, Li, & Luo, 2005). Thus, EEG measures appear to have validity in the

assessment of attentional bias to negative information and have potential in extending

understanding of how anxiety-linked selective attention to negative information

operates in individuals who vary in autistic traits. The incorporation of EGG within the

present methodological framework presents an alternative opportunity to collect

convergent, or divergent, evidence for the current findings. With regard to EEG, the

hypothesis under test in the current research programme generates the prediction that,

relative to individuals low in autistic traits, individuals high in autistic traits would


demonstrate attenuated anxiety-linked changes in ERP components (reduced peaks or

less delayed latencies) for negative stimuli relative to neutral stimuli. A future

experiment incorporating EEG recording would be able to test the validity of this

prediction.

The incorporation of complementary measures of selective attention to negative

information within the methodological framework described in the current thesis

provides a valuable direction for future research. The use of such measures may help to

extend understanding of how anxiety-linked attentional bias to negative information

operates in individuals who vary in autistic traits and provide convergent evidence of

anxiety-linked selective attention to negative information at the high end of the autism

continuum.

Assessment of alternative facets of anxiety-linked attentional bias to

negative information. When assessing anxiety-linked attentional bias in the current

research programme a distinction was not made between alternative components that

may underpin this attentional process. Two distinct components have been the subject

of increasing investigation regarding their role in the expression of attentional bias to

negative information, namely, attentional engagement and disengagement (M. I. Posner

& Petersen, 1990; Michael I. Posner, 1980). Attentional engagement refers to a shift in

attention towards a presented stimulus, while attentional disengagement refers to

shifting attention away from a stimulus that was the previous focus of attention. Both of

these components have been theorised to underpin anxiety-linked attentional bias to

negative information. With regard to attentional engagement, it has been theorised that

anxiety-linked attentional bias reflects a facilitated engagement with negative

information (e.g. LeDoux, 2000; Vuilleumier, 2005). Alternatively, it has been

suggested that anxiety-linked attentional bias may reflect a difficulty disengaging from

negative information (e.g. Fox et al., 2001). Evidence supporting both of these accounts

has been reported (see Armstrong & Olatunji, 2012; Cisler & Koster, 2010).


In-line with the facilitated engagement hypothesis, previous research has

demonstrated that individuals high in anxiety vulnerability have lower detection

thresholds for negative stimuli (Foa & McNally, 1986; Wiens, Peira, Golkar, & Öhman,

2008), and these decreased thresholds lead to an increased attentional orienting to

negative information (Mogg & Bradley, 2002). Previous research supporting the

disengagement hypothesis includes findings from spatial cueing and visual search tasks

that have suggested individuals high in anxiety vulnerability show difficulties shifting

attention away from negative information (e.g. Fox et al., 2001; Gilboa-Schechtman,

Foa, & Amir, 1999; Juth, Lundqvist, Karlsson, & Öhman, 2005; Koster, Crombez,

Verschuere, Van Damme, & Wiersema, 2006; Koster, De Raedt, Goeleven, Franck, &

Crombez, 2005; Rinck, Becker, Kellermann, & Roth, 2003; Yiend & Mathews, 2001).

While these assessments have predominately assessed the two components

independently, researchers have more recently developed novel attentional probe

methodologies that aim to disentangle the contributions of engagement and

disengagement facets to attentional bias to negative information (see Clarke, MacLeod,

& Guastella, 2013). Such paradigms have demonstrated evidence of both facilitated

engagement with negative information (e.g. Carlson & Reinke, 2008; Grafton &

MacLeod, 2014; Rudaizky, Basanovic, & MacLeod, 2014), and difficulty disengaging

from negative information (e.g. Grafton & MacLeod, 2014; Koster, Crombez,

Verschuere, & De Houwer, 2004, 2006; Rudaizky et al., 2014; Salemink, van den Hout,

& Kindt, 2007). Findings from studies assessing eye-movements have also supported

links between attentional engagement and disengagement in anxiety-linked selective

attention to negative information (see Armstrong & Olatunji, 2012). Recently it has

been suggested that these facets independently contribute to anxiety vulnerability, such

that for some individuals anxiety-linked attentional bias to negative information occurs

as a result of facilitated engagement with negative information, whilst for others the


attentional bias arises from difficulty disengaging with negative information (Grafton &

MacLeod, 2014; Rudaizky, Basanovic, & MacLeod, 2014b).

The distinction of these two facets of attentional bias to negative information

generates two conceptually different variations of the hypothesis under test in the

current research programme, each of which could account for variations in anxiety-

linked attentional bias to negative information. The first variant is that high levels of

autistic traits are characterised by an attenuation in anxiety-linked facilitated attentional

engagement with negative information. Such a hypothesis generates the prediction that

the degree to which facilitated engagement to negative information is greater in

participants with high levels of trait anxiety compared to participants low in trait anxiety

will be reduced in participants high in autistic traits relative to participants low in

autistic traits. The second variant is that high levels of autistic traits are characterised by

attenuated anxiety-linked impairments in attentional disengagement from negative

information. Accordingly, this hypothesis generates the prediction that the degree to

which attentional disengagement from negative information is delayed in participants

with high levels of trait anxiety compared to participants low in trait anxiety will be

reduced in participants high in autistic traits relative to participants low in autistic traits.

Participants at the high end of the autism continuum may show attenuation in one of

these distinct facets, neither, or both. Thus, potential differences in the magnitude and

time-course of engagement and disengagement with negative information may account

for the inconsistencies observed both in the current research programme, and in

previous research that has examined anxiety-linked attentional bias to negative

information in clinical autistic populations. The attentional probe task used in the

current research programme was not designed to discreetly assess the components of

attentional bias, and so facilitated engagement or impaired disengagement could equally

account for the findings obtained with the task used in the present research (see Clarke,

MacLeod, & Guastella, 2013). Thus, it is possible that participants high (or low) in


autistic traits demonstrate attenuation of anxiety-linked attentional bias to negative

information, but this is restricted to one facet of attentional bias that was not directly

assessed in the current research. It should be noted that one previous study did attempt

to make inferences regarding facilitated engagement and impaired disengagement in

ASD (May et al., 2015). However, as reviewed previously, this study compared an

anxious, autistic group to a non-anxious, non-autistic group and hence did not fully

unconfound autistic traits and anxiety vulnerability. Additionally, the task employed in

the May et al. (2015) study was an attentional probe task similar in design to that used

in the current research, thus facilitated engagement, or impaired disengagement, could

equally account for the findings. A valuable direction for future research would be a

programme of research that included either measures or eye-movements, or a task

specifically designed to independently assess facilitated attentional engagement, and

impaired attentional disengagement (e.g. see Grafton & MacLeod, 2014; Rudaizky,

Basanovic, & MacLeod, 2014), to systematically evaluate the contributions of each

facet of anxiety-linked attentional bias to possible differences as a function of position

on the autism continuum.

Assessment of alternative dimensions and clinical manifestations of autistic

traits. A third avenue for future research concerns the consideration of the distinct

social and non-social dimensions of autistic traits, and clinical manifestations of autistic

traits. These potential avenues for future research will now be discussed in turn.

Assessment of alternative dimensions of autistic traits. As reviewed previously,

ASD is characterised by a social dimension, consisting of difficulties in social

interaction and communication, and a non-social dimension, consisting of restricted and

repetitive behaviours. Thus, measures designed to assess autistic traits in the general

population commonly propose to have factor structures that align with social and non-

social dimensions. Indeed, the original description of the AQ measure suggested five

factors: social skill, attention switching, attention to detail, communication, and


imagination (Baron-Cohen et al., 2001). Previous research has shown that the social and

non-social dimensions obtained using measures of autistic traits are separable in

heritability (e.g. Ronald et al., 2006), and cognitive characteristics (e.g. Russell-Smith,

Maybery, Bayliss, & Sng, 2012). For instance, Davis and colleagues (2017) assessed

the relationship between social and non-social dimensions obtained from the AQ, and

differential attention to the eyes region of faces displaying neutral expressions. Findings

indicated that higher scores on the social dimension were associated with a reduced

tendency to look towards the eyes, whilst higher scores on the non-social dimension

were associated with improved facial recognition. Thus, it is possible that the social and

non-social dimensions of autistic traits may differentially contribute to anxiety-linked

attentional bias to negative information. However, limitations associated with the factor

structure of the AQ limit the suitability of this measure for adequately assessing this

possibility in an extension of the current research programme. In particular, the initial

factor structure proposed by Baron-Cohen et al. (2001) has been shown to have poor

validity. Alternative factor structures have been proposed, with varying degrees of

supporting evidence (e.g. Austin, 2005; Kloosterman, Keefer, Kelley, Summerfeldt, &

Parker, 2011; Russell-Smith, Maybery, & Bayliss, 2011). In particular, the non-social

factor(s) obtained from the AQ appear to have poor internal consistency and it has been

suggested that alternative measures of autistic traits may be more suitable for assessing

this dimension (see English, Gignac, Visser, Whitehouse, & Maybery, 2020). As such,

alternative measures of autistic traits in the general population should be pursued in

investigating the relationship between the social and non-social dimensions of autistic

traits and anxiety-linked attentional bias to negative information. A candidate measure

is the Broad Autism Phenotype Questionnaire (BAPQ; Hurley, Losh, Parlier, Reznick,

& Piven, 2007). Like the AQ, this measure was designed to measure autistic traits in the

general population, and the items map on to three factors, social difficulties,

communication and language difficulties, and restricted, rigid behaviours. Recent factor


analytic studies of the BAPQ have indicated promising support for the proposed factors

(Broderick, Wade, Reeve, Meyer, & Hull, 2015; Ingersoll, Hopwood, Wainer, & Brent

Donnellan, 2011; Sasson et al., 2013; Wainer et al., 2011). Thus, a potential avenue for

future research involves consideration of how the dimensions of autistic traits may

differentially influence anxiety-linked attentional bias to negative information.

Using alternative measures of autistic traits that tap the distinct social and non-

social dimensions would allow for more thorough examination of the hypothesis under

test in the current research. For instance, it is possible that attenuation of anxiety-linked

attentional bias to negative information is constrained to only one of the dimensions of

autistic traits. Thus, future research could test two variations of the hypothesis under test

in the present research, that is, that attenuated anxiety-linked attentional bias to negative

information is associated with either high levels of social difficulties or with high levels

of restricted and repetitive behaviours. Future studies could discriminate these alternate

hypotheses by employing the same methodological framework employed in the current

research programme. Specifically, this could be done by recruiting participants in a 2

(high versus low social/non-social traits) by 2 (high versus low trait anxiety) factorial

design and assessing selective attention using an attentional probe task or some of the

alternative methods mooted earlier in this chapter.

Assessment of clinical manifestations of autistic traits. While the current

findings provide encouraging evidence of the presence of anxiety-linked attentional bias

to negative information at the high end of the autism continuum, it is possible that

clinical and subclinical groups are differentiated in respect of this attentional bias.

Specifically, it is possible that anxiety vulnerability is qualitatively different in autistic

individuals compared to non-clinical individuals high in autistic traits. It will therefore

be important to examine whether these findings extend into clinical populations. A

programme of research could be conducted that recruits clinically diagnosed individuals

within the same methodological framework described in the current thesis. Specifically,


future research could recruit participants in a 2 (autistic vs non-autistic) by 2 (high vs

low anxiety vulnerability) design. This design will allow for testing the hypothesis that

high levels of autistic traits attenuate anxiety-linked attentional bias to negative

information in clinical autistic populations. Given the entangled nature of anxiety

vulnerability and autistic traits in clinically diagnosed ASD recruiting for this design

would present a challenge for future research, however it is imperative that future

experiments do not confound autistic traits and anxiety vulnerability in clinical research.

Within this framework, future research could also examine how methodological

variations of the attentional probe task influence the sensitivity of this task to attentional

bias to negative information effects in autistic individuals. For instance, while there was

no consistent evidence that SOA moderated the expression of anxiety-linked attentional

bias at the high end of the autism continuum in the current research programme, it is

possible that this finding will not extend to clinical populations. There is substantial

evidence to suggest delayed latency to process emotions in faces in autistic individuals

(see Aoki, Cortese, & Tansella, 2015; Harms, Martin, & Wallace, 2010 for reviews).

Thus, SOA may significantly moderate the expression of anxiety-linked attentional bias

to negative information in clinical populations. Future research could explore this

possibility by varying SOA in the attentional probe task or by incorporating continuous

measures of attention such as by employing eye-tracking.

Additional limitations of the current research programme. While a number

of methodological constraints of the current research programme have been identified

and considered within the discussion above, it is important to mention further

limitations that may have impacted the generalisability of the observed findings.

Specific limitations are the lack of measurement of state anxiety, and the gender

imbalance present in all samples throughout the thesis.

Measurement of state anxiety. Throughout the thesis, anxiety-linked attentional

bias to negative information was assessed with reference to variations in trait anxiety


(i.e. individual differences in the tendency to experience state anxiety across a range of

settings). However, significant anxiety-linked attentional bias to negative information

has also been observed when investigating individuals who differ in levels of self-

reported state anxiety (i.e. individual differences in actual situation-related reactions;

Bar-Haim et al., 2007). Further, it has been demonstrated that state anxiety moderates

the relationship between trait anxiety and attentional bias to negative information, such

that individuals with elevated trait anxiety are most likely to exhibit attentional bias to

negative information when state anxiety is also elevated (see Macleod, Grafton, &

Notebaert, 2019). For instance, Mathews and MacLeod (1988) recruited high and low

trait anxious undergraduate university students and assessed their attentional bias to

negative information using an attentional probe task 12 weeks before a major exam

(when state anxiety was relatively low), and again one week before the exam (when

state anxiety was relatively high). It was found that one week prior to the exam,

participants high in trait anxiety displayed an increased magnitude of attentional bias to

negative information, while participants low in trait anxiety showed an increase in

avoidance of negative information at this time point. Similar findings of an interactive

effect of state anxiety and trait anxiety on attentional bias to negative information were

obtained by Broadbent and Broadbent (1988), who found that higher state anxiety

predicted greater attentional bias to negative information only in participants high in

trait anxiety. These findings have been replicated in a number of studies, using both the

attentional probe task and the emotional Stroop task (Egloff & Hock, 2003; MacLeod &

Rutherford, 1992; Mogg, Bradley, & Hallowell, 1994). Thus, it is possible that state

anxiety may have moderated the expression of anxiety-linked attentional bias effects in

the thesis. Given that a measure of state anxiety was not administered during data

collection in the current research programme, this possibility is not able to be assessed

here. Future research can expand on the current findings by administering a measure of

state anxiety at the time of data collection. This would allow future researchers to


investigate the potential impact of state anxiety on anxiety-linked attentional bias to

negative information in individuals who differ in levels of autistic traits.

Autism-specific measures of anxiety vulnerability. Throughout the current

thesis, the STAI-T (Spielberger, Goruch, Lushene, Vagg & Jacobs, 1983) was used as a

general measure of trait anxiety levels. This measure was selected as the STAI-T has

been demonstrated to have good reliability and validity (Barnes, Harp, & Jung, 2002,

Grös, Antony, Simms, & McCabe, 2007; Spielberger & Sydeman, 1994), and has been

widely used to investigate anxiety-linked attentional bias to negative information in the

general population (see Bar-Haim et al. 2007). However, with growing research on the

high rates of anxiety vulnerability associated with ASD, there is an emerging literature

on the suitability of existing measures of anxiety vulnerability for use with autistic

individuals. As reviewed in the introductory chapter, there is some discussion as to

whether the high rates of anxiety vulnerability observed in ASD reflect a discreet co-

morbidity, if such symptoms are part of the ASD profile, or if they represent a unique

manifestation of anxiety (see Kerns & Kendall, 2012; Wood & Gadow, 2010). Further,

while meta-analytic studies estimate a pooled prevalence rate of co-occurring anxiety

disorders in ASD to be approximately 40% (Hollocks et al., 2019; van Steensel et al.,

2011), the prevalence rates reported in individual studies vary greatly (11%-84%; De

Bruin et al., 2007; Joshi et al., 2010; Leyfer et al., 2006; Simonoff et al., 2008). It has

been suggested that this high level of variability between studies may be in part because

measures of anxiety vulnerability that have been validated in samples of non-autistic

individuals may not be suitable for use with autistic individuals (Lecavalier et al., 2014;

Wigham & McConachie, 2014). In order to address this issue, several autism-specific

measures of anxiety vulnerability have been developed. Specifically, these include

paediatric tools such as the Anxiety for Children with Autism Spectrum Disorder (ASC-

ASD; Rodgers et al., 2016) and the Parent Rated Anxiety Scale for Youth with Autism

(PRAS-ASD; Scahill et al., 2019), and the Anxiety Scale for Autism-Adults (ASA-A;


Rodgers et al., 2020). While there is some evidence of the validity and sensitivity of the

ASC-ASD (den Houting, Adams, Roberts, & Keen, 2018; Keen, Adams, Simpson, den

Houting, & Roberts, 2019; Rodgers et al., 2016), the other two measures have received

only preliminary evaluations (Scahill et al., 2019; Rodgers et al., 2020). While the

STAI-T has been commonly used in studies that have investigated relationships

involving autistic traits and trait anxiety (e.g. English, Maybery, & Visser, 2019;

Kunihira et al., 2006; Maisel et al., 2016), to date the suitability of the STAI-T for use

with individuals with elevated levels of autistic traits has not been assessed. Thus, it is

possible that the use of the STAI-T in the current thesis contributed to the variability of

the findings across studies, as levels of anxiety vulnerability in the samples may not

have been sensitively assessed. Future research could address this issue by utilising (or

including) an autism-specific measure of anxiety vulnerability.

Gender imbalance across samples. It should be noted that a limitation of the

current research programme was a significant gender imbalance that was present in all

samples across the thesis, with female participants over-represented relative to male

participants. This gender imbalance was consistent in all experiments within the thesis,

with Experiment 1 having a gender ratio of approximately 5:7 females to males, and

Experiments 2-4 having a gender ratio of approximately 2:1 females to males. Indeed,

across the entire sample, female participants outnumbered male participants

approximately 2:1 (total number of participants = 344; total number of female

participants = 229). To date, there is no evidence to suggest that anxiety-linked

attentional bias to negative information is influenced by gender, with recent studies

finding no significant sex differences in the expression of attentional bias to negative

information (e.g. Campbell & Muncer, 2017; Carlson, Aday, & Rubin, 2019; Carlson &

Fang, 2020). However, it should be noted that the gender balance in the current thesis is

inconsistent with that associated with high levels of autistic traits. As noted in the

introductory chapter to this these, ASD is more commonly diagnosed in males than


females, with prevalence estimates suggesting a ratio of approximately 4:1 (Fombonne,

2009; Loomes et al., 2017; Werling & Geschwind, 2013). Further, in the original paper

describing the AQ, Baron-Cohen and colleagues (2001) noted that in their sample,

males scored significantly higher on the AQ than females in the control group. They

also noted a significant gender difference at high levels of the AQ, such that high levels

were endorsed more frequently by males than females, with a ratio of approximately

3.5:1. Similar findings have been reported in a number of subsequent studies of

examining the AQ in the general population (Baron-Cohen & Wheelwright, 2004;

Ruzich, Allison, Chakrabarti, et al., 2015; Ruzich et al., 2015; Wheelwright et al.,

2006). Thus, the gender ratio of males to females in the current recruited sample is not

consistent with the gender ratio reported in clinical ASD, or high levels of autistic traits.

While existing evidence does not suggest that gender significantly influences the

expression of anxiety-linked attentional bias to negative information, the gender

imbalance in the current thesis may impact the generalisability of the findings.

Therefore, future research may want to consider recruitment of samples that more

accurately reflect the gender ratio reported in the high end of the autism continuum.

Concluding Comments

The present programme of research was designed to test the hypothesis that high

levels of autistic traits are characterised by an attenuated anxiety-linked attentional bias

to negative information. The current research provided no support for this hypothesis. In

fact, the current findings indicated that when autistic traits and trait anxiety are

appropriately separated, high levels of autistic traits are associated with an anxiety-

linked attentional bias to negative information. Interestingly, across the four

experiments reported in this thesis, expression of anxiety-linked attentional bias to

negative information in participants low in autistic traits was inconsistent. Additional

findings arising from the research project demonstrated that high levels of autistic traits

were associated with reduced selective attentional control.


It has been argued that the present research findings hold important

methodological and theoretical implications concerning the understanding of anxiety-

linked attentional bias in individuals high in autistic traits. Several future avenues of

research will be important in strengthening understanding of how anxiety-linked

attentional bias to negative information operates in individuals varying in levels of

autistic traits. This future research should build upon the present research by examining

the delineated, alternative variants of the hypothesis that high levels of autistic traits are

characterised by attenuated anxiety-linked attentional bias to negative information

proposed above. This can be achieved through adopting alternative methods for

assessing selective attention such as measures of eye-movements, systematically

assessing engagement and disengagement with negative information, and assessing the

different dimensions of autistic traits. Another valuable, if difficult, path for further

research would be to examine clinical populations within the methodological

framework established in the current research programme, that is, while taking care to

appropriately separate the dimensions of autistic traits and anxiety vulnerability.

Ultimately, it is hoped that the present research programme will serve not only to

enhance understanding, but will also stimulate further research designed to further

illuminate the relationship between anxiety vulnerability and attentional bias to negative

information across the autism continuum.


REFERENCES 152

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Appendix A

This appendix contains the verbatim instruction scripts included in the

experimental tasks used throughout the thesis.

Script for Attentional Probe Task – Experiment 1, 2 and 4

You will now begin the main task.

A fixation cross will appear in the middle of the screen. This is where you need to

attend at the beginning of each trial.

After the fixation cross, two images will briefly appear on the screen followed by two

arrows - a single headed arrow ↑ or and a double headed arrow

Your task is to find the single headed arrow (pointing up or down) as

quickly as possible - but not so quickly you are making mistakes.

When you locate the arrow, indicate the direction it is pointing with the

arrows on your keyboard.

If you make a mistake you will trigger a 3s error delay, wait out this

delay and then continue the task.

You will first complete a practice before continuing to the main task.

Script for Rating Task and Attentional Probe Task – Experiment 3

You will complete two tasks in today’s experiment. In the first task you will be asked to

rate a series of images. The image will appear in the centre of the screen, and a rating

scale will appear below it. For each image, please rate how the image makes YOU

personally feel. You need to move the mouse along the scale in order to make a rating –

a rating cannot be made in the centre of the scale.

Following the rating task the main task will begin. In this task, a fixation cross will

appear in the middle of the screen. This is where you need to attend at the beginning of

each trial.

After the fixation cross, two images will briefly appear on the screen followed by two

arrows - a single headed arrow ↑ or and a double headed

201

arrow

Your task is to find the single headed arrow (pointing up or down) as

quickly as possible - but not so quickly you are making mistakes.

When you locate the arrow, indicate the direction it is pointing with the

arrows on your keyboard.

If you make a mistake you will trigger a 3s error delay, wait out this

delay and then continue the task.

You will first complete of both the rating task and the main task before continuing.

Script for Attentional Control Task – Experiment 4

For this task a number of trials will be presented arranged in blocks. On

each trial, one or more shapes will be presented, along with a target arrow.

Your task on each trial is to identify whether this arrow is pointing up or

down.

At the start of each block of trials there will be an instruction which

tells you where you should attend on each trial during the block.

For example, the instruction could instruct you to attend to the circle shape.

Please follow these instructions, as they will enable you to most quickly

identify the target arrow.

At the beginning of each trial a small cross will appear in the middle

which you should attend to.

When you are ready PRESS THE SPACE BAR TO BEGIN THE TRIAL.

Then, one or more shapes and the target arrow will appear. Please indicate

the direction of this arrow as quickly and as possible without compromising

accuracy.

Speed and accuracy to identify the arrow will be recorded on each trial.

Press the up arrow on your keyboard when the arrow is pointing up, and

the down arrow on your keyboard when the arrow is pointing down.

202

If you have a side number keypad, you have to use the up and down arrows on

this to respond.

Press the spacebar to start.

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Emily Rose South Bachelor of Science (Honours)

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