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Academiejaar 2015 - 2016Tweedesemesterexamenperiode

Implicit mentalizing in Autism Spectrum Disorder: an fMRI study

Master of Science in de Psychologie, afstudeerrichting Theoretische en Experimentele PsychologieProf. Dr. Roeljan WiersemaProf. Dr. Marcel BrassAnnabel Nijhof

01104244

Judith Goris

Abstract

In everyday life, individuals with autism spectrum disorder (ASD) experience problems

in understanding mental states of other persons. This has been explained as a Theory of

Mind (ToM), or mentalizing deficit. However, research has shown that high-functioning

individuals with ASD are capable of mentalizing when tested under controlled

experimental conditions. Therefore it has been hypothesized that a two-path ToM

system exists, consisting of an implicit and explicit path. Research indicates that high-

functioning individuals with ASD are probably capable of performing explicit ToM

tasks, since they can use their intelligence and verbal capacities in these tasks, but that

they are impaired in implicit ToM. In the current thesis, we used functional magnetic

resonance imaging (fMRI) to investigate underlying neural mechanisms during an

implicit and explicit version of a ToM task, both in adults with and without ASD. In this

false-belief task, participants form a representation of the beliefs of an agent.

Participants are unaware of this during the implicit version of the task, but are explicitly

asked to do this during the explicit version. In this way, behavioral performance and

neural correlates could be directly compared for the implicit and explicit ToM task,

between the ASD and control group. Behavioral results indicated that the paradigm

clearly worked, as reaction times of all participants were influenced by the agent’s

beliefs both in the implicit and explicit task. However, no behavioral differences were

found between the ASD and control group. This could probably be attributed to a lack

of power, since the results are in the expected direction. Regarding the neural correlates

in the explicit task, we found reduced activity in mentalizing regions in ASD,

specifically in the rTPJ, which is in accord with findings in the literature. For the

implicit task however, no activations in the control group and no differences between

the ASD and control group were found. Additional analyses are proposed that could

give more insight into this matter.

Table of contents

Introduction ...................................................................................................................... 1

What is “Autism Spectrum Disorder”? ........................................................................ 1

Overview of Cognitive Explanations of ASD .............................................................. 3

Recent Developments in ToM Theory: Implicit Mentalizing ...................................... 7

Underlying Neural Mechanisms of Implicit and Explicit Mentalizing ........................ 9

Current Study .............................................................................................................. 11

Methods .......................................................................................................................... 13

Participants ................................................................................................................. 13

Materials ..................................................................................................................... 14

Procedure .................................................................................................................... 17

Data-analysis .............................................................................................................. 17

Results ............................................................................................................................ 19

Behavioral data ........................................................................................................... 19

Neuroimaging data ..................................................................................................... 21

Discussion ....................................................................................................................... 26

References ...................................................................................................................... 30

Supplementary materials ................................................................................................ 37

1

Introduction

In daily life, people with autism spectrum disorder (ASD) have difficulties

attributing and understanding mental states of others, which has been explained in terms

of a Theory of Mind (ToM) deficit. However, performance on ToM tasks in the lab is

sometimes remarkably good for these persons, especially when they are high-

functioning (Scheeren, de Rosnay, Koot, & Begeer, 2013). So how can we explain their

impaired social performance in everyday situations? And what do we know about the

underlying brain mechanisms? These are the topics of this thesis.

We will start off with a short introduction about ASD. Next, several cognitive

theories explaining ASD are shortly discussed, but the main focus is on the ToM

hypothesis. The recent developments in this theory considering implicit mentalizing are

extensively discussed. Also, the neural mechanisms underlying this implicit mentalizing

are investigated. At the end of the introduction, the aim and hypotheses are outlined.

What is “Autism Spectrum Disorder”?

History. The term “autism” was first used by Bleuler in 1911 to describe a

symptom of schizophrenia. He defined autism as “detaching oneself from outer reality

along with a relative or absolute predominance of inner life” (Bleuler, 1911, p. 304). In

this view, being autistic thus means losing the connection with the external

environment.

Leo Kanner was the first one to scientifically describe autism as a psychiatric

disorder in children (Kanner, 1943). According to him, two symptoms were necessary

to get diagnosed with autism: (a) aloofness and apathy with respect to others and (b)

intense resistance against changes in repetitive routines (Kanner & Eisenberg, 1956).

Around the same time, Hans Asperger described a similar disorder and called it

“autistic psychopathy”. Main symptoms were according to him: lack of empathy, little

social skills, unilateral conversation, huge interest in certain things and clumsiness

(Asperger, 1944). Later this disorder would be called “the syndrome of Asperger.” The

research into these first symptom descriptions has developed over time and has led to

what we currently understand by ‘autism’.

Definition in DSM-5 and ICD-10. Nowadays the most important definitions of

autism are in the Diagnostic and Statistical Manual of Mental Disorders (DSM) version

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5 and in the International Classification of Diseases (ICD) version 10. These are the

manuals most used in diagnostic practice.

In the DSM-5, the disorder is called Autism Spectrum Disorder (ASD). The

term “spectrum” is used because manifestations of the disorder can vary greatly,

depending on severity, age and developmental level (American Psychiatric Association

[APA], 2013). The DSM-5 defines ASD as abnormal behavior in two domains: (a)

deficits in social communication and interaction and (b) repetitive, restricted behavior.

These symptoms must already be present in the early developmental period, must cause

significant impairment and cannot be better explained by intellectual disability or global

developmental delay (APA, 2013). Since this is the most commonly used name, we will

also use the term Autism Spectrum Disorder (ASD) in this thesis.

In the ICD-10, the criteria for Childhood Autism are quite similar. Abnormal or

impaired development must be present before the age of 3 years in either language as

social communication, or in social attachments or interaction, or in functional or

symbolic play. Furthermore, symptoms must be present in the areas of social

interaction, communication and repetitive, restricted behavior (World Health

Organization, 2010).

General information about ASD. The above-described symptoms of ASD

have consequences in all life domains. Often learning is impaired because of the

problems with social interaction. Eating, sleeping and general care during childhood can

be very difficult due to the insistence on routines and resistance to changes. The

problems in organization and planning often hamper establishing independence and

academic achievements during adolescence (APA, 2013).

The prevalence of ASD is estimated as 0,62% (Elsabbagh, et al., 2012). This

means that more or less 1 in 150 individuals has ASD. ASD is diagnosed four times

more in boys than in girls. This is probably because problems with social interaction

and communication are subtler in girls without intellectual impairment (APA, 2013).

In research, the terms Asperger’s Syndrome (AS) and High-functioning Autism

(HFA) are often used. Asperger’s syndrome is a disorder that was present in earlier

versions of the DSM, like the DSM-IV (APA, 2000). It is related to ASD but the main

difference is that there is no delay in language development in Asperger’s syndrome.

High-functioning Autism refers to individuals with ASD that have normal cognitive and

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language skills. There is no clear difference between the symptoms of AS and HFA.

Both terms will be used in this paper when discussing earlier research.

Overview of Cognitive Explanations of ASD

Ever since autism was first described, researchers have tried to find an

explanation that could account for the wide range of symptoms associated with this

developmental disorder. Starting from the 1980s, the focus of this research has been

placed on cognition (Rajendran & Mitchell, 2007). We were and are still looking for a

primary core deficit that can account for all the elementary symptoms of ASD. This

deficit should be universal, i.e. it should be found in all individuals with ASD. Also, it

should be specific for ASD, i.e. only found in individuals with ASD and not in other

disorders. Finally, it should be stable and present during the whole course of

development because ASD is also present during the entire life span. I will now shortly

discuss the most important cognitive theories that have been proposed.

Weak central coherence. A first theory, proposed by Frith in 1989, is called the

weak central coherence (WCC) theory (Frith, 1989). It states that typically developing

individuals process information by focusing on the overall meaning. Individuals with

ASD however, tend to focus on the details rather than on the global whole.

A specific example providing evidence for this theory comes from an

experiment by Shah and Frith (Shah & Frith, 1993), which made use of the Block

Design test, a subtest of the Wechsler Intelligence Scale (Wechsler, 1991). In this test,

participants have to rearrange blocks with various colors on each side to match a

pattern. The pattern is visually presented and can be unsegmented (i.e. without lines

indicating the block borders) or segmented (i.e. with lines indicating the block borders).

They found that children with ASD performed much better than the control group when

the block design was unsegmented and that they didn’t show a great increase in

performance in the segmented condition compared to the unsegmented condition, unlike

the control group. This suggests that individuals with ASD process the design in term of

separate blocks. Many other experiments have been conducted showing similar results

(for an overview, see Happé & Frith, 2006). However, a recent meta-analysis has found

that individuals with ASD are merely slower in global processing, but don’t show a

global processing deficit (Van der Hallen, Evers, Brewaeys, Van den Noortgate, &

Wagemans, 2015).

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This theory provides an explanation for a lot of symptoms in ASD, like the

resistance to change, restricted interests, and not taking into account the context in

social interaction. However, it fails to account for all social-communicative problems

(Happé & Frith, 2006).

Research findings suggest that weak central coherence is only found in a subset

of individuals with ASD (Jarrold & Russell, 1997; Scheuffgen, 1998), so this theory

doesn’t meet the universality condition. Also, it is not specific for ASD. Weak central

coherence has also been found in individuals with schizophrenia, Williams syndrome,

depression and right hemispheric damage (Happé & Frith, 2006).

Problems with executive functioning. Some researchers stated that the

symptoms of autism have a lot in common with the dysexecutive syndrome, a disorder

due to frontal lobe damage that is related to problems with executive functioning

(Ozonoff, Pennington, & Rogers, 1991). In this way the idea arose that autism can be

explained as a deficit in executive functions (EF). Executive functions are defined as

cognitive functions that control and integrate other cognitive functions, like inhibition,

planning, etc. (Bryan & Luszez, 2000).

This theory can account for the need for sameness, the perseverating behavior,

the difficulty in switching attention and the lack of impulse control in autism (Rajendran

& Mitchell, 2007).

Studies indeed found that individuals with ASD perform less well on EF tasks

compared to individuals without ASD, but this is not the case for all individuals with

ASD. It is not certain how much of the individuals with ASD have EF difficulties, but

estimations for the proportion range between 50% and 96% (Ozonoff, et al., 1991;

Pellicano, Maybery, Durkin, & Maley, 2006).

Also, executive deficits are not unique for ASD. Similar problems have been

found in individuals with attention deficit hyperactivity disorder, conduct disorder and

Tourette syndrome (Geurts, Vertie, Oosterlaan, Roeyers, & Sergeant, 2004; Hill 2004a).

Up to now, clear evidence for a specific deficit in one of the executive functions or a

specific pattern of EF deficits that is unique to ASD has not been found yet (Hill, 2004a;

Hill, 2004b). The difficulty in finding such a profile can partially be caused by the

problem that most EF tasks measure multiple EF’s instead of just one (Rajendran &

Mitchell, 2007).

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We can conclude that the theory of executive functioning can account for a lot of

symptoms associated with ASD. However, it is a problem that not all individuals with

ASD have EF difficulties and that this deficit is not unique to ASD.

Theory of mind. The theory of mind (ToM) hypothesis as originally described

by Premack and Woodruff (1978) states that individuals with ASD fail to “impute

mental states to themselves and others” (Premack & Woodruff, 1978, p.515). This is

manifested as the inability to ‘mentalize’ or to take into account the mental states of

others, such as beliefs, desires, intentions et cetera. While up until now, there is no

theory that can account for all symptoms of ASD, the ToM hypothesis has been the

most influential theory. Recently, there have been important developments with regard

to the ToM theory that bring a new light to this account. These developments will be

discussed later on, in this thesis.

A first test designed to investigate ToM was created by Wimmer and Perner

(1983) and is called a false belief test. In this test, the participants watch a movie in

which a doll believes a certain object is in a specific location, which may or may not be

the real location. The participants have to decide where the doll will look for the object.

The first results indicated that 80% of children with ASD failed this task (Baron-Cohen,

Leslie, & Frith, 1985) and these results have been widely replicated. However, it is

problematic that 20% of the individuals with ASD can still pass the test because this

means that the deficit is not universal, i.e. it is not found in every individual with ASD.

To account for this problem, Baron-Cohen stated that the ToM problem should be

conceptualized more as a delay than as a deficit (Baron-Cohen, 1989). In other words,

persons with ASD are not unable to mentalize but they typically develop this capacity

less and much later than individuals without ASD. To investigate this, he used a second-

order false belief task. In this task, the participants have to judge what one doll thinks

that the other doll thinks. It was found that not one individual with ASD passed the test

and therefore was concluded that individuals with ASD do not have a representational

ToM (Baron-Cohen, 1989). However, this claim was again challenged when Bowler

(1992) found that 73% of young adults with Asperger syndrome passed the second-

order false belief task.

Therefore, some other, more advanced tests were designed to test ToM in

higher-functioning individuals with ASD. Firstly, the Strange Stories test was

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developed by Happé (1994). In this test, participants read short stories that are

accompanied by a picture. In each story, a character says something that is not meant

literally. Participants have to indicate whether this sentence is true and why the

character said it. It was shown that all individuals with ASD (including higher-

functioning persons with ASD) gave wrong answers to the “why”-question, unlike the

typically-developed adults.

Another influential advanced test is the Reading the Mind in the Eyes Test

designed by Baron-Cohen and Swettenham (1997). In this task, participants have to

judge the emotion of a person in a photograph while only the eye region is displayed.

Individuals with ASD indeed performed less well on this task compared to a control

group, in this study. When whole faces were displayed, persons with ASD perform at

ceiling level. Therefore, it was concluded that they are specifically impaired in reading

information from the eyes, while they are able to understand emotional expressions in a

normal fashion. However, another study provided evidence to the contrary (Back,

Ropar, & Mitchell, 2007). In this experiment, individuals had to infer emotions from

videos in which one part was frozen. When the eyes were frozen, performance of the

individuals with ASD deteriorated to the same degree as performance of control

participants. This suggests that persons with ASD do read information from the eye

region.

A problem with these advanced ToM tests, is that they don’t measure ToM as it

was proposed by Dennett (1978). Dennett states that a minimal test of ToM must

include a causal relation between informational access and the consequent state of belief

(Rajendran & Mitchell, 2007). In the false belief tasks for example, this is the case: the

doll saw that the object was in a certain location, and didn’t see that it was moved, so

there is no reason for her belief to change. In the advanced ToM tests however, there is

no causal relation between informational access and the belief state. The Reading the

Mind in the Eyes Test for example, is merely a test for emotion recognition.

When we focus again on the false-belief tasks, we can ask ourselves the

question: how is it possible that some persons with ASD are able to pass both first- and

second-order false-belief tasks, but have however impaired social capabilities in real

life? To account for this discrepancy, a two-path ToM system has been proposed

(Apperly & Butterfill, 2009).

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Recent Developments in ToM Theory: Implicit Mentalizing

To explain the inconsistency between performance on false-belief tasks and

performance in everyday social situations, it has been proposed that ToM consists of an

implicit and explicit path (Apperly & Butterfill, 2009). The implicit system is supposed

to be a relatively effortless, fast and efficient system that develops early in life. It is

however also an inflexible system. The explicit system on the other hand is supposed to

be highly flexible. This develops at a later age, concurrent with the development of

basic language and executive functions. A disadvantage of the explicit system is that it

is inefficient and that it uses general cognitive functions (and thus is effortful). In

human adults, both systems are supposed to exist in parallel (Apperly & Butterfill,

2009).

Classical false-belief tasks, like the ones already described, rely on the explicit

ToM path. Implicit ToM is mostly tested by displaying false-belief movies without the

instruction to infer the actor’s mental state. It is then investigated if the participants’ eye

movements and reaction times to another task are influenced by the actor’s beliefs.

This was for example done by Schneider, Bayliss, Becker, and Dux (2012), who

found that typically developed adults’ eye movement patterns were indeed influenced

by the actor’s belief, while their only instruction was to press the space bar when the

actor waved. Additionally, Schneider, Nott, and Dux (2014) have investigated eye

movement patterns in three groups with different task instructions: one group was given

no instructions, a second group was instructed to track the position of the object and a

third group was instructed to track the actor’s belief. Eye movement patterns in all three

groups were influenced by the actor’s beliefs. This provides support for the existence of

an implicit ToM system. Moreover, using false-belief movies, Kovács, Téglás, &

Endress (2010) found support for implicit ToM processes in neurotypical adults and 7-

month-old infants by measuring adults’ reaction times and infants’ looking times. Note

that explicit mentalizing typically only develops at the age of four years (Wellman,

Cross, & Watson, 2001), which is thus much later than implicit mentalizing.

But what do we expect from and know about the two-path ToM system in

individuals with ASD?

Explicit and implicit mentalizing in ASD. Several authors propose that

individuals with high-functioning ASD are able to succeed in explicit ToM tasks

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because in these tasks, they have to solve a problem that is presented verbally and

explicitly. This means they can use their verbal capacities and intelligence to make

logical inferences (Frith, 2004; Frith, Happé, & Siddons, 1994). However, in real-life

situations, there is no explicit instruction. Persons rather have to process social

information in the environment spontaneously. In other words, in everyday social

situations, it is necessary to mentalize implicitly. This is a capacity that persons with

high-functioning ASD cannot compensate for with their intelligence. To test this theory,

several studies have been conducted that measure implicit mentalizing in ASD in the

laboratory.

A first study was conducted by Klin in 2000. He used the social attribution task

(Heider & Simmel, 1944) in which participants have to describe the movements of

geometric shapes. The stimuli are created in such a way that most people assign social

attributions to the shapes, as if they were “human”. However, in the first part of the

task, the participants are just instructed to describe what happened. There is no explicit

instruction to attribute mental states to the shapes, so this is an implicit mentalizing task.

It is expected that persons impaired on implicit mentalizing, attribute less or

inappropriate mental states to the shapes. This is indeed what was found in the

individuals with ASD in Klin’s study. They tended to describe the movements in

geometric or physical terms instead of the social terms that the control group used (Klin,

2000). Klin and his colleagues merely attribute this deficit in implicit mentalizing to

atypical strategies of social monitoring, i.e. paying attention to the social stimuli in the

environment (Klin, Jones, Schultz, Volkmar, & Cohen, 2002a; Klin, Jones, Schultz,

Volkmar, & Cohen, 2002b). Similar social attribution tasks were used in several other

studies and showed the same results. Abell, Happé, and Frith (2000) demonstrated

impaired implicit mentalizing for children with ASD, and Castelli, Frith, Happé, and

Frith (2002) for very high-functioning adults with ASD.

Senju, Southgate, White, and Frith (2009) used a non-verbal version of the false

belief task. Participants had to watch movies in which an actor has a false belief, while

their eye movements were recorded. An earlier study that used the same paradigm,

showed that 25-month-old children’s eye movements correctly anticipated the actor’s

behavior based on her false belief (Southgate, Senju, & Csibra, 2007). In the 2009

study, eye movements from high-functioning individuals with ASD and control

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participants were compared. The results showed that the ASD group’s eye movements

anticipated the actor’s behavior significantly worse. Schneider, Slaughter, Bayliss, and

Dux (2013) did a similar experiment while they controlled for differences in memory

and learning processes and obtained the same results.

Another study that used reaction times in false-belief tasks in an ASD and

control group, did not find a significant difference between the ASD and control

participants (Deschrijver, Bardi, Wiersema, & Brass, 2015). However, there was a

numerical difference in the hypothesized direction. Moreover, implicit mentalizing

performance correlated negatively with scores on the AQ, a very commonly used autism

questionnaire.

We can conclude that there is a lot of evidence for the hypothesis that high-

functioning individuals with ASD have a deficit specifically for implicit ToM. Less is

known about the underlying brain mechanisms. In the next paragraph I will discuss the

neural mechanisms underlying ToM.

Underlying Neural Mechanisms of Implicit and Explicit Mentalizing

Where is mentalizing located in the brain? Most investigations of brain activity

during mentalizing have found the same network of brain regions, consisting of the

temporoparietal junction (TPJ), the superior temporal sulcus (STS), the temporal poles

and the medial prefrontal cortex (MPFC) (Frith & Frith, 2003). Several studies have

shown that especially the right TPJ (rTPJ) might be specialized in the attribution of

mental states (Lombardo, Chakrabarti, Bullmore, & Baron-Cohen, 2011; Perner,

Aichhorn, Kronbichler, Staffen, & Ladurner, 2006; Saxe & Wexler, 2005). It is not

clear if the mentalizing network is the same for explicit and implicit mentalizing, since

most studies have focused on the explicit process. The few studies that investigated the

implicit process have come to inconsistent results.

Castelli and colleagues (2002) used the social attribution task (Heider &

Simmel, 1944) to investigate implicit mentalizing in a PET study. They found that the

more the participants attributed mental states to the stimuli, the higher the activity was

in the mentalizing network earlier found for explicit mentalizing (TPJ, STS, temporal

poles and MPFC). This suggests that the brain regions underlying explicit and implicit

mentalizing are more or less the same. Schultz and colleagues (2003) used the same task

in an fMRI experiment and found similar results. Furthermore, Kovacs, Kühn, Gergely,

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Csibra, & Brass (2014) used an implicit false belief task in an fMRI experiment and

found that the right TPJ and MPFC play crucial roles in implicit mentalizing. So can we

conclude that neural mechanisms are more or less the same in implicit and explicit

mentalizing? Unfortunately, other studies have shown different results.

For example, in a study by Ma, Vandekerckhove, Van Overwalle, Seurinck, and

Fias (2011), implicit mentalizing activated only the MPFC and the TPJ while explicit

mentalizing also activated other areas including the precuneus and the posterior part of

the superior temporal sulcus (pSTS). Similar findings were obtained by Rameson,

Satpute and Lieberman (2010). This suggests that implicit mentalizing activates a

certain network of brain regions, while explicit mentalizing activates the same network

but also some additional areas. This hypothesis is also supported by Ma,

Vandekerckhove, Baetens, Van Overwalle, Seurinck, and Fias (2012), who found that

left TPJ, pSTS and the precuneus are more active during explicit mentalizing than

during implicit mentalizing. Keysers and Gazzola (2007) maintain the same hypothesis,

but they state that it is the MPFC that is active in explicit mentalizing and not in implicit

mentalizing. A meta-analysis by Van Overwalle (2009) also suggests that MPFC is

associated with the explicit process.

However, two fMRI studies concluded that the MPFC is important for implicit

mentalizing (Gallagher, Jack, Roepstorff, & Frith, 2002; McCabe, Houser, Ryan, Smith,

& Trouard, 2001). In these studies, a computer task with two conditions was used. In

one condition, the participants mentalized implicitly, in the other condition they didn’t.

The MPFC was active in the first condition but not in the second. These results are

consistent with what was found in a study by Kestemont, Vandekerckhove, Ma, Van

Hoeck, and Van Overwalle (2013).

We can conclude that research evidence is not clear on this matter. Furthermore,

most of the studies described above are actually not directly comparable since they all

used different mentalizing tasks. But the most important hypotheses are (a) that implicit

and explicit mentalizing rely on the same brain regions (namely TPJ, STS, MPFC and

temporal poles, in which rTPJ might be most specialized in mentalizing) and (b) that

implicit mentalizing relies on a certain network while explicit mentalizing relies on the

same network with some additional areas. In this last hypothesis, the exact brain regions

underlying implicit and explicit mentalizing are not clear yet.

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So what do we know about these underlying neural mechanisms in individuals

with ASD?

Underlying neural mechanisms of mentalizing in ASD. Several neuroimaging

studies investigating mentalizing in ASD, have found less or different brain activation

in the mentalizing network in ASD compared to a control group (Assaf, Hyatt, Wong,

Johnson, Schultz, Hendler, & Pearlson, 2013; Castelli, et al., 2002; Kana, Keller,

Cherkassky, Minshew, & Just, 2009; Nieminen-von Wendt, et al., 2003; O’Nions,

Sebastian, McCrory, Chantiluke, Happé, & Viding, 2014; Von dem Hagen, Stoyanova,

Rowe, Baron-Cohen, & Calder, 2014). Specifically the rTPJ is claimed to be disturbed

in ASD (Kana, Libero, Hu, Deshpande, Colburn, 2014; Pantelis, Byrge, Tyszka,

Adolphs, & Kennedy, 2015). In most of these studies, performance and brain activation

of an ASD group and a control group are compared during a mentalizing task.

Typically, the ASD group performs less well on the task. Also, they do show activity in

the same brain regions as the control group, but this activity is reduced. Some studies

have focused on explicit mentalizing tasks, while other studies have used rather implicit

measures of ToM. To our knowledge, up until now, explicit and implicit mentalizing in

ASD have not been directly compared in a neuroimaging study. Therefore, a lot of

questions remain unanswered. In the current study, we aimed to directly compare neural

correlates of explicit and implicit mentalizing in a high-functioning ASD and matched

control group.

Current Study

The current study focused on explicit and implicit mentalizing in high-

functioning ASD. The main aims of the current study were (a) to compare performance

on an explicit and implicit version of a mentalizing task for a high-functioning ASD

group and matched control group, and (b) to compare brain activity during explicit and

implicit mentalizing for these two groups. Additionally, we were also interested in the

neural correlates of the implicit mentalizing task in the typically developed group.

Therefore, we used an implicit and explicit version of a false-belief task in 12

high-functioning adults with ASD and 12 matched control participants, while brain

activation was investigated using functional magnetic resonance imaging (fMRI). In this

false-belief task, participants observe the movements of a ball either disappearing

behind an occluder or rolling out of the scene. Crucially, another agent observes the

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movement of the ball, leaving the scene either before the ball reaches the final position

or after the ball reaches the final position. As an implicit measure of performance,

participants had to press a button whenever the ball was behind the occluder when the

occluder fell. The difference between the explicit and implicit version of the task lies in

the so-called catch questions that participants answer after some trials. In the implicit

version of the task, these questions concern an irrelevant feature of the agent. In the

explicit version of the task, the beliefs of the agent are explicitly questioned. Thus, the

implicit and explicit version of the task are totally identical, except that participants are

instructed to keep in mind the perspective of the agent in the explicit task. In this way,

we can directly compare performance and neural activation in the explicit and implicit

version of the mentalizing task.

In an earlier fMRI study (Kovacs, et al., 2014), using a similar implicit

mentalizing task, it was found that the rTPJ was only active during implicit mentalizing,

when a false belief attributed to the agent had a positive content, but not when this false

belief had a negative content. This suggests that a certain content selectivity exists in the

implicit mentalizing system. Therefore we will additional analyses investigate the effect

of the belief content of the agent in the current thesis.

Hypotheses. We expected that performance would be disturbed in the ASD

group for implicit ToM, as measured by reaction times. More specifically, we expected

that the pattern of reaction times in the ASD group would be less influenced by the

agent’s beliefs. We expected this to be the case in both the explicit and implicit task.

There was an explicit measure of ToM only during the explicit task, namely the catch

questions about the beliefs of the agent. Since we used a high-functioning ASD group,

we hypothesized that performance on these catch questions in the explicit task would be

equal to performance of the control group.

As for the comparison of neural activation between the ASD and control group,

we hypothesized that we would see reduced activation in brain regions in the

mentalizing network in the ASD group, especially in rTPJ, when compared to the

control group. We expected this difference both in the explicit and implicit mentalizing

task. For the explicit mentalizing task, we also expected to find compensatory activity in

other brain regions in the ASD group.

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As described above, research evidence about brain regions underlying implicit

mentalizing is scarce, and fairly inconsistent. Therefore, no clear prediction could be

made regarding brain areas involved in implicit mentalizing in the control group.

However, based on the literature, we considered the following hypotheses most

probable: (a) implicit and explicit mentalizing rely on the same brain regions

incorporating TPJ, STS, and MPFC and (b) in addition to these areas, explicit

mentalizing may rely on additional areas. We thus hypothesized that in the control

group, there would be more activation in the explicit task compared to the implicit task,

either in the mentalizing network or in additional brain areas, or both. Additionally, it is

possible that we find the same content selectivity during the implicit version of the task

that was found in the study of Kovacs and colleagues (2014), meaning that there may be

an effect of a positive vs. negative false belief attributed to the agent.

Methods

Participants

Participants consisted of an adult ASD group and an age- and gender-matched

control group. Age ranged from 19 to 43 years (M = 29, SD = 7). Each group consisted

of 12 participants (8 female). Important to note is that the final sample is planned to be

larger (at least 20 participants per group), but that the thesis is restricted to a subsample

due to time reasons. All participants were right-handed. Participants were matched one-

to-one on gender and age (± 2 years).

Intelligence tests are currently not yet conducted, but planned for a later stage so

that participants will also be matched on intelligence level. Intelligence will be assessed

using the KAUFMAN 2 short form WAIS-III, since this has proven to be an accurate

measurement of IQ in ASD (Minshew, Turner, & Goldstein, 2005). We will only

include participants with a normal intelligence level (full-scale IQ ≥ 85), thus focusing

on high-functioning ASD (HFA). However, we do believe the current sample already

consists (mostly) of individuals with high-functioning ASD, since it was clearly stated

in the announcement that participants should have a normal intelligence level.

All ASD participants had received a formal diagnosis of ASD (including autistic

disorder, Asperger’s syndrome and PDD-NOS) from a psychiatrist or multidisciplinary

team, including a psychiatrist. This diagnosis will be verified using an Autism

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Diagnostic Observational Schedule 2 (ADOS-2) (Lord, et al., 2000) Module 4 with a

trained researcher, in a later stage of the experiment.

Participants with additional neurological or psychiatric diagnoses were

excluded. The Ghent University medical ethics committee approved the study. All

participants gave written informed consent before participation and were financially

compensated.

Materials

Experimental tasks. The false belief task is designed by Bardi, Desmet, Nijhof,

Wiersema & Brass (submitted) and is an adapted version of the task of Kovacs and

colleagues (2010). In this task participants watch videos (720x480) that each last 13,850

ms.

Each movie consists of two phases: the belief formation phase and the outcome

phase. In the belief formation phase, participants form a belief about the location of a

ball. Also, an agent is present in (a part of) the movie. In this way, the agent also forms

a belief about the location of the ball. This agent’s belief could either be a true belief

(matching participant’s knowledge) or a false belief (not matching participant’s

knowledge). In the outcome phase, an occluder is lowered behind which the ball is

believed to be, in some trials. However, the ball is present in exactly 50% of trials, and

the presence of the ball is completely random, not depending on the belief formation

phase.

The belief formation phase begins with the agent (Buzz Lightyear from the

movie Toy Story) placing a ball on a table in front of an occluder. Then the ball rolls

behind this occluder. After this, the video could continue in four ways. The final

location of the ball varied: either it left the scene or it stayed in the same location. Also

the moment Buzz Lightyear left the scene varied: he left the scene either before or after

the ball reached its final location. This is 5000 ms or 9874 ms after the beginning of the

video. In this way the beliefs of the participant and the beliefs of the actor in the video

were manipulated. The participant could believe the ball was behind the occluder (P+)

or that the ball was not behind the occluder (P-). Also the actor could believe the ball to

be behind (A+) or not behind (A-) the occluder, this will be referred to as the belief

content of the agent (A+ for positive, A- for negative). At the end of the movie, Buzz

came back (at 12,694 ms after the beginning of the video). In the outcome phase, the

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occluder was lowered (at 13,250 ms after the beginning of the movie) and in half of the

trials of all the conditions, participants saw the ball behind the occluder. This thus

results in 2x2x2 conditions: participants’ belief, agent’s belief and the actual outcome

were all manipulated independently from each other. The task of the participants was to

press a button when they saw the ball behind the occluder at the end of the movie.

Figure 1. Overview of the experimental task.

The crucial conditions are the P-A- and the P-A+ conditions. In both these

conditions, the participant does not expect the ball. In the P-A- condition, Buzz

Lightyear also does not expect the ball. However in the P-A+ condition, he does expect

it. So the only difference between these two conditions is the belief content of the agent.

The reaction time difference between these two conditions indicates if the participants

took the perspective of the agent into account or not.

20% of the trials were catch trials. 4 catch trials were before the break in each

task, and 4 after the break. These trials made the difference between the implicit and

explicit version of the task. Catch trials are normal trials that are followed by a question.

In the implicit version, this question was: “Did Buzz wear a blue hat?” In the explicit

version, this was: “Did Buzz think that the ball was behind the screen?” Participants had

to answer as quickly as possible. In this way, participants had to think about these

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questions during all the trials. So in the explicit version, they kept in mind the

perspective of Buzz, while this was not the case in the implicit version. Catch trials

stayed on screen for 1000 ms. Participants had to respond with “yes” or “no”.

The inter-trial interval (ITI) was jittered between 1000 ms and 7400 ms, with an

average of 4200 ms.

The 8 videos were each shown 8 times, both for the implicit and the explicit

version. This resulted in 64 x 2 trials. In the middle of both the implicit and explicit

task, there was a break.

Questionnaires. After the implicit task, participants filled out a debriefing

questionnaire in the scanner. This questionnaire is an adapted and translated version of

the debriefing questionnaire used by Schneider and colleagues (2012). It measured

whether participants could guess the aim of the study, and thus whether mentalizing was

indeed implicit. The questionnaire is added in the supplementary materials.

Both ASD and control participants also filled out the Autism Spectrum Quotient

(AQ). The AQ is the most well studied questionnaire to measure autistic traits (Baron-

Cohen, Wheelwright, Skinner, Martin, & Clubley, 2001; Dutch version: Hoekstra,

Bartels, Cath, & Boomsma, 2008). Control participants with too high scores on the AQ

were excluded from the study. In our sample, control participants scored sufficiently

well below the cut-off of 26 (M = 11.91, SD = 4.72) (Woodbury-Smith, Robinson,

Wheelwright & Cohen, 2005). ASD participants scored well above the cut-off (M =

33.73, SD = 6.21). There was a significant difference between the control and ASD

group, t (18.66) = -9.27, p < .001.

Functional magnetic resonance imaging data acquisition and preprocessing.

We first conducted an anatomical scan during 5 minutes. Next, functional scanning was

conducted for the implicit task (20 minutes) and explicit task (20 minutes) with breaks

in between. Also, participants conducted a flanker task in the scanner for an additional

study (10 minutes). So, subjects were in the scanner for approximately one hour.

Images sensitive to blood-oxygen level dependent (BOLD) contrast were

acquired on a 3T Siemens Magnetom Trio scanner (Erlangen, Germany) with a 32-

channel radiofrequency head coil. 176 high-resolution anatomical images were obtained

using a T1-weighted 3D MP-RAGE GRAPPA sequence (TR = 2530 ms, TE = 2.58 ms,

image matrix = 256 x 256, FOV = 220 mm, flip angle = 78, slice thickness = 0.90 mm,

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voxel size = 0.9 x 0.86 x 0.86 mm (resized to 1 x 1 x 1 mm)). Functional images were

obtained by using a fast T2*-weighted 3D EPI sequence, with high temporal resolution

(TR = 2000 ms, TE = 28 ms, image matrix = 64 x 64, FOV = 224 mm, flip angle = 80˚,

slice thickness = 3.0 mm, distance factor = 17%, voxel size = 3.5 x 3.5 x 3.0 axial

slices). To minimize participants’ movements, pillows and tape were used. Earphones

were used to minimize the scanner noise.

Data preprocessing was performed using SPM 8 software (Wellcome

Department of Cognitive Neurology, London, UK) in MatLab. First we removed the

first five volumes to control for T1 equilibration. Then, functional images were spatially

realigned using a rigid body transformation and slice time corrected with respect to the

first slice. Next, the high-resolution structural image of each subject was co-registered

with the mean image of the EPI series. The structural scans were brought in line with

the tissue probability maps available in SPM during segmentation. After this, all

anatomical and functional scans were normalized to the standard MNI template. Finally,

all data was spatially smoothed using an isotropic 8mm full width at half maximum

(FWHM) Gaussian kernel.

Procedure

The experiment was completed in 2 sessions per participant. During the first

session, the fMRI procedure was performed, consisting of the implicit and explicit task

and the debriefing questionnaire. Also, participants filled out the AQ. In the second (not

yet conducted) session, participants will be tested on the short form WAIS-III. The ASD

participants will also be tested on the ADOS.

Data-analysis

Behavioral data. RStudio was used for data-analysis of the behavioral data.

Reaction times for the detection of the ball at the end of each movie were recorded. This

is thus an implicit measure of ToM, both in the implicit and explicit version of the task.

Since participants only had to respond when the ball was present when the occluder fell,

reaction time analyses were conducted only for these trials. A repeated-measures

ANOVA on reaction times was conducted, with task (implicit, explicit), belief (true

belief, false belief) and belief content of the agent (positive, negative) as within-subject

factors and group (ASD, control) as between-subject factor.

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fMRI data. SPM8 software (Wellcome Department of Cognitive Neurology,

London, UK) in MatLab was used for fMRI data-analysis. The general linear model

(GLM) was used to perform the subject-level statistical analyses. In this thesis, only

analyses on the belief formation phase of the task will be discussed. The model

contained separate regressors for all possible combinations of belief (false belief, true

belief) and belief content of the agent (positive, negative). In total, there were 4

regressors of interest for the implicit task and 4 regressors of interest for the explicit

task. Six subject-specific regressors accounting for head motion that were obtained

during the realignment were also added to the model. The resulting vectors were

convolved with the canonical haemodynamic response function (HRF) to form the main

regressors in the design matrix. Statistical parameter estimates were computed

separately for each voxel for all columns in the design matrix. Contrast images were

created at the first level and then entered into the second level analyses, with subject as

random variable. T-tests were used to make contrasts at this group level.

To identify regions involved in false belief processing, a contrast was computed

as follow: false belief (P-A+ and P+A-, participant’s and agent’s belief do not match) >

true belief (P-A- and P+A+, participant’s and agent’s belief match). The interaction

between belief and task (explicit, implicit) was calculated as (false belief explicit > true

belief explicit) > (false belief implicit > true belief implicit). The regions involved in false

belief processing in the explicit and implicit task separately were computed as: false belief

explicit > true belief explicit, and false belief implicit > true belief implicit. In addition,

the following contrast was computed to study the neural effect of tracking positive beliefs

of the agent: A+ (positive belief content of the agent) > A- (negative belief content of the

agent). This was done separately for the implicit and explicit task. The interaction

between belief and belief content was computed as FB vs. TB positive content > FB vs.

TB negative content, more specifically: (P-A+ > P+A) > (P+A- > P-A-), separately for

the implicit and explicit task. Furthermore, the contrasts P-A+ > P-A- and P+A+ > P+A-

were used to investigate the effect of positive beliefs of the agent in more depth, also

separately for the explicit and implicit task. All these contrasts were first ran on the

control group only. Then, the same contrasts were used to identify differences between

the ASD and control group, investigating both TD > ASD and ASD > TD.

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A cluster-extent based thresholding approach was used to correct for multiple

comparisons (Friston et al., 1996). A primary uncorrected threshold of p < .001 was first

used at voxel level to identify groups of supratreshold voxels. Then, a cluster-level extent

threshold, represented in units of contiguous voxels (k), was determined by SPM 8 (p <

.05 FWE cluster corrected threshold). Coordinates are reported according to the MNI

coordinate system.

Results

Behavioral data

Debriefing questionnaire. Some of the participants tried to guess the aim of the

experiment. However, none of them reported that the experiment was related to the

beliefs of the agent Buzz Lightyear. Therefore, we can conclude that participants did not

mentalize explicitly during the implicit version of the experiment.

Catch questions. Both the control group and ASD group performed above

chance level on catch questions, both in the implicit and explicit version of the task. For

the implicit task, the ASD group (M = 78%, SD = 20%) had significantly worse

performance than the control group (M = 90%, SD = 10%) as shown with an

independent samples one-sided t-test, t (15.95) = 1.94, p = .04. There was no

performance difference for the explicit task, tested with an independent samples one-

sided t-test (ASD: M = 82%, SD = 19%; TD: M = 86%, SD = 12%).

Reaction times. A repeated-measures ANOVA on reaction times was

conducted, with task (implicit, explicit), belief (true belief, false belief) and belief

content of the agent (positive, negative) as within-subject factors and group (ASD,

control) as between-subject factor.

This revealed a significant effect of belief F (1, 21) = 7.43, p = .013. A post-hoc

one sided t-test indicated that reaction times were shorter for false belief conditions (P-

A+ and P+A-) compared to true belief conditions (P-A- and P+A+), t (1401.7) = -2.14,

p = .016.

Also, a marginally significant effect of belief content of the agent was found, F

(1, 21) = 3.59, p = .072. Reaction times were marginally shorter when the belief content

of Buzz Lightyear was positive, as indicated with a post-hoc one-sided t test, t (1400.2)

= -1.59, p = .056.

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More importantly, a significant interaction was found between belief and belief

content, F (1, 21) = 16.33, p < .001. Pairwise comparisons between conditions, using

two-sided t-tests, revealed that reaction times in the P-A- condition were longer than in

all other conditions (all p’s < .01). This result pattern overlaps completely with similar

studies (Deschrijver, et al., 2015; Kovacs et al., 2010). The fact that reaction times in

the P-A+ conditions were shorter than in the P-A- conditions indicates that participants

implicitly represented the agent’s belief.

We did not find any significant main effect of or interaction effect with task.

Crucially, there was also no significant main or interaction effect of group,

contrary to our hypotheses. This indicates that there are no differences between the ASD

and TD group on the behavioral level.

However, post-hoc comparisons between the two most important conditions, the

P-A+ and the P-A- condition, revealed that the results in the implicit task are in the

expected direction. The difference between reaction times in the P-A+ and the P-A- is

crucial as a measure of implicit mentalizing, and has previously been called the ToM-

index (Deschrijver et al., 2015). In the implicit task, the ToM-index was significantly

different from zero in the control group, t (11) = 2.75, p = .009, while this was not the

case in the ASD group, t (11) = 0.58, p = .288. However, the difference in ToM-indexes

between the ASD and control group was not significant, t (16.44) = 0.75, p = .233.

(control group: M = 28.22, SD = 35.54; ASD group: M = 11.49, SD = 69.07). For the

explicit task, the ToM-index of the control group was not significant, t (11) = 1.24, p =

.121. Also the ToM-index of the ASD group was not significant, t (11) = 0.70, p = .249.

The difference between these two ToM-indexes was also not significant, t (21.97) =

0.40, p = .345 (control group: M = 23.68, SD = 66.28; ASD group: M = 12.93, SD =

63.81).

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Neuroimaging data

In this thesis, we will focus only on the belief formation phase.

One control participant was excluded due to incomplete data collection.

Control group. First we focused on the neuroimaging results of the control

group. We aimed to identify regions involved in processing false beliefs as compared to

true beliefs (contrast: FB > TB) across task versions. There were no significant results.

Next, we tested for significant activations in the interaction between belief and task

(contrast: (FB > TB explicit task) > (FB > TB implicit task)). We found a marginally

significant cluster of activation in the right temporoparietal junction (rTPJ), see table 1.

This suggests that the rTPJ was more involved in the explicit task compared to the

implicit task. When tested for the explicit task separately (contrast: FB explicit task >

TB explicit task), significant activation was found in the rTPJ and extrastriate body area

(EBA), and marginally significant activation in the lTPJ, see table 1. When tested for

the implicit task separately (contrast: FB implicit task > TB implicit task), no significant

390

400

410

420

430

440

450

P+A+ P+A- P-A+ P-A-

ASD group - explicit task

350

360

370

380

390

400

410

P+A+ P+A- P-A+ P-A-

control group - explicit task

390

400

410

420

430

440

P+A+ P+A- P-A+ P-A-

ASD group - implicit task

350

360

370

380

390

400

410

P+A+ P+A- P-A+ P-A-

control group - implicit task

Figure 2. Reaction time means per condition, per task and per group. Reaction times were significantly

longer in P-A- conditions than in all other conditions. There was no significant main or interaction effect of task

type or group.

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clusters of activation were found. Regarding the belief content of the agent, we did not

find any significant clusters of activation in the contrast A+ > A-, neither for the explicit

nor the implicit task. Also, in both tasks there was no significant activation in the

interaction between belief and belief content. Furthermore, no significant neural

activation was found in the contrast P+A+ > P+A- neither for the explicit nor the

implicit task. However, in the P-A+ > P-A- there was significant activation in the rTPJ

during the explicit task, see table 1. There was no significant activation in the implicit

task for this contrast.

Table 1. Summary of activations in the control group.

MNI peak

coordinates xyz

Cluster size FWE corrected p

value

(FB > TB explicit task) > (FB > TB implicit task)

rTPJ 54 -58 22 36 .095

42 -61 31

FB > TB explicit task

rTPJ 42 -61 49 64 .026

51 -52 40

EBA 63 -40 -5 65 .024

57 -49 -11

lTPJ -57 -61 25 45 .085

-45 -58 34

P-A+ > P-A- explicit task

rTPJ 54 -58 34 194 < .001

60 -52 28

48 -55 46

Note. FB stands for false belief. TB stands for true belief. rTPJ stands for right temporoparietal

junction. EBA stands for extrastriate body area. lTPJ stands for left temporoparietal junction.

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Figure 3. Brain activity for contrast (FB > TB explicit task) > (FB > TB implicit task). We found

marginally significantly more activity in the rTPJ in the control group during the explicit task compared

to the implicit task. The activation in the lTPJ visible in the figure, was not significant.

Comparison between the ASD and control group. First, we tested whether

there was a difference in activation during false belief processing between the control

and ASD group across task versions (contrast: (FB > TB control group) > (FB > TB

ASD group)). This was not the case. Next, we tested the interaction between belief, task

and group (contrast: ((FB > TB explicit task) > (FB > TB implicit task)) control group)

> ((FB > TB explicit task) > (FB > TB implicit task)) ASD group)). Here, we found

significant activation in the rTPJ and marginally significant activation in the lTPJ, see

table 2. This indicated that the difference in rTPJ activation between the explicit and

implicit task is higher for the control group than for the ASD group. When tested for the

explicit task separately (contrast: (FB > TB explicit task control group) > (FB > TB

explicit task ASD group)), we also found significant activation in the rTPJ and

marginally significant activation in the lTPJ, see table 2. When tested for the implicit

task separately (contrast: (FB > TB implicit task control group) > (FB > TB implicit

task ASD group)), we did not find any significant activation. Additionally, there was no

significant difference in activation in the contrast A+ > A-, neither for the explicit nor

the implicit task. There was also no difference for the interaction between belief and

belief content. Furthermore, for the contrast P+A+ > P+A-, no difference in activation

was found, neither in the explicit nor the implicit task. However, in the P-A+ > P-A-

contrast, it was found that the control group had significantly more activation in the

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rTPJ during the explicit task, see table 2. There was no difference for this contrast

during the implicit task.

Also, there was no single contrast in which the ASD group showed more brain

activation than the control group.

Table 2. Summary of difference in activations for control group > ASD group.

MNI peak

coordinates xyz

Cluster size FWE corrected p

value

(FB > TB explicit task) > (FB > TB implicit task)

rTPJ 54 -58 22 64 .041

42 -58 25

lTPJ -54 -61 25 52 .075

FB > TB explicit task

rTPJ 51 -55 28 127 .005

51 -52 37

42 -55 34

lTPJ -51 -58 31 54 .087

P-A+ > P-A- explicit task

rTPJ 48 -52 34 140 .003

Note. FB stands for false belief. TB stands for true belief. rTPJ stands for right temporoparietal

junction. lTPJ stands for left temporoparietal junction.

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Figure 4. Difference in brain activity for TD > ASD group in the contrast (FB > TB explicit task) >

(FB > TB implicit task). We found significantly more activity in the rTPJ in the control group than the

ASD group during the explicit task compared to the implicit task. Also, there was marginally significantly

more activity in the lTPJ.

Figure 5. Difference in brain activity for TD > ASD group in the contrast FB > TB explicit task. We

found more activity in the rTPJ in the control group than the ASD group during the explicit task. Also,

there was marginally more activity in the lTPJ.

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Discussion

In this thesis, we aimed to investigate explicit and implicit mentalizing in adults

with high-functioning ASD. Since there are indications that high-functioning

individuals with ASD are capable of explicit mentalizing, but do have problems with

implicit mentalizing, we expected a significant deficiency in the implicit measure in the

ASD group, but no difference for the explicit mentalizing measure between the ASD

and control group. As for the neural correlates, in the control group we had the

following hypothesis: (a) to find the same active regions in both the explicit and implicit

task, namely TPJ, STS, MPFC and temporal poles, or (b) to find a network of regions

that is active in the implicit task, while the explicit task activates the same network with

some additional regions. As for the neural differences between the ASD and control

group, we hypothesized that the ASD group would show reduced neural activity in the

mentalizing network, especially in the rTPJ, in both the explicit and implicit task, since

this was already found in other explicit mentalizing studies in ASD (Assaf, et al., 2013;

Castelli, et al. 2002; Kana, et al., 2009; Kana, et al., 2014; Nieminen-von Wendt, et al.,

2003; O’Nions, et al., 2014; Pantelis, et al., 2015; Von dem Hagen, et al., 2014).

Furthermore, we expected to find compensatory activation in additional brain areas in

the ASD group during the explicit task.

For performance on the implicit measure of the mentalizing tasks (i.e. reaction

times on the different conditions), we found that participants (of both the ASD and

control group) were indeed influenced by the agent’s belief, as they were faster when

only the agent expected the ball compared to when neither the participant nor the agent

expected the ball. This is a replication of earlier results (Deschrijver, et al., 2015;

Kovacs, et al., 2010) and indicates that the paradigm worked. This was the case in both

the explicit and implicit version of the task. Furthermore, the debriefing questionnaire

showed that participants were unaware of the aim of the implicit task, and that

mentalizing was thus really implicit in this version of the task. Surprisingly, we did not

find any significant differences between the ASD and control group. This could either

mean that the ASD and typically developed population do have the same level of

performance on explicit and implicit mentalizing, or it could be that no difference was

found due to a lack of statistical power because of the small sample size. Given the

previous reports in the literature about deficient implicit ToM in ASD (Abell, et al.,

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2000; Castelli, et al., 2002; Klin, 2000; Schneider, et al., 2013; Senju, et al., 2009), we

assume the latter possibility to be more likely. In line with this explanation, reaction

time differences did go in the expected direction for the implicit task, as also found in

the study of Deschrijver and colleagues (2015). That is, the difference in reaction times

between the P-A+ and the P-A- condition was smaller in the ASD group than in the

control group, in the implicit task. However, this was not the case for ToM-index in the

explicit task. This could mean that the ToM system is not per se defect in high-

functioning ASD, but is only activated when explicitly instructed to, as this would

predict a deficit in ToM during the implicit but not the explicit version of the task. Still,

of course, we cannot draw any conclusions from these results since the difference in

ToM-index during the implicit task was not significant. It is clear that the current study

has to be extended with more participants, in order to obtain enough power.

For the explicit ToM measure during the explicit task (the explicit catch

questions), we did not find a difference between the ASD and control group, as

expected. Surprisingly, for the catch questions during the implicit task (“Did Buzz wear

a blue hat?”), the ASD group performed significantly worse than the control group. We

do not know why this was the case, but it can be hypothesized that this is related to

problems with focusing on task-relevant stimuli (Keehn, Nair, Lincoln, Townsend, &

Müller, 2016) or related to working memory deficits in ASD (Barendse et al., 2013),

although one could argue that this would influence performance in the explicit task as

well, which was not the case. Since the implicit task was the first task conducted in the

fMRI scanner, it is also possible that the unfamiliarity of the scanning situation has

influenced performance, since aversiveness to unfamiliarity is one of the main

symptoms of ASD (APA, 2013).

The neuroimaging results of the typically developed group indicate that the rTPJ

was marginally significantly more active during the explicit than the implicit task.

Further analyses showed that the explicit task activated the rTPJ, the EBA and

marginally the lTPJ. These results are not surprising, given that the TPJ is part of the

mentalizing network. It is even argued that rTPJ is the region that is specialized in

attributing mental states (Lombardo, et al., 2011; Perner, et al., 2006; Saxe & Wexler,

2005). Since the EBA is believed to be responsible for processing biological motion

(Downing, Jiang, Shuman, & Kanwisher, 2001), we assume that keeping in mind the

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false belief of the agent has led the participants to be more focused on the movements of

the agent in the explicit task. The absence of activity in other regions of the mentalizing

network during the explicit task, could be due to the low power. Alternatively, it is

possible that the rTPJ is the only region of the mentalizing network that is selectively

activated by attributing mental states (Saxe & Wexler, 2005). In the current experiment,

we collected neuroimaging data during the belief formation phase, unlike most other

mentalizing studies, that were interested in neural mechanisms when participants had to

make a decision about the mental states of another person. It is therefore possible that

our study taps more effectively the process of attributing mental states specifically, and

that therefore only activation in the rTPJ was found. Also, in our study we investigated

the difference between false belief and true belief processing, while mentalizing of

course also takes place in true belief conditions. It is possible that because of this

contrast, certain mentalizing areas are not activated in this thesis.

Surprisingly, we did not find any significantly activated regions during the

implicit task in the control group. This is an indication that participants might not have

been engaged in implicit mentalizing during this task. Reaction time effects in the

implicit task do however suggest that participants have implicitly kept in mind the

perspective of the agent during this task. The null result regarding neural activations is

thus more likely due to our small sample size. Kovacs and colleagues (2014) reported

that the rTPJ was only active during implicit mentalizing, when a false belief attributed

to the agent had a positive content, but not when this false belief had a negative content.

Therefore, we conducted additional analyses to investigate the effect of the belief

content of the agent. However, in the implicit task we did not find any significant

activation in these contrasts. Since Kovacs and colleagues (2014) only found this

content-selective effect in the rTPJ using a region-of-interest (ROI) analysis, it is not

surprising that we have not found it using a whole-brain analysis. Thus, additional ROI

analyses are necessary to investigate if a content-selective activation of the rTPJ can

also be found in our dataset.

Comparison of the neural activations between the ASD and control group

revealed a significant interaction effect between belief, task and group. More

specifically, the rTPJ was more active in the explicit compared to the implicit task, in

the control group compared to the ASD group. This is most likely caused by the fact

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that in the explicit task, the rTPJ was more active in the control group than in the ASD

group. This is consistent with the literature about a TPJ deficiency in ASD (Kana, et al.,

2014; Pantelis, et al., 2015) and with earlier studies that have found reduced activity in

the mentalizing network in ASD (Assaf, et al., 2013; Castelli, et al. 2002; Kana, et al.,

2009; Nieminen-von Wendt, et al., 2003; O’Nions, et al., 2014; Von dem Hagen, et al.,

2014).

Surprisingly, there were no differences for the implicit task. If more precise ROI

analyses can find activations in the implicit task in the control group, for example in

false belief conditions in which the agent has a positive belief content, then possibly

differences between the ASD and TD group can be found in these contrasts. In addition,

a larger sample will increase the power to detect condition and group effects.

Summarized, the null results in the brain correlates of the implicit task found in the

current thesis, are not in line with the behavioral results or the literature, and should

therefore be investigated further.

We also did not find compensatory activation in the ASD group in regions

outside of the mentalizing network. It is possible that there are large individual

differences in compensatory strategies, associated with activation in different brain

areas and that therefore no significant activation can be found at the group level,

especially with a limited sample size.

Summarized, this thesis was the first to directly compare neural correlates of

explicit and implicit mentalizing in a high-functioning ASD and matched control group.

In sum, we can conclude that the behavioral results indicate that our paradigm clearly

worked, as reaction times of all participants were influenced by the agent’s beliefs both

in the implicit and explicit task. The fact that there are no differences between the ASD

and control group at the behavioral level can probably be attributed to the lack of power,

since the results are in the expected direction for the implicit task. Regarding the neural

correlates in the explicit task, we can conclude that our results confirm the reduced

activity in mentalizing regions in ASD, especially for the rTPJ, as found in earlier

studies. The lack of group differences in the implicit task, is probably due to the null

result of activations during the implicit task in the control group, which is a matter that

requires additional investigation.

30

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Supplementary materials

Debriefing questionnaire

1. Had je een idee van het doel van dit experiment?

2. Is je tijdens deze taak iets ongewoons opgevallen aan de filmpjes?

3. Is je een bepaald patroon of thema opgevallen aan de filmpjes?

4. Had je een bepaald doel of strategie tijdens het bekijken van de filmpjes?