CORRELATES WITHIN A

ERROR MONITORING AND EMPATHY:

EXPLORING NEUROPHYSIOLOGICAL

CORRELATES WITHIN A

MULTIDIMENSIONAL FRAMEWORK

Noor Azhani binti Noor Amiruddin MA(Hons.) PGCert.

This thesis is presented for the degree of Doctor of Philosophy, and in partial fulfilment of

the requirements for the Master of Psychology (Clinical) degree, of the University of

Western Australia.

Neurocognitive Development Unit

School of Psychological Science

The Faculty of Science

University of Western Australia

2017

“We’re all so desperate to be understood,

we forget to be understanding”.

- Beau Taplin

THESIS DECLARATION

I, Noor Azhani binti Noor Amiruddin , certify that:

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

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

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

No part of this work will, in the future, 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.

The work(s) are not in any way a violation or infringement of 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 #: RA/4/1/5926 and RA/4/1/4623

This thesis contains published work and/or work prepared for publication, some of

which has been co-authored.

Signature:

Date: 27 January 2017

ABSTRACT

Neurophysiological studies have shown a relationship between empathy and

performance monitoring, specifically in the detection of errors. This relationship has

previously been assessed using self-reported global measures of empathy. Concurrently,

there is a growing body of evidence to suggest that empathy is not a single process but a

combination of two processes: cognitive and affective empathy. Cognitive empathy is

defined as the ability to take the perspective of another, while affective empathy is the

ability to recognise the emotions of another and share it. It must be noted though that

there is discrepancy in the literature, with researchers suggesting empathy to be either a

unidimensional or a two-dimensional construct. Therefore, the thesis aimed to map the

cognitive and affective empathy framework using error monitoring indices. The thesis

did this in two ways. The first experiment examined error monitoring indices and

empathy in children and adults, as children were more likely to show dissociation of

these processes. A validated measure of objective empathy was then used to compare

these processes in both children and adults. The thesis then aimed to compare error

monitoring indices with a purpose-built, self-reported measure of cognitive and

affective empathy in adults.

The following methods were used to address these aims. Three

neurophysiological studies, comparing error monitoring indices and empathy, were

performed and are reported in chapters 2 and 3. Error monitoring indices were elicited

by flanker tasks whilst the electrophysiological data were recorded. In Chapter 2 we

reported the association between the error-related negativity with a behavioural measure

of empathy (Faux Pas task) in both children and adults (N = 53; child sample, N=17;

adult; N = 36). A quantitative review (N = 1078) on existing Faux Pas literature is also

reported. Chapter 3 reported the relationship between error monitoring/empathy in

adults using multidimensional measures (chapter 3.1; N = 24), and a global empathy

measure (chapter 3.2; N =36). A meta-analysis (N =135) on the existing error

monitoring/self-reported empathy literature was also reported in Chapter 3. Within

chapter 4, we investigated the construct of empathy using confirmatory and unrestricted

factor analyses (N = 209) with self-reported and behavioural empathy measures.

The first study failed to observe a significant effect between error monitoring

and empathy using behavioural empathy measures (Chapter 2; N = 53). Firstly, there

was no evidence supporting the relationship between the error-related negativity and an

objective measure of empathy. Surprisingly, our child sample had greater Faux Pas

detection than our adult sample. To investigate the possibility that the findings were

unique to our samples (Chapter 2.1), a quantitative review was performed, comparing

the relationship between Faux Pas detection and age. The review indicated Faux Pas

detection gradually improved with age. However, the observed developmental trajectory

of Faux Pas detection did not resemble the developmental trajectory of the error-related

negativity. Consequently, the thesis focused on exploring error monitoring indices and

multidimensional empathy in adults.

To address the second aim, a neurophysiological study comparing error

monitoring indices and purpose-built, self-reported measure of cognitive and affective

empathy in adults was performed (Chapter 2). The study did not show support for the

relationship between error monitoring indices and cognitive and affective empathy. A

follow-up investigation comparing the error-related negativity and a commonly used

global measure of empathy also did not show support for the relationship. To examine

potential between-study variability in the effects reported, a meta-analysis was also

performed. The results of meta-analysis failed to suggest a statistically significant

association between the error-related negativity and empathy. However, we were unable

to evaluate potential moderating variables due to the limited error-related negativity and

empathy literature available for investigation.

Consequently, a psychometric investigation of the empathy construct was

performed, using confirmatory and unrestricted factor analyses (Chapter 4). Chapter 4

involved the evaluation of the unidimensional and two-dimensional framework of

empathy on existing objective and self-reported measures of empathy. The investigation

found support for the proposed two-dimensional construct incorporating affective and

cognitive empathy. However, findings indicated commonly used measures of cognitive

and affective empathy did not map onto these two constructs as expected. Therefore,

existing neurophysiological empathy studies may be confounded by the incongruity

between theoretical and statistical measures of empathy.

Thus, this thesis proposed two conclusions. Firstly, this thesis raises concerns

about whether the reported relationship between error monitoring and empathy is a true

effect, indicating the possibility of an existing publication bias in the literature.

Secondly, empirical support was found for the distinction between cognitive and

affective empathy and highlights that these constructs need to be independently assessed

when attempting to delineate the neurophysiological indices associated with empathy.

ACKNOWLEDGEMENTS

This research was supported by an Australian Government Research Training

Program (RTP) Scholarship. I would also like to thank my supervisors, Allison and

Gilles, for their support throughout my PhD candidacy. By providing more than just

‘practical support’, they went above and beyond their means to help me grow as an

individual. Without their encouragement and guidance, I would not be in the position

of submitting what I thought would have been the impossible.

I owe endless amounts of gratitude to my family, Ayah, Umi, Lin, Jan, Eizwan,

and Hassan. Thank you for your endless support in helping me fulfil my dreams. I look

forward to making you all proud to have me as the ‘Doctor’ of the family.

I would also like to thank Amanda, An, Elise, Shen, and Simone who supported

me through thick and thin. Thank you for all the dinner debriefs, fried chicken runs,

catch-up meals, and providing a shoulder for me to cry on. You have no idea how much

every little text and phone call meant to me. I can’t wait to see you all at the finish line

as successful Psychologists (and researcher).

I would also like to thank my clinical supervisors, Carmela, David, and Zamia

for supporting my research throughout my placements. Thank you for your patience and

leniency for letting me rush off to complete my PhD deadlines. Additionally, thanks to

the NDU lab, and 2013 Clinical Psychology girls. I cannot wait to work alongside you

all in the near future. As for my friends overseas, thank you for accommodating me with

Skypes, Facetimes, and late text replies. I endeavour to see you all very soon.

Lastly, but definitely not least, I would like to thank Brent. Thank you for

helping me up for every time I’d fallen down. I will always remember your words of

support: “Cutie, you’re doing really well and you’re making lots of progress” (Foreman,

2016). I am proud to finally say that I have done well, and this is what I have achieved.

STATEMENT OF CANDIDATE CONTRIBUTION

Error Monitoring and Empathy

Noor Azhani binti Noor Amiruddin –2017 1

CONTENTS

1. General Introduction .................................................................................................. 09

An Overview of Empathy and its Neurophysiological Associations .............. 11

Brief History of Empathy Models ................................................................... 12

Cognitive and Affective Empathy .................................................................. 15

Cognitive and Affective Empathy in Clinical Populations........................... 16

Cognitive and Affective Empathy in Typical Populations ........................... 17

Empathy and the Anterior Cingulate Cortex ............................................... 18

Error Monitoring and Empathy ...................................................................... 19

The Current Thesis .......................................................................................... 21

References ....................................................................................................... 21

2. The Error-Related Negativity and Behavioural Empathy: Electrophysiological

Explorations in Children and Adults. ................................................................. 34

Abstract ........................................................................................................... 35

Introduction ..................................................................................................... 36

Error Processing Indices and Empathy ...................................................... 37

The Development of Inhibitory Control in Children .................................... 38

Current Study .................................................................................................. 40

Chapter 2.1 ...................................................................................................... 40

Methods ........................................................................................................ 40

Data Analysis ............................................................................................... 43

Results .......................................................................................................... 44

Discussion .................................................................................................... 48

Chapter 2.2 ...................................................................................................... 50


2 Noor Azhani binti Noor Amiruddin – 2017

Methods ........................................................................................................ 50

Results .......................................................................................................... 51

General Discussion ......................................................................................... 55

Functional Significance of ERN and Empathy ............................................ 55

Limitations ................................................................................................... 57

Conclusion ...................................................................................................... 58

References ....................................................................................................... 59

3. Error Monitoring and Empathy: Explorations Within a Neurophysiological Context 68

Abstract ........................................................................................................... 69

Introduction ..................................................................................................... 70

Chapter 3.1 ...................................................................................................... 73

Methods ........................................................................................................ 73

Results ......................................................................................................... 77

Discussion .................................................................................................... 79

Chapter 3.2 ...................................................................................................... 82

Methods ........................................................................................................ 82

Procedures and Data Analysis ..................................................................... 83

Statistical Analysis ....................................................................................... 84

Results .......................................................................................................... 84

Discussion .................................................................................................... 84

Chapter 3.3 ...................................................................................................... 86

Methods ....................................................................................................... 86

Results .......................................................................................................... 88

Discussion .................................................................................................... 89

General Discussion ......................................................................................... 91



Endnotes .......................................................................................................... 95

References ....................................................................................................... 96

4. The Measurement of Empathy: Factorial Validity of Self-Report and Behavioural

Tasks .................................................................................................................... 107

Abstract ......................................................................................................... 108

Introduction ................................................................................................... 109

Commonly Used Measures of Empathy: Self-Report ................................. 110

Commonly Used Measures of Empathy: Behavioural Tasks ..................... 114

Gender Differences in Empathy ................................................................. 115

Purpose of Current Study ........................................................................... 116

Methods ......................................................................................................... 117

Data-Analytic Strategy ............................................................................... 119

Results ........................................................................................................... 120

Confirmatory Factor Analyses ................................................................... 120

Unrestricted Factor Analyses ................................................................... 121

Discussion ..................................................................................................... 123

Two-Factor Model of Empathy: Self-Reported Measures ......................... 123

Two-Factor Model of Empathy: Behavioural Measures .......................... 125

Empathy and Gender ................................................................................. 127

Potential Clinical Implications .................................................................. 128

References ..................................................................................................... 131

5. General Discussion ................................................................................................... 141

Chapter Summary ........................................................................................ 142

Measurement of Empathy .......................................................................... 143

Neural Correlates of Empathy ................................................................... 144



Limitations .................................................................................................... 145

Future Directions .......................................................................................... 146

Conclusion .................................................................................................... 147

References ..................................................................................................... 149



LIST OF TABLES

Table 2-1. ERN Amplitude (µV) Means and Standard Deviations (in parenthe-

ses).………….............................................................................................................45

Table 2-2. Study Details of the Quantitative Review including Faux Pas Mean, Par-

ticipant Age, and Sample

Size……………………………………………….....................................................52

Table 3-1. Descriptive Statistics for Empathy Scores, ERN Amplitude (µV), Partici-

pant Accuracy, Mean Reaction Times (milliseconds) on Congruent, Incongruent,

and Reversed Trials for Study

1……………………………………......................................................................…75

Table 3-2. Pearson’s Correlations and Bayes Factors between ERN and QCAE

measures…………………………………………………………………….…....…77

Table 3-3. Descriptive Statistics of Empathy Scores and ERN Amplitude (µV), Par-

ticipant Accuracy, the Median Reaction Times (milliseconds) on Congruent, Incon-

gruent, and Reversed Trials for Chapter

3.2.…………………………….........................................................................….....84

Table 3-4. Study and Participant Characteristics for the Meta-Analyses..................86

Table 4-1. Descriptive Statistics of Empathy Measures……………………...…...118

Table 4-2. Internal Consistency and Pearson Correlations of the Measures of Empa-

thy………………………………………………………………........….............…119



Table 4-3. Maximum Likelihood Unrestricted Factor Analytic Pattern Matrix

(Standardised)……………………………………………………………....……...120



LIST OF FIGURES

Figure 2-1. The flanker stimuli for each condition. The fish on the left represent the

congruent condition, followed by the incongruent condition, and the reversed condi-

tion on the right. ...................................................................................................... 39

Figure 2-2. Grand averaged ERP waveforms for child responses from 600 to 1000

ms showing amplitude distribution of ERN following incorrect post-response during

the flanker task. ERN peak negative amplitude at 68 ms at Cz. ............................. 42

Figure 2-3. Grand averaged ERP waveforms for adult responses from 600 to 1000

ms showing amplitude distribution of ERN following incorrect post-response during

the flanker task. ERN peak negative amplitude at 72 ms at FCz. ........................... 42

Figure 2-4. Scatterplot of mean ERN amplitude and Faux Pas performance in adults.

................................................................................................................................ 46

Figure 2-5. Scatterplot depicting the linear and quadratic association between Age

and Faux Pas performance. ..................................................................................... 50

Figure 3-1. Average ERP waveforms from 600 ms before to 1000 ms after a re-

sponse during the flanker task, and the topographic map showing the amplitude dis-

tribution of ERN following incorrect responses at the ERN peak latency of 56 ms

post-response. The topographic map was created using ERP lab (Lopez-Calderon &

Luck, 2014). ............................................................................................................ 74

Figure 3-2. Grand-averaged waveforms from 600 ms before to 1000 ms after an er-

ror response during the flanker task, and the topographic map showing the distribu-

tion of the ERN following incorrect responses. Peak ERN amplitude was shown at



64 ms with a 24 to 80 ms3-1

interval. Topographic map was created by ERP lab

(Lopez-Calderon & Luck, 2014)............................................................................. 81

Figure 3-3. ERN and empathy correlations (r), z-values, p-values, and mean effect

sizes. ........................................................................................................................ 87



LIST OF ABBREVIATIONS AND ACRONYMS

Anterior Cingulate Cortex ACC

Confirmatory Factor Analyses CFA

Electroencephalogram EEG

Error-Related Negativity ERN

Empathy Quotient EQ

Event-Related Potential ERP

Interpersonal Reactivity Index IRI

Feedback Related Negativity FRN

Functional Magnetic Resonance Imaging fMRI



1. General Introduction



AN OVERVIEW OF EMPATHY AND ITS NEUROPHYSIOLOGICAL ASSOCIATIONS

Empathy allows individuals to notice another person’s feelings, understand

‘where they are at’, and respond appropriately to that person’s state of mind. It can lead

individuals to pro-social behaviours, such as helping, sharing or providing comfort for

others (Zahn-Waxler, Radke-Yarrow, Wagner & Chapman, 1992; Eisenberg, 2000)

and/or amoral behaviours (Decety & Cowell, 2014). By understanding the origins and

development of empathic processes, it can allow us to uncover the underlying

localisations of empathic deficits in individuals.

Though empathy is proposed to have multiple processes (Davis, 1980; 1983;

Mehrabian & Epstein, 1972), some have argued that these processes are difficult to

delineate due to the overlap of processes (Baron-Cohen & Wheelwright, 2004; Leslie,

1987) and therefore choose to measure empathy using global measures. However, there

are researchers who have dissociated empathy processes through the use of psychometric

measures (Jolliffe & Farrington, 2006; Muncer & Ling, 2006; Reniers, Corcoran, Drake,

Shryane, & Völlm, 2011) and neuroimaging (Adolphs, 2002; Blair, 2005) and have

argued that it is also possible to measure these separate processes. Using factor analysis,

it has been suggested that empathy consists of two processes: cognitive and affective

empathy (Jolliffe & Farrington, 2006; Reniers et al., 2011). One must note though, that

despite the evidence that two processes are dissociated, there are few questionnaires that

are purpose-built to measure cognitive and affective empathy (Chrysikou & Thompson,

2015; Jolliffe & Farrington, 2006; Reniers et al., 2011).

Thus, more recent empathy research is endeavouring to localise cognitive and

affective empathy using functional magnetic imaging (fMRI) and event-related potentials

(ERP). Recent neuroscientists have suggested that the anterior cingulate cortex (ACC) is

associated with empathy (Bellebaum, Broadmann, & Thoma, 2014; Singer et al., 2004).



The ACC has been found to relate to other cognitive and emotional processes (Bush, Luu,

& Posner, 2000), with studies reporting evidence of empathy to correlate with error

monitoring processes (Bellebaum, Brodmann, & Thoma, 2014; Larson, Good & Fine,

2010; Santesso & Segalowitz, 2009; Rak, Bellebaum, & Thoma, 2013; Thoma, Norra,

Juckel, Suchan, & Bellebaum, 2014). However, there are few investigations that have

evaluated the relationship between error monitoring and empathy within the cognitive and

affective empathy framework.

The aim of this thesis is to map the cognitive and affective empathy framework

using error monitoring indices. Within this chapter, we discuss the theoretical models of

empathy and how the past and current research would measure its processes. Secondly,

we discuss the two-dimensional model of empathy; cognitive and affective empathy.

Lastly, we discuss how ERP investigations can provide insight into empathy processes

through using electrophysiological (EEG) indices like the error-related negativity.

BRIEF HISTORY OF EMPATHY MODELS

It has been agreed that empathy consists of multiple processes (Baron-Cohen &

Wheelwright 2004; Hogan, 1969; Reniers et al., 2011). However, the bone of contention

within these empathy models is whether each process can be decoupled from each other

with initial measures of empathy measuring empathy as a whole (Hogan, 1969; Stotland,

1969). However, even within these questionnaires, there were items that were based on a

combination of processes. These processes would include how empathic individuals

would interact in social interactions (Hogan, 1969) or how a therapist may interact with

clients (Davis, 1983: Mehrabian & Epstein, 1972). These measures were proven to be

problematic, as their outcomes were often affected by poorly defined items on their scales

(Jolliffe & Farrington, 2006; Reniers et al., 2011) and variations due to individual



response styles (Jackson & Messick, 1958). Consequently, researchers endeavoured to

develop a more scientific measure of empathy.

Baron-Cohen and Wheelwright (2004) stated that the lack of consistency across

past measures was due to failed attempts to disentangle the processes of empathy through

various questionnaires. Thereafter, they created a measurement of empathy by examining

clinical populations with deficits in empathy, such as Autism Spectrum Disorder (Baron-

Cohen et al., 1999; Enticott et al., 2014; Stone, Baron-Cohen, Calder, Keane & Young,

2003) and psychopathy (Munro et al., 2007). Through these explorations, they developed

a global measure of empathy known as the empathy quotient (EQ). The EQ is a self-

report questionnaire and is currently one of the most widely used measures of empathy

(Baron-Cohen, 2011). Baron-Cohen and Wheelwright (2004) proposed that empathy

should be measured through a global measure, involving items that assess the multiple

processes of empathy.

However, other researchers persisted with quantifying the distinct processes of

empathy. Contemporary statistical methods allow for an objective quantification of a

particular construct. Confirmatory factor analysis is a multivariate statistical procedure

that evaluates whether the resulting factor structure is consistent with the researcher’s

understanding of the construct. The analysis attributes a proportion of shared variance of

the measured variables to one or more factors (Reise, Moor, & Haviland, 2010). Muncer

and Ling (2006) performed a confirmatory factor analysis on EQ scores of university

students and their parents. Their results show that the EQ is an unfit measure of global

empathy. Using the items in the EQ, they created a three dimensional measure, which

they found to be more statistically suitable. Their dimensions included cognitive empathy,

emotional reactivity, and social skills. Cognitive empathy is the understanding of another

person's beliefs and intentions. Emotional reactivity is the individual's emotional response



to another's emotions. The Social skills subscale is how an individual would interact with

another during a social situation. These measures have gained traction in empathy

literature and are used within clinical and experimental research settings. Their measure

has been included in the assessment of clinical traits such as schizotypy (Henry, Bailey,

& Rendell, 2008; Russell-Smith, Bayliss, Maybery, & Tomkinson, 2013), traumatic brain

injury (Sousa et al., 2010), and autism spectrum disorder (Mathersul, McDonald &

Rushby, 2013).

Another multidimensional measurement that is also commonly used is

Interpersonal Reactivity Index (IRI; Davis, 1980; 1983). The scale consists of four

different measures: perspective taking, empathic concern, fantasy, and, personal distress.

Perspective taking is the ability to take the mental view of another, similar to the concept

of cognitive empathy. Fantasy is similar to perspective taking as it examines an

individual’s ability to take the perspective of another in a fictional situation. Empathic

concern assesses feelings of sympathy and compassion towards another from an

emotional perspective. Personal distress, on the other hand, involves the respondent’s

tendency to feel emotional distress when perceiving another in distress. Davis

hypothesised that individuals adept at empathising would reflect high scores in

perspective taking, empathic concern, and fantasy. They would further show lower

personal distress levels when empathising with another. The IRI has been used in both

experimental (Davis & Franzoi, 1991; Schutte et al., 2001) and clinical research settings

(Bellebaum, et al., 2014; Thoma, et al., 2015).

However, others have argued that certain subscales may not be relevant to the

measurement of empathy. For instance, others have suggested EQ Social Skills (Muncer

& Ling, 2006) to be more of a socialisation measurement than an empathy measure

(Russell-Smith et al., 2013). Similarly, others have omitted measures like fantasy (IRI)



and personal distress (IRI), as they did not find it to ‘fit’ the empathy construct (Jolliffe &

Farrington, 2006; Reniers et al., 2011). Empathy research continues to move forward with

developing a multidimensional construct of empathy using statistical techniques.

The more recent theories lean towards empathy consisting of two dimensions;

cognitive and affective empathy (Jolliffe & Farrington, 2006; Reniers et al., 2011;

Vanman, 2016; Smith, 2006). Cognitive empathy is defined as the understanding of the

cognitive mental states of others, whilst affective empathy is defined as sharing emotions

with another. This two-dimensional framework is proposed to be a more precise measure

of distinct empathy processes (Gonzalez-Liencres, Shamay-Tsoory, & Brüne, 2013;

Jolliffe & Farrington, 2006; Reniers et al., 2011). This is because others have suggested

the framework includes processes that are more representative of empathy (Jolliffe &

Farrington, 2006; Reniers et al., 2011). However, there are few empathy measurements

that are built to fit the two-dimensional framework. Therefore, current research

endeavours to map the cognitive and affective empathy framework using neuroimaging

and neurophysiology (Bellebaum et al., 2014; Blair, 2005; Larson et al. 2010; Thoma et

al., 2015).

COGNITIVE AND AFFECTIVE EMPATHY

Though there is widespread controversy on the definition of the psychological

construct of empathy (Cuff et al., 2016), many accept the two-dimensional construct of

empathy. Neuroscientific research has shown evidence supporting the dissociation

between cognitive and affective empathy. The evidence can be seen in two ways. Firstly,

there are both fMRI and behavioural evidence that report dissociations between cognitive

and affective empathy in clinical populations. Secondly, there are studies that show the

dissociation of these processes within the typical population. Along with the dissociation

of cognitive and affective empathy, research has also started to map and localise these



processes (Decety, 2011; Bernhardt & Singer, 2012). One particular area, the ACC has

been linked to affective empathy in adults (Decety & Michalska, 2010; Jackson, Meltzoff,

& Decety, 2005; Lamm, Decety & Singer, 2011; Langford et al., 2006; Singer et al.,

2004). However, there are few studies that have used purpose-built, multi-dimensional

measures of empathy in relation to the ACC. Therefore, I conclude by proposing the use

of more refined measurements to map cognitive and affective empathy in relation to the

ACC.

COGNITIVE AND AFFECTIVE EMPATHY IN CLINICAL POPULATIONS

Blair (2005) proposes that there is a neural dissociation between cognitive and

affective empathy through reviewing the profiles of clinical populations with autism and

psychopathy. He suggests that individuals with autism have deficits in cognitive empathy,

as evidenced by diminished activations in more perspective-taking neural areas, medial

pre-frontal cortex. (Adolphs, 2002). Conversely, studies have shown psychopaths to have

no difficulty with cognitive empathy tasks and showed no difference in neural activations

(Blair et al., 1996; Kosson, Suchy, Mayer & Libby, 2002). However, psychopaths exhibit

reduced activations in neural areas associated with emotional recognition (Blair, 2005;

Kosson et al., 2002).

Other clinical investigations exhibited dissociations in empathy processes when

comparing clinical populations with empathic deficits to typical populations. Ozonoff and

colleagues (1999) compared the performance of autistic individuals with a control group,

matched on verbal IQ, on cognitive empathy tasks and emotion perception. They found

no differences in emotion perception between groups, yet the autistic group performed

significantly worse on cognitive empathy tasks. Ozonoff et al. (1999) suggest that

individuals with autism may have difficulty in cognitive empathy, despite having an

intact ability to identify emotions.



COGNITIVE AND AFFECTIVE EMPATHY IN TYPICAL POPULATIONS

More recent research in typical populations indicates cognitive and affective

empathy to have distinct developmental trajectories through the use of purpose-built

behavioural empathy tasks. Behavioural tasks are objective and have the additional

benefit of testing individuals who may not have insight into their abilities (Stone,

Bachrach, Jobe, Kurtzman, & Cain, 1999), including clinical populations (Berthoz,

Armony, Blair, & Dolan, 2002; Blair et al., 1996; Kosson et al., 2002) and children

(Decety & Michalska, 2010; Ozonoff, Pennington, & Rogers, 1999). Others have also

labelled these tasks as measures of state empathy (Loggia, Mogil & Bushnell, 2008; Van

der Graaf et al., 2016). Behavioural empathy tasks are measures associated with the

subject feeling empathic concern in the ‘here and now’ (Batson, 2009). These tasks are

built to evoke a feeling, allowing for a real time snapshot of how a person may react

when faced with a situation (Loggia et al. 2008; Van der Graaf et al., 2016). These tasks

also differ from self-reported questionnaires often focus on capturing the participant’s

empathic trait characteristics (Batson, 2009).

Decety and Jackson (2010) reviewed fMRI evidence of child development and

have found differences in activation of neural structures in cognitive and affective

empathy in children. They found infants aged zero to two to exhibit activations in neural

areas associated with emotional components of empathy, such as recognition of emotions

and pro-social behaviour. These behaviours are synonymous with the definition of

affective empathy (Cuff et al., 2016; Reniers et al., 2011). However, further

neurodevelopmental evidence found a link between executive function development and

the ability to separate thoughts of their selves and others (Carlson & Moses, 2001). The

development of these skills was commonly evident in ages 4 to 12 (Carlson & Moses,

2001). These findings suggest the possibility of another component of empathy which

allows the individual to understand that another person is capable of having different



feelings and actions (Decety & Jackson, 2004). This component was known as cognitive

empathy (Blair, 2005; Reniers et al., 2011). However, it is important to note that adults

are also found to not be consistently reliable in their use of cognitive empathy abilities, as

they still have the tendency to assume that others would have the same knowledge and

beliefs as they do (Keysar, Lin, & Barr, 2003).

EMPATHY AND THE ANTERIOR CINGULATE CORTEX

Studies in empathy have suggested specific components of empathy to be linked

to the anterior cingulate cortex (Eres, Decety, Louis, & Molenberghs, 2015; Fan, Duncan,

de Greck, & Northoff, 2011; Singer et al., 2004). Some investigations have found self-

reported measures of cognitive empathy to be associated with ACC activations (Eres et

al., 2015; Fan et al., 2011). Other research, such as Singer et al., (2004)’s study, have

found a relationship between high levels of affective empathy and activations in the ACC.

Singer et al. (2004) administered a pain stimulus to adult participants and their partners

and assessed the participants' brain activity using fMRI. The participants were also given

self-reported affective empathy questionnaires, capturing the participants’ empathic traits.

Participants with higher empathy scores had greater activation in the ACC, whereas those

with lower affective empathy scores had reduced activation of this region. Singer et al.

(2004) suggested that the ACC is associated with affective empathy through both

behavioural and self-reported measures. Since then, other studies (see Jackson et al.,

2005; Lamm et al., 2011; Langford et al., 2006) have also replicated similar relationships,

using pain as a measure of behavioural affective empathy.

Due to variation in findings, others have debated what pain represents within the

cognitive and affective empathy framework. Some have proposed pain to measure

affective empathy (Fan & Han, 2008; Han et al., 2008; Singer et al., 2006), whilst other

propose it to be a separate category of ‘pain empathy’ (Fitzgibbon, Giummarra,



Georgiou-Karistianis, Enticott & Bradshaw, 2010; Gonzalez-Liencres et al., 2013;

Jackson, Rainville, & Decety, 2006). Consequently, using other purpose-built,

behavioural empathy measures can allow for a valid and objective evaluation of how an

individual empathises with others. There are a number of putative measures which may

be indicative of individual differences in empathic skills (Stone et al. , 2003; Baron-

Cohen et al., 2015; Reid et al., 2013) and an evaluation of the dimensionality of empathy

could be fruitfully evaluated based on the inclusion of a combination of self-reported and

behavioural measures.

ERROR MONITORING AND EMPATHY

Studies have also reported a relationship between empathy scores and error

monitoring (Larson et al., 2010; Santesso & Segalowitz, 2009; Thoma & Bellebaum,

2012), a skill associated with the ACC (Carter et al., 1998; Carter & van Veen, 2007;

Heilbronner & Hayden, 2016; Lockwood, Apps, Roiser, & Viding, 2015; Ullsperger,

Fischer, Nighbur & Endrass, 2014; van Veen & Carter, 2002). Error monitoring involves

observing one's performance and regulating behaviour in response to errors (Carter et al.,

1998). Following commission of erroneous responses, the stimuli elicit an ERN,

indicating that an error has been detected. The detection of the error signals the need to

exert greater cognitive control and subsequently adjust their behaviour to improve

performance (Heilbronner & Hayden, 2016; Ullsperger et al., 2014; van Veen & Carter,

2002).

Other investigations have indicated a relationship between empathy levels and the

amplitude of the ERN in error monitoring tasks, such as the flanker task (Larson et al.,

2010; Santesso & Segalowitz, 2009, Thoma & Bellebaum, 2012). Santesso and

Segalowitz (2009) found that groups who scored higher on empathy had greater ERN

amplitudes elicited by errors during the flanker task. This suggests that individuals who



are more likely to notice errors, as denoted by larger ERN amplitudes, are more likely to

have higher levels of empathy.

At present, the functional significance of the ERN in relation to empathy has been

debated. Some have stated that the ERN signifies a compensatory index (Gehring et al.,

1993). Consequently, individuals with greater ERN amplitudes are more likely to be more

cautious or correct their subsequent response following their error. Within the context of

ERN/empathy, individuals with higher empathy are more likely to be vigilant of their

own environment and the action of others (Larson et al., 2010). Studies have found

individuals with larger ERNs to take more cautious choices (Hewig et al., 2007) and are

more willing to accept norms and abide by the rules (Dikman & Allen, 2000).

Others have suggested the ERN to reflect a reinforcement learning signal within a

neural reward system (Holroyd & Coles, 2002; Larson et al., 2010; Santesso &

Segalowitz, 2009). Within the context of the neural reward system, the individual will

receive a reinforcement signal as reflected by a neural response (i.e. ERN) to reinforce

what the individual has learned is a ‘correct’ response (Holroyd & Coles, 2002).

Subsequently, individuals will be more likely to seek more rewarding experiences and

amend errors that were made. Within the context of empathy, more empathic individuals

may hypothetically be rewarded for interacting socially (Larson et al., 2010; Santesso &

Segalowitz, 2009), with the ERN signalling the individual to provide a desirable response

when communicating with another.

However, there is little clarification on the significance of the ERN and how it

relates to cognitive and affective empathy. At present, there is only one study to date that

has compared both error monitoring indices and cognitive and affective empathy (Larson

et al., 2010). Larson et al. (2010) found the ERN to associate to both cognitive and

affective empathy, yet this study has not been replicated since. Follow-up investigations



have instead shown inconsistent findings. For instance, existing functional magnetic

resonance imaging (fMRI) studies have found the ACC to be linked to affective empathy

using behavioural tasks (Fan & Han, 2008; Han et al., 2008; Singer et al., 2004), whilst

others have found the ACC to link to cognitive empathy (Fan et al., 2011; Eres et al.,

2015) . Others have instead found no associations between empathy and other event-

related potentials originating from the ACC (Bellebaum et al., 2014). Consequently, there

is a necessity to clarify how error monitoring indices map on to the cognitive and

affective empathy framework using more precise measures.

THE CURRENT THESIS

To summarise, the literature suggests that the two components of empathy,

cognitive and affective, can be dissociated. However, the evidence of the mapping of

cognitive and affective empathy in relation to the ACC remains inconsistent (see

Carrington & Bailey, 2009; Gallagher & Frith, 2003; Kanske, Böckler, Trautwein, &

Singer, 2015; McCabe, Houser, Ryan, Smith, & Trouard, 2001). Furthermore, there are

few investigations which have looked at error monitoring indices within the cognitive and

affective empathy framework. Prior neuroimaging studies have attempted to delineate

cognitive and affective empathy through comparing behavioural task performance in

different age groups (Decety & Michalska, 2010; Killgore & Yurgelun-Todd; 2007).

Others have used purpose-build cognitive and affective empathy tasks and measures to

compare with ACC indices (Bellebaum et al., 2014; Larson et al., 2010; Santesso &

Segalowitz, 2009; Singer et al., 2004). This thesis addresses the gaps in literature, which

includes (i) exploring the association between error monitoring and empathy through

comparing children and adult performance (Chapter 2), (ii) disassociation of cognitive

and affective empathy using purpose-built, multidimensional measures (Chapter 3), and

(iii) a statistical evaluation of the cognitive and affective empathy framework using both



self-reported and behavioural tasks (Chapter 4). We report how we determined our

sample size, all data exclusions (if any), all manipulations, and all measures in each

study.

Thus, Chapter 2 aims to clarify the relationship between error monitoring and

empathy in both a child and adult sample. By confirming whether or not error monitoring

is linked to a global empathy measurement in children, we can move towards comparing

purpose-built, multidimensional behavioural empathy tasks with error monitoring indices

in children. The study includes a two-part exploration. The first part investigates whether

the ERN is associated with empathy using a behavioural measure of global empathy in

typical children and adults. To elicit the ERN, a child-friendly visual flanker task as used

in Brydges and colleagues’ (2014) study was used to elicit the ERN. To investigate the

possibility that our findings were unique to our samples, a quantitative review of existing

literature was performed assessing the relationship between Faux Pas detection and age.

Chapter 3 examines the error monitoring and empathy relationship using a

purpose-built, multidimensional measure of cognitive and affective empathy. Chapter 3.1

investigates the relationship of error monitoring indices and performance on the

Questionnaire of Cognitive and Affective Empathy (QCAE). A vertical visual flanker

task as adapted from Mansefield and colleagues' (2013) study, contrasting from Chapter

3.1, was used to promote greater task engagement. Following the results of Chapter 3.1,

Chapter 3.2 then examines the relationship between an error monitoring index and a

global measure of empathy using a horizontal visual flanker, as it was used in Chapter 1

to keep it consistent with prior error monitoring and empathy studies1. Lastly, Chapter 3.3

reviews the strength of the ERN/empathy relationship in existing literature through a

meta-analysis.



Chapter 4 aims to validate existing measures of behavioural and self-reported

empathy on the cognitive and affective empathy framework through the use of

confirmatory factor analyses. To date, there are no known evaluations of empathy using

both behavioural and self-reported measures. Following the results, the study uses an

unrestricted factor analysis to find a suitable fit of the existing measures of empathy.

To address the proposed aims of the thesis, a final discussion chapter will attempt

to draw conclusions from the results of the empirical investigations. Furthermore, it will

propose new directions of how empathy and its neurophysiology should be measured and

interpreted within the context of the cognitive and affective framework.



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2. The Error-Related Negativity and

Behavioural Empathy:

Electrophysiological Explorations in

Children and Adults.



ABSTRACT

Prior investigations suggest that a neurophysiological marker, the error-related negativity

(ERN), is associated with measures of self-reported empathy. Currently, it is unclear how

the ERN relates to behavioural measures of empathy. Our current study (N = 53) explored

the developmental relationship between the ERN and the Faux Pas task in both children

and adults. Both samples exhibited enhanced negativities following error commission,

with greater ERNs in adults than children. However, children were shown to have greater

Faux Pas performance than adults. There was also no significant association between the

ERN and the Faux Pas understanding in the adult samples. To investigate whether the

findings were unique to our sample, a follow-up quantitative review (N=1078) was

performed comparing age and Faux Pas performance. Results demonstrated an increase in

Faux Pas performance with increasing age. Our findings suggest that the developmental

trajectory of the ERN may not resemble the developmental trajectory of Faux Pas

understanding.

Keywords: Error-Related Negativity, Empathy, Faux Pas Recognition Test, Error

Monitoring, Performance Monitoring, Anterior Cingulate; quantitative review



INTRODUCTION

Empathy is the ability to understand the mental states of others and respond to

their thoughts and feelings with an appropriate emotion (Baron-Cohen, 2011; Baron-

Cohen & Wheelwright, 2004). Studies have found empathy to associate with activations

of the anterior cingulate cortex, a neural area linked with cognitive and emotional

processes (Bush, Luu, & Posner, 2000). Like empathy, the anterior cingulate cortex is

linked to other processes, such as the development of pro-social skills, (Decety, 2010;

Decety & Michalska, 2010), emotion regulation (Segalowitz & Dywan, 2009), and error

monitoring (Bellebaum, Brodmann, & Thoma, 2014; Larson, Good & Fine, 2010;

Santesso & Segalowitz, 2009; Rak, Bellebaum, & Thoma, 2013; Thoma, Norra, Juckel,

Suchan, & Bellebaum, 2014).

Recent investigations have found a link between error monitoring indices,

specifically the error-related negativity (ERN), and self-reported empathy (Larson et al.,

2013; Santesso & Segalowitz, 2009) within an adult sample. Prior studies have used self-

reported measures as they are convenient for data collection (Reniers, Corcoran, Drake,

Shryane, & Völlm, 2011). However, self-reported measures are limited, as participant

groups like children (Anastassiou-Hadicharalambous & Warden, 2008) and clinical

populations (Brook & Kosson, 2013) may not have insight into their abilities to

empathise. As a result, other researchers have focused on using behavioural measures of

empathy to allow for an objective evaluation (Baron-Cohen, O’Riordan, Stone, Jones &

Plaisted, 1999; Stone, Baron-Cohen, Calden, & Young, 2003; Stone, Baron-Cohen, &

Knight, 1998). The aim of our studies was to examine the relationship between ERN and

empathy, as measured by a behavioural empathy task in both children and adults. A

follow-up quantitative review was performed to account for possible floor and ceiling

effects in the behavioural empathy task performance.



ERROR PROCESSING INDICES AND EMPATHY

The anterior cingulate cortex is associated with various forms of cognitive and

emotional processes, including error monitoring (van Veen & Carter, 2002), and empathy

(Heilbronner & Hayden, 2016; Ullsperger , Fischer, Nighbur & Endrass, 2014).

Neurophysiological studies show that error monitoring indices are associated with

personality traits (Hajcak, McDonald, & Simons, 2003; Myer et al., 2011; Pourtois et al.,

2010), clinical disorders (Hajcak & Simons, 2002; Santesso, Segalowitz, & Schmidt,

2006), and empathic processes (Larson et al., 2010; Munro, Dywan, Harris, McKee,

Unsal, & Segalowitz, 2007; Santesso & Segalowitz, 2009). Previous studies have found

empathy to correlate with error monitoring indices, specifically with the ERN (Larson et

al., 2010; Santesso & Segalowitz, 2009). The ERN is a negative deflection that is elicited

when an individual detects an error within an error monitoring task (Carter et al., 1998). It

allows the individual to be more cautious in trials following an error and further improve

their performance (Navarro-Cebrian, Knight & Kayser, 2016; Rabbitt, 1966). Studies

indicate that individuals with greater ERN amplitudes are likely to have higher levels of

empathy, as measured by self-reported empathy scores (Larson et al., 2010; Santesso &

Segalowitz, 2009).

However, the reasons behind the ERN/empathy relationship remain relatively

unknown. Santesso and Segalowitz (2009) propose that the ERN reflects a reinforcement

learning signal. The reinforcement learning signal is a neural response that is mediated by

a neural reward system (Holroyd & Coles, 2002). Holroyd and Coles (2002) propose that

individuals who seek rewarding experiences are more likely to receive a neural response

(i.e. the ERN) at the absence of a reward. For instance, empathic individuals may find

pro-social behaviours rewarding and are informed neurobiologically when an antisocial

behaviour occurs. Therefore, empathic individuals are more likely to avoid situations

which results in antisocial behaviours.



Others have proposed that the ERN/empathy relationship reflects one’s vigilance

for both the environment and actions of others (Larson et al., 2010). For example,

individuals with these traits may be more likely to seek higher successes and avoid errors.

Studies have found that individuals with larger ERN are more cautious in social situations

(Hewig et al., 2007) and more likely to abide by the rules (Dikman & Allen, 2000).

THE DEVELOPMENT OF INHIBITORY CONTROL IN CHILDREN

There are currently no existing studies that directly examine the relationship

between ERN/empathy in children. Rather, developmental studies have investigated the

relationship between perspective-taking abilities and inhibitory control (Chasiotis et al.,

2006). Inhibitory control is a type of executive function that is implicated within error

monitoring (Garavan, Ross, Murphy, Roche, & Stein, 2002). Chasiotis and colleagues

(2006) found that children with greater inhibitory control are more adept at behavioural

empathy tasks. Inhibitory control and error monitoring have also been shown to develop

with age (Davies, Segalowitz, & Gavin, 2004). For instance, Davies and colleagues

(2004) reports that ERN amplitudes significantly increase from ages 7 to 18 years.

Likewise, the development of inhibitory control parallels the development of error

monitoring (Kerns et al., 2004). Researchers have attributed the developmental increase

of the ERN and inhibitory control to both biochemical and structural changes in the

anterior cingulate cortex (Davies et al., 2004)

Empathy is also influenced by structural neural changes, specifically in the

anterior cingulate cortex (Blair, 2005). Studies have shown that empathy develops from a

combination of environmental influences (Harris & Núntez, 1996) and structural neural

changes (Decety, 2010; Decety & Michalska, 2010). Others have also attributed the

development of empathy to concurrently develop with executive function abilities

(Chasiotis et al., 2006; Leslie, 1987; Leslie, Friedman, & German, 2004; Wellman, Cross,



& Watson, 2001). These executive function abilities also include inhibitory control

(Chasiotis et al., 2006; Wellman et al., 2001).

MEASUREMENT OF EMPATHY IN CHILDREN

As the ERN/empathy field focuses on using self-reported measures, self-reported

measures assume that participants have good quality insight into their abilities. However,

children may not have the verbal capacity to comprehend or communicate their cognitive

and affective processes (Harris & Núñez 1996). As a result, researchers have begun

relying on behavioural empathy tasks to assess individual differences in empathy to

detect subtle empathy deficits (Baron-Cohen et al., 1999; Stone et 1998; 2003).

Behavioural empathy tasks, like the Faux Pas task, have been used to detect

perspective-taking abilities in children, autistic traits in higher functioning individuals

(Baron-Cohen et al., 1999), and empathy deficits in adults with orbitofrontal damage

(Stone et al., 1998; 2003). The Faux Pas task involves scenarios of a character saying

something awkward or embarrassing. The situation usually elicits a reaction of

embarrassment for another character involved in the story. Consequently, the reader must

take the perspective of multiple characters and be aware of the embarrassing moment to

successfully understand a Faux Pas.

Krach and colleagues (2011) have found an association between activations of the

anterior cingulate cortex and a perspective-taking task, similar to the Faux Pas task.

Krach et al. (2011) presented vignettes of embarrassing situations and measured their

participants’ neural activations using fMRI. They found that respondents who rated the

vignette as being more embarrassing elicited greater anterior cingulate cortex activations,

and obtained high scores on self-reported measures of empathy. Krach et al. (2011)

suggest that the ability to recognise another’s embarrassment can be seen as an

integration of cognitive and interpersonal processes.



CURRENT STUDY

To investigate the relationship between ERN and empathy, we used the ERN as a

measure of anterior cingulate cortex activation and examined the association with the

Faux Pas task. There are few who use consistent and validated measures of empathy, like

the Faux Pas task, to examine the relationship between error monitoring and empathy.

The aim of this study was to assess the relationship between ERN and behavioural

measure of empathy. We examined the relationship between the ERN and scores on Faux

Pas detection in both children and adult samples. As we tested both children and adults,

we kept the methodology consistent across both samples. This included using a child-

friendly flanker task, as used in Brydges and colleagues (2014). This task had shown to

elicit flanker effect in both children (Brydges, Fox, Reid, & Anderson, 2014) and adults

(Brydges et al. 2012; Brydges, Anderson, Reid, & Fox, 2013). Additionally, a child-

friendly Faux Pas Recognition Task (Baron-Cohen et al., 1999) was used, which included

home and school scenarios.

CHAPTER 2.1

METHODS

Participants

The child participants included 24 children aged 8 to 9 years. They were recruited

from a holiday activity program held at the Neurocognitive Development Unit, at The

University of Western Australia from 2013 and 2014. All participants were screened for

visual impairments, indicating normal, or corrected vision, and were not colour-blind.

The children participated voluntarily. Each activity represented a test within a

standardised test battery. The adult sample included 39 undergraduate students aged 18 to

37 years enrolled at The University of Western Australia. They participated in the study



for course credit. All adult participants had normal or corrected-to-normal vision and

were not colour-blind.

Materials

Modified Visual Flanker Task. A modified visual flanker, presented as a game, was used

to elicit the ERN. Each participant had to feed the central fish. Five fish in a row were

presented on a blue background (see Figure 2-1). Each fish had an arrow on its body to

indicate the direction the fish was facing (i.e. left or right). Participants indicated the

direction of the central fish by pressing one of two keyboard keys, as indicated by red felt

patches on the ‘Z’ and ‘/’ keys. There were three conditions: the congruent condition (0.5

probability), where all the fish were green and facing the same direction; the incongruent

condition (0.25 probability), where all the fish were green, and the target fish faced the

opposite direction of the flankers; and the reversed condition (0.25 probability), where all

the fish were red, and participants were to press the key opposite to the direction of the

target fish. A fixation cross was presented for 500 ms in the centre of the screen prior to

the presentation of each stimulus. The stimuli were presented for 300 ms and were

presented in a random order of 176 trials. Both speed and accuracy were equally

emphasised. A practice block of eight trials was administered to ensure that participants

understood the task requirements.

Figure 2-1. The flanker stimuli for each condition. The fish on the left represent the con-

gruent condition, followed by the incongruent condition, and the reversed condition on

the right.

Faux Pas Recognition Task. Participants were asked to complete a Faux Pas recognition

task (Baron-Cohen et al., 1999). The Faux Pas task consisted of vignettes of a character



talking about another character. This conversation is designed to upset the other character

within the vignette. The aim of the task was to assess whether participants are able to take

the perspective of the characters and empathise with the characters within the story. A

total of ten Faux Pas stories and ten control stories, in which no Faux Pas was committed

were included in the task. The order of the presentation of the stories was randomised,

and the same randomised order was presented in the same order to all participants. The

task was presented on a computer monitor, with the vignettes displayed on white text on

black background. The vignettes were also verbally presented through headphones. Each

vignette was followed by four questions. The first question asked whether the story

contained a Faux Pas. If a Faux Pas was identified, a question was asked for details of the

identified Faux Pas. The third question was a comprehension question to check whether

the participants had paid attention to and understood the vignette. The fourth question

checked whether the participant felt the character in the story had malicious intent, i.e. a

false belief. Participants completed the Faux Pas task by listening to the audio recording

via headphones and reading the vignettes and questions on the computer monitor. Child

participants responded verbally, and an adult tester typed up the answers. Adult

participants typed their answers directly into the keyboard. Participants received one

point for each Faux Pas detected; with scores ranging from 0 to 10. Participants who

scored below seven for the false belief questions were excluded (Baron-Cohen et al.,

1999).

Procedures

Participants were fitted with an EasyCapTM

and completed a modified visual

flanker task. Electroencephalogram (EEG) was recorded continuously throughout the

task. Participants also completed the Faux Pas task.

Electroencephalogram (EEG) recording



The EEG were continuously recorded using Ag/AgCl electrodes at 33 scalp

locations (FP1, FP2 , F3, F4, F7, F8, Fz, FC1, FC2, FC5, FC6, FCz, FT9, FT10, C3, C4,

Cz, T7, T8, CP1, CP2, CP5, CP6, P3, P4, P7, P8, Pz, PO9, PO10, O1, O2, Iz). A ground

electrode was positioned at a frontal midline point, AFz, and the right mastoid was used

as an online reference. One electrode was positioned 2 cm above and another below the

left eye to measure eye movement. The EEG was amplified using NuAmps 40-channel

amplifier and digitised at a sampling rate of 250 Hz. A 0.05-30 Hz bandpass and 50 Hz

notch filters were used online and the EEG was subsequently filtered offline using a 1-30

Hz zero phase shift band-pass filter (12 dB roll off). Ocular movements were then

corrected offline using the ocular artefact reduction algorithm in Scan 4.33 after re-

referencing offline to an averaged mastoid reference.

DATA ANALYSIS

Event-Related Potential Analysis

Response-locked epochs from the midline sites from -600 to 1000 ms post-

response were extracted offline. All epochs were then baseline-corrected relative to -600

to -400 ms interval to avoid contamination of stimulus-locked ERP responses across

samples (Davies et al., 2004; Santesso & Segalowitz, 2009; Segalowitz & Dywan, 2009).

Based on visual inspection of the event-related potentials, the central lobe (Cz)

showed the largest grand averaged ERN amplitude, peaking at 68 ms for children (see

Figure 2-2). Adults showed a grand average ERN amplitude of 72 ms (see Figure 2-3).

Luck (2014) recommends using an interval of at least 50 ms to quantify the amplitude of

a peak, as it is less sensitive to high-frequency noise. It also mitigates the effect of

overlapping event-related components. The child sample ERN was then quantified as the

mean amplitude over an interval from 40 to 96 ms. The ERN interval for the adult sample

was identified to be a 44 to 100 ms. The incorrect minus correct waveform was chosen as



a measure of the ERN to account for potential between-subjects variability (Larson et al.,

2010).

Figure 2-2. Grand averaged ERP waveforms for child responses from 600 to 1000 ms

showing amplitude distribution of ERN following incorrect post-response during the

flanker task. ERN peak negative amplitude at 68 ms at Cz.

Figure 2-3. Grand averaged ERP waveforms for adult responses from 600 to 1000 ms

showing amplitude distribution of ERN following incorrect post-response during the

flanker task. ERN peak negative amplitude at 72 ms at FCz.

RESULTS

The data from two child participants were excluded, as they did not perform

above chance level on the flanker task. Furthermore, five child participants did not fulfil

the minimum requirements for the comprehension and/or false belief questions on the

Faux Pas task. Thus, the final total child sample consisted of 17 participants (M = 8.82,

SD= .39). Data from three adult participants were excluded, as they had less than six

errors across conditions in the flanker task (Olvet & Hajcak, 2008). Thus, the final adult

sample consisted of 36 participants (18 females; M = 23.19; SD = 4.34). The final data

were screened for univariate and bivariate outliers. None were identified. Furthermore,



the data were found to be normally distributed, (skew < 1.0). Faux Pas and modified

flanker task descriptive statistics are included in Table 1.

Behavioural Performance Analysis

Firstly, we looked at the characteristics of behavioural responses from the flanker

task to see whether participants were exhibiting a flanker effect. To test whether

participants were more accurate in the congruent trials, in comparison to the incongruent

and reversed trials, a one-way within subjects ANOVA was performed. Task conditions

(congruent, incongruent, and reversed) were used as the independent variable and

accuracy as the dependent variable. Paired t-tests were also performed for follow-up post-

hoc tests.

For the child sample, there were significant differences in accuracy across task

conditions, F (2, 32) = 21.39, p < .001. Specifically, children were, on average, more

accurate in the congruent, in comparison to the incongruent, condition, t (16) = 4.19, p =

.001, and the reversed trials condition, t (16) = 4.63, p < .001. By contrast, there was no

significant difference in accuracy between the incongruent and reversed trial, t (16) =

1.23, p = .236.

Similarly, our analyses indicated a significant difference in conditions for adult

accuracy, F(1, 35) = 39.53, p <.001. Adults were, on average, significantly more accurate

in the congruent condition than the incongruent, t (35) = 3.46, p = .001, and the reversed

condition, t (35) = 6.29, p < .001. Additionally, participants were more accurate in the

incongruent than the reversed condition, t (35) = 4.49, p < .001.

Secondly, to test whether participants had slower reaction times for correct trials

than incongruent and reversed trials, we performed a second one-way ANOVA, with task

conditions (congruent, incongruent, reversed) as the independent variables and reaction

time as the dependent variable. Paired t-tests were performed for follow-up analyses.



For the child sample, there was a significant difference in reactions across

conditions, F (2, 32) = 25.36, p < .001. Follow-up t-tests indicated that children were

significantly slower in the reversed, t (16) = 7.01, p < .001, and incongruent, t (16) =

6.14, p < .001, than the congruent condition. There was no significant difference in

reaction times for correct responses between the reversed and incongruent condition, t

(16) = 1.11, p = .286.

For the adult sample, analyses exhibited a significant difference in reaction time

for correct responses across conditions, F(2, 58) = 25.43, p <.001. Adult participants were

slower to respond correctly on the incongruent, t (35) = 13.47, p <.001, and reversed con-

dition, t(35) = 9.95, p <.001, in comparison to the congruent condition. Adult were also

slower in the reversed condition than the incongruent condition, t (35) = 8.88, p < .001.

Children Adult

Faux Pas scores 8.82 (.39) 8.00 (1.60)

Error Related Negativity µV -4.35 (4.60) -7.65 (4.25)

Accuracy (Percentage correct)

Children Adult

Congruent 90.80 (8.02) 94.32 (5.40)

Incongruent 85.86 (8.35) 89.52 (9.68)

Opposite 83.66 (9.40) 83.14 (13.28)

Reaction Time (ms)

Children Adults

Correct Responses Congruent 810.07 (193.46) 435.57 (72.64)

Correct Responses Incongruent 899.51 (218.63) 480.23 (76.82)

Correct Responses Reversed 920.38 (191.58) 523.92 (76.54)

Note. N = 53



Table 2-1. ERN Amplitude (µV) Means and Standard Deviations (in parentheses).

Event-Related Potential Analyses

Our first substantive aim was to compare the difference in performance in the

ERN amplitude and Faux Pas performance across groups. Such analysis was conducted to

confirm that error monitoring (Davies et al., 2004) and Faux Pas understanding (Baron-

Cohen et al., 1999) increased with age. To address this aim, we performed two independ-

ent samples t-tests, with mean ERN amplitude and Faux Pas performance as the depend-

ent variables. The second aim of our analyses was to compare the relationship between

the error monitoring index and Faux Pas task performance in the child and adult samples.

To this effect, we estimated two Pearson’s correlations and tested the difference between

the two sample correlations statistically. Finally, we estimated Bayesian factors for the

correlations with JASP (JASP team, 2016; jasp-stats.org).The child versus adult sample t-

tests indicated adults to have significantly larger ERN amplitudes than children, t (51) = -

2.57, p = .018, d = .74. Cohen’s d (1992) effect size value suggested a large meaningful

effect.

However, adults had significantly poorer Faux Pas performance than children, t

(51) = 2.90, p = .006, d = .70. Cohen’s d (1992) effect size value indicated a large mean-

ingful effect. It was noted that the child sample did not exhibit individual variability, as

their Faux Pas scores only ranged from 8 to 9. In contrast to the child scores, adults ex-

hibited individual variability with Faux Pas scores ranging from 4 to 10.

As there was no individual variability in the child sample, we were unable to ex-

amine the association between ERN and empathy. Within the adult sample, the ERN am-

plitude was not found to correlate significantly with Faux Pas scores, r (35) = .02, p =

.910, BF01 = 4.66. Thus, in the adult sample, results supported the null hypothesis to be



4.66 times more likely than the alternative hypothesis, representing substantial evidence

(Jeffreys, 1961).

Figure 2-4. Scatterplot of mean ERN amplitude and Faux Pas performance in adults.

DISCUSSION

The aim of this investigation was to examine the relationship between ERN and

empathy, as measured by a behavioural task, in both children and adults. Adults exhibit

larger mean ERN amplitudes, in comparison to children. Surprisingly, children showed

greater Faux Pas detection than adults, though exhibiting only a small effect (Cohen,

1992). This is probably due to the lack of individual variability within the child sample.

Therefore, we are unable to compare the relationship between ERN and empathy in chil-

dren.

Within our adult sample, our findings failed to identify a statistically significant

association between ERN and the Faux Pas task. Furthermore, the Bayes Factor indicated

‘substantial’ evidence in favour of the null hypothesis (adult sample; Jeffreys, 1961).

Thus, these findings are inconsistent with the current ERN/empathy literature. In the dis-

cussion that follows, we consider the possible factors that could affect the relationship

between ERN and Faux Pas understanding.

A possible reason for the lack of significant ERN/empathy effect is probably due

to the developmental differences in Faux Pas performance between groups within our

sample. Based on the available evidence in the literature, there is little clarity on the



developmental trajectory of Faux Pas understanding. Our child sample indicated very

high performance on the Faux Pas task, with scores ranging from 8 to 9. This contrasts

from Baron-Cohen et al’s. (1999) findings who found kids aged 9 to have scores ranging

from 4 to 7. As we had recruited a small sample of children without deficits, there is a

possibility that they are more likely to perform better than typical children.

However, it must also be noted that our child participants below the age of eight

(N = 5; M = 7.50) were excluded as they discontinued early on from completing the task.

This is consistent with other investigations whom have also reported difficulties with

children under the age of nine (Cashion, 2009; Pearson & Pillow, 2016). For example,

Cashion (2009) finds that children below the age of 8 to exhibit floor effects on the Faux

Pas task. Similarly, Pearson and Pillow (2016) notes that younger children are less likely

to consider the intent of the characters within the Faux Pas tasks and did not accurately

recognise the Faux Pas in the vignettes.

Furthermore, our study is limited as our final sample only involved children aged

8 to 9, we are unable to grasp the distribution of Faux Pas detection across a

developmental trajectory. Previous research has shown ERN amplitude to increase

linearly, from childhood to adulthood (Davies et al., 2004; Rubia, Smith, Taylor, &

Brammer, 2007). However, there are no known studies to date that have explored the

developmental trajectory of perspective-taking across the ages, specifically the

development of Faux Pas understanding. Perhaps young children below the age of 8 are

unable to exhibit Faux Pas understanding. Instead, there is a possibility that this skill is

‘switched-on’ past the age of 8. However, this is not observed within our adult sample

some adults also performed below a score of 8. To investigate the possibility that our

findings were unique to our samples, we next examined the developmental trajectory of

the Faux Pas task scores across age, based on a series of published empirical studies.



CHAPTER 2.2

METHODS

A search of published work was performed in PsychINFO and the Web of

Knowledge databases. Only English language journals were considered. Fifteen

experimental studies, which reported Faux Pas were identified as a measure of empathy.

Additionally, key authors who have published articles using the original Faux Pas task

from Baron-Cohen et al. (1999) or the adult adaptation of the Faux Pas task in Stone et al.

(2003) were contacted. Details regarding their age of samples tested, and mean and

standard deviation of Faux Pas scores from a 0 to 10 point scale were requested. If no

response was given, scores were adapted to the 0 to 10 points, based on their scoring

details within their methods section. The above search procedure identified 30 data sets.

A total of 1038 participants, with ages ranging from 7 to 75 (M = 21.66) were included.

Data Extraction and Processing

Each of the identified articles had the following information extracted: authors,

publication status, journal, year of publication, sample size, age, and the means and

standard deviation of the Faux Pas detection. The quantitative review was conducted to

generate a weighted mean of Faux Pas scores from a 0 to 10-point scale across studies.

Individual study details, sample size, participant age, and Faux Pas mean and standard

deviation are provided in Table 2-2.

Due to the limited information provided in papers, we collapsed the performance

of Faux Pas across males and females (in those studies that disaggregated the results). For

the studies included in the investigation, the mean ages were rounded to the nearest .5. In

instances where more than one study was associated with the same mean age, a weighted

mean of Faux Pas performance was calculated. A Pearson’s correlation with Bayesian



factor comparing age and weighted mean scores of Faux Pas detection was also

performed.

RESULTS

Age and mean Faux Pas tasks scores were found to be statistically significant

associated, r (30) = .48, p = .008. The Bayes Factor10 was estimated at 6.48, exhibiting

substantial support for the alternative hypothesis (Jeffreys, 1961). Thus, the alternative

hypothesis was 6.48 times more likely than the null hypothesis. As can be seen in Figure

2-5, the effect was primarily linear in nature, however, there was also the suggestion of a

non-linear effect. Consequently, a supplementary hierarchical multiple regression

analysis was performed with age entered at step 1 and age2 (squared) entered at step 2.

The addition of the age2 variable offered the opportunity to test for a curvilinear

association between age and Faux Pas performance (a quadratic effect, specifically). The

quadratic effect was not found to be statistically significant, b = -.001, t = -1.31, p = .201,

semi-partial r = -.22.

Figure 2-5. Scatterplot depicting the linear and quadratic association between Age and

Faux Pas performance.

DISCUSSION The quantitative review indicated a significantly positive relationship between

Faux Pas performance and age. Furthermore, it exhibited a steep development of Faux



Pas understanding from ages seven to nine. However, it appears as though the

development of Faux Pas understanding plateaued past the age of nine. It must be noted

that Faux Pas performance was shown to reduce aged 56 onwards. These results indicated

a downward trajectory, though not significant, in detecting Faux Pas past the age of 56.

In contrast to the developmental trajectory of Faux Pas understanding, the ERN

has been reported to develop differently across age (Davies et al., 2004; Rubia et al.,

2007). Though there are no direct comparisons between the developments of

ERN/empathy across age, studies have indicated the development of ERN amplitude to

differ between males and females (Davies et al., 2004; Rubia et al., 2007). For instance,

females exhibit a significant development of ERN amplitudes from ages 10 to 15,

whereas males exhibited greater development of the ERN from ages 16 to 20 (Davies et

al., 2004). Perhaps gender and age play a role in moderating the ERN/empathy

relationship in early ages. As the developmental trajectory between ERN and Faux Pas

are suggested to be dissimilar, it may not be worth comparing these two factors between

children and adults. However, more empirical research in ERN and Faux Pas is needed

prior to confirming these findings.

However, there is limited access to Faux Pas data across ages. It must be noted

that Faux Pas tasks are typically used in children (Baron-Cohen et al., 1999; Cashion,

2009) and clinical populations (Adenzato & Poletti, 2013; Ibanez et al., 2013; Ozel-Kizil

et al., 2012; Riveros et al., 2010). Additionally, others have also noted that the inclusion

criteria for these studies are strictly screened (Cox et al.; 2014; 2016). Consequently,

individuals may perform better on the Faux Pas task, and do better in detecting Faux Pas

overall in comparison to people in the general population (Cox, personal communication,

16 December, 2016).



Also, we noticed a lack of consistency in scoring Faux Pas scores across samples.

For example, child Faux Pas scores were usually scored from a 0 to 10 point scale (i.e.

Barlow, Qualter, & Stylianou, 2010; Baron-Cohen et al., 1999). However, others may

count each question as a point, resulting in maximum scores of 40 to 50 (i.e. Cox et al.,

2014, 2016; Goldstein & Winner, 2010). For papers that did not clarify their scoring

methods, we were unable to include them within our review. Consequently, we were

unable to collate all mean of Faux Pas performance which, in turn, could affect the

relationship between Faux Pas and age. Future studies exploring the relationship between

age and Faux Pas investigation are warranted.



Faux Pas eans Age Age SD Age Range N

Barlow et al. (2010) Boys 5.95 9.25 - 8-10 65

Barlow et al. (2010) Girls 6.68 9.16 - 8-10 44

Baron-Cohen et al. (1999) boys 2.9 7 0 - 10



Baron-Cohen et al. (1999) girls 5.8 7 0 - 10



Bird et al. (2004) 9.5 62 0 - 12

Cashion (2009) 5.55 6.71 0.42 - 70

Cashion (2009) 7.11 8.88 0.44 - 71

Cashion (2009) 8.29 11.23 0.45 - 75

Cox et al. (2014) 9.2 73 - - 90

Cox et al. (2016) Bilingual 8.33 74.54 0.31 - 26

Cox et al. (2016) Monolingual 7.66 74.45 0.32 - 64

Current study 7.5 8 - 8 2

Current study 8.73 9 - 9 15

Faísca et al. (2016) 6.74 33 12 18-60 200

Goldstein & Winner (2010) Actor 8.67 9.42 0.83 - 11

Goldstein & Winner (2010) Dancer 8.48 9.33 0.92 - 14

Goldstein & Winner (2010) Summer

Camp*

7.63 9.08 0.79 - 11

Goldstein & Winner (2011) Actor 7.66 9 2 - 35

Goldstein & Winner (2011) Nonactor 8.43 8 0 - 40

MacPherson (2002) Middle-Aged 9.7 50.3 5.7 40-59 30

MacPherson (2002) Old 7.9 69.9 5.5 61-80 30

MacPherson (2002) Young 8.6 28.8 6 20-38 30

Stone et al. (1998) 10 56.54 13.79 34-80 5

Stone et al. (2003) 8.88 56.9 4.46 52-67 34

Zalla et al. (2008) 6.62 27.8 4.5 - 15

Zalla et al. (2011) 7.6 26.6 6.5 20-47 33

Table 2-2. Study Details of the Quantitative Review including Faux Pas Mean, Participant Age, and

Sample Size



GENERAL DISCUSSION

The aim of these studies was to examine the relationship between error

monitoring, specifically the ERN, and behavioural empathy. In the first study, we were

unable to find a relationship between ERN and empathy, as assessed by the Faux Pas

task, in both children and adults. Surprisingly, we also found children to have greater

Faux Pas performance than adults in our sample. However, we are unable to conclude

these findings due to small sample sizes within first study. Consequently, we did a

follow-up quantitative review on Faux Pas performance across age groups. We found a

gradual development of Faux Pas detection across the ages. These do not resemble the

developmental trajectory of ERN development as reported in past literature (Davies et al.,

2004; Rubia et al., 2007)

Thus, Faux Pas understanding may not be comparable with the development of

error monitoring performance. However, it is perhaps premature to confirm the

developmental trajectory of Faux Pas performance due to limited data sets available.

Consequently, it is worth discussing other possible moderating factors as to why the

relationship between ERN/empathy was not found in our study. We propose two

additional reasons as to why we were unable to find an effect between ERN and empathy.

Firstly, we attribute these differences in findings due to differences between self-report

and behavioural measures of empathy. Secondly, we propose the lack of effect due to the

lack of clarity between ERN/empathy within the multidimensional framework of

empathy.

FUNCTIONAL SIGNIFICANCE OF ERN AND EMPATHY

There is a possibility that the ERN/empathy relationship represents how an

individual evaluates themselves and not how they interact with each other. Larson and

colleagues (2010) propose that the ERN/empathy relationship to signify an individual’s



underlying concern for positive or successful outcomes. As empathy can be seen as a

desirable trait, demonstrating empathy for another in distress can lead to positive

consequences (Larson et al., 2010). However, an individual’s representation of how they

may perceive themselves can differ from the actions they portray (Stone et al. 1998).

Individuals who may believe that they are highly empathic may not partake in pro-social

behaviour (Hein & Singer, 2008).

Secondly, there is a lack of clarity on which type of empathy is associated with

the ERN. Contemporary literature indicates that empathy is a multidimensional process,

which involves cognitive and affective facets (Jolliffe & Farrington, 2004; Reniers et al.,

2011; Smith, 2006). Cognitive empathy has been defined as the ability to take the

perspective of another’s mental states, whereas affective empathy is the ability

understand the emotions and share the experience with them (Bellebaum et al., 2014;

Larson et al., 2010; Reniers et al., 2011; Thoma et al.., 2015).

More recent literature may have denoted the Faux Pas task as a cognitive empathy

task (Adenzato & Poletti, 2013; Ozel-Kizil et al., 2012; Riveros et al., 2010). However,

the existing neurophysiology of multidimensional empathy remains debated (Larson et

al., 2010; Singer et al., 2004). For instance, studies like Larson et al.’s (2010) found

evidence for the ERN/empathy relationship using self-reported cognitive and affective

empathy measures. In contrast, Singer and colleagues (2004) found an association

between activations of the anterior cingulate cortex, self-reported affective empathy, and

a behavioural measure of empathy. Subsequently, Singer et al. (2004) suggested that

activations in the anterior cingulate cortex relate only to self-reported affective empathy.

However, neither studies used the Faux Pas task nor did they examine the ERN/empathy

relationship in children. Thus, more research is encouraged to elucidate the associations



between ERN and cognitive and affective empathy, using both behavioural and self-

reported measures.

LIMITATIONS

Our studies mark the starting point of further investigations between error

monitoring indices and empathy. For instance, we did not include a measure of self-

reported empathy as a baseline comparison, as there is currently no existing gold-standard

measure of self-reported empathy for children. Consequently, we are unable to validate

whether the Faux Pas task measures empathy within our study. Nonetheless, prior

literature has rarely reported the reliability and validity of the behavioural empathy scores

(Cashion, 2009). Though the Faux Pas task was reported to be validated with the EQ

(Baron-Cohen et al., 1999), replication studies suggest otherwise. Newer investigations

have shown behavioural empathy tests to have small concurrent validity with self-

reported empathy measures in adults (r =.29; Spek, Scholte, & Van Berckelaer-Onnes,

2010). Therefore, there is a possibility that the relationship between self-reported

measures and behavioural empathy measures may not measure the same processes as

initially suggested.

Secondly, both studies focused on using a typical sample across the age group.

The Faux Pas task was initially developed to assess autistic traits in children (Baron-

Cohen et al., 1999) or perspective-taking deficits in individuals with brain damage (Stone

et al., 1998; 2003). Consequently, we may have been unable to assess the full breadth of

individual differences in Faux Pas understanding. Perhaps using a clinical sample may

result in greater individual variability in Faux Pas performance and demonstrate

differences in the ERN/empathy relationship.



CONCLUSION

Previous studies have reported a statistically significant association between ERN

and empathy. By contrast, this investigation’s studies failed to observe a statistically

significant effect in both children and adult samples. This could be due to the nature of

the Faux Pas task, as prior ERN/empathy research has not used validated measures of

behavioural empathy. Furthermore, emerging literature is progressing towards empathy as

a two-dimensional construct, such as cognitive and affective empathy. Therefore, future

investigations in the ERN/empathy literature should include behavioural measures within

the context of cognitive and affective empathy.



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68

3. Error Monitoring and Empathy:

Explorations Within a

Neurophysiological Context

A version of this chapter was accepted for publication in Psychophysiology on 25

January, 2017.


69

ABSTRACT

Past literature has proposed that empathy consists of two components: cognitive

and affective empathy. Error monitoring indices indexed by the error-related negativity

(ERN) have been associated with empathy. Studies have found that a larger ERN is

associated with higher levels of empathy. We aimed to extend upon previous work by

investigating how error monitoring relates to the independent theoretical domains of

cognitive and affective empathy. Chapter 3-1 (N = 24) explored the relationship between

error monitoring indices and subcomponents of empathy using the Questionnaire of

Cognitive and Affective Empathy and found no relationship. Chapter 3.2 (N = 38)

explored the relationship between the error monitoring indices and overall empathy.

Contrary to past findings, there was no evidence to support a relationship between error

monitoring indices and scores on empathy measures. A subsequent meta-analysis

(Chapter 3.3) summarising the relationship across previously published studies together

with the two studies reported in the current paper indicated that overall there was no

significant association between ERN and empathy and that there was significant

heterogeneity across studies. Future investigations exploring the potential variables that

may moderate these relationships are discussed.

Keywords: ERN, Error-Related Negativity, Empathy, Cognitive Empathy, Affective

Empathy, Error Monitoring, Conflict Detection, Error Processing, Performance

Monitoring, Anterior Cingulate, error


70

INTRODUCTION

Empathy involves multiple processes, including noticing another person's

feelings, making an inference of the mental state of another, and responding appropriately

to that person's state of mind. Past literature suggests that empathy may be associated

with activation of the anterior cingulate cortex (ACC), an area of the brain that is

involved in vicarious experience and conflict detection (Bernhardt & Singer, 2012; Carter

& van Veen, 2007; Lockwood, Apps, Roiser, & Viding, 2015). Researchers suggest that

the ACC plays a key role in the overlap between cognitive and affective processes

(Egner, Etkin, Gale, & Hirsch, 2008; Ochsner, Hughes, Robertson, Cooper, & Gabrieli,

2009). For instance, individual differences in empathic abilities are observed to be

associated with variation in the amplitude of the ACC-related electrophysiological

responses, like the error-related negativity (ERN; Larson, Fair, Good & Baldwin, 2010,

Santesso & Segalowitz, 2009) and the feedback-related negativity (FRN; Bellebaum,

Brodmann, & Thoma, 2014; Thoma, Norra, Juckel, Suchan & Bellebaum, 2015).

The ERN and FRN are frontal-centrally distributed negativities that peak

following an incorrect response (Carter et al., 1998) or when given negative performance

feedback (Gehring & Willoughby, 2002) during cognitive choice response tasks. When

individuals are aware of an error, the respondent may slow down to adjust their

performance (Navarro-Cebrian, Knight, & Kayser, 2016; Rabbitt, 1966). The existing

literature suggests that performance monitoring indices, such as the ERN and FRN,

signify one’s sensitivity to negative outcomes (Holroyd & Coles, 2002). Therefore,

individuals with larger ERNs and FRNs are more likely to detect errors, which in turn

results in adjustment of their behaviours for future trials to prevent repeating the error

(Bress & Hajcak, 2013; Miltner, Braun, & Coles, 1997; Potts, George & Barratt, 2006).


71

The ERN and FRN have been compared with self-reported empathy measures

(Bellebaum et al., 2014; Larson et al., 2010; Santesso & Segalowitz, 2009; Thoma et al.,

2015) and have demonstrated inconsistent findings. Initial findings demonstrated that

larger ERN amplitudes were associated with greater empathy scores on the Empathy

Quotient (EQ; Baron-Cohen & Wheelwright, 2004) in male adolescents (Santesso &

Segalowitz, 2009). These findings were also replicated in both male and female samples

(Larson et al., 2010).

Though the reasons behind performance monitoring/empathy relationship remain

relatively unknown, researchers suggested that indices within the ACC, reflected in the

ERN and/or FRN, represent a reinforcing learning ‘signal’ within a biochemical neural

reward system to deter individuals from negative outcome (Bellebaum et al., 2014;

Holroyd & Coles, 2002; Larson et al., 2010; Santesso & Segalowitz, 2009; Thoma et al.,

2015). Subsequently, they proposed that a highly empathic individual might be more

likely to receive a ‘signal’, when they have detected a negative situation within social

contexts. Such an event would lead the empathic individual to adjust their behaviours to

gain a desirable outcome. Conversely, Larson et al. (2010) proposed that the performance

monitoring/empathy relationship represented the respondents’ vigilance for the

environment and actions of others. Studies have shown that individuals with larger ERNs

are more likely to be more cautious (Hewig et al., 2007) and more willing to abide by the

rules (Dikman & Allen, 2000). Correspondingly, these individuals may have more regard

on how to behave acceptably with others.

More recent investigations have looked to examine the relationship between

performance monitoring and more specific processes of empathy. Current explorations in

the empathy field have theorised empathy to consist of two-dimensions: cognitive and

affective empathy (Baron-Cohen, 2011; Jolliffe & Farrington, 2004; Reniers, Corcoron,


72

Drake, Shryane & Völlm, 2011; Smith, 2006). Cognitive empathy is defined as the ability

to take the mental perspective of another, whereas affective empathy is defined as the

ability to take the emotional perspective of another and share the experience (Reniers et

al., 2010). Cognitive and affective empathy are typically assessed via composite scores of

subscales from the Interpersonal Reactivity Index (Davis, 1980; 1983). Cognitive

empathy is measured via a composite score of perspective taking and fantasy. Affective

empathy, on the other hand, is measured by summing empathic concern and personal

distress subscale scores.

Although contemporary research has shifted to using the two-dimensional

empathy framework, there have been conflicting findings within the performance

monitoring and empathy literature. Empirical research has found FRN to inversely

correlate with cognitive empathy within a major depressive disorder sample (Thoma et

al., 2014). Contrastingly, another study found no significant correlations between FRN

and either measure of cognitive and affective empathy in both typical and autism

spectrum disorder samples (Bellebaum et al., 2014).

Within a typical sample, Larson et al. (2010) found the ERN to correlate with only

two specific subscales from the Interpersonal Reactivity Index (IRI; Davis, 1980; 1983).

These subscales were fantasy, the ability to take the perspective of a fictional character,

and emotional reactivity, the ability to understand and share the emotion of another

(Davis, 1983). It could be argued that the performance monitoring/empathy relationship is

perhaps modulated by specific empathy characteristics and not all components of

empathy.

Though Larson et al.’s (2010) investigation show a valuable contribution to

literature, there were some limitations with their study. For example, some researchers

have omitted fantasy and personal distress subscales, as they were suggested to not be


73

valid measures of empathy (Jolliffe & Farrington, 2004; Reniers et al., 2011).

Furthermore, others have attributed the inconsistencies in findings due to variations in use

and interpretation of the IRI subscales (Chrysikou & Thompson, 2016).

Subsequently, in response to empirical inconsistencies in literature (Chrysikou &

Thompson, 2015; Jollife & Farrington, 2006), Reniers and colleagues (2011) developed

the Questionnaire of Cognitive and Affective Empathy (QCAE). Using exploratory and

confirmatory factor analysis of various self-report measures of empathy, including the

Empathy Quotient, IRI, and other measures, they modelled distinct cognitive and

affective empathy subscales to fit the recent two-dimensional framework. We

consequently aimed to expand this area of literature by looking specifically at the ERN

with a validated multidimensional empathy measure. Our first study examined the

relationship between the performance-monitoring indices and empathy using this well-

validated measure of the two empathy dimensions. Based on the results obtained, we

subsequently examined the error monitoring/empathy relationship with a global measure

of empathy in a second study. We further followed up by collating the observed

relationships in a meta-analysis of the available literature looking at conflict monitoring

electrophysiology and its associations with self-reported empathy.

CHAPTER 3.1

METHODS

Participants

Twenty-eight undergraduate students between the ages 19 to 36 (22 females)

participated in the study and provided written informed consent prior to the

commencement of the study. All participants had normal or corrected-to-normal vision.

Materials


74

Modified Visual Flanker-Go/No-Go Task. The task used to elicit the ERN was a

visual flanker-go/no-go task adapted from Mansfield, van der Molen, Falkenstein, and

van Boxtel, (2013). Participants were presented with an array of three arrows measuring

4.6 cm (2.0º) on a black background, arranged vertically, and each arrow measured 1.4

cm (0.62º) in width and height. Each arrow pointed either left or right, and participants

were asked to indicate the direction of the target (central arrow), whilst ignoring the

flanking arrows. Participants responded by pressing one of the two keys on the keyboard,

‘Z’ and ‘/’ keys, representing left and right. There were four conditions within the task:

congruent, incongruent, reversed, and no-go. In the congruent condition, all the arrows

were green and pointed in the same direction (0.40 probability). In the incongruent

condition, all the arrows were green; however, the flanking arrows were pointing in the

opposite direction of the target (central arrow; 0.20 probability). In the reversed

condition, all arrows were red and pointed in the same direction, and participants were

asked to press the button opposite to the direction of the target (0.20 probability). In the

no-go condition, during which participants were asked to withhold responding, all the

arrows were blue and pointed in the same direction (0.20 probability). A practice block of

eight trials was initially administered to ensure that participants understood the task,

followed by six experimental blocks, which consisted of 50 trials in each block. Each

block consisted of 20 congruent, 10 incongruent, 10 NoGo and 10 reversed trials. The

order of trials was randomised within each block. On each trial, a fixation cross was

presented for 500 ms in the centre of the screen, followed by the stimulus which was

presented for 200 ms. Participants were given 1000 ms to respond but were encouraged to

respond as quickly and as accurately as possible. Visual feedback (‘oops’ for incorrect,

‘good’ for correct’ and ‘Too slow’ for incorrectly inhibited items) was presented for 900

ms, 700 ms following the response. Speed and accuracy were equally emphasised.


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Questionnaire of Cognitive and Affective Empathy (QCAE) The QCAE (Reniers et al.,

2011) measures an individual’s cognitive and affective empathy. It was based on factor

analytic explorations on past empathy measures, which included 15 items from the EQ

(Baron-Cohen & Wheelwright, 2004), two items from the Hogan Empathy Scale (Hogan,

1969), eight items from the empathy subscale of the Impulsiveness, Venturesomeness,

and Empathy Scale (Eysenck & Eysenck, 1978) and seven items from the IRI (Davis,

1983). Reniers et al. reported high convergent validity in comparison to the Basic

Empathy Scale (Joliffe & Farrington, 2006), another cognitive and affective empathy

measure. The QCAE consists of 19 cognitive empathy items and 12 affective empathy

items. Items are rated using a four-point Likert scale with the following response options:

4 (strongly agree), 3 (slightly agree), 2 (slightly disagree), and 1 (strongly disagree). The

internal consistency reliability of the QCAE for our study was acceptable, α = .81.

Procedures

Participants were fitted with an EasyCapTM

and completed the modified visual

flanker-go/no-go task followed by the QCAE.

Electroencephalograms (EEG) Recording

The EEG was continuously recorded using Ag/AgCl electrodes at 33 scalp

locations (FP1, FP2, F3, F4, F7, F8, Fz, FC1, FC2, FC5, FC6, FCz, FT9, FT10, C3, C4,

Cz, T7, T8, CP1, CP2, CP5, CP6, P3, P4, P7, P8, Pz, PO9, PO10, O1, O2, Iz). A ground

electrode was positioned at the frontal midline point, AFz, and the right mastoid was used

as an online reference. The EEG was amplified using NuAmps 40-channel amplifier,

digitised at a sampling rate of 250 Hz, and filtered online using a 0.05-30 Hz band-pass

filter and 50 Hz notch filter. Electrodes were positioned 2 cm above and below the left

eye to measure eye movement. Ocular movements were corrected offline using the ocular


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artefact reduction algorithm in Scan 4.5 after re-referencing offline to an averaged

mastoid reference.

Event-Related Potentials Analysis

The incorrect minus correct waveform was chosen as a measure of the ERN to

account for possible between-subjects variability (Larson et al., 2010). Based on visual

inspection of the grand averaged event-related potentials (ERP) and scalp maps shown in

Figure 3-1, it can be seen that the topography of the peak was fronto-centrally maximal.

Response-locked epochs from -600 to 1000 ms post-response were extracted

offline. The epochs were then baseline-corrected relative to the -600 to -400 ms interval

to avoid contamination of the stimulus-locked ERP components (Santesso & Segalowitz,

2009). Luck (2014) recommended calculating the grand amplitude over an interval of at

least 50 ms, as it is less sensitive to high-frequency noise and mitigates effects of

overlapping event-related components. Therefore, the ERN amplitude was quantified

over the 24 to 80 ms1

interval, centred on the peak latency of the peak obtained from the

grand averaged waveform.

Figure 3-1. Average ERP waveforms from 600 ms before to 1000 ms after a response

during the flanker task, and the topographic map showing the amplitude distribution of

ERN following incorrect responses at the ERN peak latency of 56 ms post-response. The

topographic map was created using ERP lab (Lopez-Calderon & Luck, 2014).


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RESULTS

Scores from one participant were excluded, as he/she did not complete all items

on the questionnaire. Data from three participants were excluded as they obtained fewer

than six errors during the task (Olvet & Hajcak, 2008). This resulted in a final sample of

24 participants (17 females, mean age M = 22.89, SD = 4.37). The descriptive statistics

associated with ERN and behavioural performances on the modified flanker task are

presented in Table 3-1.

Table 3-1. Descriptive Statistics for Empathy Scores, ERN Amplitude (µV), Participant

Accuracy, Mean Reaction Times (milliseconds) on Congruent, Incongruent, and Reversed

Trials for Study 1.

Note. N = 24

Behavioural Data Analyses

Firstly, we analysed participant accuracy and response times to see if participants

exhibited a flanker effect from our modified visual flanker. We did not analyse the no-go

trials as participants did not commit enough errors on those trials. To test whether

participants were more accurate in the congruent trials than incongruent and reversed


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trials, a one-way within-subjects ANOVA was performed with stimulus type (congruent,

incongruent, and reversed) as the independent variable and accuracy as the dependent

variable. Paired t-tests were also performed for follow-up post-hoc tests.

Based on the first within-subjects ANOVA, the omnibus null hypothesis of no

difference in accuracy across the three conditions was rejected, F (2, 46) = 18.85, p <

.001, ε = .642. Follow-up analyses suggested that the participants were more accurate in

the congruent condition than the incongruent, t (23) = 5.31, p < .001, and the reversed

condition, t (23) = 5.78, p < .001. The difference between incongruent and reversed failed

to reach statistical significance after Bonferroni corrections, t (23) = 2.58, p = .017.

Subsequently, to test whether participants had slower reaction times for correct

trials on incongruent and reversed trials, we performed a one-way within-subject

ANOVAs on response times to correct trials. Paired t-tests were also performed for

follow-up post-hoc tests.

For reaction times, we found significant differences in response times across

conditions, F (2, 46) = 148.12, p < .001. Follow-up analyses indicated that participants

responded slower on incongruent trials, t (23) = 10.01, p < .001 and reversed trials, t (23)

= 9.22, p < .001 in comparison to the congruent trials. Participants were also slower to

respond to reversed trials than incongruent trials, t (23) = 9.22, p < .001.

Event-Related Potentials Analyses

The aim of the study was to compare the relationship between error monitoring

indices and empathy measures. To test the hypothesis that the error monitoring indices

were associated with empathy scores, we used Pearson’s correlations comparing ERN,

the sub-scales of the QCAE empathy, and a composite QCAE score. We further used

Bayesian statistics to indicate the probability of the null hypothesis across conditions.


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The hypothesis that there would be a negative association between ERN and

empathy was not supported (see Table 3-2). The Bayes Factors show that the null

hypothesis predicted the data better than the alternative hypothesis (Jarosz & Wiley,

2014).

Table 3-2. Pearson’s Correlations and Bayes Factors between ERN and QCAE measures.

Note . N = 24; r = correlation coefficient; p = significance level; BF₀₋ = Bayes Factor

statistic for null hypothesis, BF10 = Bayes Factor statistic for alternate hypothesis.

DISCUSSION

We further expanded error monitoring/empathy research by looking at a

questionnaire that demarcated empathy into two separate dimensions: cognitive and

affective empathy. Prior studies found error monitoring indices correlating with global

empathy measures and IRI subscales that represent cognitive and affective empathy. In

contrast, our findings indicated no significant associations between any error monitoring

indices and measures of QCAE. The differences across error monitoring/empathy

findings could be due to a lack of uniform measures used to quantify cognitive and

affective empathy across error monitoring/empathy literature.

Prior research had designated certain subscales from the IRI as a measure of

cognitive and affective empathy. However, not all these subscales have shown consistent

relationships with performance monitoring indices. Larson et al. had only found a

correlation between the fantasy subscale from the IRI and the ERN. In regards to

r p BF₀₋

ERN – Cognitive Empathy .25 .237 1.64

ERN – Affective Empathy .23 .283 2.04

ERN – Total QCAE .29 .173 2.28


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cognitive empathy, their study found no relationship between other error monitoring

indices and the perspective taking subscale from the IRI, which other studies have utilised

as a measure of cognitive empathy (Thoma et al., 2015; Bellebaum et al., 2014).

However, other explorations have found conflicting associations between other

neurophysiological indices associated with the ACC and cognitive empathy. For instance,

consistent with Larson et al.'s findings, clinical groups of ASD individuals who had

impaired cognitive empathy were associated with attenuated FRNs (Bellebaum et al.,

2014). However, they did not find a relationship between FRN and self-reported empathy

scores. Bellebaum and colleagues (2014) suggested that the lack of correlations between

FRN and empathy could be due to the task eliciting the FRN having little emotional

significance to the participants. Likewise, our study found no significant relationships

between error monitoring indices and empathy subscales. Perhaps our sample reflected a

similar response and held little emotional significance in response to our flanker task,

consequently exhibiting only cognitive and not emotional processes.

In addition, there is a lack of clarity in how affective empathy relates to the ACC.

Singer and colleagues (2004), using functional magnetic resonance imaging, found

associations between ACC activation and self-reported affective empathy, as measured by

the empathic concern subscale from the IRI and the Balanced Emotional Empathy Scale

(Mehrabian & Epstein, 1972). In contrast, Larson et al. (2010) found a significant

correlation between the ERN and only one affective empathy subscale from the IRI,

empathic concern. In contrast, studies involving neurophysiological measures of ACC

activation (Bellebaum et al., 2014; Thoma et al., 2014) found no significant associations

with affective empathy measures. Research has also shown that using combinations or

separate subscales of the IRI may not reliably represent cognitive and affective empathy


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(Chrysikou & Thompson, 2015). The results of this investigation suggest that future

research should consider the use of questionnaires in relation to the measurement of

cognitive and affective framework of multidimensional empathy.

However, our study had some limitations. Firstly, we had used a different measure

of multidimensional empathy instead of the IRI. There is a possibility that the previously

observed association between ERN and empathy was influenced by the items that were

omitted in the version of the questionnaire we used. However, there is also a possibility

that current self-reported cognitive and affective measures are not sensitive enough to

capture the separate dimensions of empathy and its associated neurophysiology.

Secondly, our modified visual flanker was unique in that it presented stimuli in a

vertical array. Though the ERN has been noted to be reliable and valid across tasks

(Riesel, Weinberg, Endrass, Meyer, & Hajcak, 2013), prior error monitoring and empathy

investigations used flanker (see Santesso & Segalowitz, 2006) and Stroop tasks (see

Larson et al., 2010) with horizontal stimuli. On the other hand, Mansfield et al. (2013)

proposed the use of vertical arrays in flanker tasks, as it encouraged greater behavioural

interference. Arguably, the difficulty of the vertical array would promote task

engagement (Gendolla, 1999). However, others have noted that the presentation of visual

stimuli in a vertical array can activate other neural areas, in addition to areas associated

with performance monitoring (Whitney & Levi, 2011). Consequently, there is a

possibility that the role of the ACC may be less salient when participants are presented

vertical stimuli.

To account for these limitations, our follow-up experiment sought to confirm the

relationship between error monitoring indices/empathy with a horizontal flanker task.

This visual flanker task included three conditions (i.e. congruent, incongruent, and

reversed) with horizontal stimuli to maintain task difficulty (Mansfield et al., 2013) and


82

engagement (Gendolla, 1999). Furthermore, the flanker task has been used in multiple

studies (see Almabruk, Iyer, Tan, Roberts, & Anderson, 2015; Brydges et al. 2012;

Brydges, Anderson, Reid, & Fox, 2013; Rueda, Posner, Rothbart, & Davis-Stober, 2004).

Finally, we used a global measure of empathy to exclude the possibility of inconsistencies

in findings as a result of using different types of multidimensional measures.

CHAPTER 3.2

METHODS

Participants

Thirty-eight undergraduate students aged 18 to 37 years (M = 24.37; SD = 7.28;

17 females) participated in the study and provided written informed consent prior to the

commencement of the study. All participants had normal or corrected-to-normal vision.

Materials

Tasks

Modified Visual Flanker Task. A modified visual flanker was presented as a game

in which the participants had to feed the hungry central fish. Participants were presented

with five fish in a row on a blue background. Each fish had an arrow on its body which

indicated the direction the fish was facing (i.e. left or right). Participants were asked to

indicate the direction of the target (central) fish by pressing one of two keys on the

keyboard (red felt patches on the ‘Z' and ‘/' keys). There were three conditions: the

congruent condition (0.5 probability) where all the fish were green and facing the same

direction, the incongruent condition (0.25 probability), where all the fish were green, and

the target fish faced the opposite direction to the flankers; and a reversed condition (0.25

probability), where all the fish were red, and participants were asked to press the key

opposite to the direction of the target fish. A fixation cross was presented for 500 ms in

the centre of the screen before the presentation of each stimulus. The stimuli were


83

presented in a random order for 300 ms, and visual feedback was presented 300 ms

following the participant's response. Both speed and accuracy were equally emphasised.

A practice block of eight trials was administered to ensure that participants understood

the task requirements prior to an experimental block of 176 trials.

Short form of the Empathy Quotient The short form of the Empathy Quotient (EQ-

Short, Wakabayashi, et al., 2006) consisted of 22 items that subjectively measured an

individual’s empathic understanding. Participants indicated their responses to the items

on a four-point Likert scale. The 22-item version is correlated with the 60-item EQ (r =

0.93, Baron-Cohen & Wheelwright, 2004). The test was scored out of 44, and the internal

consistency of the EQ-Short form was acceptable in the current study, α = .85.

PROCEDURES AND DATA ANALYSIS

The procedures and data analysis recording were performed as per Chapter 3-1.

Based on visual inspection of the grand-averaged ERPs and scalp maps, it was noted that

the FCz showed the largest ERN amplitude (see Figure 3-2).

Figure 3-2. Grand-averaged waveforms from 600 ms before to 1000 ms after an error re-

sponse during the flanker task, and the topographic map showing the distribution of the

ERN following incorrect responses. Peak ERN amplitude was shown at 64 ms with a 24

to 80 ms3-1

interval. Topographic map was created by ERP lab (Lopez-Calderon & Luck,

2014).


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STATISTICAL ANALYSIS

Statistical analyses for behavioural performance on the flanker task were

completed as per Chapter 3-1. Behavioural performance in Chapter 3-2 indicated similar

patterns to study 1 with a significant difference in conditions for participant accuracy, F

(1, 37) = 40.43, p < .001. Follow-up analyses suggested that participants were more

accurate on congruent trials than incongruent trials, t (37) =3.41, p=.002, and reversed

trials, t (37) =6.36, p<.001. Participants were also more accurate on the incongruent than

reversed trials, t (37) = 4.65, p < .001.

Participants demonstrated significant differences for correct responses in reaction

times across conditions, F(2, 74) = 144.75, p <.001, ε = .820. Follow-up analyses

indicated that participants were slower to respond correctly following presentation on the

incongruent, t (37) = 14.29, p < .001, and reversed stimuli, t (37) = 10.15, p < .001, than

the congruent stimuli. Participants were also slower for correct responses on the reversed

than the incongruent trials, t (37) =9.20, p <.001

RESULTS

As can be seen in Table 3-3, the results showed no evidence for support of a

statistically significant correlation between error monitoring indices and EQ scores: ERN,

r (36) = .16, p = .328, BF₀₋ = 3.12. From a Bayesian perspective (JASP team, 2016; jasp-

stats.org), the null hypothesis (beta prior width = 1) predicted the data 3.12 times better

than the alternative hypothesis (i.e. negative correlation) for the ERN/EQ association.

DISCUSSION

Our second study found no significant correlation between error monitoring

indices and a global empathy measure, consistent with the results from Chapter 3-1. We

discuss potential factors that could affect the relationship between error monitoring and

empathy.


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Table 3-3. Descriptive Statistics of Empathy Scores, Behavioural Performance, and ERN

Amplitude (µV) for Chapter3.2.

Differences in affect and personality traits can affect an individual's ERN and

empathy levels. Anxious (Luu, Collins, & Tucker, 2000) and neurotic individuals

(Segalowitz & Dywan, 2009) are more likely to have larger ERN amplitudes. These

characteristics have also been associated with higher levels of empathy (Eysenck &

Eysenck, 1978). However, these attributes were not measured in our studies or past error

monitoring/empathy research. Other limitations include looking solely at a typical sample

of University students. Therefore, our samples may not consist of individuals who

manifest extreme levels of empathy. Consequently, exploring existing individual

variability through a meta-analysis in past error monitoring/empathy studies could

moderate the relationship between conflict detection indices and self-reported empathy.

Demographics

Mean (SD) Min Max

Empathy Quotient Short Form 25.42 (7.33) 11 40

Error Related Negativity µV -8.52 (6.45) -22.85 4.49

Accuracy (Percentage correct)

Mean (SD) Min Max

Congruent 94.32 (5.32) 78.41 100

Incongruent 89.77 (9.57) 59.09 100

Opposite 83.37 (13.25) 45.45 100

Reaction Time (ms)

Mean (SD) Min Max

Correct Responses Congruent 442.65 (77.90) 326.30 659.70

Correct Responses Incongruent 487.60 (85.13) 339.25 733

Correct Responses Reversed 539.44 (83.60) 336.30 767.20


86

Meta-analysis is a methodology that summarises effect sizes from the relevant

research and involves an integration of past findings (Cheung & Viyajakumar, 2016).

This method allows us to look at the homogeneity of different mean effect sizes to

determine whether the studies show more variation than would be expected from

sampling error alone (Jolliffe & Farrington, 2004; Lipsey & Wilson, 2001). We

undertook a meta-analysis summarising all available existing studies that have explored

the relationship between error monitoring and self-reported empathy, including those

reported in this manuscript.

CHAPTER 3.3

METHODS

Search and Inclusion

We performed an extensive systematic search to locate the primary articles

published in peer-reviewed journals and non-published articles between January 1980 and

April 2016. Search terms contained adjectives or derivatives of empathy, error-related

negativity, ERN, conflict detection, and error monitoring. These terms were combined

with a series of Boolean and/or operators. These combinations were used to search in

PsycINFO and the Web of Knowledge databases. Only English language journals were

considered. An initial pool of 12 non-overlapping studies were identified as meeting the

criteria for error monitoring and empathy. References of the included articles were hand-

searched, producing no additional relevant articles.

We looked at studies which reported an association between the ERN and at least

one measure of empathy. Abstracts retrieved from the databases, and subsequent papers

were screened for relevance. Ten studies were excluded as the articles were not in English

(n = 1), did not report new data in a format suitable for inclusion (e.g. case report or


87

review) (n = 3), or did not report results on the error-related negativity (n = 6). This

process generated two data sets for inclusion in the meta-analysis. We also included the

two data sets from the studies reported in the current manuscript, resulting in four data

sets.

Data Extraction and Analysis

Data extracted and coded included authors, publishing status, journal, year of

publication, sample size, age, and the reported correlation between the ERN and total

empathy scores. Individual study details, sample size, participant demographics, global

empathy measures used, participant mean and standard deviation scores on the empathy

measures, and mean and standard deviation of participant ERN voltages were provided in

Table 3-4.

Table 3-4. Study and Participant Characteristics for the Meta-Analyses

*Note: A pooled calculation was used for this statistic as Santesso and Segalowitz (2009)

provided information for two groups based on a median split within their study.

The two main measures included in the meta-analysis were ERNs and self-

reported empathy. To keep the correlations consistent, we looked at the ERN as a

negative voltage across all studies. Consequently, a negative correlation indicates that a

larger ERN is associated with higher self-reported empathy scores. The correlation for

Santesso and Segalowitz’s (2009) study was reversed scored for consistency. We were


88

interested in looking at the relationship in overall empathy; consequently, we only

extracted overall empathy scores instead of subscale scores for different empathy

measures. We looked at total empathy scores in Santesso and Segalowitz (2009), Larson

et al. (2010), and study 3-1 and 3-2 in Amiruddin et al. (current studies).

Data Processing

We calculated the effect sizes and created a forest plot using Comprehensive

Meta-Analysis version 2.2.064 (Borenstein, Hedges, Higgins & Rothstein, 2011).

Statistical significance was denoted by p-values less than .05. The meta-analyses were

conducted to generate average weighted correlations between the ERN and self-reported

empathy scores.

The meta-analyses were conducted with a random effects model; this allowed for

the analyses to assume a ‘true’ effect across different sample demographics in each study

(Rosenthal, 1995). Although heterogeneity in effect sizes is commonly tested in meta-

analyses with statistics such as Cochrane’s Q and I2 (Borenstein, 2010), such analyses are

known to be associated with questionable validity when the number of effect sizes

evaluated is less than 8 (von Hippel, 2015).

RESULTS

A total of four independent correlations between ERN amplitude and total self-

reported empathy were included in the meta-analysis. As can be seen in Figure 3-3 (left-

side), the meta-analytic correlation (N = 132) was estimated at r = -.10 (p = .246, 95% CI:

-.277/ .077). Thus, there was an absence of evidence to suggest an association between

ERN amplitude and self-reported empathy. For thoroughness, a forest plot was also

reported in Figure 3-3 (right side).


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Figure 3-3. ERN and empathy correlations (r), z-values, p-values, and mean effect sizes.

DISCUSSION

The results of the meta-analysis failed to suggest a statistically significant

association between ERN amplitude and self-reported empathy. Furthermore, an

examination of the forest plot suggested the possibility of heterogeneity in the effects

associated with the four empirical investigations. However, a proper evaluation of

heterogeneity was not feasible, due to the limited number of studies available.

The non-significant meta-analytic correlation of r = -.11 suggests that there may

not be an association between ERN amplitude and self-reported empathy. Of course, the

absence of evidence is not evidence for absence. With such a dictum in mind, it is

nonetheless worthwhile to speculate why there may not be an effect between ERN

amplitude and self-reported empathy. Weinberg and colleagues (2012) proposed in a

literature review that the ERN may be an initial evaluation of the salience of a particular

error. The ERN elicited from a cognitive control task may signal how an individual's

current state would reflect how they detect an error when faced with conflict. However,

an individual's clinical pathology or personality traits may shape the individual's ability to

respond to conflict/errors. For instance, people with obsessive-compulsive disorder are

reported to have larger ERN amplitudes and exhibit overly cautious behaviour (Endrass

& Ullsperger, 2014). This, in turn, could reflect the individual's ability to regulate his/her


90

behaviour within social situations. Consequently, the ERN may not necessarily reflect

empathy levels but instead what signals may be salient to the individual.

Additionally, studies have shown how psychiatric disorders can affect error

monitoring indices, including affecting the magnitude of the ERN (see Olvet & Hajcak,

2008; Santesso, Segalowitz & Schmidt, 2006; Weinberg et al., 2012). Clinical

populations, such as Autism Spectrum Disorder and psychopaths, have been found to

have a diminished ERN in various cognitive control tasks (Munro, Dywan, Harris,

McKee, Unsal, & Segalowitz. 2007; Vlamings et al., 2008). Likewise, these populations

are also known to have deficits in empathy. However, these associations are not as clear

within error monitoring/empathy literature. For instance, Munro and colleagues (2007)

compared 15 typical participants and 15 violent offenders on both a letter flanker task and

a face flanker task. The face flanker task consisted of faces that depicted anger or fear,

with participants having to make a judgment on whether the central face was angry or

fearful. Munro et al. (2007) found that the control group and offenders did not differ on

error monitoring performance or ERN amplitudes on the letter flanker task, whereas the

offenders performed worse in the face flanker and had reduced ERNs to errors in

comparison to the control group. These results indicated that individuals with

psychopathic personalities were less likely to notice errors and have smaller ERN in

emotion distinction tasks, a quality associated with affective empathy traits (Blair, 2005;

Shamay-Tsoory, 2008)

Furthermore, other clinical groups have shown differences in the ERN, such as

larger ERNs elicited from high levels of trait anxiety (Hajcak et al., 2003a; Meyer et al.,

2011; Pourtois et al., 2010); and high negative affect (Hajcak, McDonald, & Simons,

2004; Luu et al., 2000a, b). However, these population groups have shown inconsistent

empathic traits, with individuals with depression and/or anxiety are also known to have


91

normal to higher levels of empathy (O’Connor, Berry, Weiss, & Gilbert, 2002) yet are

less likely to be proactive in social interaction.

Personality traits could also moderate the error monitoring/empathy relationship.

For example, individuals with high levels of neuroticism have been shown to also have

larger ERNs (Weinberg et al., 2012). It has been suggested that neurotic individuals may

be more likely to be hyper-vigilant of their own performance in cognitive control tasks,

which in turn affects how they interact with the environment. Furthermore, neurotic

individuals have also been associated with higher levels of affective empathy (Joliffe &

Farrington, 2006). These results suggest how neurotic individuals can be overly cautious

in the context of social situations. Other personality characteristics, such as agreeableness

and openness (Batson, 1987; Joliffe & Farrington, 2006), have also been shown to

correlate positively with other personality characteristics. These overlapping factors are

also worth exploring within the context of error monitoring and its neurophysiology.

GENERAL DISCUSSION

The aim of these studies was to examine past associations between performance

monitoring indices, specifically the ERN and self-reported empathy; however, this

research failed to observe a significant effect between error monitoring and empathy. In

the context of contemporary psychology which recognises the implications associated

with the lack of replication of studies (Maxwell, Lau, & Howard, 2015; Stroebe & Strack,

2014), this paper may be considered valuable and raises a question of whether it is a true

effect. However, it must be noted as a limitation that the current investigation cannot

explain the inconsistency in the results across studies relevant to the association between

performance monitoring and empathy. It is possible the effect at the population level is

zero (or nearly so). However, it would be unbalanced not to suggest that there may be a

moderator of the effect between performance monitoring and empathy. As more


92

empirical studies in the area accumulate, more sophisticated analyses such as a meta-

regression, will be possible.

Another possible area of exploration includes how different components of the

ACC network relate to empathy. Studies have shown that the ACC interconnects with

both the limbic (Weinberg et al., 2012) and the prefrontal neural areas (Carter et al., 1998;

Gehring & Knight, 2000; Kiehl, Smith, Hare, & Liddle, 2000; Turken & Swick, 2008;

Ullsperger & von Cramen, 2006). The system is activated in response to conflict

detection (Carter et al., 1998) and emotional responses to affective stimuli (Bush, Luu, &

Posner, 2000). Research has also found differences in ACC activations when dealing with

both cognitive and emotional conflict. These researchers propose distinct neural systems

that process cognitive and emotional conflict separately yet share a slight overlap in their

conflict detection indices (Egner et al., 2008; Ochsner et al., 2009). As our current and

past studies show a greater focus on cognitive conflict, it is worth delineating empathic

processes and comparing them with emotional conflict.

There are, however, limitations within the current performance

monitoring/empathy field. Empathy is often quantified using self-reported measures. Self-

reported measures are more convenient for data collection and show longstanding validity

and reliability (Reniers et al., 2011), and these measures have been thoroughly developed

both qualitatively and quantitatively. For example, Larson et al. (2010) stated that their

sample obtained high self-reported empathy scores. As their sample was selected from

healthy young adults enrolled in a religious college, Larson et al. (2010) suggested that

empathy could be well-regarded characteristic within their school and participants may,

therefore, have chosen to present themselves in a more favourable light. Perhaps future

research should consider using a combination of self-reported and objective measures of

empathy to account for potential self-reporting bias.


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Furthermore, self-reported measures of empathy also look at empathy as a trait

measure, indicating an individual's overall particular style of social interaction.

Questionnaires used within the error monitoring/empathy literature reflect a participant's

empathic traits instead of how an individual responds to a given situation. Studies have

shown empathy to not be a static trait and can be affected by mood (Luu et al., 2000) and

environmental context (Berndhardt & Singer, 2012; Eisenberg, Lennon, & Roth, 1983;

Engen & Singer, 2013). Individuals may be more likely to help people they know or feel

a sense of belonging with (Long et al., 1999, Singer et al., 2003; Stephan & Finlay, 1999).

Likewise, conflict monitoring can also be affected by state-related manipulations, such as

motivation (Chiu & Deldin, 2007; MacNamara & Hajcak, 2009; Weinberg et al., 2012).

For example, Luu and colleagues (2000) found that individuals with higher self-reported

negative affect were observed to have enhanced ERNs at the start of a conflict

interference task. They found that participants' mean ERN amplitudes reduced in

amplitude towards the latter half of the testing which they suggested could have been due

to task disengagement. Theorists have suggested that motivation plays a significant role

in prosocial behaviour within an empathic state (Batson, Eklund, Chermok, Hoyt, &

Ortiz, 2007; Bernhardt & Singer, 2012). Future studies could directly compare

performance monitoring indices with a measure of state empathy, which encapsulates

how an individual situationally performs in regards to empathy.

In conclusion, our findings encourage a more comprehensive evaluation of the

neurophysiological correlates of empathy. Future research should provide clarity on

possible factors that moderate the association between ERN and empathy. It may benefit

isolating different components of empathy, such as emotional recognition or perspective-

taking, and incorporate it into cognitive control tasks. These modifications would allow


94

for the saliency of the ERN and other neural indices associated to the ACC, and provide

clarity in the neurophysiology of empathy.


95

ENDNOTES

3-1 The event-related potential waveforms were extracted using various methods, includ-

ing the peak-to-peak quantification, the methods in Santesso and Segalowitz’s study, and

Guthrie and Buchwald (1991)’s technique. All methods of ERN quantification yielded the

same pattern of results and amplitudes were highly correlated.


96

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4. The Measurement of Empathy:

Factorial Validity of Self-reported and

Behavioural Tasks


108

ABSTRACT

Empathy has been contended to be a combination of cognitive and affective processes.

However, the literature is inconsistent as to which scales are indicative of cognitive and

affective empathy. To date, the majority of empathy research has relied upon self-

reported measurement. Nevertheless, there are two putative behavioural tasks of empathy

which have not been examined: the Simone and the Faux Pas tasks. Consequently, we

evaluated the dimensionality of empathy, based on a combination of self-reported and

behavioural measures. A theorised restricted correlated-two factor model was not

supported. A follow-up unrestricted factor analysis yielded a correlated two-factor

solution (cognitive and affective; r=.35) with three noteworthy cross-loadings. Both

behavioural tasks loaded moderately onto the affective factor. Gender was related to the

affective factor (λ=.48) but not the cognitive factor (λ=-.09). The results were interpreted

to be supportive of a two-factor model of empathy. Furthermore, the justifiable

interpretation of empathy total scale scores was questioned.

Keywords: empathy, theory of mind, cognitive empathy, affective empathy, confirmatory

factor analysis, exploratory factor analysis


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INTRODUCTION

The ability to empathise consists of a combination of processes, including taking

the perspective of another, recognising the emotions of another, and responding to their

emotions appropriately (Baron-Cohen & Wheelwright, 2004; Reniers, Corcoran, Drake,

Shryane, & Völlm, 2011). Self-reported measures are more commonly used to assess

empathy, as they are advantageous for efficient data collection. Additionally, self-

reported empathy scores tend to be associated with acceptable levels of reliability

(Reniers et al., 2011). However, self-reported measures are dependent on the subjective

responses of the respondents, and the accuracy of the participants’ subjective responses

may be questionable (Stone, Bachrach, Jobe, Kurtzman, & Cain, 1999).

Behavioural measures, by contrast, allow for the possibility of an objective

evaluation of the degree to which an individual empathises with others. There are a

number of putative behavioural measures of empathy (e.g., Faux Pas task, Baron-Cohen,

O’Riordan, Stone, Jones & Plaisted, 1999; Simone task; Batson, Eklund, Chermok, Hoyt,

& Ortiz, 2007); however, their basic psychometric properties have not yet been evaluated.

Furthermore, there is a non-negligible amount of inconsistency in the literature relevant to

which scales are indicative of cognitive versus affective empathy (for both self-reported

and behavioural tasks; Jolliffe & Farrington, 2004; Reniers et al., 2011). Consequently,

the purpose of this investigation was to evaluate the dimensionality of empathy, based on

a collection of commonly administered self-reported and behavioural measures of

empathy.


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COMMONLY USED MEASURES OF EMPATHY: SELF-REPORTED

According to Baron-Cohen (2011), empathy is dependent upon inferences of the

mental states of others through observation and using one’s experiences. To address the

assessment of empathy in both clinical and typical populations, Baron-Cohen and

Wheelwright (2004) developed one of the most widely-used self-reported questionnaires

of empathy, the Empathy Quotient (EQ). The underlying rationale for the development of

this scale was to create a global measure of empathy. From an applied perspective, the

scale can be used to compare scores derived from clinical settings with a normative

sample (Baron-Cohen & Wheelwright, 2004). Subsequent factor-analytical research on

the EQ has found it to fit a multidimensional model. For example, Muncer and Ling

(2006) performed a factor analysis on the EQ scores of university students and their

parents. Muncer and Ling (2006) found the single-factor model to be associated with

unacceptable levels of model close-fit (e.g., CFI = .57). Subsequently, they identified a

modified three-factor model to be associated with essentially acceptable levels of model

close-fit (CFI = .91): cognitive empathy, emotional reactivity, and social skills. Cognitive

empathy (EQ Cognitive) was defined as the understanding of another person’s beliefs and

intentions (Lawrence, Shaw, Baker, Baron-Cohen, & David, 2004). Emotional reactivity

(EQ Emotional), on the other hand, was defined as the individual’s emotional response to

the affective state of another (Baron-Cohen & Wheelwright, 2004; Muncer & Ling,

2006). Finally, the social skills (EQ Social Skills) factor represented how individuals

interact within a social situation. These three dimensions inter-correlated positively,

suggesting the presence of a general empathy factor (EQ Cognitive and EQ Emotional, r

= .54; EQ Cognitive and EQ Social Skills, r =.51; and EQ Social Skills and EQ

Emotional, r = .17). These three subscales have occasionally been used as an assessment

of clinical traits, including schizotypy (Henry, Bailey, & Rendell, 2008; Russell-Smith,


111

Bayliss, Maybery, & Tomkinson, 2013), traumatic brain injury, and autism spectrum

disorder (Mathersul, McDonald, & Rushby, 2013).

In contrast to the EQ, the IRI was developed originally as a multidimensional self-

reported instrument of empathy (Davis, 1980, 1983). The IRI consists of four different

subscales: perspective taking, empathic concern, fantasy, and personal distress.

Perspective taking is the ability to take the mental view of another. Similarly, the fantasy

subscale is similar to perspective taking as it examines an individual’s ability to take the

perspective of another within a fictional situation. Empathic concern assesses feelings of

sympathy and compassion towards another from an emotional perspective. Personal

distress involves the respondent’s tendency to feel emotional distress when perceiving

another in distress. There is empirical evidence to suggest that the IRI is consistent with a

four-factor model for both male and female samples (Davis, 1980; Chrysikou &

Thompson, 2015; Pulos, Elison, & Lennon, 2004). Furthermore, Davis (1980)

emphasised the independence of these subscales, as they were associated with relatively

small inter-correlations across the four subscales (r = .01-.33). The IRI is used in both

experimental (Davis & Franzoi, 1991; Schutte et al., 2001) and clinical research settings

(Bellebaum, Brodmann, & Thoma, 2014; Thoma, Norra, Juckel, Suchan & Bellebaum,

2015).

Although there is some factorial validity research which supports the use of the

EQ and the IRI, it should be noted that there are inconsistencies in how the IRI is scored

in the literature. Specifically, some have theorised empathy to consist of only two

dimensions, cognitive and affective empathy. Furthermore, some have used the IRI to

measure these two dimensions (Jolliffe & Farrington, 2004; 2006; Reniers et al., 2011;

Smith, 2006). Cognitive empathy is the ability to take the perspective of one’s mental

states. By contrast, affective empathy is the ability to understand another’s emotions and


112

share that experience with them (Bellebaum et al., 2014; Larson, Fair, Good, & Baldwin,

2010; Reniers et al., 2011; Thoma et al., 2015). Researchers tend to classify the

perspective taking and fantasy subscales within the IRI as cognitive empathy measures.

For instance, some would use a single subscale, either perspective-taking or the fantasy,

as a measure of cognitive empathy (see Bellebaum et al., 2014; Larson et al., 2010;

Thoma et al., 2015). Correspondingly, other investigators have used either the empathic

concern or personal distress subscales as indicators of affective empathy (Larson et al.,

2010; Singer et al., 2004). However, these measures do not consistently represent

cognitive and affective empathy within experimental and clinical research. Instead, other

studies have ‘created’ cognitive and affective empathy measures through combinations

of IRI subscales (Bock & Hosser, 2014; Harari, Shamay-Tsoory, Ravid, & Levkovitz,

2010; Thoma et al., 2015), i.e. use composite scores of perspective taking and fantasy to

represent cognitive empathy, and use composite scores of empathic concern and personal

distress to represent affective empathy. Such variations in the use and interpretation of

the subscales would naturally lead to inconsistency in the results reported across

investigations (Chrysikou & Thompson, 2015).

Subsequently, empirical research has focused on using cognitive and affective

empathy as predictors of psychopathologies, including personality disorders (Blair, 2005;

Harariet al., 2010), alcoholism (Maurage et al. 2011), and identifying neural correlates of

empathy (Bellebaum et al., 2014; Larson et al., 2010; Singer et al., 2004). However,

statistical explorations have found conflicting relationships, with both cognitive empathy

subscales reflecting a small correlation (r = .25, p <.01; Chrysikou & Thompson, 2015) or

no correlation (r = .11 for males, and r = .01 for females; Davis, 1980). Furthermore, the

correlations between affective empathy subscales from the IRI have also shown

inconsistent relationships, (r = .09; Chrysikou & Thompson, 2015; r =.33 and r = .01 for


113

females; Davis, 1980). As a result, other researchers have omitted certain subscales, like

fantasy and personal distress (Jolliffe & Farrington, 2004; Reniers et al., 2011).

In response to the statistical and theoretical inconsistencies in the literature,

Reniers and colleagues (2011) conducted a multi-inventory factorial validity investigation

of empathy. Specifically, Reniers's et al. (2011) administered the EQ, the IRI, the Hogan

Empathy Scale (Hogan, 1969), and the empathy subscale of Impulsiveness-Venturesome-

Empathy Inventory (Eysenck & Eysenck, 1978) to a sample of 660 adult participants. In

the first instance, Reniers et al. (2011) performed an exploratory factor analyses on all

items from the combined five inventories of empathy. The analysis identified five lower-

order facets of empathy. Reniers et al. (2011) identified the following subscales as

indicators of cognitive empathy: perspective-taking, taking the perspective of another,

and online simulation, to consciously imagine what the person is feeling. The affective

empathy facets included emotional contagion, proximal responsivity, and peripheral

responsivity. Emotional contagion is defined as automatic mirroring, whereas proximal

responsivity involves the respondent wanting to take action. Similarly, peripheral

responsivity involves measuring how much the respondent would feel the need to respond

in a more detached situation. Subsequently, Reniers et al. (2011) followed up with a

confirmatory factor analysis (N = 925), where the five facets of empathy were used to

define two latent variables: cognitive and affective empathy. Reniers et al. (2011)

reported acceptable levels of model close-fit (e.g., CFI = .925). Furthermore, the

correlation between the two second-order factors was moderate at r = .31.

Although the Reniers et al.’s (2011) investigation should be viewed as a valuable

contribution to literature, it should be noted that Reniers et al. (2011) selected and omitted

certain items from the well-known scales of empathy to help increase the chances of

fitting the two-factor model. For example, they included only 15 items from the EQ. They


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also omitted the fantasy and personal distress subscales from the IRI. However, many

researchers continue to use all items of the EQ and the IRI as measures of cognitive and

affective empathy. An additional limitation associated with the Reniers et al.’s (2011)

investigation is that all of the measures included in the analysis were self-reported in

nature, despite the fact that behavioural measures of empathy have been developed and

are fairly commonly used in clinical settings, as described next.

COMMONLY USED MEASURES OF EMPATHY: BEHAVIOURAL TASKS

Although popular, self-reported measures of empathy assume that participants

have good quality insight into their abilities. Such an assumption has been questioned,

particularly in clinical settings (Baron-Cohen et al., 1999; Stone et al. 1998).

Consequently, researchers have developed behavioural tasks to measure individual

differences in empathy. These tasks have been used to investigate the development of

social skills (Hiruma, 2014; Ozonoff & Miller, 1995), emotional intelligence (Reid et al.,

2013), and theory of mind understanding (Happé, 1994; Leslie, 1987), for example.

However, there are disagreements on how to classify the commonly used measures of

behavioural empathy within the context of the multidimensional framework of empathy.

One purpose developed cognitive empathy behavioural task is known as the Faux

Pas task. The Faux Pas task was initially developed as a task to detect autistic traits in

higher-functioning individuals (Baron-Cohen et al., 1999). Additionally, it has been used

as a measure of theory of mind deficits in adult patients with orbitofrontal damage (Stone

et al., 1998; 2003). The Faux Pas task consists of various scenarios of a character saying

something that elicits an ‘uh-oh!’ emotion to another character involved. Thus, this task

aims to assess how well an individual may behaviourally take the perspective of another

and empathise with the characters in the vignettes. The applicability of the Faux Pas task

can include assessments of social cognitive deficits in a wide variety of populations,


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including children aged seven onwards (Cashion, 2009), autistic individuals (Baron-

Cohen et al., 1999), individuals with schizophrenia (Ozel-Kizil, Baskak, Uran, Cihan,

Zivrali, Ates, & Cangoz, 2012; Riveros et al., 2010), personality disorders (Ibanez et al.,

2013), and older adults with neurodegenerative disorders (Adenzato & Poletti, 2013).

Despite the fact that some consider the Faux Pas task as a measure of cognitive empathy,

to-date, the factorial validity of the Faux Pas task has not been investigated.

Like cognitive behavioural empathy tasks, there is a paucity of factorial validity

research on behavioural measures of affective empathy. Various behavioural approaches

to the measurement of affective empathy were attempted. For example, the Simone task

was developed on the basis of Batson et al.’s (2007) theory of affective empathy, in

which individuals are able to feel empathy without actively adopting the person’s

perspective. This task has been shown to associate with the affective subscale on the EQ

(Baron-Cohen & Wheelwright, 2004) within a typical sample (r = .54; N = 80). The

Simone task involves presenting participants with a series of vignettes. The participants

are asked to rate the degree to which the vignettes make them feel soft-hearted, warm,

compassionate, tender, and moved. The Simone task is one of the few behavioural

affective empathy measures that have been psychometrically validated with self-reported

affective measures of empathy (Russell et al., 2013). However, to date, there has been no

comprehensive evaluation of the Simone task within the broader measurement approach

to empathy (self-reported and behavioural).

GENDER DIFFERENCES IN EMPATHY

The empirical research suggests that females score higher, overall, on empathy

scales than males (Baron-Cohen & Wheelwright, 2004; Davis, 1980, 1983; Jolliffe &

Farrington, 2004, Reniers et al., 2011). However, there appears to be a trend for females

to score higher on affective-related dimensions of empathy, rather than cognitive related


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dimensions of empathy (e.g., Ang & Goh, 2010; Wai & Tiliopoulous, 2012; Derntl et al.,

2009). Furthermore, males tend to score higher on scales relevant to psychopathic

characteristics, a trait negatively associated with affective empathy (Blair, 2005; Munro et

al., 2007). Additionally, affective empathy has been theoretically and empirically linked

to emotional intelligence (EI) (Mayer, DiPaolo, & Salovey, 1990). Correspondingly,

females tend to score higher on measures of EI (e.g., Cabello, Fernandez-Pinto,

Extremera, Fernandez-Berrocal, 2016; Gomez-Baya, Mendoza, Paino, & de Matos,

2017).

In contrast to affective empathy, recent research suggests that there are little in the

way of gender differences on cognitive empathy scales (Baron-Cohen et al., 2015).

Furthermore, cognitive empathy has been shown to be associated positively with other

aptitudes such as executive functioning and inhibitory control, both of which have

evidenced negligible gender differences (Carlson, Moses, & Breton, 2002; Ozonoff,

Pennington, & Rogers, 1991). In summary, although a substantial amount of research

suggests that females score higher on empathy than males, it may be suggested that the

effect resides principally on the affective dimension only. Consequently, for this

investigation, gender was considered a valuable external non-psychometric marker

variable that could be used in a factor analysis to help define the hypothesised dimensions

of empathy: cognitive and affective. Based on the research reviewed above, it was

anticipated that gender would be correlated meaningfully with an affective dimension of

empathy, but not a cognitive dimension of empathy.

PURPOSE OF CURRENT STUDY

In light of the above, the purpose of this investigation was to potentially further

substantiate the correlated two-factor model of empathy (cognitive and affective) at the

subscale level across a diversity of measures. Correspondingly, as the analyses were at


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the subscale level, an additional purpose of the investigation was to potentially help

clarify which commonly used subscales may be better considered indicators of cognitive

empathy and which may be better considered indicators of affective empathy.

METHODS

Participants

A total of 217 students (101 male, 125 females, one other) aged 17 to 55 years

from the University of Western Australia participated for course credit. The study took

approximately 30 to 45 minutes to complete. Ethical approval for studies was granted by

the University of Western Australia Research Ethics Committee.

Measures

Two measurement methods were used to measure each of the two hypothesised

dimensions of empathy: self-reported questionnaires and behavioural task-based

measures. For self-reported measures, we selected commonly used measures of empathy,

i.e. the EQ and the IRI. For behavioural empathy measures, we selected the Faux Pas

Recognition Task as a representation of Cognitive Empathy, as it had shown higher

internal coefficient of variation in comparison to other higher level Theory of Mind tasks

(Cashion, 2009). We selected the Simone task as a behavioural measure of affective

empathy as it had shown to strongly correlation with affective empathy measures, such as

the EQ: Emotional Reactivity (Russell-Smith, et al., 2013).

Interpersonal Reactivity Index. The Interpersonal Reactivity Index (IRI; Davis,

1980; 1983) consists of four subscales (seven items each). Participants were asked to

indicate how well each item described themselves by choosing an appropriate letter on a

four-point response scale (A, B, C, E, and E; A—does not describe me well—to D—

describes me very well). Six of the 28 items were reverse-scored. The perspective taking


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and fantasy subscales were used as evaluations of cognitive empathy. The empathic

concern and personal distress subscales were used as evaluations of affective empathy.

Muncer and Ling’s (2006) Model of Empathy Quotient. Muncer and Ling (2006)

performed an exploratory factor analyses on items on 60 items of the Empathy Quotient

(EQ; Baron-Cohen & Wheelwright, 2004) and proposed a three-factor scale: cognitive

empathy (EQ Cognitive), emotional reactivity (EQ Emotional), and social skills (EQ

Social Skills). Each scale involved four items, and participants were given the following

options: Strongly Agree, Slightly Agree, Slightly Disagree, and Strongly Disagree.

Mildly empathic behaviours would be scored 1-point, and strongly empathic behaviours

would be scored 2-points. EQ Cognitive was used as a Cognitive Empathy measure, and

EQ Emotional was used as an affective empathy measure. Furthermore, EQ Social Skills

was also used as a cognitive empathy measure within the hypothesised factor analytic

models, as it focused on taking perspective within social situations.

Faux Pas Task. Participants completed a Faux Pas recognition test (Baron-Cohen

et al., 1999) The test used in this investigation consisted of ten Faux Pas stories and ten

control stories in which no Faux Pas was committed. The stories were presented in white

text on a black background and verbally presented through headphones. Participants

typed in their answers into the computer. One point was given to every correctly observed

Faux Pas with a maximum score of up to ten points.

Simone Task. The Simone task was based on Batson and colleagues’ (2007)

behavioural measure of affective empathy. Participants were presented with a vignette

and asked to rate (on a 6-item measure) the degree to which it makes them feel soft-

hearted, warm, compassionate, tender, and moved. Responses are provided on a seven-

point response scale (1—not at all—to 7 –extremely) across the six items. The responses

are then summed to provide a total score. The specific vignette involved a character


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named ‘Simone’ describing her distress having just learned that her father has been

diagnosed with a terminal brain tumour.

DATA-ANALYTIC STRATEGY

First, a single-factor latent variable model was tested. Next, a correlated two-

factor latent variable model was tested. Within the correlated-factor model, one of the

latent variables was defined by the following scales: perspective taking, fantasy, EQ

Cognitive, EQ Social Skills, and the Faux Pas task. The second latent variable was

defined by the following scales: empathic concern, personal distress, EQ Emotional, and

Simone task. Both latent variable models were estimated with maximum likelihood and

performed with AMOS 21.0 (Arbuckle, 2012). Identification was achieved by fixing the

latent variable variances to 1.0. A model was considered well-fitting if the CFI and TLI

values were .950 or greater. Additionally, SRMR and RMSEA values of .08 or less were

considered indicative of an acceptably well-fitting model.


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RESULTS

The data were screened for univariate outliers. None were identified. Eight cases

were removed due to a substantial amount of missing data (missing at random). Thus, the

final sample size was 209. Descriptive statistics for the scale scores are presented in Table

4-1. The Pearson correlations between the measures are presented in Table 2. It can be

observed that a number of the scales intercorrelated with each other positively, as

expected.

Table 4-1. Descriptive Statistics of Empathy Measures

Note. M = Mean, SD = Standard Deviation, IRI = Interpersonal Reactivity, EQ =

Empathy Quotient.

CONFIRMATORY FACTOR ANALYSES

The single-factor model which was not found to be acceptably well-fitting, χ2 (27,

N = 209) = 154.75, p<.001, CFI = .660, TLI = .546, RMSEA = .151, SRMR = .111. Next,


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the correlated two-factor model was tested and was not found to be well fitting, χ2 (27, N

= 209) = 154.65, p<.001, CFI = .660, TLI = .546, RMSEA = .151, SRMR = .111.

Table 4-2. Internal Consistency and Pearson Correlations of the Measures of Empathy

Note. N = 209; correlations greater than |.13| were statistically significant (p < .05);

internal consistency reliabilities on the main diagonal.

UNRESTRICTED FACTOR ANALYSES

In light of the CFA results, we performed an unrestricted factor analysis with the

nine empathy measures as inputted variables. Parallel analysis was used to determine the

number of statistically significant factors within the data based on a p < .01 criterion (99th

percentile) to control for family-wise error inflation. The results suggested that two

factors should be extracted (i.e., two eigenvalues above the randomly generated

eigenvalues). Thus, two maximum likelihood estimation derived factors were extracted

and rotated via Promax with Kaiser Normalisation. As can be seen in Table 3 (left-hand

side), the first factor was defined principally by EQ Emotional Reactivity and was

labelled ‘affective empathy’ (rotated eigenvalue = 2.67). The second factor was labelled


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‘cognitive empathy’ as it was principally defined by EQ Social (rotated eigenvalue =

1.71). The two factors correlated at r = .35, p <.001. The two extracted factors accounted

for 35.81% of the total variance.

Table 4-3. Maximum Likelihood Unrestricted Factor Analytic Pattern Matrix Standard-

ised).

Note. N = 208; solutions based on promax rotation.

Contrary to expectations, the fantasy subscale and Faux Pas task evidenced mod-

erate loadings on the affective empathy factor, but essentially no loading on the cognitive

empathy factor. Furthermore, personal distress was found to load onto both the affective

(λ = .51) and the cognitive (λ = -.54) empathy factors. Additionally, the EQ Emotional


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and the empathic concern subscales yielded cross-loadings onto the cognitive empathy

factor (λ = .34)

To explore the validity of the two unrestricted factors, a second unrestricted factor

analysis was performed with the addition of gender to the nine empathy measures (males

= 1; females = 2). As can be seen in Table 3 (right-hand side), gender loaded substantially

onto the affective empathy factor (λ = .48). Thus, females scored higher on the affective

empathy factor than males.

DISCUSSION

The results of this investigation suggest that empathy is defined by two

intercorrelated (r = .35) dimensions: cognitive and affective. Furthermore, although some

subscales were observed to load clearly onto a single dimension, others were observed to

be associated with substantial cross-loadings. Additionally, the two behavioural tasks

loaded moderately on the affective factor. Lastly, we found that gender was associated

with affective empathy only. Although the results of this investigation support a

correlated two-factor model of empathy, which is consistent with Reniers et al.’s (2011),

it was not exactly the model specified based on theory and previous research. Instead, the

results of the unrestricted factor analysis revealed that several subscales did not load onto

their hypothesised dimension and/or some subscales evidenced cross-loadings.

TWO-FACTOR MODEL OF EMPATHY: SELF-REPORTED MEASURES

The self-reported subscales analysed in this investigation yielded a series of clear

loadings onto the two unrestricted factors, as well as cross-loadings. First, we discuss the

subscales associated with the simple structure. Next, we deal with the cross-loaded

subscales.

The affective dimension of empathy uncovered in this investigation was most

strongly and clearly defined by the EQ Emotional and empathic concern subscales.


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Interestingly, the fantasy subscale evidenced a substantial positive loading on the

affective factor. Some have contended that the fantasy subscale is a representation of

cognitive empathy (Bellebaum et al., 2014; Larson et al., 2010; Thoma et al., 2015).

However, the results of this investigation suggest that it is an indicator of affective

empathy. This could be due to the wording of some of the items such as, ‘I really get

involved with the feelings of the characters of the novel,’ and ‘after seeing a play or

movie, I have felt as though I were one of the characters.' Thus, there is a tendency

towards ‘feeling’ and less so on taking the perspective of another’s mental state. Such

emotionally relevant content would naturally render the fantasy subscale a meaningful

indicator of affective empathy.

For the second factor, EQ Social Skills exhibited the highest loading.

Consequently, the factor was interpreted as a dimension of cognitive empathy. Muncer

and Ling (2006) initially suggested EQ Social Skills to be a separate dimension of

empathy, based on the notion that proficient social interaction alongside cognitive and

affective empathy is necessary for empathic processes. However, the second factor

consisted of other cognitive measure loadings, such as EQ Cognitive and perspective

taking. Furthermore, EQ Social exhibited higher correlations with the cognitive empathy

subscale (r = .40), which is consistent with previous research (Muncer & Ling, 2006).

The loadings of EQ Social on the cognitive dimension suggest the importance of

understanding social situations within the context of perspective-taking, as exemplified

by items such as: ‘I do not tend to find social situations confusing,’ and ‘I often find it

difficult to judge if something is rude or polite’ (reverse scored).

Interestingly, three subscales showed cross-loadings onto the cognitive and

affective factors: EQ Emotional, empathic concern (IRI), EQ Emotional, personal distress

(IRI). Chrysikou and Thompson (2015) stated that self-report questionnaires, like the IRI,


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held an inherent bias towards cognitive empathy. For example, item 18 from the empathic

concern subscale states, ‘When I see someone being treated unfairly, I sometimes don't

feel very much pity for them’ (reverse scored). Similarly, EQ Emotional exhibits

cognitive empathy qualities, such as item 27, ‘If I say something that someone else is

offended by, I think that is their problem, not mine.’ Respondents would have to put

themselves in a situation before answering the item, which would imply an element of

cognitive empathy.

Furthermore, personal distress also evidenced a salient cross-loading onto both

factors, with a positive loading on the affective factor and a negative loading on the

cognitive factor. Although such a result may be surprising, previous research has

associated empathy with measures of emotional responsivity, i.e. how likely an individual

will respond to an emotional situation (Frick & White, 2008; Gonzalez- Liencres, et al.,

2013; Vachon & Lynam, 2015). Perhaps the personal distress subscale measures internal

conflict when in the presence of another person’s distress, regardless of the other person’s

mental state. Obviously, another possible explanation for why the personal distress

subscale yielded a cross-loading is that some of the items may measure more affective

qualities, whilst others measure more cognitive qualities. Additional item-level factor

analytic research is indicated to help resolve this issue, ideally with a large (N > 500) and

representative sample.

TWO-FACTOR MODEL OF EMPATHY: BEHAVIOURAL MEASURES

In contrast to the self-reported measures, the results associated with the

behavioural measures of empathy were simpler in nature. Our findings showed that the

Simone task was an indicator of affective empathy, though only moderately. Such a result

is consistent with other research. For example, Russell-Smith et al. (2013) found that the

Simone task correlated positively (r = .54) with the EQ Emotional subscale. Although we


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found the EQ Emotional subscale to cross-load onto both the cognitive and affective

factors, it loaded much more substantially on the affective empathy factor. Nonetheless,

we would recommend that others use the Simone task cautiously, as it loaded onto the

affective empathy factor at only .26. Further psychometric research on the Simone task is

encouraged to help improve its validity.

Surprisingly, the Faux Pas task loaded onto the affective factor. This result

contradicts the existing empathy literature, as the Faux Pas task was theoretically

considered to be a cognitive empathy measure (Bons et al., 2013; Gleichgerrcht et al.,

2012. It will be noted, however, that the Faux Pas task has been used as an affective

measure within theory of mind assessments (Völlm et al., 2006). For instance, the Faux

Pas task requires the respondent to read scenarios in which an ‘uh-oh’ reaction is elicited

(Baron-Cohen et al., 1999). Subsequently, the respondent may feel what is known as

fremdschämen, a German word that describes the feeling of vicarious and empathic

embarrassment (Lehmann, 2012). In light of the above, it may be suggested that to

recognise a Faux Pas, successfully, requires the individual to detect embarrassment,

which taps into affective-relevant characteristics.

Although the Faux Pas task evidenced some factorial validity as an indicator of

affective empathy, it is worth noting that the Faux Pas task scores were associated with

relatively low internal consistency reliability (α = .55). Correspondingly, other studies

have found behavioural empathy measures to have similarly low levels of internal

consistency reliability (e.g., Reading in Mind’s Eyes test, α= .54; Cashion, 2009).

Unfortunately, few studies have reported the internal consistency associated with

behavioural empathy measure test scores (Cashion, 2009). Consequently, it is difficult to

evaluate the area more broadly. Furthermore, the Faux Pas task was initially developed to

measure general empathy in children and clinical populations (Baron-Cohen et al., 1999;


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Stone et al., 1998; 1999; 2003). For instance, Stone et al. (2003) considered the Faux Pas

task to measure ‘epistemic ‘ mental states, which is defined as the attribution of ‘belief,'

the intention of another, and/or the attribution of affective mental states (i.e. desire, fear,

or anger). Therefore, it is possible that it measures more than one facet of empathy at an

item-level, which may reduce the level of homogeneity associated with the items. A

rigorous item-level psychometric evaluation of the Faux Pas task would be a valuable

contribution to the literature.

It is worth noting that there was a slight ceiling effect observed in the Faux Pas

tasks scores, though it was not deemed serious. Specifically, the coefficient of variation

(SD/M) in our sample was estimated at .28, which is comparable to other cognitive

abilities such as memory span (Gignac & Watkins, 2015). Thus, the psychometric

limitations associated with the Faux Pas task are probably unrelated to a lack of

variability in the test scores obtained from adults. It would appear that a behavioural,

cognitive empathy measure has yet to be established empirically to measure cognitive

empathy, rather than affective empathy. Most, if not all, behavioural measures of

cognitive empathy were initially developed on the basis of theoretical literature, with few

studies testing their psychometric properties (Reniers et al., 2011; Sebastian, Fontaine,

Brid, Blakemore, Brito, McCrory, & Viding, 2012). More work is indicated in this

important area.

EMPATHY AND GENDER

Prior empirical research has suggested that females score higher on self-reported

measures of empathy (see Baron-Cohen & Wheelwright, 2004; Davis, 1983; Reniers et

al., 2011). However, the second unrestricted factor analysis performed in this

investigation, which included gender, suggested that females score higher than males on

affective empathy, but not cognitive empathy. Such a finding is broadly consistent with


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Reniers et al.’s (2011) investigation, where greater gender differences were observed for

affective empathy (d = .83) than cognitive empathy (d = .41). Thus, it would appear that

females, on average, are more experientially emotional with empathy-related behaviours.

Such a position is also consistent with the finding that females score higher on scales of

emotional intelligence (e.g., Cabello, Fernandez-Pinto, Extremera, Fernandez-Berrocal,

2016; Gomez-Baya, Mendoza, Paino, & de Matos, 2017). By contrast, there was no

meaningful association between gender and the cognitive empathy factor, which is

consistent with the results of Baron-Cohen et al. (2016). In light of these findings,

statements about gender differences in empathy should be qualified. Specifically, they

should be restricted to affective empathy. Although previous empirical investigations may

have observed differences in empathy scale scores or lack thereof, it should be

acknowledged that it is difficult to interpret those results. That is, several of the

commonly used empathy scales in the literature may be differentially imbued with

cognitive and affective empathy elements. A strength of the current investigation is that it

used factor analysis, which helped “purify” the two dimensions of empathy. More

research of this nature would be valuable to help replicate the results.

POTENTIAL CLINICAL IMPLICATIONS

Though empathy assessments are predominantly used in a research environment,

there has been an emerging use of empathy as a clinical qualifier for clinical diagnosis. It

is noteworthy, however, that current clinical diagnoses fail to distinguish between

affective and cognitive empathy. For example, the clinical classifications of narcissistic

personality disorder and conduct disorder include ‘empathy’ as a diagnostic indicator

and/or specifier (American Psychiatric Association, 2013). Specifically, one of the

diagnostic criteria for narcissistic personality disorder states that a narcissistic individual,

’lacks empathy: is unwilling to identify the feelings and needs of others’ (American


129

Psychiatric Association, 2013, p. 670). Furthermore, a specifier for conduct disorder

states that these individuals are ‘callous [with] lack of empathy. [The individual]

disregards and is unconcerned about the feelings of others. The individual is described as

cold and uncaring.’ (American Psychiatric Association, 2013, p.470). Though current

diagnostic criteria are based on qualitative assessments, these two criteria do not clarify

whether clinicians should be focusing on affective or cognitive empathy deficits (or both).

Additionally, many researchers tend to rely on only total scale empathy scores

(see Hartung, Burke, Hagoort, & Willems, 2016, Montgomery et al., 2016). However, the

measurement of empathy from a total scale perspective, though perhaps convenient and

simple, may lead to imprecision in measurement and interpretation, as there appears to be

only a modest, positive correlation between cognitive and affective empathy. Based on

the method demonstrated by Gignac (2014), the total scale empathy scores in this

investigation were associated with a total scale score reliability of only .52. Subsequently,

its reliability is arguably not sufficiently high for even basic research purposes, much less

important clinical decision-making purposes, based on conventional standards (Lance,

Butts, & Michels, 2006).

Finally, we note that the non-negligible difference between males and females on

the affective empathy dimension raises several questions. First, the difference may arise

due to some level of item bias (Teresi, Kleinman, & Ocepek-Welikson, 2000). Thus, a

differential item-functioning analysis would be a worthwhile contribution to the area.

Secondly, if females score higher than males on affective empathy in a justifiable manner

(i.e., not due to item bias), then it may be necessary to develop gender-specific empathy

norms.

LIMITATIONS


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Although the sample size was adequate in this investigation (N = 209), it was not

especially large. Consequently, further factor analytic work at the subscale level with

larger sample sizes (N > 500) would be valuable to help replicate the results reported in

this investigation. Secondly, our study did not include all existing self-reported and

behavioural empathy measures (see Henry, von Hippel, Molenberghs, Lee, & Sachdev,

2016),. Thus, future investigations may find different factors and/or loadings with the

inclusion of subscales derived from additional empathy measures, such as the Basic

Empathy Scale for Adults (BES-A; Carré et al., 2013), Affective and Cognitive Measure

of Empathy (Vachon & Lynam, 2015) and The Awareness of Social Inference Test

(TASIT; McDonald, Flanagan, & Rollins, 2003). Lastly, the sample consisted of

university undergraduates which limit the generalisability of the results. Future research

should consider investigating the factor structure of empathy using clinical samples

composed of individuals with deficits in empathy, such as some personality disorders

and/or autism spectrum disorder.

CONCLUSION

The results of this investigation and others suggest relatively convincingly that

empathy should be conceptualised, measured, and interpreted from the perspective of a

two-dimensional model: cognitive and affective. Consequently, it is perhaps time to

seriously consider the cessation of the calculation and interpretation of total scale

empathy scores. Instead, the application of the psychometric distinction between

cognitive and affective empathy may be expected to offer much richer insights into how

people engage in empathic behaviour, both in research and clinical settings.


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5. General Discussion


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CHAPTER SUMMARY

The overall aim of this thesis was to dissociate between cognitive and affective

empathy via neurophysiological and psychometric processes. Three neurophysiological

investigations (Chapter 2.1, 3.1, 3.2) comparing error monitoring indices and empathy

were performed. All studies failed to reject the null hypothesis, regardless of the type of

empathy measures used. A meta-analysis was also performed and findings failed to

suggest a statistically significant association between the error-related negativity (ERN)

and empathy. Following these findings, factor analytic evaluations of empathy was

performed. The thesis showed support for the two-factor model: cognitive and affective

empathy. However, findings indicated that commonly used behavioural measures of

empathy measures lacked reliability and/or validity. Furthermore, some commonly used

self-reported measures were associated with cross-loadings.

Four primary conclusions can be drawn from this thesis. Firstly, our studies

suggest that there may not be an association between ERN and empathy. However, we

were limited by the paucity of ERN/empathy literature, making it difficult to establish a

conclusion for the lack of ERN/empathy effect. Secondly, commonly regarded measures

of cognitive and affective empathy may not necessarily measure cognitive and affective

dimensions of empathy in a respectable manner. Therefore, existing investigations may

be confounded by the incongruity between theoretical and statistical measures of empathy

(Chrysikou & Thompson, 2015). The following discussion explores the significance of

these findings within the context of measurement and electrophysiology.


143

MEASUREMENT OF EMPATHY

Firstly, it is important to flag the inconsistencies across empathy measures. As

empathy is progressing towards using a two-dimensional framework, newer

investigations modify existing measures to fit the cognitive and affective dimensions of

empathy without statistical validation (Bellebaum, Brodmann, & Thoma, 2014; Larson,

Good, & Baldwin, 2011; Thoma, Norra, Juckel, Suchan & Bellebaum, 2015).

Consequently, the lack of uniformity in empathy measurement can affect the outcomes of

experimental investigations (Chrysikou & Thompson, 2015). This has been evidenced by

conflicting findings (Bellebaum et al., 2014; Larson et al., 2010; Thoma et al., 2015) or

lack of significant effects (Chapter 1 and 2).

Secondly, there are few statistically valid behavioural measures of empathy. As

the behavioural measures used in the thesis indicated moderate affective empathy

loadings (Chapter 4), the development of newer and more precise behavioural measures

of empathy are encouraged. However, it must be noted that behavioural measures of

empathy had initially been developed to assess empathy in populations that may not have

insight into their abilities (Stone, Bahrach, Jobe, Kurtzman, & Cain, 1999). Therefore, the

basis of behavioural empathy assessment is rarely validated within the cognitive and

affective empathy framework. Perhaps, the cognitive and affective empathy framework

may not necessarily apply to behavioural empathy measures.

Lastly, a majority of literature has indicated compelling support for a two-factor

model, as indicated in Chapter 4. However, existing clinical (Baron-Cohen et al., 2015;

Montgomery et al., 2016) and developmental (Faísca et al., 2016) research continues to

use global measures of empathy. As more studies indicate distinct cognitive and affective

dimensions (Chapter 4; Jolliffe & Farrington, 2006; Reniers, Corcoran, Drake, Shryane &


144

Völlm, 2011), using a global empathy measure can lead to irregularity in experimental

and clinical findings. Future investigations must discourage the use of global measures

and develop more precise and convenient measures of cognitive and affective empathy.

NEURAL CORRELATES OF EMPATHY

This thesis failed to replicate the ERN/empathy effect in three experimental

chapters (Chapter 2a, Chapter 3a, Chapter 3b). These results, or lack thereof, suggest the

possibility of publication bias within the ERN/empathy literature. Recent commentary on

the scientific discipline of psychology has highlighted a lack of replication studies

(Maxwell, Lau, & Howard, 2015; Stroebe & Strack, 2014). It is possible that other,

unpublished, investigations with null results exist in the area of ERN/empathy. Overall,

the lack of empirical investigations in the area limit the possibility of firm conclusions.

The very least, the results of this thesis emphasise a note of caution on the accepted effect

between ERN and empathy.

There is a possibility that an association exists between state empathy and the

ERN. As the ERN demonstrates an individual’s current state of conflict detection (Chiu

& Deldin, 2007; MacNamara & Hajcak, 2009; Weinberg et al., 2012).perhaps comparing

it with a state-like measure of empathy can reflect a stronger relationship between ERN

and empathy. Future studies could directly compare error monitoring indices with a

measure of state empathy, which encapsulates how an individual situationally performs in

regards to empathy.

Another possibility is that the ERN/empathy relationship is moderated by other

neurophysiological waveforms originating from the anterior cingulate cortex. A few

investigations that have compared the feedback-related negativity (FRN; Bellebaum et

al., 2014; Thoma et al., 2015) and the P3 (Fan & Han, 2008; Han et al., 2008) with

empathy and have found it to correlate. The FRN is an event-related potential (ERP)


145

associated with negative performance feedback during cognitive choice response tasks

(Gehring & Willoughby, 2002). On the other hand, the P3 is a stimulus-locked response

associated with the evaluation of a particular outcome (Friedman, Cycowicz, & Gaeta,

2001). Studies have shown that the ERN and these event-related potentials highly

correlate with each other (Riesel, Weinberg, Endrass, Meyer, & Hajcak, 2013). However,

there are few studies that have looked into the relationship between these waveforms and

empathy. Others have also reported inconsistent findings between these waveforms and

empathy (Bellebaum et al., 2014; Thoma et al., 2015). Like existing ERN/empathy

literature, these investigations have used non-standardised assessments of empathy, which

could also lead to variation in findings (Chrysikou & Thompson, 2015). Perhaps

exploring these indices in relation to the standardised cognitive and affective empathy

measures may provide more clarity in the neurophysiology of empathy.

LIMITATIONS

There are two primary limitations associated with this thesis. Firstly, this

thesis focused on the assessment of the cognitive and affective empathy framework.

However, other researchers have proposed a possible third dimension of empathy known

as ‘motor empathy’ (Blair, 2005; Bons et al., 2013; Eres & Molenberghs, 2013; Vanman,

2016). Motor empathy is proposed to incorporate more automatic empathy processes,

including the simulation of another individual (Goldman, 1992; Iacaboni & Dapretto,

2006). Other empathy literature suggests that the observation of others performing an

action will elicit the same neural activations, such as the anterior cingulate cortex (Fan &

Han, 2008; Han et al., 2008; Singer et al., 2004) when performing the actions themselves.

These observations allow the individual to mentally simulate the actions and infer the

associated thoughts and feelings (Fitzgibbon et al., 2016; Heyes, 2010; Hickok, 2009;


146

Eres & Molenberghs, 2013; Vanman, 2016). Perhaps behavioural empathy may be more

likely to relate to this existing dimension.

Secondly, this thesis involved testing typical populations, specifically children

screened to be suitable for the Children’s Activity Program (Chapter 2) and University

students (Chapters 3 and Chapter 4). This limits the generalisability of the results, or

absence thereof. These populations may be restricted in age range and personality

attributes, therefore the results may exhibit attenuated effects. Similarly, strict screening

can also lead to greater performances on experimental tasks in comparison to their

community counterparts (Cox, personal communication, 2016).

FUTURE DIRECTIONS

Firstly, I will discuss the importance of using purpose built cognitive and affective

measures of empathy for future studies. Secondly, I encourage researchers to investigate

and develop a gold-standard measure of empathy for children. Lastly, I encourage future

research to investigate the development of neurophysiological correlates and empathy

from children to adults.

Firstly, future studies must consider using purpose-built, cognitive and affective

empathy measures for reliability and consistency. A longstanding issue within the field of

empathy is the lack of consistency in measurement (Mehrabian, 1969). However, its issue

maintains as many researchers still choose to define it subjectively (Cuff et al., 2014).

With the possibility of using statistical methods to define a construct, it is important to

consider developing and using a measure that validly measures empathy.

Secondly, it must be noted that there are no existing explorations that compare

error monitoring indices and empathy in children. This is due to the lack of gold standard

measures of empathy for children. Furthermore, there are also no explorations to date of

whether a cognitive and affective framework is applicable for existing measures of


147

empathy for children. Factor analytic explorations of existing child measures would be

encouraged prior to exploring the relationship between performance monitoring and

empathy.

Lastly, studies have indicated greater dissociations of cognitive and affective

processes in children (Decety & Michalska, 2010; Killgore & Yurgelun-Todd, 2007).

However, these processes are potentially moderated by developmental differences

between males and females. Future studies exploring cognitive and affective empathy

processes between males and female from a developmental perspective can provide

insight of the possible moderating factors between ERP correlates and empathy.

CONCLUSION

Psychology is limited in which we try to define a construct like empathy which

exists beyond our measures. Nevertheless, this thesis forewarns future literature to be

cautious of significant associations. Greater clarity in empathy measurement is needed

prior to localising its processes. Furthermore, incorporating statistical validation of

empathy measures can help clarify its associated neural correlates. Future research must

consider using more standardised assessments of empathy, prior to generalising it to

clinical contexts. As the DSM-5 proposes the use of ‘empathy’ as a future potential

diagnostic criterion (American Psychiatric Association, 2013), there lacks a gold standard

measure of assessing this criterion. At present, assessment of empathy is based on

observations of poor social functioning, DSM-5 specific items from clinical

questionnaires such as, ‘I try to be nice to other people. I care about their feelings’

(Strengths and Difficulties Questionnaire; Meltzer, Gatward, Goodman, & Ford, 2000), or

personality inventories that measure traits associated with high levels empathy (Lee &


148

Ashton, 2004). It is important to clarify the discrepancies in existing literature prior to

using it in a clinical setting.


149

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