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Body Image and Cognitive Bias i

Attention and memory bias for body image and health related information using an

Emotional Stroop task in a non-clinical sample.

By

Kate Mulgrew

Bachelor of Psychology (Honours)

Supervisor: Dr Nathan Moss

Associate Supervisor: Dr Doug Mahar

A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor

of Philosophy

School of Psychology and Counselling

Institute of Health and Biomedical Innovation

Queensland University of Technology

2008

Body Image and Cognitive Bias ii

Keywords

Body image, Emotional Stroop, attention, memory, cluster analysis, classification,

Body Image and Cognitive Bias iii

Acknowledgements

I would first like to acknowledge and sincerely thank my supervisors Dr

Nathan Moss and Dr Doug Mahar for their continued support, invaluable feedback,

and generosity with their time. Finishing a PhD is no easy task, but neither is

supervising one. You were both there for me as mentors, but also as friends. I am so

grateful for the knowledge I have gained from working with you both. I honestly

don’t think I would be where I am today without the opportunities you both provided

me to help my career advancement. I would especially like to thank Nathan for his

attention to detail while reading through numerous drafts, and to Doug for taking the

time to write the Stroop program for me. The financial support and resources of QUT

are also gratefully acknowledged.

I would also like to give a big thank-you to my postgraduate colleagues for the

friendship, support, and many hours of laughter that you provided. I would not have

emerged (somewhat) sane without you. A special thanks to my family for all the

support and encouragement they provided. Nothing ever seemed quite so bad after

eating Mexican food and drinking margaritas with my sister. Another special thanks

to my work colleagues at ACU, especially Anne, for your enduring faith in me, the

support you provided, and listening to me complain constantly about the PhD. Thank-

you also to the students who thoughtfully ask me every semester whether I am a

doctor yet. Finally, I would like to thank the participants of this study who gave up

their time to take part in this research, and to anyone who has taken interest in my

research. Sincere thanks to the external markers who provided thorough and useful

feedback.

Body Image and Cognitive Bias iv

Statement of Original Authorship

The work contained in this thesis has not previously been submitted for a degree or

diploma at any other higher education institute. To the best of my knowledge and

belief, the thesis contains no material published or written by another person except

where due reference is made.

Name: Kate Mulgrew

Signed:

Date:

Body Image and Cognitive Bias v

Abstract

It has been proposed that body image disturbance is a form of cognitive bias wherein

schemas for self-relevant information guide the selective processing of appearance-

related information in the environment. This threatening information receives

disproportionately more attention and memory, as measured by an Emotional Stroop

and incidental recall task. The aim of this thesis was to expand the literature on

cognitive processing biases in non-clinical males and females by incorporating a

number of significant methodological refinements. To achieve this aim, three phases

of research were conducted. The initial two phases of research provided preliminary

data to inform the development of the main study.

Phase One was a qualitative exploration of body image concerns amongst males and

females recruited through the general community and from a university. Seventeen

participants (eight male; nine female) provided information on their body image and

what factors they saw as positively and negatively impacting on their self evaluations.

The importance of self esteem, mood, health and fitness, and recognition of the social

ideal were identified as key themes. These themes were incorporated as psycho-social

measures and Stroop word stimuli in subsequent phases of the research.

Phase Two involved the selection and testing of stimuli to be used in the Emotional

Stroop task. Six experimental categories of words were developed that reflected a

broad range of health and body image concerns for males and females. These

categories were high and low calorie food words, positive and negative appearance

words, negative emotion words, and physical activity words.

Phase Three addressed the central aim of the project by examining cognitive biases

for body image information in empirically defined sub-groups. A National sample of

males (N = 55) and females (N = 144), recruited from the general community and

universities, completed an Emotional Stroop task, incidental memory test, and a

collection of psycho-social questionnaires. Sub-groups of body image disturbance

Body Image and Cognitive Bias vi

were sought using a cluster analysis, which identified three sub-groups in males

(Normal, Dissatisfied, and Athletic) and four sub-groups in females (Normal, Health

Conscious, Dissatisfied, and Symptomatic). No differences were noted between the

groups in selective attention, although time taken to colour name the words was

associated with some of the psycho-social variables. Memory biases found across the

whole sample for negative emotion, low calorie food, and negative appearance words

were interpreted as reflecting the current focus on health and stigma against being

unattractive. Collectively these results have expanded our understanding of processing

biases in the general community by demonstrating that the processing biases are

found within non-clinical samples and that not all processing biases are associated

with negative functionality.

Body Image and Cognitive Bias vii

Table of Contents

Keywords……. ................................................................................................ ii

Acknowledgements .......................................................................................... iii

Statement of Original Authorship .................................................................... iv

Abstract………… ............................................................................................ v

List of Tables .................................................................................................. xiii

List of Figures .................................................................................................. xv

Chapter One: Body Image Disturbance in Non-Clinical Samples: A General

Overview of the Literature. .............................................................................. 1

1.1 Overview ........................................................................................ 1

1.2 Definition of Key Terms ................................................................ 1

1.3 Significance of the Problem ........................................................... 2

1.4 Vulnerability Factors ...................................................................... 4

1.4.1 Weight and Self-Esteem .................................................. 4

1.4.2 Attitudes Towards One’s Body ....................................... 5

1.4.3 Eating Disorder Symptomology ...................................... 6

1.4.4 Dysfunctional Behaviours as Weight Loss Strategies ..... 7

1.4.5 Other Psychopathology ................................................... 9

1.4.6 Drive for Muscularity ...................................................... 10

1.5 Classification and Sub-Typing of Body Image .............................. 11

1.5.1 Sub-Typing Eating Disorders .......................................... 12

1.5.2 Sub-Typing Non-Clinical Samples ................................. 14

1.6 Critique of the Literature ................................................................ 15

1.7 Summary of the Chapter ................................................................ 17

Chapter Two: Theoretical Review: Biased Attention and Memory for

Disorder Consistent Material .......................................................................... 19

2.1 Introduction .................................................................................... 19

2.2 Key Theories .................................................................................. 21

2.2.1 Markus’s Self-Schema Theory ........................................ 21

2.2.2 Williamson et al. Information Processing Model ............ 23

2.2.3 Thompson et al.’s Cognitive Processing Model. ............. 24

2.2.4 Vitousek and Hollon Schema Framework ...................... 25

2.2.5 Depression and Anxiety Models ..................................... 26

2.2.5.1 Williams et al. Integrative Model ......................... 27

2.2.5.2 Beck’s Schema Theory and Content Specificity

Hypothesis ........................................................................ 30

Body Image and Cognitive Bias viii

2.2.6 Bower’s Network Theory ................................................ 31

2.2.7 Encoding of Personal Information .................................. 33

2.3 Integration of Theories and Summary of Theoretical Review

Chapter.. ............................................................................................... 34

Chapter Three: Attention and Memory Bias for Body Image Information ..... 37

3.1 Overview of Chapter ..................................................................... 37

3.2 Background to the Research ......................................................... 37

3.3 The Stroop task as a Measure of Attentional Bias ........................ 38

3.4 The Emotional Stroop Task .......................................................... 39

3.4.1 The Emotional Stroop Task with Eating Disorders ........ 41

3.4.2 The Emotional Stroop Task in Non-Clinical Samples .... 46

3.4.3 Methodological Limitations ............................................ 51

3.4.3.1 Card vs Computer technique .............................. 51

3.4.3.2 Blocked vs Random Presentation of Words ...... 52

3.4.3.3 Response Strategy .............................................. 53

3.4.3.4 Order of Tasks .................................................... 54

3.4.3.5 Stimulus Sets ...................................................... 54

3.4.3.6 Sample Limitations ........................................... 56

3.4.4 Limitations Addressed in the Current Project ................ 58

3.5 Memory Bias ................................................................................. 59

3.6 Summary of Attention and Memory Bias Chapter ....................... 64

Chapter Four: Overview of Research Program ................................................ 65

4.1 Aims, Research Questions, and Hypotheses .................................. 65

Chapter Five: A Qualitative Exploration of Body Image Concerns Amongst

Men and Women From a Non-Clinical Population (Phase One) .................... 69

5.1 Overview of Chapter ...................................................................... 69

5.2 Method ........................................................................................... 69

5.2.1 Participants ..................................................................... 69

5.2.2 Procedure ....................................................................... 70

5.2.3 Data Analysis ................................................................. 72

5.3 Results ............................................................................................ 73

5.3.1 A Healthy Body Image Does Not Negatively Affect

One’s Life ............................................................................... 73

5.3.2 Having a Strong Self-Esteem / Self Image is a Protective

Factor ..................................................................................... 75

5.3.3 Importance of Health and Fitness .................................. 76

Body Image and Cognitive Bias ix

5.3.4 Recognition of Societal Ideal ......................................... 78

5.3.5 Mood Can Affect How Events are Interpreted .............. 80

5.3.6 Other Factors .................................................................. 81

5.4 Discussion ...................................................................................... 82

5.4.1 Summary of Findings ...................................................... 82

5.4.2 Limitations ...................................................................... 87

5.5 Summary of Chapter ...................................................................... 88

Chapter Six: Selection of Stroop Stimuli (Phase Two) ................................... 89

6.1 Overview of Chapter ...................................................................... 89

6.2 Stage One and Two: Generation of Stroop categories and words . 89

6.2.1 Method ............................................................................ 89

6.2.1.1 Procedure ........................................................... 89

6.2.2 Results and Discussion ................................................... 90

6.3 Stage Three: Rating and Final Selection of Experimental Stroop

Words .................................................................................... 94

6.3.1 Method ............................................................................ 94

6.3.1.1 Participants .......................................................... 94

6.3.1.2 Procedure ............................................................ 94

6.3.1.3 Materials ............................................................. 94

6.3.1.4 Method of Analysis ............................................. 95

6.4.1 Results and Discussion .................................................. 95

6.4 Stage Four: Matching of Neutral and Experimental Words ....... 96

6.4.1 Method ............................................................................ 96

6.4.1.1 Procedure ............................................................ 96

6.4.2 Results and Discussion ................................................... 96

6.5 Summary of Phase Two ................................................................. 99

Chapter Seven: Cognitive Method used to Assess Biased Processing (Phase

Three) .............................................................................................................. 100

7.1 Method ......................................................................................... 100

7.1.1 Participants ...................................................................... 100

7.1.2 Design ............................................................................. 102

7.1.3 Materials ......................................................................... 102

7.1.3.1 Emotional Stroop task ......................................... 102

7.1.3.2 Distracter Task .................................................... 104

7.1.3.3 Incidental Memory Task ..................................... 104

7.1.3.4 Demographic Form ............................................. 104

Body Image and Cognitive Bias x

7.1.3.5 Hunger Levels ..................................................... 104

7.1.3.6 Negative Mood .................................................... 104

7.1.3.7 Reasons for Exercising ....................................... 105

7.1.3.8 Dysfunctional Behaviours ................................... 105

7.1.3.9 Eating Disorder Behaviours ................................ 105

7.1.3.10 Body Mass Index .............................................. 106

7.1.3.11 Self-Esteem ....................................................... 106

7.1.3.12 Social Functioning ............................................ 106

7.1.3.13 Body Attitudes .................................................. 106

7.1.3.14 Drive for Muscularity ....................................... 108

7.1.3.15 Dietary Restraint ............................................... 108

7.1.4 Procedure ........................................................................ 108

Chapter Eight: Results and Discussion for Biased Cognitive Processing (Phase

Three)…….... .................................................................................................. 110

8.1 Preliminary Analyses ..................................................................... 110

8.1.1 Overview of Analyses ..................................................... 110

8.1.2 Data Screening ................................................................ 110

8.1.3 Scoring of the Cognitive Data ......................................... 111

8.1.3.1 Scoring of the Stroop Data .................................. 111

8.1.3.2 Scoring of the Memory Data ............................... 111

8.1.4 Identification of Sub-Groups: The Use of Cluster

Analysis ............................................................................ 112

8.1.4.1 Overview of the Cluster Analysis Technique .... 112

8.1.4.2 Results of the Cluster Analysis for Women ....... 113

8.1.4.3 Results of the Cluster Analysis for Men ............ 119

8.2 Results and Discussion for Females .............................................. 124

8.2.1 Descriptive Information .................................................. 124

8.2.2 Biased Attention .............................................................. 125

8.2.2.1 The Role of Demographic Factors in Biased

Attention ........................................................................ 128

8.2.2.2 The Role of Vulnerability Factors in Biased

Attention ........................................................................ 130

8.2.2.3 Sub-Group Differences on Biased Attention .... 136

8.2.3 Biased Memory ............................................................... 137

8.2.3.1 Percentage of Words Recalled ........................... 137


Body Image and Cognitive Bias xi

Attention ......................................................................... 139

8.2.3.3 Sub-Group Differences on Biased Memory ...... 141

8.2.3.4 Relationship Between Biased Attention and

Memory .......................................................................... 143

8.2.4 Error Rates ...................................................................... 141

8.3 Results and Discussion for Males .................................................. 145

8.3.1 Descriptive Information .................................................. 145

8.3.2 Biased Attention .............................................................. 145

8.3.2.1 The Role of Demographic Factors in Biased

Attention ........................................................................ 146


Attention ........................................................................ 146

8.3.2.3 Sub-Group Differences in Biased Attention ..... 151

8.3.3 Biased Memory ............................................................... 153

8.3.3.1 Percentage of Words Recalled ........................... 153


Attention ......................................................................... 154

8.3.3.3 Sub-Group Differences on Biased Memory ...... 155

8.3.3.4 Relationship Between Biased Attention and

Memory .......................................................................... 156

8.3.4 Error Rates ...................................................................... 156

8.4 Sex Differences in Biased Cognitive Processing ........................... 158

Chapter Nine: General Discussion of Phase Three Findings and Theoretical

Integration ……... ............................................................................................ 161

9.1 Overview ..................................................................................... 161

9.2 Integration of Past Research and Theories ..................................... 161

9.2.1 High- and Low-Calorie Food Words .............................. 162

9.2.1.1 Integration of Findings with Past Research for

Females ........................................................................... 164


Males ............................................................................... 169

9.2.1.3 Theoretical Explanations .................................... 171

9.2.2 Positive and Negative Appearance Words ...................... 173


Females .......................................................................... 174


Body Image and Cognitive Bias xii

Males .............................................................................. 178

9.2.2.3 Theoretical Explanations ........................................ 180

9.2.3 Negative Emotion Words ................................................. 183


Females ........................................................................... 183


Males ............................................................................... 185


9.2.4 Physical Activity Words ................................................... 189


Females ........................................................................... 190


Males ............................................................................... 191


9.2.5 Sex Differences in Cognition .......................................... 193

9.3 Summary of Chapter. ..................................................................... 194

Chapter Ten: Overview, General Discussion, and Conclusions ...................... 196

10.1 Overview ...................................................................................... 196

10.2 Integration of Key Findings ......................................................... 197

10.3 Theoretical and Practical Implications ......................................... 200

10.4 Strengths, Limitations, and Suggestions for Future Research ..... 205

10.4.1 Strengths of the Research .............................................. 205

10.4.1.1 Sample Characteristics ..................................................... 205

10.4.1.2 Methodological Refinements ............................... 206

10.4.2 Limitations of the Research .......................................... 208

10.5 Summary of the Chapter .............................................................. 211

References ........................................................................................................ 212

Appendix A: Information and Consent Forms used in the Qualitative Study

Of Phase One ................................................................................................... 237

Appendix B: List of Questions and Prompts for Interview in Phase One ....... 240

Appendix C: Non-Copyrighted Questionnaires used In Phase Three ............. 241

Appendix D: Information and Consent Forms used in Phase Three ............... 244

Appendix E: Overview of Cluster Analytical Technique and Sub-Group

Formation ......................................................................................................... 247

Body Image and Cognitive Bias xiii

List of Tables

Table 6.1: Initial List of Words Generated and Expected Categorisation.

Words Proceeded with an “*” Indicate Final Selection …………93

Table 6.2: Frequency of Usage and Word Length Data Used for Matching

the Experimental and Neutral Categories ……………………..... 97

Table 8.1: BAQ Sub-Scale Scores for the Four Cluster Solution in

Women…………………………………………………………...114

Table 8.2: Differences in Demographic and Social Functioning Scores

Between the Four Sub-Groups Identified in Women

(Standard Deviations in brackets) …………………………….....117

Table 8.3: Descriptive Data for the MBSRQ Sub-Scales for the Three

Cluster Solution in Males……………………...…………………121

Table 8.4: Differences in Demographic and General Well-Being Scores

Between the Three Clusters Identified in Males ……………..….124

Table 8.5: Descriptive Statistics and Reliability Values for the Mood

and General Well-Being Measures in Women …………………..125

Table 8.6: Descriptive Statistics and Reliability Values for the Body

Image Measures in Women ……………………………………...126

Table 8.7: Mean Reaction Time and Interference Indexes (in Milliseconds)

for the Stroop Colour-Naming Task in Females (N = 144) …...…127

Table 8.8: Correlations Between Hunger Levels and Interference Indexes

in Women ……………………………………………………..….129

Table 8.9: Percentages and Stroop interference scores for women who

reported engaging in risky eating and weight management

techniques in the last six months (N = 144). Significant

differences in interference scores are noted with“*” ………….…133

Table 8.10: Mean interference scores for women classified as restrained

or unrestrained eaters on the Stroop categorie……………………135

Table 8.11: Mean Stroop Interference (in Msec) Scores Across the

Sub-Groups Identified in Women ……………………………..…136

Table 8.12: Percentage of Words Recalled Across Each Category for

Females …………………………………………………...…...…138

Table 8.13: Percentage of Words Recalled for Women who Reported

Engaging in Risky Eating and Weight Management

Techniques in the Last Six Months (N = 144). Significant

Body Image and Cognitive Bias xiv

Differences in Interference Scores are Noted with “*” …….……142

Table 8.14: Percentage of Words Recalled in Each of the Experimental

Categories from the Stroop Task Across Sub-Groups

in Women ……………………………………………….…...…..143

Table 8.15: Mean number of colour naming errors made on the Stroop

task by females…………………………………………………..144

Table 8.16: Descriptive Statistics and Reliability Values for the Mood and

General Well-Being Variables in the Male Sample …………..….145

Table 8.17: Descriptive Statistics and Reliability Values for the Body

Image Variables in the Male Sample ………………………….…147

Table 8.18: Mean Reaction Times and Interference Indexes

(in Milliseconds) for the Stroop Task in Males (N = 54) ……......148

Table 8.19: Percentage of Men who Reported Engaging in Risky Eating

and Weight Management Techniques in the Past Six Months

and Stroop Interference Scores (N = 54) …………………….…..150

Table 8.20: Mean Stroop Interference (in Milliseconds) by Sub-Group

in men. Standard Deviations in Parentheses ……………….....…152

Table 8.21: Percentage of Words Recalled Across Stroop Categories

for Males …………………………………………………..….….154

Table 8.22: Percentage of Words Recalled by Sub-Group in Men…………....156

Table 8.23: Mean number of colour naming errors made on the Stroop

task for males……………………………………………………..157

Table 8.24: Means (and Standard Deviations) for Stroop Interference

Scores Between Males (N = 55) and Females (N = 143) .............158

Table 8.25: Means (and Standard Deviations) for the Percentage of Words

Recalled Between Males (N = 55) and Females (N = 143) ..........159

Table 8.26: Means (and Standard Deviations) for the Number of Errors

Made During the Emotional Stroop Task for Males and

Females………………………………………………………….160

Body Image and Cognitive Bias xv

List of Figures

Figure 1.1: Williamsons’ et al. (2002) cognitive information processing model of

body image disturbance………………………………………….…24

Figure 5.1: Summary of Inter-Relationships Between Key Themes Identified in the

Qualitative Analysis…………………………………………….….82

Body Image and Cognitive Bias 1

Chapter One: Body Image Disturbance in Non-Clinical Samples

1.1 Overview

This thesis presents a program of research designed to increase our knowledge

about the spectrum of body image disturbance and biased cognitive processing within

non-clinical samples. This chapter provides a background to the research by defining

key terms and examining the prevalence and consequences of body image

disturbance. A range of psycho-social vulnerability factors that have been implicated

in body image disturbance will be briefly reviewed. Chapter Two will then outline the

cognitive theoretical framework used, while Chapter Three will examine the role of

biased attention and memory in more depth. Following this literature review, Chapters

Five to Eight will outline the research conducted, followed by a general discussion of

the results of the program of research.

1.2 Definitions of Key Terms

Body image can be defined as the internal view that one holds about their

outer appearance (Garner, 1997). This internal view is subjective, and may be

influenced by a variety of factors including norms prevalent in ones' culture, biology,

history of weight fluctuations, media, social pressure, and individual characteristics;

all of which interact in complex ways to form an unstable representation of ones' body

shape and size (Slade, 1994; Rievers & Cash, 1996). Generally, body image is thought

to involve three components: an attitudinal component, a cognitive component, and a

behavioural component (Slade, 1994).

In this thesis, the term body image disturbance is used to denote a broad range

of problems associated with one’s body image, such as disturbances in eating

behaviours, excessive exercising, and unhappiness with one’s appearance or body

shape. Body image disturbance occurs on a spectrum ranging from individuals who

are only mildly concerned with their appearance to those for whom body image

concerns severely impact on day to day living. According to Levine and Piran (2004),


“negative body image refers not only to body dissatisfaction but also to excessive

cognitive and behavioural investment in one’s physical appearance in defining one’s

sense of self” (p. 57). That is, negative feelings about appearance may translate into a

negative view of the self.

A specific type of body image disturbance is body dissatisfaction. Perhaps one

of the most widely used terms, and widely measured constructs in the literature, body

dissatisfaction is commonly defined as the degree of satisfaction or dissatisfaction

with one’s body. This can be a global measure (i.e., how satisfied are you with your

general appearance?) or can focus on specific bodily areas (i.e., how satisfied are you

with your stomach/thighs/hips?). In this thesis the term body dissatisfaction is defined

as a specific type of body image disturbance.

1.3 Significance of the Problem

Much of the existing literature has focused on clinical levels of body image

disturbance, despite figures indicating that a larger proportion of people fail to reach

this level of disturbance (Cash & Hicks, 1990). According to the DSM-IV-TR, the

Eating Disorder Anorexia Nervosa currently afflicts about 0.5% of women, while

Bulimia Nervosa affects slightly more women at approximately 1-3%. The respective

rates for these conditions in males are about half that seen in women (American

Psychiatric Association; APA, 2000). Community based research into body image

disturbance predominantly conceptualises this construct as dissatisfaction with

various aspects of body shape and physical appearance. When asked whether women

are satisfied with their appearance and/or shape, the majority of respondents answer

“no” (Tiggemann & Lynch, 2001). Most women report they want to be thinner

(Frederick, Peplau, & Lever, 2006), but also more toned and muscular (Butler &

Ryckman, 1993). While men also report similar levels of body dissatisfaction (Pope,

Phillips, & Olivardia, 2000), their dissatisfaction with appearance can translate into

either wanting to loose weight, or to be more muscular (Drewnowski & Yee, 1987).


Thus, current estimates of the prevalence of body dissatisfaction indicate that non-

clinical body image concerns affect a substantially larger proportion of the population

than do clinical concerns.

The importance of understanding body image disturbance is underscored by

the variety of psychological problems associated with disordered eating patterns and

dysfunctional body images. Kenardy, Brown, and Vogt (2001) found that frequent

dieting was associated with poorer mental health. Women who reported dieting five

times or more in the previous year were 45% more likely to be depressed than non-

dieters. Body dissatisfaction and any subsequent dieting have also been found to

produce psychological and physical distress (Garner & Wolley, 1991), health risks

associated with weight cycling (Lissner et al., 1991), nutritional deficits (Kenardy et

al., 2001), and a substantially increased risk of death from cardiovascular problems

(Jeffrey, 1996). In the United States, it is reported that Anorexia Nervosa has the

highest mortality rate compared to any other type of psychiatric condition, and can be

as costly to treat as Schizophrenia, whereas Bulimia Nervosa and Binge Eating

Disorder are similar in treatment cost as Obsessive Compulsive Disorder (Agras,

2001).

Some women who are dissatisfied with their appearance may subjectively see

their body shape as larger than what it is objectively. Tiggemann and Lynch (2001)

reported that over 50% of Australian women wanted to be thinner, despite being

within a normal weight range. Cash and Hicks (1990) reported that women and men

who classified themselves as overweight, when objectively they were of normal

weight, reported poor well-being, such as low life satisfaction, and feelings of

loneliness and depression. Based on a large Danish sample, 10% of women who were

objectively underweight considered their body weight to be too heavy (Kjærbye-

Thygesen, Munk, Ottesen, & Krüger Kjær, 2004). Thus, belief about one’s

appearance seems to be more important than actual body size (Cash & Hicks, 1990).


Two decades ago body dissatisfaction was so prevalent it was labelled a

normative discontent (Rodin, Silberstein, & Striegel-Moore, 1984) of epidemic

proportions (Hutchinson, 1982). Ten years ago, a worldwide survey by Psychology

Today revealed that over half of women surveyed were dissatisfied with their overall

appearance and weight. Alarmingly, 39% of female respondents said they would

forego between three and five years of their lives if they could be their ideal weight

(Garner, 1997). Australian prevalence rates reflect this ‘normative discontent’ and

show a large proportion of the population experiencing some form of body image

disturbance. For example, 77% of Australian adolescent girls (Grigg, Bowman &

Redman, 1996), and over half of Australian women (Tiggemann & Lynch, 2001)

report that they want to thinner. Whereas women receive pressure from society to be

thin, men receive pressure to be larger and bulkier (Cohane & Pope, 2001). Men are

equally as likely to report wanting weight gain as loss (Drewnowski & Yee, 1987).

1.4 Vulnerability Factors

A number of vulnerability factors have been identified in the research that

precipitate or are typically associated with body image disturbance. A brief review of

some of these vulnerability factors follows, and demonstrates the multifaceted nature

of body image disturbance. The reader is reminded that the aim of Phase Two of the

research was to identify the key vulnerability factors within non-clinical males and

females.

1.4.1 Weight and Self-Esteem

Self-esteem is the extent to which one is satisfied with themselves (Rosenberg,

1965). Low self-esteem has been recognised as a risk factor in the development of

body image disturbance in women (Paxton & Phythian, 1999) and men (Green &

Pritchard, 2003). It may also interact with other variables such as perfectionism to

make an individual more vulnerable to body dissatisfaction (Cooley & Toray, 2001).

Self-esteem contributes highly to the prediction of body satisfaction. For example,


Monteath and McCabe (1997) found that self-esteem contributed significantly to the

prediction of various measures of body satisfaction, including deviation from societal

ideal. Thus, self-esteem plays an important role in understanding body image.

Body weight is typically measured by the Body Mass Index ratio (BMI: kg /

m²) which provides an indication of adiposity relative to ones height. Monteath and

McCabe (1997) found that a higher BMI was related to higher levels of body

dissatisfaction among women. These researchers also found that BMI was a

significant predictor of various measures of body dissatisfaction. Self esteem may

interact with BMI and determine how much importance is placed on weight. For

example, women with lower self-esteem and/or a larger body weight are generally

more dissatisfied with their bodies than other women (Monteath & McCabe, 1997).

1.4.2 Attitudes Towards One’s Body

Attitudes towards one’s body have typically been measured as feelings of

body dissatisfaction (Ben-Tovim & Walker, 1991). Body dissatisfaction is recognised

as one of the key features of eating disorders (APA, 2000), even though it is much

more common in non-disordered individuals. However, extreme feelings of body

unattractiveness and disgust have been shown to be important discriminators between

those with eating disorders and non-eating disordered individuals (Ben-Tovim &

Walker, 1992).

A range of negative attitudes towards one’s body are frequently found among

women such as high levels of body dissatisfaction, perceived pressure to diet, and

feelings of fatness (Lam, Stewart, & Leung, 2002). The importance of feeling

attractive was demonstrated by Stokes and Frederick-Recascino (2003) who found

that women with higher levels of satisfaction with their appearance and feelings of

attractiveness also had high levels of life satisfaction. Level of attractiveness has also

been found to be a frequent comparison issue amongst adolescent girls, who report

comparing their attractiveness to models and celebrities (Jones, 2001). As such, it is


proposed that feelings of attractiveness are a fundamental aspect for understanding an

individual’s experience of body image.

Feeling fat is an affective component of body image (Slade, 1994). Research

has indicated that women often report feeling fat when objectively they are of normal

weight (Tiggemann, 1996). Consistent with this finding, Ben-Tovim and Walker

(1991) report only a moderate correlation between BMI and feeling fat. Thus, women

can feel fat regardless of their objective weight. Subjective feelings of fatness appear

to be an important variable in understanding body dissatisfaction. Tiggemann (1996)

found that feelings of fatness added unique variance in the prediction of body

dissatisfaction over and above objective and cognitive variables. Feelings of fatness

have also been shown to be a dominant concern in non-clinical populations (Ben-

Tovim & Walker, 1991). These feelings of fatness have been shown to be the most

frequently cited reason for dieting amongst adolescent girls (Wertheim, Paxton,

Schutz, & Muir, 1997). Thus, a range of attitude’s held by individuals need to be

included in the measurement of body image.

1.4.3 Eating Disorder Symptomology

Drive for thinness has been recognised as a key risk factor for the development

of Anorexia (APA, 2000). Drive for thinness refers to excessive thoughts about

dieting, increased importance placed on weight, and strong desire to be thin, over and

above that which is regarded as normal (Garner, Olmsted, & Polivy, 1983). Like body

image, drive for thinness has been recognised as a multidimensional construct

encompassing perceptual, affective and behavioural components (Sands, 2000). A

drive for thinness may be the outcome of a perceived discrepancy between ones’

actual and ideal figures. Therefore, drive for thinness may be the process of reducing

feelings of body dissatisfaction (Sands, 2000).

The importance of drive for thinness in non-clinical samples was demonstrated

by Wiederman and Pryor (2000) who compared disordered women and non-


disordered college women. In both groups, drive for thinness accounted for unique

variability in the prediction of body dissatisfaction beyond that of other variables such

as depression and bulimia. In fact, after the effects of drive for thinness had been

removed, the severity of bulimia was no longer related to amount of body

dissatisfaction. Thus, drive for thinness is an important element in understanding body

dissatisfaction in non-clinical samples as well (Wiederman & Pryor, 2000).

Other eating disorder symptomology have been found in high levels within

university samples. Using the Eating Attitudes Test (Garner & Garkinkel, 1979),

Clarke and Palmer (1983) found that 11% of female respondents reporting similar

levels of eating and weight pathology as women with anorexia. Australian women

show similar prevalence rates. Using the Eating Disorder Inventory (Garner, Olmsted,

& Polivy, 1983), Ball and Lee (2002) also found high levels of eating disorder

symptomology in a sample of Australian women aged between 19 and 24 years. Grigg

et al. (1996) found that 33% of Australian adolescent females reporting disordered

eating, such as binge eating, vomiting to achieve weight control, and continued weigh

loss attempts while classified as underweight. Over half of the sample (57%) reported

engaging in unhealthy dieting behaviours. An American study revealed that 69% of

women had reporting using diet pills (Ceclio et al., 2006). Therefore, individuals

within a non-clinical sample report a range of risky weight management techniques.

The presence of these symptoms has been shown to be predictive of the later

development of eating disorders (Polivy & Herman, 1985). As such, eating disorder

symptomology is important to consider within non-clinical samples.

1.4.4 Dysfunctional Behaviours as Weight Loss Strategies

In addition to eating disorder symptomology, there are a range of

dysfunctional behaviours that are also important for the understanding of body image.

One of these dysfunctional behaviours is dieting. The average age of onset for dieting

in Australian women has found to be 15.4 years (Kenardy et al., 2001). During this


adolescent period, body weight and shape are changing dramatically, and many girls

report dieting to compensate for these changes. One Australian study has found that

57% of adolescent girls reported engaging in unhealthy dieting, and an additional 36%

were classified as engaging in extreme dieting (use of laxatives, slimming tablets etc

to achieve weight loss; Grigg et al., 1996). It appears that this figures remain stable

into early adulthood, with Kenardy et al. (2001) reporting that 46% of Australian

women aged from 18 to 23 years had engaged in dieting behaviours in the last year.

Thus, a large proportion of young Australian women engage in dieting behaviours.

If dieting alone is not perceived to produce the desired weight loss, then other

compensatory strategies may be used. These include excessive exercising and purging

behaviours such as the use of laxatives, vomiting, and slimming tablets. Australian

studies indicate that 9% of non-clinical adolescent females report vomiting, 6% use

diet pills and laxatives, and 3% used diuretics to achieve weight loss (Wertheim et al.,

1992). Again, similar figures for purging behaviours are found in Australian young

adult women, with figures ranging between 2.7% for non-dieters, to 33% for frequent

dieters (Kenardy et al., 2001). These figures illustrate that dysfunctional weight loss

behaviours are not restricted to those with clinical eating disorders.

Unlike the above behaviours, exercise has a range of positive health outcomes

(DiBartolo & Shaffer, 2002). However, engaging in excessive amounts of exercise

can result in a range of negative physical and psychological results. Excessive

exercise is defined in terms of duration, frequency, and intensity (Hausenblas &

Downs, 2002). Not all individuals, however, exercise for weight loss reasons. In one

of the first empirical investigations to explore the reasons that men and women

exercise, Silberstein, Striegal-Moore, Timko, and Rodin (1988) developed the

Reasons for Exercise Inventory from which seven reasons emerged: exercising for

weight control, for fitness, health, improving body tone, physical attractiveness,

mood, and enjoyment. Men and women cited similar reasons for exercising, however


women reported exercising significantly more for weight control. Eating disorder

symptomology was found to be associated with the Weight Control and Mood

Regulation subscales. Thus, while some forms of exercise are engaged in as part of a

healthy lifestyle, other types of exercising may be related to body image disturbance.

Therefore, the type of exercise, and the reasons for engaging in exercise are important

variables to consider.

1.4.5 Other Psychopathology

In addition to the core disturbances in eating, weight, and shape found in

those with body image disturbance, a range of comorbid mental disorders have been

noted. Negative body image and depression have been found to be associated (Levine

& Smolak, 2002), and negative emotionality was found to be one of the strongest

predictors of developing an Eating Disorder (Leon, Fulkerson, Perry, & Cudeck,

1993). It has been proposed that the current ideals of attractiveness and thinness,

coupled with the importance placed on these attributes, leads to depression in women

(McCarthy, 1990). A number of body image disturbance factors (such as body

dissatisfaction and thin-ideal internalisation) have been found to be predictive of

increases in levels of depression (Stice & Bearman, 2001). For example, Stice,

Hayward, Cameron, Killen, and Taylor (2000) found that a combination of body

image disturbance factors was able to significantly predict which adolescent girls

would develop depression. Australian research has also found that early onset of

dieting is associated with depression (Kenardy et al., 2001).

The role of perfectionism in eating disorders has been well established by

research (Davis, Claridge, & Fox, 2000; Pumariega & LaBarbera, 1986; Bastiani,

Rao, Weltzin, & Kaye, 1995; Hewitt, Flett, & Ediger, 1995). Perfectionism is a

multidimensional construct involving unrealistic standards and fear of, and

overemphasis on, failure (Hewitt et al, 1995). Within Anorexia perfectionism

manifests itself as an all-encompassing drive to achieve a thin physique (Pumariega et


al., 1986; Hewitt et al., 1995). Bastiani et al. (1995) found that women with Anorexia

scored substantially higher than controls on various measures of perfectionism, a

pattern that persists even after weight gain. More recent research has found that

maladaptive aspects of perfectionism to be strongly related to weight preoccupation,

as measured by the Drive for Thinness, Body Dissatisfaction, and Bulimia subscales

of the Eating Disorder Inventory (Davis et al., 2000).

According to Fichter, Quadflieg, and Rehm, (2003), general well-being and

the presence of comorbid psychopathology are just as important, and in some cases

more important, than core eating disturbance variables in understanding and

predicting the course of the disorder long term. Therefore, general well-being is

important to consider in understanding body image disturbance.

1.4.6 Drive for Muscularity

The ideal male standard is becoming increasingly larger and more muscular; a

standard difficult for most men to achieve. The desire to achieve muscle mass has

been termed a drive for muscularity (McCreary & Sasse, 2000), and has been

identified as an important facet of men’s body image (McCreary, Sasse, Saucier, &

Dorsch, 2004). Men with a high drive for muscularity experience dissatisfaction with

their muscle mass and see themselves as less muscular than they are objectively

(McCreary & Sasse, 2000). In order to achieve this lean yet muscular physique, men

may engage in excessive weight-lifting, body building, or steroid use (Smolak,

Murnen, & Thompson, 2005).

Exercise is an important means for weight regulation, improving tone, and

generally engaging in a healthy lifestyle. Men spend a considerable amount of time

exercising and weight lifting (Phillips & Drummond, 2001), however exercise is not

always related to improvements in body satisfaction (Paxton et al., 1991). Given the

female ideal is becoming increasingly toned (Butler & Ryckman, 1993), drive for

muscularity is an important facet to consider in male and female body image.


1.5 Classification and Sub-Typing of Body Image

A number of psycho-social vulnerability factors have been identified as

important to understanding male and female body image. Research remains limited,

however, as to the inter-relationship between these variables. Further, group

comparisons are often conducted between ‘high’ and ‘low’ scorers on a particular

measure; a distinction that is sometimes arbitrary. To overcome this limitation, and to

improve our understanding of the spectrum of body image disturbance within non-

clinical samples, it is useful to consider classification of individuals along body image

measures.

Classification presents a useful means for the identification of

psychopathology. It aids clinicians with the detection and treatment of mental

disorders by specifying traits that are characteristic of each condition. The purpose of

The Diagnostic and Statistical Manual of Mental Disorders (DSM IV-TR; APA,

2000) is to provide a classificatory system for just this purpose. Currently,

classifications of Anorexia Nervosa and Bulimia Nervosa are available to explain the

extreme dissatisfaction with appearance and weight that some women experience.

There is, however, no empirical data to identify what groups exist in non-eating

disordered samples. There may exist, for example, a group of women who evidence a

high level of weight preoccupation without any resultant negative affect. The

identification of sub-groups of body image disturbance will aid group comparison by

providing empirical support for non-clinical typologies. Although clinical levels of

eating disorders are not of prime focus in the current research, the literature will be

briefly reviewed to illustrate the classification process.

The available research focuses primarily on the clinical eating disordered

groups and questions whether the range of symptomology occurs on a continuum. The

continuity/discontinuity (or dimensional versus categorical) debate revolves around

how disorders are best conceptualised. The continuity perspective states that


symptomology occurs on a continuum ranging from no symptoms, to moderate

disturbance, to high levels of symptomology indicating a clinical disorder (Franko,

Wonderlich, Little, & Herzog, 2004; Trull & Durrett, 2005). That is, differences

between clinical eating disordered groups and normal body image disturbance occurs

in degree of symptomology, or along certain dimensions. For example, a woman with

weight and eating disturbances could experience the same symptoms as a woman with

Anorexia, only to a lesser extent.

Conversely, the discontinuity perspective states that there are distinct

differences between those experiencing the particular disorder, and those with sub

clinical levels of the disorder (Franko et al., 2004; Trull & Durrett, 2005). That is,

individuals with an eating disorder experience a distinct set of symptoms compared to

those with non-clinical disturbances. This perspective states that classification is most

useful with a categorical approach, and is the approach adopted by the DSM-IV-TR

(APA, 2000). Research that examines the continuity / discontinuity perspective

typically starts with pre-defined groups and determines whether the differences

between the groups are qualitative or quantitative in nature.

1.5.1 Sub-Typing Eating Disorders

The DSM-IV-TR (2000) has identified two sub-types of Anorexia Nervosa:

restricting type and binge-eating/purging type. The restricting sub-type describes

those women who primarily engage in restrictive activities (i.e., fasting and dieting) to

achieve weight loss. The binge-eating / purging type describes those women who

primarily engaging in binging (eating a large amount of food in a small period of

time) and purging (removing ingested food through vomiting, diuretics, etc.);

behaviours similar Bulimia Nervosa. Two sub-types of Bulimia Nervosa have also

been specified. The purging sub-type is used to describe individuals who engage

primarily in purging behaviour, while the non-purging sub-type does not involve the


purging behaviours (such as vomiting) traditionally associated with Bulimia Nervosa

(APA, 2000).

Sub-typing eating disorders based on bulimic and anorexic behaviours has

provided evidence for a range of categories. Bulik, Sullivan, and Kendler (2000)

found support for six distinct clusters in a large sample of 2100 female twins. Three

classes emerged that were similar to the DSM classifications of Anorexia Nervosa,

Bulimia Nervosa, and Binge Eating Disorder. The other three presented as a range of

sub-clinical manifestations. There were two groups that had low weight without

eating disorder symptomology, and one group that had shape and weight

preoccupations without low weight. While these findings support the distinction of a

range of eating disorders, the findings also suggest that an additional cluster of

women exist that hold some eating disordered symptomology.

Two distinct sub-types have been found in women with Bulimia Nervosa

(Stice & Agras, 1999) and Binge Eating Disorder (Grilo, Masheb, & Wilson, 2001).

Sub-typing occurred along a range of dieting and negative affect dimensions. A “pure

dietary” sub-type emerged which is consistent with the traditional idea of dieting

being a core symptom of Bulimia. A “mixed dietary-depressive” sub-type also

emerged, which had similar levels of dieting behaviours to the pure dietary sub-type,

in addition to a range of other symptoms such as depression and other mood disorders,

and social maladjustment. The emergence of these two sub-types across two disorders

supports the validity of these categories and the methodologies used.

The value of sub-typing along a wider range of dimensions was demonstrated

by Westen and Harnden-Fischer (2001). Taking into account personality variables in

addition to weight and eating disturbances, three distinct subgroups of eating

disorders emerged. The first cluster was a “high-functioning / perfectionist” group

that generally had a low level of pathology with high levels of self-critique. The

second cluster was “constricted / over controlled” type that primarily consisted of


Anorexia-like symptoms and a range of personality problems. The third type that

emerged was an “emotionally dysregulated / under controlled” group. This group

primarily consisted of Bulimia-like symptoms and was associated with extreme

personality problems.

The research summarised above is intended to be a brief overview to show the

utility and importance of sub-typing. The review has show that even within clearly

defined categories, not all individuals experience the same set of pathology.

1.5.2 Sub-Typing Non-Clinical Samples

The research presented above has looked at sub-groups within eating

disorders. There is very little literature assessing what groups, if any, exist in the

normal or non-clinical population. Only one study could be located on this topic

(Garner, Olmsted, & Garfinkel, 1983; Garner, Olmsted, Polivy, & Garfinkel, 1984) 1.

The aim of their paper was to explore the differences between women diagnosed with

the eating disorder Anorexia Nervosa and women experiencing weight preoccupation

drawn from a university and ballet sample. Women were classed as weight

preoccupied if they scored highly on the Drive for Thinness subscale of the Eating

Disorder Inventory (EDI; Garner, Olmsted, & Polivy, 1983). This sub-scale assesses

“preoccupation with body weight, excessive concern with dieting, and morbid fear of

becoming fat” (Garner et al., 1983, p. 13). A non-weight preoccupied group consisted

of women who scored on the lower end of the drive for thinness subscale. This group

was included to complete the range of weight preoccupation.

Very similar EDI scores were found in the groups characterised by high

weight preoccupation and Anorexia Nervosa. Only three subscales (Ineffectiveness,

Interoceptive Awareness, and Awareness) differentiated the two groups, with the

1 This paper was published by Garner, Olmsted, and Garfinkel (1983) in the International Journal of Eating Disorders. A similar paper by Garner, Olmsted, Polivy and Garfinkel was published the following year (1984) in the journal of Psychosomatic Medicine. Even though the two papers address slightly different research questions, the data appears identical. As such, for the purposes of this literature review, these papers will be treated as the one study.


‘Anorexic’ group scoring higher on these three. There were significant differences

between the high- and low-weight preoccupied groups on all subscales except

Interpersonal Distrust. These results indicate that the differences between the clinical

and non-clinical, yet symptomatic, group are minimal. It does indicate, however, that

there exists a set of symptoms that are found only in those with clinical levels of

eating disorders; differences that were only found by examining mean levels of

symptomology.

A cluster analysis was used to determine what subgroups would emerge within

the weight preoccupation categories. Garner et al. (1983) found that two distinct

clusters of weight preoccupation emerged. The first cluster represented a group of

weight preoccupied women who had elevated scores equal to the group with Anorexia

on all EDI subscales except Ineffectiveness. Thus, this subscale seems to be important

in distinguishing clinical from sub-clinical women. The second cluster represented

those women who experienced some symptomology on the Drive for Thinness, Body

Dissatisfaction, and Perfectionism sub-scales, but otherwise low scores on all other

subscales. Garner et al. (1983) noted that this group probably represents “normal

dieters” (p. 18) that pursue weight loss, and experience some level of body

dissatisfaction, yet who’s symptoms are not associated with any significant

psychopathology.

These findings add strength to the argument that non-clinical groups must not

be presumed homogeneous in nature. The findings from Garner et al. (1983) show

that there are groups of women who experience very similar symptomology to clinical

eating disordered groups. The inclusion of these women in control groups that are

presumed to be asymptomatic represents a serious methodological flaw.

1.6 Critique of the Literature

While the above literature is useful in demonstrating the multi-faceted nature

of body image disturbance, a number of significant limitations were identified and


addressed in this thesis. Problems were identified surrounding the limited way

classification has been applied to non-clinical samples and the general limitations of

using self-report measures.

The eating disorders of Anorexia Nervosa and Bulimia Nervosa have been

extensively researched, while there is comparatively little knowledge about body

image and body image disturbance in non-clinical populations. According to Ben-

Tovim and Walker (1991), the understanding of atypical levels of eating and weight

disturbances cannot be reached without an understanding of the more typical, normal

experiences. Previous research has typically viewed non-eating disordered individuals

as a homogeneous control group, without exploring the psychological make up of the

individuals comprising this group. As control groups are typically drawn from

university samples, and university women have been shown to experience high levels

of body image disturbance (e.g., Frost & McKelvie, 2004), there is a need for a

greater understanding of body image in non-clinical populations. Thus, the current

thesis addressed this limitation by expanding the knowledge of non-eating disordered

individuals.

The use of sub-typing has been used in limited way in past research. Sub-

typing along already defined categories such as “Anorexia” or “Bulimia” restricts the

emergence of groups showing mixed symptoms. This thesis did not constrain group

membership by using pre-defined categories but rather explored what categories

naturally emerged. Limited research has examined sub-groups within a non-clinical

sample. Again, this research sub-typed within an already defined group (i.e., weight

preoccupation). Finally, previous sub-typing was been along limited dimensions (i.e.,

eating disorder symptomology or personality variables). This project expanded this

line of research by classifying along a wider range of psycho-social variables.

A review of the literature indicated an array of variables that are important for the

understanding of body image. Traditionally, these are psychosocial variables


measured using self-report questionnaires. The crucial problem with this type of

measurement is the reliance on self-report methods for a problem that may not be

amenable to self reporting. Symptom severity may be downplayed purposively, or

certain aspects of body image disturbance may not be available to conscious

awareness (Couturier & Lock, 2006; Nisbett & Wilson, 1977; Vitousek, Daly, &

Heiser, 1991). Therefore, in addition to psychosocial variables, cognitive

methodologies were also used. The current thesis proposed that increased attention,

processing, and memory for disorder-consistent material serves to develop or maintain

the disorder (Mathews & MacLeod, 1994). Information regarding this biased

processing of information is not accessible using traditional paper and pencil tests, but

is best tested by using techniques that directly assess automatic attention and selective

memory. A limitation noted by some researchers (Vitousek & Hollon, 1990; Vasey,

Dalgleish & Silverman, 2003) is that the previous research has been driven by

measurement rather than theory. Thus, a solid framework for understanding body

image that recognises psychosocial and cognitive factors is needed. The application of

cognitive approaches, in addition to psychosocial variables, will allow a multifaceted

understanding of body image and body image disturbance.

1.7 Summary of Chapter

This chapter identified a number of psycho-social variables that have been

associated with body image disturbance. From this review, the variables of self-

esteem, BMI, perfectionism, eating disorder behaviours, drive for muscularity,

reasons for exercising, and general body image attitudes were highlighted as

potentially important constructs. Despite the range of variables identified, past

research has simply compared ‘high’ versus ‘low’ scorers on a particular construct.

The use of classification was then reviewed which showed how past research has

attempted to identified sub-groups of body image disturbance along a combination of

variables. However a number of limitations were noted in this research which


precludes an understanding of sub-groups within a non-clinical sample. Additionally,

a number of researchers have questioned the usefulness of self report measures when

assessing body image disturbance. Finally, limited research has been conducted on

male body image concerns. Therefore, in order to provide a more thorough and multi-

faceted understanding of body image in both males and females, both cognitive and

psycho-social variables were used in the current thesis.

Cognitive Bias and Body Image 19

Chapter Two: Theoretical Review: Biased Attention and Memory for Disorder Consistent

Material.

2.1 Introduction

A number of researchers have argued that body image disturbance represents a

form of cognitive bias (Sarason, Sarason, & Pierce, 1994; Vitousek & Hollon, 1990;

Williamson, 1996). A range of cognitive distortions have been noted in women with

eating disorders. Women with Anorexia Nervosa and Bulimia Nervosa hold a set of

negative attitudes and beliefs around food and body shape that have been implicated in

not only the development, but also in the maintenance of these disorders (Dobson &

Dozois, 2004; Sarason et al., 1994). Research has shown that individuals who are chronic

dieters become obsessed and preoccupied with food and eating, and show hypervigilance

towards food (Lee & Shafran, 2004). Individuals with extreme body dissatisfaction will

report seeing themselves as either larger (women; Tiggemann & Lynch, 2001), or less

muscular (men; Pope et al., 2000) than they are currently. These findings implicate the

role of biased cognitive processing in body image disturbance.

In order to provide objective measures of this biased processing, paradigms such

as the Stroop task (Channon, Hemsley & de Silva, 1988), dot probe task (Boon,

Vogelzang, & Jansen, 2000), dichotic listening task (Schotte, McNally, & Turner, 1990),

and incidental memory task (Unterhalter et al., 2007) have been adapted for use from the

wider cognitive literature. Studies that focus on biased encoding examine how self-

relevant information is processed. The time taken to respond to such stimuli is measured

and considered to be related to the salience or personal relevance of the information

(Ingram & Reed, 1986). It is proposed that threatening information has a lowered


threshold for awareness receives disproportionately more attention (Williams, 1988).

Studies that focus on biased retrieval typically use incidental memory tests as an indirect

measure of the degree of elaborative processing the information has received (Ingram &

Reed, 1986). In evolutionary terms, this rapid detection and recall of threat information

meant survival for a species (Beck & Clark, 1997).

If biased cognitive processing does underlie body image disturbance, then it’s

important to determine that the processing is indeed selective for certain types of

information. According to MacLeod (1996) one way to test this is to measure

performance on a simple task in the presence of different types of distracters. Neutral

distracters or stimuli that have little importance to the individual should not impede task

performance. The presence of more threatening distracters produces a shift in attention

toward this distracter and away from the primary task. This division of attention results in

decreased task performance (MacLeod, 1996; Sarason et al., 1994). One of the most

common techniques to measure this division of attention has been the Stroop task (Ball et

al., 2004). A detailed discussion of the Stroop task and how it has been adapted as a

measure of psychopathology is provided in Chapter Three.

Individuals with a high level of body image disturbance may view stimuli related

to negative appearance and/or food as threatening. For instance, dieters are expected to

avoid foods with a high fat content and instead eat foods with a lower caloric content

(Huon & Brown, 1996). When food or appearance related distracters are presented,

performance deficits on a primary task may be experienced by those with higher levels of

body image disturbance. It may be that those with Eating Disorders, or higher levels of

body image disturbance, have difficulty in ignoring such information. Moreover, this


interference effect is selective in nature, in that performance for emotionally neutral

information should not be affected.

2.2 Key Theories

Many of the theories to be reviewed regarding biased processing implicate

schemas as responsible for selective attention and memory. Schemas can be viewed as

cognitive stores of information that serve to simplify and guide the processing of further

schema-consistent information (Winfrey & Goldfried, 1986). Schemas may be

conceptualised as similar to memories or the organisation of knowledge and experiences

that one accumulates. Schemas can both facilitate or interfere with the processing of

information depending on the nature of the task (Winfrey & Goldfried, 1986; Vitousek &

Hollon, 1990). Schemas are proposed to guide both the encoding and retrieval of self

relevant information (Winfrey & Goldfried, 1986). The enhanced retrieval of schema

consistent information is proposed to result from the highly developed and interconnected

knowledge structures found in schemas (Winfrey & Goldfried, 1986). More detail on the

role of schemas in biased processing is now provided in the theoretical review.

2.2.1 Markus’s Self-Schema Theory

According to Markus (1977), schemas represent cognitive stores of information

that serve to process self-relevant material. As a person cannot attend to every aspect of

their environment, schemas determine what information a person will attend to in any

given situation. The development of these schemas serves to selectively filter information

and “determine whether information is attended to, how it is structured, how much

importance is attached to it, and what happens to it subsequently” (Markus, 1977, p. 64).

The information that a person will attend to is dependent upon which attributes that the

person deems important to their self-concept.


In an experimental test of self-schemata, Markus (1977) concluded that people

who place more importance on a particular dimension (schematic) will process

information in a different manner to people for whom that dimension is irrelevant

(aschematic). Schematics showed significantly faster response times to material directly

related to their schema (that is, facilitated processing), and longer response times to

indirectly related material (interference). What these findings suggest is that there are

systematic differences in the processing of information about the self between schematic

and aschematic individuals.

The way most people view themselves will undoubtedly have a body image

component. However, there will be differences in the importance attributed to this aspect

of the self. Markus et al. (1987) proposed that there are at least two types of self-schemas:

universal and particularistic schemas. Universal schemas are those that everyone holds to

some degree or another. For example, everyone is aware of what they look like, but only

in certain people will physical appearance be an important aspect of self-concept. These

people hold a particularistic schema for physical appearance.

Markus et al. (1987) explored the self-schema of body weight to see its effects on

the processing of weight-relevant information. According to Markus et al. (1987), a body

weight schema is an example of both a universal and particularistic schema. It can be

expected that everyone has at least a rudimentary schema concerning their bodies. For

some people, body weight will be an integral component of their self-concept and they

will selectively attend to and process weight related information in the environment.

These people can be considered schematic for body weight, while those for whom body

weight isn’t important can be considered aschematic (Markus et al., 1987).


Evidence for self schemas have been noted in depression (Segal, Hood, Shaw, &

Higgins, 1988; Segal & Vella, 1990), and for assertiveness (Bruch, Kaflowitz, & Berger,

1988). Both of these studies, despite the use of different methodologies, have concluded

that schematic individuals processed self-relevant information differently than

aschematics.

2.2.2 Williamson et al. Information Processing Model

Williamson, Stewart, White, and York-Crowe (2002) describe a cognitive

information-processing model that describes how people with body image disturbance

process information. This model is presented in Figure 1.1. Central to this theory is the

notion of schemas which guide the processing of information. For instance, a body shape

schema will process body shape-related information. The more a schema is activated, the

denser the associated networks become within the schema. These networks link together

memories, and can also become associated with certain situations or cues. Thus a schema

can be activated by certain stimuli. The result of this activation is cognitive bias in

susceptible people. This may be evidenced as selective attention where only certain

aspects of the stimuli are attended to (such as selective interference in a Stroop task), or

selective memory (where body-related words are recalled more than neutral words). The

result of this selective attention is increased negative affect (Williamson et al., 2002). For

example, a woman who has a highly developed body schema may attend to stimuli in the

environment that are associated with fatness. As such, she experiences changes in mood

including increased depression, and increased body dissatisfaction. These changes may

feed back to the body schema, wherein negative affect is then associated with fatness

related stimuli.


Figure 1.1: Williamsons’ et al. (2002) cognitive information processing model of body

image disturbance.

2.2.3 Thompson et al.’s Cognitive Processing Model

The presence of a body image schema which helps to organise information related

to one’s view of his or her physical appearance is central to this perspective (Thompson

et al., 1999). These body image schemas serve to interpret information in the

environment, but may do so in a biased manner. If the person has a negative view of their

appearance, then they may misinterpret information in a social situation, and concepts in

their mental encyclopaedia or lexicon such as “fat” take on negative connotations such as

‘bad’. The body image schema may dominate other larger domains such as views of the

self, so that physical appearance becomes the main determinate of self-worth. This

perspective predicts that physical appearance related information will be processed in a

halla

This figure is not available online. Please consult the hardcopy thesis available from the QUT Library


facilatatory or inhibitory manner. Either way, the presence of a negative body image

schema will affect “the normal input, storage, and retrieval processes of the mental

encyclopaedia” (Thompson et al., 1999, p. 272).

2.2.4 Vitousek and Hollons’ Schema Framework

Building upon the work of Markus and colleagues (Markus et al., 1977, 1987) on

schemata, Vitousek and Hollon (1990) proposed a framework to understand the cognitive

processes of women with Anorexia Nervosa and Bulimia Nervosa that emphasised a

central role of schemata. These researchers propose that the persistence of eating

disorders can be partly explained by the presence of organised cognitive structures. These

schemas influence the thought content, emotional experience, interpretation, and

behaviour of eating disordered individuals because of the close association between

issues of weight and self-concept.

Vitousek and Hollon proposed three types of schemata relevant to the eating

disorders: self-schemata, weight-related schemata, and weight-related self-schemata. The

notion of self-schemata is consistent with Markus’ ideas that these schemata serve to

process information regarding the self. Weight-related schemata process information

relevant to the stereotypes associated with varying weight status. However, these views

are not distinctive to eating disordered individuals, and may be more reflective of cultural

ideals of weight. It is proposed though, that these views are more emotionally charged

and elaborate in individuals with eating disorders. Weight-related self-schemata represent

the specific concerns of the eating disordered, as they process information about the self

in terms of weight. This is a more enduring schemata, while fleeting thoughts about body

weight can be influenced by environmental factors.


In order to determine the presence of these schemata, Vitousek and Hollon (1990)

proposed a number of indicators indicative of schemata. Only those relevant to the

current research will be reviewed here, but the interested reader is directed to the work of

Markus and colleagues (1977, 1987), and Vitousek and Hollon (1990) for further

information.

One indicator is the ease and speed of information processing related to the

schemata. This has been typically assessed using the Stroop task which will be reviewed

in depth in the next chapter. Briefly, it is assumed that interference (typically longer

response times) when presented with body weight and shape words is reflective of the

core concerns of the individual. In addition to biases in attention, evidence for schemata

can also be inferred from memory biases. Selective memory for weight related

information serves to support the central role of weight concerns in the lives of eating

disordered individuals. Thus, the cognitive biases of selective attention and memory, are

all assumed to be indicative of core schemata that selectively seek out information

consistent with current concerns.

The preceding four theories indicate that schemas hold a central role in

understanding biased information processing. All of these theories have been developed

within the body image literature. Pertinent theories within the depression and anxiety

literature have also been identified that are expected to contribute to the understanding of

biased processing. These are briefly reviewed below.

2.2.5 Depression and Anxiety Models of Biased Cognitive Processing

Research examining biased attention and memory has proliferated within the

depression and anxiety literature. This literature is guided by strong theoretical

frameworks and carefully designed methodologies. Given that biased cognitive


processing is still a relatively new area within the body image literature, theories from the

depression and anxiety literature will be adapted. Just as negative affect material is salient

for those with depression, and social threat information is important within anxiety, food

and appearance related stimuli are anxiety-provoking within body image. In all cases, self

relevant emotional information is expected to produce biased cognitive processing.

Three of the most influential cognitive theories in the depression and anxiety

literature are those of Beck (Beck 1967; Beck & Clark, 1988), Bower (1981), and the

work by MacLeod and colleagues (e.g., Williams, Watts, MacLeod, & Matthews, 1988,

1997). Each of these theories and their applicability to understanding body image will be

explored in turn

2.2.5.1 Williams et al. integrative model. One of the more developed cognitive

theories of the role of emotion in depression and anxiety was proposed by Williams et al.

(1988, 1997). First, the distinction is made between priming and elaboration. Priming is

defined as an automatic process (a process that occurs rapidly without conscious

awareness) that activates the internal representation of a stimulus. This priming serves to

make that representation more accessible when only part of the stimulus is later

presented. For example, if the word “failure” is presented, activation will enhance

recognition (i.e., make it more readily accessible) if part of the word is later presented.

Elaboration however is a strategic process that requires conscious thought. It involves

linking new concepts together, or strengthening old connections, which also makes the

stimulus more retrievable. This elaboration process may be responsible for the negative

connotations that food and body shape take in those with severe body image disturbance.

One distinctive feature of this theory is that it can explain why biases in attention

do not always lead to biases in memory, a finding that other theories had difficulty


explaining. Williams et al. (1988, 1997) explain that the processing requirements of

selective attention tasks involve priming, while memory tasks require elaboration of

previously learnt material. Depending upon the amount of elaboration that occurred at

encoding, and the nature of the disorder (i.e., whether attention is directed toward or

away from the stimuli), enhanced or inhibited retrieval of previously viewed emotional

material may occur.

The model states that information received is subject to a pre-attentive ‘decision

mechanism’. This decision mechanism makes a judgement about the affective valance of

the stimulus that determines resource allocation. State anxiety can affect the output of the

decision mechanism. Williams et al. (1988, 1997) state that non-emotionally disturbed

people hold a protective response that will serve to deflect processing of the material

away. Thus, the negative information does not receive processing. Anxious individuals

however, allocate resources toward processing of the negative material at this pre-

attentive stage. Negative material is deemed to be a priority, and thus processed as such.

The process of resource allocation is affected by trait anxiety, reflecting a more

permanent manner of responding.

The increased resource allocation acts as a prime so that the word is more likely

to be reproduced at a later stage. Williams et al. (1988, 1997) state that this priming will

occur irrespective of any elaboration that may also occur.

Elaboration occurs at the strategic stage of encoding. It involves making

associations between the stimulus and other items in memory. A further decision

mechanism evaluates the affective valence of the stimulus, as in the pre-attentive stage.

More elaborate encoding between the stimulus and associated concepts will occur if extra

resources are allocated, which serve as cues for later retrieval. The effects of elaborative


coding differ between disorders, such that conditions is which attention is directed away

from the stimulus (such as phobics’) will not involve elaboration with the feared

stimulus, which serves to make it less retrievable.

The distinction between automatic and strategic processing is also relevant to the

retrieval of the information from memory. Williams et al. (1988, 1997) suggest that

certain types of emotional states can affect both the passive (the information is recalled

without conscious awareness) and active (active search strategies are employed) recall of

information. Accordingly, anxiety is associated with passive recall through priming,

whereas depression affects strategic retrieval.

One of the strengths of this approach is the dissociation between priming and

elaboration whereby a bias may occur with one, but not the other. Other theoretical

approaches in depression and anxiety have not made this distinction which may be

important for understanding the differences between the two disorders. It also provides a

means for understanding the interaction between state and trait anxiety. There may be

differences in the resource allocation in response to threat between those with high and

low trait anxiety. For example, it may be that individuals with high trait anxiety respond

by allocating additional resources toward processing of the threat, while individuals with

low trait anxiety direct attention away from the threat (Williams et al., 1988, 1997).

State anxiety can produce selective processing of threat related material in the

environment. This is similar to the diathesis-stress models of mental disorders, wherein

persistent maladaptive attention toward threat (diathesis) coupled with state anxiety

inducing selective attention (stress) can result in pathological levels of anxiety. The

interplay of state and trait anxiety can be seen as cyclical in nature – selective attention to


threat in the environment results in a perception of the world being threatening in nature,

which serves to reinforce the disproportionate attention toward threat.

This theory is one of the most comprehensive models that seek to explain both

anxiety and depression, and is generally supported as a preliminary understanding of the

disorders. The main utility of this model in the current case, is how well it can be applied

to understanding body image. Given that research examining cognitive bias in body

image disturbance is still relatively new, it is difficult to ascertain the applicability of this

model to body image. For example, it not clear whether attention is directed toward or

away from, stimuli relating to negative appearance and food. Just as words signifying

social embarrassment present a threat for individuals with panic disorder (Ehlers,

Margraf, Davies, & Roth, 1988), it may be argued that words surrounding negative

appearance are threatening for those with high levels of body image disturbance. This

thesis examined the applicability of Williams et al’s model within body image

disturbance.

2.2.5.2 Beck’s schema theory and content-specificity hypothesis. The central role

of schemas is also emphasised in Beck’s influential theories of depression and anxiety

(Beck 1967; Beck & Clark, 1988). These theories state that processing of information is

guided by schemas. Information consistent with existing schemas is sought out in the

environment, and this information receives additional elaboration and encoding,

compared to schema-inconsistent information. For example, depressed individuals hold

schemata that contain negative information about the self, the world, and the future.

Attention is then drawn toward negative information in the environment consistent with

these schemata. Within anxious individuals the dominant concern is a focus on

threatening information. Threatening stimuli in the environment are attended to,


interpreted, and later recalled in a biased manner. According to Beck (1967, 1991), these

schemas remain dormant between periods of depression or anxiety, until they are

triggered by environment sources. These schemas then become ‘activated’ and guide the

processing of self-relevant information in a maladaptive way.

The content-specificity hypothesis was developed to explain the differences

between anxiety and depression in the types of information that is processed (Beck &

Clark, 1988). This hypothesis states that differences between anxiety and depression are

found in the types of information to which is primarily attended (Greenberg & Beck,

1989). That is, depressed individuals will selectively process negative information, while

anxious individuals will selectively process information related to threat. Support for the

content-specificity hypothesis was found for depression, where individuals with

depression recalled significantly more negative stimuli, and significantly less positive

stimuli, than non-depressed individuals (Greenberg & Beck, 1989).

2.2.6 Bower’s Network Theory

Bower’s network theory (1981) built upon Collins and Quillian’s (1969) semantic

network model. Both theories propose that concepts are stored in memory as nodes.

Similar concepts or nodes are linked together with other concepts through pathways.

When a certain node is activated by information in the environment, then the pathways

connected it to other nodes are also activated; a process known as spreading activation.

A distinctive feature of Bower’s model is the recognition that emotions are also

represented as nodes within the network. When an emotion such as depression is

experienced the corresponding emotional node becomes activated along with connected

nodes. This spreading activation makes information related to the particular mood

disproportionately more accessible. For example, the individual becomes more aware of


concepts relating to depression such as “failure” and thoughts of “I am no good”. This

priming effect is thought be an automatic process, such that the individual may not be

consciously aware of the trigger for the emotion. This theory helps to explain the mood

congruent findings in the cognitive literature such as mood-dependent recall, in that

mood activates or primes certain networks of nodes. Recall is better for mood congruent

information as it’s more salient in memory (Winfrey & Goldfried, 1986).

According to Markus et al. (1987) everyone holds schemas or knowledge stores about

their appearance, however there are differences between individuals in the strength of

these cognitive stores, how elaborate they are, and the ease of accessibility of these

schemas. It may be that schematic individuals have appearance schemas that are closely

linked in memory to negative emotions and views of the self. These schemas may be

readily accessible because they are linked to so many other concepts in a person’s

memory. For example, a woman who is dieting may have a strong and close link between

concepts of “fat” and “food”. Further, these concepts may also be strongly linked and

activation of the concepts primes the activation of the other.

According to MacLeod and Rutherford, (1992) the benefit of Bower’s theory

over Beck’s theory is the distinction between state and trait levels of anxiety. According

to Rofey, Corcoran, and Tran, (2004) “both [models] propose that emotional stimuli

attract disproportionately more processing resources due to the activation of specific

knowledge structures representing threatening information” (p. 42). The key distinction is

that Beck identifies cognitive bias as associated with enduring trait levels of anxiety,

while Bower proposes that state levels of anxiety can influence processing. Individuals

with a clinical level of a particular disorder are likely to experience both state and trait

levels of disturbance. Non clinical individuals however will not be experiencing either


state or trait levels of disturbance, unless specifically primed. MacLeod and Mathews

(1988) found that the relationship between state and trait anxiety was ambiguous. In an

attempt to disentangle the effects of state and trait anxiety, students high and low on trait

anxiety were compared at two points: once when state anxiety was low (early in a

university semester), and again when state anxiety was high (before an important test). It

was found that the effects of state anxiety on cognitive performance differed between

those students with high and low trait anxiety. When state levels of anxiety were low,

neither group showed selective attention on a probe detection task. However, when levels

of state anxiety were high, the students with high trait anxiety showed selective attention

towards threatening information, while those students with low trait anxiety showed

avoidance of the material.

Williams et al. (1988) note a number of findings that are difficult to explain under

Bower’s theory. First, Williams notes that not all types of mood have the same effects on

processing. Attention and memory for positive and negative information are different, in

that negative can sometimes result in reduced elaboration and recall. Additionally, even

when considering different types of negative mood (such as depression and anxiety),

differences are noted in attention and memory. Second, Williams et al (1988) notes that

viewing moods as nodes may be too simplistic. Moods are more diffuse than cognitions,

and often gradually increase in intensity rather than being an ‘all-or-none’ activation.

2.2.7 Encoding of Personal Information

This theory is similar to the classic Levels of Processing work (Craik & Lockhart,

1972; Craik & Tulving, 1975) which showed that information placed in context benefits

from greater elaboration and therefore better recall. A number of researchers have

implicated the importance of selecting personally relevant and meaningful stimuli


(Rogers, Kuiper, & Kirker, 1977; Ulterhalter, Farrell, & Mohr, 2007; Wingenfeld et al.,

2006). For example, Rogers et al. (1977) found that the presentation of personally

relevant information resulted in more stable memories. Rogers et al. used an incidental

memory task where participants were asked questions about a set of words that required

different types of processing ranging from shallow to deep. Their results showed that

participants recalled significantly more adjectives that they had previously rated as

describing them. However, even adjectives that participants rated as “not like them”

benefitted from enhanced recall, indicating that information receives increased processing

in a self referent encoding task. The self referent encoding task also produced higher

recall than words in the semantic condition. Therefore, understanding a word and putting

it into context results in inferior recall compared to applying the word personally. It

appears that simply thinking about how the concept relates to you, regardless of whether

it does or not, results in enhanced recall.

When applied to an Emotional Stroop task, this theory states that words which are

both personally relevant and emotionally valanced will produce greater interference than

words that are simply of an emotional nature (Cohen, Dunbar, & McClelland, 1990;

Wingenfeld et al., 2006). For example, Wingenfeld et al. found that participants were

slower to respond to personally relevant words compared to negatively valanced or

neutral words. This theory highlights the importance of carefully selecting relevant word

stimuli, an issue which is further elaborated in Section 3.4.3.5.

2.3 Integration of Theories and Summary of Theoretical Review Chapter

The purpose of this chapter was to provide a theoretical basis for the

understanding of how biased information processing within psychopathology occurs.

Theoretical development regarding biased processing has occurred more within the


depression and anxiety literature, hence it was deemed useful to consider these models.

The applicability of these models to body image is yet to be tested.

All of the theories reviewed implicate the central role of schemas in guiding

attention. Schemas determine what information is attended to or avoided, the importance

placed on this information, and the amount of elaboration the information receives.

Markus’ self schema theory (Markus et al., 1977, 1987) was the first to identify and

empirically test weight and appearance related schemata. Vitousek and Hollon (1990)

incorporated these ideas into a more comprehensive model of how schemas guide

processing in eating disorders. The central role of schemas is also recognised by

Williamson et al. (2002) and Thompson et al. (1999), with Williamson developing a

model how schemas may be activated by various internal and external cues. Models from

the depression and anxiety literature were explored, and these models highlighted the

important distinction between priming and elaboration (Williams et al., 1988, 1997), and

identified that attention and memory biases occur for information that is self referential

(Rogers et al., 1977) and related to current concerns (Beck & Clark, 1988). The model of

Bower (1981) suggests that memory bias is related to the strength of interconnections in a

network of associated concepts. Women with body image concerns may develop a highly

elaborate set of associations between sense of self, appearance, and mood, such that

negative feelings about one’s appearance become integrated with general feelings of self-

worth. Bower’s model, in addition to subsequent work by MacLeod and Mathews (1988),

implies that state and trait levels of disturbance can influence memory.

To summarise, threatening information is proposed to receive disproportionately

more attention and memory. Exposure to self relevant, content-specific information

activates congruent self-schemata that incorporate the material into already existing,


dense elaborations in memory. The associated biased attention and memory for this

material serves to confirm the processing biases of the schemata. It is therefore

hypothesised that biased attention and memory for body image stimuli will be found in

individuals with high levels of body image disturbance because of their highly developed

schemas around weight and appearance. The following chapter reviews the evidence for

these theories using an Emotional Stroop task.


Chapter Three: Attention and Memory Bias for Body Image Information

3.1 Overview of Chapter

The purpose of this chapter is to review the literature on biased attention and

memory for body image information. A review of the literature will demonstrate that the

Stroop task is the most commonly used method for assessing selective attention. Despite

the Stroop tasks’ wide usage, the existing research suffers from a number of important

limitations which precludes a thorough understanding of how cognitive biases apply to

non-clinical samples. The limitations identified from past research informed the

development of an emotional Stroop task and incidental memory task assessing a wide

variety of body image concerns for both males and females.

3.2 Background to the Research

As Chapter Two demonstrated, over the past 20 years there has been an increased

recognition of the role that cognitive processes play in the understanding of body image

disturbance. The adaptation of methodologies from cognitive psychology has provided a

unique set of tools to assess cognitive disturbances. According to Williamson (1996), “of

the new paradigms from which we might reformulate the concept of body image, I

believe that the cognitive, or information-processing, perspective has the most potential”

(p. 47-48). According to a cognitive framework, psychopathology is thought to arise from

biased processing of negative information. It’s proposed that concerns an individual hold

result in automatic and biased processing of such information in the environment

(Williamson, 1996). For example, it has been noted that individuals experiencing

depression are more likely to notice and remember negative information in their

environment, as opposed to positive information (Watkins, Mathews, Williamson, &

Fuller, 1992). This increased attention, processing, and memory for disorder-consistent


material may help to develop or maintain the psychopathology (Mathews & MacLeod,

1994). Applied to body image disturbance, a person who views themselves as too fat may

be more likely to notice an advertisement of a thin attractive model. This automatic

processing could then result in negative mood and more feelings of fatness within the

individual.

Limited information regarding this biased processing of information is accessible

using traditional paper and pencil tests, which may be open to distortion as they rely on

self report (Beck, Stanley, Averill, Baldwin, & Deagle, 1990; Cassin & von Ranson,

2005; Vitousek, Daly, & Heiser, 1991). Thus, selective processing of disorder-relevant

information is best tested by using techniques that directly assess automatic attention and

the accessibility of emotional information. Research has shown the Stroop task to be a

valid indicator of these biases in attention. In fact, MacLeod (1992) has termed it “the

‘gold standard’ of attentional measures” (p. 12). Measures of biases in attention are

expected to significantly add to the understanding of body image and body image

disturbance.

3.3 The Stroop Task as a Measure of Attentional Bias

The Stroop task (Stroop, 1935) is an example of how a methodology borrowed

and adapted from cognitive psychology has provided an enhanced understanding of a

range of disorders. In the original Stroop task, a range of words were presented and the

participants’ task was to name the colour of ink the word was written in as quickly as

possible. In the colour-consistent condition, there was a match between the colour the

word was written in, and the meaning of the word. For example, the word “blue” would

be presented in blue ink, and the correct response is “blue”. This task presented very little

cognitive demand for participants, and reaction times were typically fast. The colour-


inconsistent condition provides more of a challenge and division of resources for

participants. This time there is a mis-match between the meaning of the word and the

colour used. For example, the word “blue” is now presented in red ink, and the correct

response would be “red”. Delayed reaction times are typically found, as the semantic

meaning of the world detracts attention away from the naming the colour. That is,

participant’s attention was drawn to the meaning of the written word because this is an

automatic process in reading. Participants found it difficult to ignore the most salient

stimuli, the words, even though they were instructed otherwise. This has become known

as attentional bias.

The original Stroop task showed enormous success as a measure of bias in

information processing (Ball et al., 2004). Since the original paper, over 70 years ago,

research using the Stroop task has proliferated, and has been applied to a diverse range of

areas.

3.4 The Emotional Stroop Task

Clinical psychology has since applied the Stroop task to a range of disorders as a

measure of attentional bias. Instead of the names of colours being presented, an

Emotional Stroop task presents words related to a particular disorder. For example, an

individual experiencing a spider phobia may be highly sensitive to words such as “hairy”

and “spider”. Despite being instructed to ignore the meaning of the words and simply

name the coloured ink, attention is automatically drawn to the meaning of the stimuli,

with difficulty experienced ignoring some types of words. Words such as “hairy” are

expected take on a particular salience wherein additional resources are allocated for their

processing. The reaction time for these disorder-related words are expected to be slower

than for neutral words, as difficulty is experienced ignoring the meaning of the words. As


neutral words (such as “table”) are not expected to hold any emotional relevance, the

colour naming task should be easier, and the reaction times for these words quicker. As

the neutral words are typically matched to the target words on a number of important

dimensions (e.g., length, frequency of occurrence in the English language), any

differences in reaction times is due to the emotional salience of the words. Evidence of

attentional bias can be measured two ways: either as the time difference between colour-

naming the target words compared to the neutral matched words (i.e., a within-subjects

design), or the time difference between disordered and non-disordered participants on the

target words (i.e., a between-subjects design). Any difference in reaction times to target

versus neutral words is known as interference. Response to the experimental words can

either be faster than neutral words (facilitation) or slower (interference).

Indeed, research has shown that individuals with a spider phobia do show longer

reaction times when asked to colour name spider related word compared to general threat

words, or colour-conflicting Stroop words (Watts, McKenna, Sharrock, & Trezise, 1986).

This unique effect of spider-related words as compared to general threat words supports

the notion that biased attention is limited specifically to disorder-consistent material; that

is, there is specificity in interference.

Interference effects, as measured by the Stroop task, have been found in a range

of different disorders such as depression (Gotlib & McCann, 1984), anxiety (Matthews &

MacLeod, 1985; MacLeod & Rutherford, 1992) and morbid jealousy (Intili & Tarrier,

1998). For example, Ehlers, Margraf, Davies, and Roth (1988) found that individuals

with both clinical and non-clinical panic disorder showed longer reaction times to colour-

name threat related words than neutral words. Chapter Two proposed that this


preferential attention for information is the result of highly developed schemas developed

around the person’s concerns.

3.4.1 The Emotional Stroop Task with Eating Disorders

The Stroop task has also been applied to Eating Disorders, and to a lesser extent,

eating disturbances in non-clinical samples. This task provides a direct and objective test

of selective attention toward eating, shape, and weight related stimuli. According to Ben-

Tovim, Walker, Fok and Yap (1989), the Stroop task is “cheap, simple to apply, easy to

score, and only takes little time” (p. 686). Additionally, Dobson and Dozois (2004) in

their meta-analysis of the use of the Stroop task in eating disorders conclude that it

provides a useful task for examining attentional bias.

The Stroop applied to body image presents categories of words relevant to the

unique concerns of those with eating disorders, namely food words and body

shape/weight words. For example, participants may be asked to colour-name such words

as “fat”, “chips”, or “ugly”. Women who view their appearance negatively, or who

consistently avoid fatty foods would be expected to show an interference effect. As with

other disorders, interference is measured as increased latencies to colour-name target

words. Given the higher incidence of eating disorder in women than men (APA, 2000),

very few studies have used male participants. Therefore the research reviewed below

focuses exclusively on interference effects in females, with a discussion of possible

effects in males provided under the “Limitations” section 3.4.3.

Early studies into interference effects focused on comparing women with eating

disorders to a control group of women on colour-naming of food words and body shape

words. Channon et al. (1988) found that while both groups showed interference effects

for the food words (i.e., longer to colour name the food words than control words), the


effect was larger for the women classified as Anorexic. No interference effects were

noted for the body shape words in either group. A similar result was found by Ben-Tovim

et al. (1989) who compared Eating Disordered and non-disordered women on colour-

naming of food words and shape words. The results showed that all the women showed

some interference for food words and shape words. The women with Bulimia Nervosa

and Anorexia Nervosa however, took significantly longer to colour name the food words

than did the non-disordered control group. Additionally, the women with Bulimia

Nervosa also showed a significant interference for shape words. Thus, while evidence of

selective attention for body image related information was found in all women, it was

those women with Eating Disorders who showed larger effects.

Collectively, these early studies demonstrated the usefulness of applying a Stroop

methodology to understanding body image concerns. The presentation of stimuli related

to the core concerns of women with Eating Disorders received increased processing

compared to neutral information. Identification of these automatic processing biases may

help to elucidate the ways in which Eating Disorder are developed and maintained.

Additionally, these early findings also suggest that processing biases are not limited to

those Eating Disorders, but may also be found in other women, albeit to a lesser extent.

Since these original studies, research has continued to examine attentional bias in

individuals with Eating Disorders compared to control non-clinical samples. Studies have

either compared the eating disorder groups to each other, or to control groups. The

majority of research has focused on clinical Eating Disordered samples. As such, the

literature focusing on clinical groups will be reviewed prior to the literature concerning

non-clinical groups.


Women with Bulimia Nervosa have shown Stroop interference for eating-,

weight-, shape-, and emotion-related words compared to control groups (Cooper,

Anastasiades & Fairburn, 1992; Fairburn et al., 1991; Jones-Chester et al., 1998; Lokken,

Marx, & Ferraro, 2006). Women with Anorexia Nervosa have evidenced selective

interference for food words (Channon et al., 1988; Green, McKenna, & de Silva, 1994;

Sackville et al, 1998) and body shape words (Green, Corr & de Silva, 1999; Sackville et

al, 1998) as compared to control groups of women. Sakville et al. (1998) expanded the

types of Stroop words typically used and found that women with Anorexia Nervosa show

selective interference for a wider range of stimuli than previously identified. As well as

the usual high calorie food words and the negative weight words, additional categories of

positive weight words, low calorie food words, and positive and negative emotion words

were used. Compared to the non-disordered control group, the women with Anorexia

Nervosa showed a significant interference for negative and positive shape words, a trend

for high calorie food, and no differences for low calorie food or the emotion words.

These results indicate that Anorexia Nervosa may be associated with interference for a

wider range of stimuli, both positive and negatively valanced, than previously thought.

Studies that have included both women with Anorexia Nervosa and women with

Bulimia Nervosa allow finer discrimination between the processing biases in these

disorders. The findings seem to be consistent in suggesting that Eating Disorder groups,

when compared to an assumed homogenous control group, show selective interference.

The findings are inconsistent however, regarding the specificity of the type of

interference and the type of disorder. The most consistent finding is that both groups

show a selective interference for words related to food and eating (Ben-Tovim, et al.,


1989; Cooper & Todd, 1997; Jones-Chester et al., 1998; Perpina et al., 1998). This

confirms the central role that eating and food has in these disorders.

The effects of body size/shape words are less consistent. Some studies have found

interference in women with Anorexia Nervosa and Bulimia Nervosa, (Jones-Chester et al,

1998), other studies report effects in women with Bulimia Nervosa only (Ben-Tovim et

al., 1989) or Anorexia Nervosa only (Cooper & Todd, 1997). Yet other studies, (Perpina

et al., 1998) have found that neither women with Anorexia Nervosa nor Bulimia Nervosa

have shown interference for body/shape words. These inconsistent results may be due to

differences in the type of stimuli used. Very few studies use the same set of words in the

Emotional Stroop task. Recently, attempts have been made to develop a standardised list

of words to overcome this limitation (Cassin & von Ranson, 2005). The problems with

the stimuli used in past research are examined in detail under the “Limitations with

methodology” in section 3.4.3 of this chapter.

To date, only three studies have examined the role of emotion-related words such

as “anxious” or “depressed” within body image disturbance. The comorbidity between

mood disorders and eating disorders leads some researchers to question whether

interference effects are a result of body image concerns, or associated mood disorders

(Cooper, 1995). Sackville et al. (1998) found no differences in interference effects

between women classified as Anorexic, or high and low dietary restraint, although a

mean level interference increased with increasing symptomology. Jones-Chester et al.

(1998) found that only women with Bulimia Nervosa showed interference for these

emotion words, while no interference effects were noted within the non-clinical or

“Anorexic” groups. However, it should be noted that these interference effects were only

found when a blocked presentation of words was used, and no interference effects were


noted in any groups when the emotion words were mixed with other types of words.

Finally, Seddon and Waller (2000) found that younger women (< 21 years of age) with

high bulimic symptomology showed a tendency for avoidance of the information (i.e.,

quicker reaction times to the material), while older women (> 21 years of age) showed

greater attention toward this information. Therefore it appears that selective interference

for negative emotion information may be more specifically related to Bulimia Nervosa,

or Bulimia-like symptoms than to general body image disturbance. This finding does

need to be considered within the results of general research examining the selective

processing of mood-related information which has suggested that people automatically

attend to negative, rather than positive information (Smith et al., 2006). Clearly, more

research is needed to ascertain the nature of these differences.

Given the inconsistent findings of the literature reviewed above, and the recent

increase in papers using the Stroop task, two recent meta-analyses have emerged. Based

on all available Stroop studies investigating Eating Disorders from 1935 to May 2001,

Dobson and Dozois (2004) concluded that attentional bias can be found in, and is

confined to, Eating Disordered samples. The results showed a trend for women with

Bulimia Nervosa to show interference across a range of word categories, while the results

for women with Anorexia Nervosa were more inconsistent, but interference seemed to be

most prominent for body shape related words. This meta-analysis concluded there were

typically no differences between those with dieting concerns and non-clinical samples on

the body Stroop.

One year later, another meta-analysis was published (Johansson, Ghaderi, &

Andersson, 2005a) which used a slightly different selection criteria for study inclusion

and came to a different conclusion than did Dobson and Dozois. Twenty-seven studies


examining food/eating and/or body weight/shape words from 1978 to 2003 that met a

number of criteria were included. Comparisons were made between different word

categories, and between women with Anorexia Nervosa, Bulimia Nervosa, non-Eating

Disordered yet symptomatic groups, and controls. While Dobson and Dozois found no

interference effects in the non-clinical groups, Johansson et al found evidence of small

interference effects. It was concluded that all groups showed longer latencies to colour-

name target versus neutral words, but the effect size was small in the non-clinical groups,

and medium in clinical groups. The Eating Disordered women did show more

interference for all target categories than the non-clinical women. These differences may

be due to the studies included in the analysis, as often the psychological characteristics of

non-clinical control groups are not reported.

These meta-analyses, while supporting the existence of interference effects in

Eating Disordered samples, have not been able to provide a clear answer to whether

attentional biases are found in non-clinical groups. Additionally, given the number of

significant methodological limitations in past research (soon to be described under

section 3.4.3 “Methodological Limitations” of this chapter), any conclusions reached

must be tentative. Clearly, the question of whether biased cognitive processing is found

within non-clinical groups warrants further research.

3.4.2 The Emotional Stroop Task with Non-Clinical Samples

The majority of studies have used non-clinical samples primarily as a control

group against which to compare women with Anorexia and/or Bulimia Nervosa. As such,

these control groups are assumed to be homogenous in nature, when in fact they may

have elevated, albeit not clinical, levels of body image disturbance. Few studies have

specifically focused on non-disordered or sub clinical samples taking into account their


levels of symptomology. In Williamsons’ (1996) review of attentional bias using the

Stroop task, it was concluded that interference was not unique to those with Eating

Disorders. However, it is restricted to those with concerns about their appearance and/or

weight. Thus, the lack of research examining interference effects in non- and sub-clinical

groups is unfounded. The following section reviews the existing evidence of selection

attention for body image related information in non- or sub-clinical samples.

Studies that have looked at symptoms rather than diagnosis have generally

supported selective interference in non-clinical, but symptomatic samples. For example,

Black, Wilson, Labouvie, and Heffernan (1997) found that women classified as restrained

eaters, unrestrained eaters, and Bulimic could not be differentiated by their responses to

weight/shape words. All groups showed interference compared to neutral words. Perpina,

Hemsley, Treasure, and de Silva (1993) found that women who scored highly on restraint

or drive for thinness had comparable colour-naming interference to women with

Anorexia and Bulimia. Drive for thinness was associated with body concerns, while

restraint was associated with food concerns. Cooper and Fairburn (1992) found that

symptomatic dieters (women who had shown eating disorder symptoms previously) along

with Anorexic and Bulimic groups showed significant interference for food words

compared to the neutral words. Thus, eating disorder symptomology may be more

important than actual diagnosis.

However, not all studies have been able to show differences between clinical and

non-clinical groups, even when acknowledging the heterogeneous nature of the control

group. Ben-Tovim and Walker (1991) found no differences between women who scored

high and low on drive for thinness. Both groups were significantly faster at colour

naming target words than the eating disordered groups. However, the control group was


significantly younger, which is problematic as Seddon and Waller (2000) have found that

the type of Stroop bias depended upon age. Lokken et al. (2006) found differences

between women with Bulimia and non-symptomatic women on eating-, weight-, and

shape-related words, but when the Bulimic group were compared against a sub-clinical

group, only differences on the shape words emerged. Additionally, there was no

difference between the normal and sub-clinical group on weight words or shape words.

Other studies have found no differences between dieters and non-dieters on shape words

(Lovell, William, & Hill, 1997), or between high and low restraint groups (Sackville et

al., 1998). Collectively, these findings suggest that the differences between clinical, sub-

clinical, and control groups are not clearly defined. Alternatively, it may be that the

groups of women have not been clearly defined, as each study seems to compare women

on a different set of vulnerability factors.

Studies that have focused primarily on non-clinical samples have again found that

greater interference was associated those who had eating or weight disturbances. Green

and Rogers (1993) concluded that an attentional bias for body shape words and food

words is present in a large proportion of non-disordered females. Interference for food

words appears to be consistently found in women who reported restrained eating. Francis,

Stewart, and Hounsell (1997), and Stewart and Samoluk (1997) found that women

classified as restrained eaters showed interference effects for food words compared to

neutral words, while women classified as low in dietary restraint did not show this effect.

However, Jansen, Huygens, and Tenney (1998) found that restrained eaters did not show

an interference effect for weight and body shape words when words were presented either

subliminally or supraliminaly. The difference between these final two studies may be due

to the differences in cut-off scores used to define the restrained and unrestrained groups,


with Frances et al using a higher cut off. As such, the participants in Francis’s study may

reflect a more extreme, homogeneous group of restrained eaters. Alternatively, it may be

that restrained eaters only show interference for food words (as assessed in Francis’s

study) and not body or weight words (as assessed in Jansen’s study). However, this

discrepancy remains to be resolved in future research.

Changes in mental state prior to the Stroop task is a variant in methodology that

helps to understand how emotional state and attentional bias interact. According to Cash

(1994), certain contexts or events can serve as primes and activate appearance schemas.

Recall that Markus and colleagues (1977, 1987) proposed the existence of schemas,

which determine what information receives attention. Schematic individuals place

importance on that dimension, and process information preferentially compared to

aschematics, for whom that dimension is not relevant.

Priming methods that have been included as variants within the Stroop task

include being weighed in front of a mirror (Labarge, Cash, & Brown, 1998), consumption

of calorie-laden food (Mahamendi & Heatherton, 1993), the presentation of still pictures

representing the thin-ideal (Hargreaves & Tiggemann, 2003), short-term fasting

(Channon & Hayward,1990; Stewart & Samoluk, 1997), and staged interactions focusing

on physical appearance (Tantleff-Dunn & Thompson, 1998). These various priming

techniques have produced conflicting findings with non-clinical samples. Labarge et al.

(1998) reported that the greatest interference for physical appearance words was found in

those women who rated appearance as important, and who were weighed before hand

(i.e., primed schematics). This effect was not present when no prime was used,

suggesting the importance of contextual cues even in women who place a high


importance on appearance. No interference effect was found in aschematic women (those

for whom appearance is not important).

Mahamedi and Heatherton (1993) compared dieters (restrained eaters) and non-

dieters to see if the consumption of a high calorie meal would produce interference in

colour naming food and body shape words. The results indicated that this high calorie

preload affected the cognitive performance of both dieters and non-dieters in naming

body words, although this effect was stronger in dieters. This difference disappeared

without the preload, again supporting the importance of contextual cures. However, there

was no difference in the colour naming of food words even with a preload.

Just as the consumption of high calorie food was shown to affect Stroop

interference, inducing hunger through fasting also appears to produce selective attention.

Channon and Hayward (1990) found that long term fasting (24 hrs) produced interference

effects in both males and females for food words, as compared to neutral words, and for

those who did not fast before the Stroop task. However, it appears that short-term fasting

does not produce comparable results, as Stewart and Samoluk (1997) found that fasting

for 6 hours did not produce interference for food words. The results again point to the

central role of environmental cues in eliciting selective attention in non-clinical samples.

Collectively, these findings suggest that selective interference may not be limited

to those with a clinical Eating Disorder (Green & Rogers, 1993; Francis et al., 1997).

However, this statement warrants further investigation due to the inconsistent findings

reported above. There are a number of significant limitations that need to be addressed in

order to further elucidate the Stroop tasks’ utility in assessing cognitive interference in

clinical and non-clinical samples. A number of these limitations are now discussed.


3.4.3 Methodological Limitations

There are a number of methodological decisions to be made when implementing a

Stroop task, and there is no agreed upon ‘best practise’ identified in past research. A

diverse range of variations within the Stroop task have been used. Some of the biggest

discrepancies between studies have been the use of a card versus computerised technique

to present the words, blocked versus randomised presentation of words, response

strategy, and scoring of interference effects. These issues warrant further discussion, as

they may affect the results and therefore the comparability of studies (Dobson & Dozois,

2004; Faunce, 2002.

3.4.3.1 Card vs computerised technique. Many earlier studies used large cards to

present a matrix of words, and the time taken to respond to all words on the card was

measured via a stopwatch (e.g., Ben-Tovim et al., 1989; Channon et al., 1988). However,

this technique is also commonly used in later studies (e.g., Carter, Bulik, McIntosh, &

Joyce, 2000; Lokken et al., 2006; Stewart & Samoluk, 1997), despite the availability of

more sophisticated methods. Computerised tasks typically present words individually,

and reaction time for each Stroop category is averaged over the various presentations of

the individual words. Computerised tasks, while more time consuming to initially

program, allow the researcher more control over randomisation procedures, more precise

timing, and an enhanced ability to examine or eliminate error rates. However, research to

date has tended to use the card-based methodology more frequently. A recent meta-

analysis found no differences in interference patterns between card and computerised

presentations (Johansson et al., 2005a), however caution should be used when comparing

interference indexes between these two methods, as card-based methods typically do not

allow for randomisation to occur.


3.4.3.2 Blocked vs random presentation of words. Blocked presentation occurs

when all words from one semantic category are presented together. For example, all of

the food words would be presented, followed by all of the neutral words. Randomised

presentation occurs when the order of words across all categories are randomly ordered.

For example, the food and neutral words are mixed together in the same presentation.

Typically, a blocked presentation is used for a card methodology, while computerised

presentation allows for more randomisation to occur. The presentation of large blocks of

semantically related words has been found to influence reaction time in both clinical and

non-clinical samples (Green et al., 1999). Highly associated neutral word lists were found

to produce as much interference in the Anorexic sample as body shape words, while in a

non-clinical group this interference effect for highly associated words was greater than

other control categories with less semantic relatedness. These differences in

methodologies may account for some of the inconsistent findings in past research given

the unstandardised method of word selection.

According to Jones-Chesters et al. (1998), the use of a card-based, blocked

methodology can present both theoretical and practical problems. Theoretically, is it not

clear whether a reaction time taken for the entire card results from specific items, or the

culmination of anxiety from viewing all items. On a practical level, a computerised

presentation allows for finer temporal precision so that reaction times for individual items

can be examined, and anxiety effects are reduced as only one word is viewed at a time.

Additionally, error rates for individual items can be examined and excluded if necessary

(Davidson & Wright, 2002).

In their examination of the differences between mixed and blocked computerised

presentations, Jones-Chesters et al. (1998) found differences for emotion words only, and


only in the women with Bulimia Nervosa. When words were blocked, the Bulimic group

showed significantly more interference for the weight, food, and emotion categories,

while the difference in emotion words was not found using a mixed presentation. For the

Anorexic group, the same pattern of interference was found across presentation types. It

was also noted that the interference effects were greater overall using a blocked

methodology for both Eating Disordered groups, yet not for the non-clinical group. This

finding supports the notion that the sequential presentation of words from some semantic

categories can result in anxiety which in turn interferes with cognitive processing.

The differences between blocked and mixed presentations may only be limited to

emotion-related words, as a recent meta-analysis concluded that method of presentation

for food and body related words did not seem to influence the pattern of interference

(Johansson et al., 2005). Therefore, a computerised task with individual presentation of

words allows for more precise timing, randomisation, and other control procedures to

occur.

3.4.3.3 Response strategy. In a Stroop task, participants can be required to either

respond either verbally or through a button press. Computerised presentation of Stroop

words typically requires participants to respond via coloured keys, although some studies

have required a verbal response in addition to, or instead of, a verbal response (e.g.,

Sackville et al., 1998). A card presentation requires participants to verbally label the

colours of the set of words.

One potential problem noted by Davidson and Wright (2002) with voice-activated

controls, is the possibility of external noises being registered, or response not being loud

enough, which results in a loss of data for those trials. Conversely, the requirement to

press a button as a response allows for mistakes to be measured more accurately. In a


direct comparison of a voice-activated versus button press methodologies, Davidson and

Wright (2002) conclude that the button press is a more sensitive and reliable method, due

to the number of potential problems faced when using voice-activated responses.

Significant differences were however only noted for control participants on the colour-

conflicting condition, while no differences were noted on food categories and body shape

categories. A recent meta-analysis also noted no consistent differences were found in

interference patterns between these two response types (Johansson et al., 2005a).

However, given the practical problems with voice response, a button response is deemed

more appropriate.

3.4.3.4 Order of tasks. A further limitation identified involves the order of tasks in

the experiment. Some studies have given participants questionnaires to fill out before

completing the Stroop task (e.g., Cooper et al., 1992). This may sensitise or prime

participants to the topic, subsequently influencing their Stroop performance. It is

recommended that questionnaires are filled out after the Stroop task to avoid any

potential priming effects these questionnaires may have.

3.4.3.5 Stimulus sets

Perhaps one of the most important methodological limitations is the use of

inappropriate word stimuli. If the words are not carefully selected to reflect the unique

concerns of the particular group, or if they are not carefully matched to appropriate

control words, interpretation of the findings becomes problematic. For example, stimuli

such as “fingernails”, “lipstick”, and “stockings” have been used as appearance words

(Labarge et al., 1998). It is unlikely that these words represent serious appearance

concerns for many women. Further, it is even more unlikely that these words are relevant


for men, who were also included in Labarges’ study. Therefore it is important to ensure

that the words represent highly salient concerns of the particular sample.

Additionally, the use of a combined target category is problematic. Some studies

have presented the target words as one category combining food, weight, and shape

related words (e.g., Fairburn et al., 1991), which does not allow the subtle differences

between word categories and disorders to be partialed out. Thus, in order to gain a clearer

understanding of cognitive interference, more specific target word sets need to be used.

Similarly, a stronger distinction is needed in the research between the categories of

eating/food and body size/shape. For example, Sackville et al. (1998) found that it was

not only high calorie food that anorexics show interference for, but also low calorie

foods, along with both positive and negative shape words. Additionally, highly restrained

eaters show a general interference for food words, rather than just high calorie forbidden

foods (Francis et al., 1997). Thus, research needs to be expanded beyond the traditional

Stroop categories used, as the pattern of interference has important theoretical and

practical implications.

Careful matching of words in the target and neutral categories needs to be made to

allow for proper comparisons in response times (Larsen, Mercer, & Balota, 2006; Lee &

Shafran, 2004). In order to conclude that participants show a slower colour-naming

response to target words, there needs to be a group of words that a comparison can be

made against. As the target words are typically from one semantic category (i.e., shape

words, appearance words), the control words should also be semantically related. This

should ensure that any priming effects are similar across the categories. For example,

Green et al. (1999) have found that highly associated neutral categories produce as much


interference as the target category. That is, participants were as slow to colour name the

highly related neutral words as they were in naming the target words.

Similarly, the list of target and neutral words need to be carefully matched on

parameters that affect word recognition (Larsen et al., 2006). In their paper, Larsen et al

outline compelling reasons, reviewing evidence that demonstrates a slow-down in

recognition for infrequently used words compared to more frequently used words, and

longer words compared to shorter words. As an Emotional Stroop task does not use the

same list of words in each condition (as with a traditional Stroop task), the comparability

of response times is dependent upon the lexical equivalence of the word lists. Otherwise,

it is not clear whether differences in response times for the target and neutral words stem

from the emotional valence, or lexical differences between the words. In their review of

the lexical features of 32 studies that have compared interference for emotion versus

neutral words, Larsen et al found that the negative emotion words were significantly

longer and less frequently used than the neutral words. Therefore, target and neutral

words need to be carefully matched on length and frequency of occurrence.

3.4.3.6 Sample limitations. Most of the Emotional Stroop research to date has

focused on attentional bias in clinical groups, while there is comparatively little work

focusing exclusively on non-clinical samples. Often, the inclusion of non-clinical groups

is simply for comparison purposes, and little information is provided regarding the nature

of body image disturbances and whether they reflect a truly asymptomatic group. The

possible diverse nature of these control groups is problematic. Williamson (1996) in his

review of the evidence to date regarding attention bias using the Emotional Stroop,

concluded that interference is not unique to those with eating disorders. It is however,

restricted to those with concerns about their appearance and/or weight. Thus,


considerations need to be given to participants’ feelings/attitudes toward their appearance

and weight.

As Eating Disorders are found predominately in women, the existing body of

research represents a gendered understanding of body image disturbance. To date, there

are only three published papers that have tested male samples on an Emotional Stroop

task, and they found no differences in attentional biases between males and females (Ben-

Tovim, Walker, & Douros, 1993; Channon & Hayward, 1990; Fairburn et al., 1991).

Fairburn et al. (1991) found that there was no difference between males and females in

colour-naming eating, shape, and weight related words, nor did either group show

interference compared to the neutral words. Similarly, Ben-Tovim et al (1993) found no

differences in males and females on body shape and weight stimuli. Channon and

Hayward (1990) also found no differences between males and females in colour-naming

food and body size after being fasted for 24 hours. However, both males and females who

were in the fasting condition showed interference for the food words. Thus, the research

to date indicates that there are no gender differences in selective attention as measured by

the Stroop task. These findings may be misleading, however, for several reasons. First,

the target words used were not developed specifically for male samples and do not reflect

concerns specific to men’s body image. As males typically report wanting to be more

muscular and larger in size (Silberstein et al., 1988), while women want to be thinner, the

target words used need to reflect these differences. It is likely that further investigation of

selective attention in non-disordered and disordered males with appropriate stimuli will

yield more conclusive findings. Both these studies are, however, important first steps in

the investigation of selective interference in non-clinical males. Thus, the current thesis

used words specifically designed to reflect both male and body image concerns.


3.4.4 Limitations Addressed in the Current Project

The aforementioned limitations preclude a thorough understanding of body image

disturbance within non-clinical samples. The primary aim of the current thesis was to

expand the current literature by addressing a number of the aforementioned

methodological flaws and gaps in the research. Separate categories of target words were

used which were matched to groups of neutral words from one semantic category.

Additionally, the current line of research was expanded by including new categories of

words that have not been explored previously. Traditionally, categories that have been

used are food/eating words (e.g., Green & Rogers, 1993; Mahamedi & Heatherton, 1993),

weight/shape words (e.g., Cooper et al., 1992; Lovell et al., 1997), and appearance words

(e.g., Labarge et al., 1998). Research has also been criticised for focusing on negatively

valanced stimuli (Vitousek & Hollon, 1990), therefore both high calorie foods and low

calorie foods, in addition to positive appearance and negative appearance words were

used. A new category comprising physical activity words was also developed to reflect

the current concern within society to fit and healthy; a key theme that emerged in the

qualitative analysis. It was anticipated that words such as “jogging” and “aerobics”

would produce interference for some groups of people. As there has been no research

using this category before, its inclusion was exploratory. Similarly, words specially

designed to reflect male body image concerns were constructed. Interviews with both

males and females in Phase One of the research elucidated relevant body image concerns

within non-clinical samples. Further, the aim of Phase two was to develop appropriate

word stimuli.

Notwithstanding the limitations previously mentioned, research indicates a trend

for some forms of attentional bias in non-clinical samples. An attentional bias indicates


that increased attention has been allocated to that stimulus. Given this preferential

treatment at the encoding stage, it is plausible that preferential attention is also given at

later stages of information processing. Many information processing theories state that

increased attention to stimuli may result in enhanced memory for that information (e.g.,

Atkinson & Shriffin, 1968). Therefore, in order to provide a comprehensive model of the

way information of processed, memory bias in addition to attention bias must also be

considered.

3.5 Memory Bias

In addition to attention bias, Vitousek and Hollon (1990) propose that evidence

for weight / appearance schemata can be gained from examining memory bias. These

researchers propose that biased memory for weight related information supports current

levels of body image disturbance. Increase attention to body and shape related stimuli

may be related to greater elaboration at encoding. This enhanced elaboration means that

information is linked to numerous other concepts in memory (Williams et al., 1988,

1997). As such, there will be many cues that could activate body image concerns

(Sebastian, Williamson, & Blouin, 1996). According to Williams et al. (1988), women

with body image related problems build up a stronger network of memories through their

recurrent thoughts about body weight. The outcome of this increased cognitive

processing is the enhancement of retrieval of such information.

There is currently only limited work examining memory biases in individuals

with body image disturbance (Lee & Shafran, 2004). Only four studies to date have

examined memory bias after an Emotional Stroop task (Channon et al., 1988; Labarge et

al., 1998; Lavy & van den Hout, 1993; Mendlewicz, Nef, & Simon, 2001), whereas a

small number of studies have examined memory biases more generally in response to a


self-referent encoding task (Baker, Williamson, & Sylve, 1995; Sebastian et al., 1996), or

reading an essay (King, Polivy & Herman, 1991).

Using an incidental memory task following an Emotional Stroop task, no

evidence of implicit (Channon et al. 1988; Lavy & van den Hout, 1993; Mendlewicz et

al., 2001) or explicit (Labarge et al., 1998; Lavy & van den Hout, 1993) memory bias has

been found in women. Channon et al reported no recognition bias in Anorexic or control

women after colour naming food and body size words, despite an attentional bias

observed in both groups for the food words. Similarly, Mendlewicz found no recognition

bias in women with Anorexia Nervosa or asymptomatic control women for high calorie

food words, low calorie food words, or anxiety provoking body areas, despite attentional

bias observed two to three days earlier. Labarge et al. found no recall bias for appearance

words in participants classified as either appearance schematic or appearance aschematic,

even after half the participants had been primed (weighed) beforehand. However, a small

yet significant correlation (r = .20) was found between level of appearance schemacity

and number of appearance words recalled. Finally, Lavy and van den Hout (1993) found

no evidence of either a recall or recognition bias for food words in asymptomatic women

even in those who fasting for 24 hours beforehand, even though an attention bias was

noted. These results consistently show that strategic processing biases are not found in

women for appearance and food stimuli. Additionally, these results suggest that

differences in attention may not always reflect enhancements in memory. It may be that

observed processing biases are short-lived; however more research is needed to

substantiate this claim.

A larger volume of research has examined memory biases within body image

more broadly. Typically, participants are required to complete some type of task such as


learn a list of words, read a story, or complete a self referent encoding task (where they

are asked to imagine themselves in a situation with the word, or asked if the word is

relevant to them). Some of the material in the word lists or interspersed in the story is

related to food / appearance. Recall is then tested a short time later to assess whether the

target or neutral information is recalled better.

The most frequently used way for assessing memory bias has been through a self

referent encoding task. Participants are presented a list of words and asked to imagine a

situation involving themselves and the word. It is proposed that women concerned with

appearance and weight integrate this material into already elaborate schemas. When

asked to recall the information, they have more cues to trigger recall and hence show

greater recall of this material (Vitousek & Hollon, 1990; Williams et al., 1997).

Using this technique, Sebastian et al. (1996) found that memory bias for fat

related information was uniquely found in women with eating disorders, and not

symptomatic or asymptomatic women. As the eating disordered and weight preoccupied

groups were equal on degree of weight preoccupation, it was suggested that a factor

unique to the eating disorders produced the results. However, Baker et al. (1995)

concluded that memory bias fatness stimuli were found in symptomatic women. Women

with high body dysphoria, but not low body dysphoria, showed a biased recall for fatness

words and reduced recall or thinness words. Negative mood induction did not change

this, suggesting that biased memory is stable and not influenced by environmental cues.

Using a wider variety of word categories Hunt and Cooper (2001) found a general

memory bias in all women. Asymptomatic women and women with Bulimia Nervosa

were assessed for memory bias for positive weight / shape words, negative shape / weight

words, positive emotion words, negative emotion words, high calorie food words, low


calorie food words, and neutral body areas. The women overall recalled more of the

weight / shape words than neutral words, while the control group of women recalled

significantly more of the food words than the women with Bulimia Nervosa.

Finally, in the only study to include male participants, and use specifically

designed word categories to reflect male and female body image concerns, Unterhalter et

al. (2007) found that men showed a memory bias for positive muscularity words, but not

negative muscularity, or positive weight / shape, or negative weight / shape. The women

showed a general memory bias for the positive and negative weight / shape categories.

Therefore, inconsistent results have been found regarding memory bias, and the

specificity of that memory bias, in women. Using a similar methodology, one study has

concluded that memory bias is restricted to women with eating disorders (Sebastian et al.,

1996), one found memory bias in symptomatic women (Baker et al., 1995), and two

found general memory biases in all women (Hunt & Cooper, 2001; Unterhalter et al.,

2007). Clearly, more research is needed to examine this issue.

Other techniques to explore memory bias have been to present word lists and ask

participants to rate the pleasantness of the words followed by an incidental memory task

(Israeli & Stewart, 2001). Using this technique, women classified as high on dietary

restraint remembered more forbidden food words than neutral words; a bias that was not

found in the women classified as low on dietary restraint. King et al. (1991) assessed

memory bias in clinical (women with Anorexia Nervosa and women classified as obese)

and non-clinical samples (restrained compared to unrestrained eaters), after they read an

essay which was interspersed with statements about weight and appearance. Memory

biases associated with individual concerns were found in both the clinical and non-

clinical groups. The women classified as restrained eaters, obese, and with Anorexia


Nervosa recalled significantly more weight and food related aspects of the story

compared to other information neutral information in the essay. Additionally, the

restrained group recalled significantly more weight information than unrestrained

women. These findings suggest that memory biases for appearance and weight related

information is not distinctive to eating disordered individuals

Memory biases for body image related information have not been as extensively

examined as attentional biases. Only four studies to date has examined incidental

memory following an Emotional Stroop task. While more research has been conducted

using other techniques, such as a self referent encoding task, it is likely that the

mechanisms underlying biased memory in these two tasks are different. For example, in

time pressures in an Emotional Stroop task mean that threatening information must be

processed more rapidly (Sharma & McKenna, 2001). Clearly more research is needed to

examine the nature of memory biases, and the relationship between attention and memory

bias. The mechanism through which this bias occurs is not yet clear. The examination of

both biased attention and memory within the one study allows greater consideration of

whether processing biases occur at encoding, retrieval, or at both stages (Sebastian et al.,

1996).

Many of the limitations noted in the preceding section on attentional biases also

relate to the research on memory biases. Specifically, matching of neutral and target

words does not always occur (e.g., Unterhalter et al., 2007), or the neutral words may not

form a homogeneous category (e.g., Hunt & Cooper, 2001). Further, few studies have

looked beyond memory bias for food or appearance words, or beyond words with a

negative connotation. Finally, limited research has included males, resulting in a


gendered understanding of memory bias. Therefore, our understanding of memory bias

within non-clinical women and men is limited.

3.6 Summary of Attention and Memory Bias Chapter

This chapter has critically reviewed research on the Emotional Stroop task as a

measure of attentional bias. Based on the inconsistent findings and methodological

limitations, a key question remains as to whether processing biases are limited to those

with Eating Disorders, or whether they can be found within a non-clinical sample.

Limited research examining memory biases was also presented. Based on this review of

the literature, a number of significant limitations were noted that preclude a thorough

understanding of processing biases in men and women. The identification of these

limitations has informed the design of the Emotional Stroop task and incidental memory

task to be used in Phase Three of the research (discussed in Chapters Seven and Eight).


Chapter Four: Overview of the Research Program

This thesis presents a program of work designed to examine the role cognitive

biases play in sub-types of body image disturbance within a non-clinical sample. The role

of selective attention and memory for body image related words was measured using an

Emotional Stroop task and incidental memory test. Additionally, a range of psychosocial

questionnaires were administered to assess various facets of health behaviours and body

image. To achieve this aim, three phases of research were conducted. The initial two

phases of research provided preliminary data to inform the development of the main

Stroop study.

4.1 Aims, Research Questions and Hypotheses

The purpose of Phase One was to conduct a qualitative exploration of body image

concerns amongst males and females. Participants provided information on their own

body image, and what factors they see as either positively or negatively impacting on

their self evaluations. The outcomes of this phase were used to inform later phases of the

research by identifying body image concerns that are specifically relevant within a non-

clinical sample. Specifically, the purpose of Phase One was to provide support for the

types of words to be used in the Emotional Stroop task and memory task, and also inform

the selection of psycho-social vulnerability factors.

Phase Two involved the selection and initial testing of words to be used in the

later Stroop study. Males and females provided appropriateness ratings on an initial

selection of categories of words, and the words themselves. Words receiving the highest

ratings were used, ensuring that the stimulus used was empirically supported.

Finally, Phase Three was a large-scale study assessing the central aim of the

research; the role of cognitive biases for body image information and how these biases


differed between empirically defined sub-groups. Males and females were primarily

recruited through the general community and tested on emotional Stroop task and

incidental memory test. Following the cognitive tests, a battery of health related

questionnaires were administered. A wide range of psychosocial variables were included,

such as body image concerns, exercise behaviours and attitudes, quality of life, negative

affect, muscularity concerns, and eating disorder symptomology. The selection of

psycho-social variables was driven by the outcomes of the qualitative analysis in Phase

One.

Based on the limitations noted in past research, the cognitive measures benefited

from a number of methodological refinements. In order to increase the generalisability of

the findings, the sample included both males and females of a wide age range recruited

from the general community and universities. Bias on the cognitive measures was

examined two ways. First, the sample as a whole was examined for attention and memory

bias at a group level, followed by a comparison between the empirically derived sub-

groups. Second, cognitive measures were examined for their relation to a wide number

of vulnerability factors that go beyond the typically used body image variables.

The limitations noted in the research also informed the development of the

cognitive tasks. The research included more appropriate and carefully developed stimulus

sets that include both positively and negatively valanced words. Both the experimental

and neutral words were semantically related and matched on relevant lexical

characteristics. The types of cognitive bias were expanded by including new categories of

words and covering multiple facets of body image. Finally, the use of a computerised

Stroop task allowed for more sensitivity in timing, and randomisation of stimulus sets.


The results from this study substantially add to our understanding of biased cognitive

processing.

Research questions and hypotheses were developed around three main areas of

enquiry. The first aim of the research was to examine whether attentional biases exist for

body image and health related information in a non-clinical sample. Specifically, it was

hypothesised that attentional biases, as evidenced by slower reaction times to body

image-related words, would be found in symptomatic participants only. No attentional

biases would be found in asymptomatic individuals.

The second aim of the research was to examine whether memory biases for this

body image and health related information would be shown. Specifically, it was

hypothesised that memory biases, as evidenced by the recall of more target words, would

only be found only in symptomatic individuals.

The final area of investigation was to examine whether gender differences would

emerge in attention to, and memory for, body image information. Specifically, it was

hypothesised that males and females would differ in their processing of sub-categories of

Stroop words given the different emphasis on body image concerns. However, given the

lack of past research in this area, the nature of these specific differences could not be

developed. These hypotheses are presented below.

Hypothesis 1: Attentional biases, as evidenced by slower reaction times to body image-

related words, will be found in symptomatic participants only. No attentional biases will

be found in asymptomatic individuals.

Hypothesis 1a: Interference effects will be different between sub-groups.

Hypothesis 1b: Greater interference effects will be associated with a range of

vulnerability factors.


Hypothesis 2: Memory biases, as evidenced by the recall of more target words, will only

be found only in symptomatic individuals.

Hypothesis 2a: Memory bias will differ between the sub-groups.

Hypothesis 2b: Greater biases will be associated with a range of vulnerability

factors.

Hypothesis 3: Differences in processing biases between males and females may be found

in the sub-categories of Stroop words.

Now that a background to the research has been provided, the following three

chapters present the research undertaken. Chapter’s Five and Six describe the qualitative

phase and the stimuli selection phase of the research respectively. These preliminary

stages provided background information before the main Emotional Stroop phase was

undertaken.


Chapter Five: A qualitative Exploration of Body Image Concerns Amongst Men and

Women from a Non-Clinical Population (Phase One).


The aim of Phase One was to gain an in-depth understanding of individuals’

experiences with their body image, their body image concerns, and how these concerns

affected their lives. Particular attention was given to understanding what factors

individuals perceive as influencing their body image. The inclusion of both males and

females across a broad age spectrum increased the representativeness of the sample, and

allowed for age and gender differences to be explored. The outcomes of this phase were

then used to inform subsequent phases of the thesis, by helping to select a range of

measurement tools and stimuli that are relevant to a non-clinical sample. This chapter of

the thesis reports the methodology and outcomes of the qualitative study that was

undertaken.

5.2 Method

5.2.1 Participants

In an attempt to increase the representativeness of the sample, both males and

females were recruited. A total of 17 participants, eight of whom were male and nine of

whom were female, voluntarily participated from May to August 2005. Data collection

continued until data saturation occurred. Recruitment primarily took place through a large

Queensland university. Participants could sign up for the study via a university

recruitment notice. The notice invited people of all ages to participate in an interview

about body image and factors that shaped one’s view of themselves. In addition, the

researcher visited some classes and gave a small speech about the study. To improve the


scope of the sample a ‘snowball’ technique was used and resulted in four participants

from outside the university.

The age of participants ranged from 18-56 years, with a mean age of 29.18 years

(SD = 11.85). Half of the sample was young adults, aged between 18 and 23 years. The

sample was well educated, with most participants currently attending university. Two of

the older participants reported completing up until grade 8 or 9 of high school. No other

demographic or descriptive information was gathered. There was no criterion for

participating in the study and some participants were offered course credit. One

participant was excluded from the analysis as she provided only minimal responses to the

questions. This case is further discussed in the Limitations section (5.4.2).

5.2.2 Procedure

Ethical clearance was received before commencement of the study through the

QUT University Human Research Ethics Committee. Participants provided written

consent after reading the information sheet and consent letter and were given the

opportunity to ask questions. A copy of the Information and Consent forms can be found

in Appendix A. Participants were asked whether they objected to the interview being tape

recorded, and all provided consent. Only demographic information of age, education, and

country of birth were provided on tape. It was explained to participants that only the PhD

candidate and the transcription company would hear the tapes. Individual interviews took

place with the PhD candidate, a young female, who had previous experience with

qualitative design.

Building rapport between the interviewer and participants was an important part

of the interview, as it was anticipated that stories and experiences regarding body image

could be difficult for some of the participants. It was the intention of the interviewer that


all participants felt comfortable and secure enough to talk about their thoughts and

experiences openly. All participants were carefully told their rights as a participant, and

told that they could stop the interview at any time without penalty. In addition,

participants were told that only needed to answer questions with which they felt

comfortable.

Once the preliminary signing of information and consent forms and rapport

building was done, sessions ranged in length from 19 - 72 minutes, with most interviews

taking about 40 minutes to complete. The length of time was guided by the participant

and how much detail or how many experiences they wished to disclose. The interviews

were guided by semi-structured questions that were determined by the research team, and

resembled an informal guided conversation. The list of questions and prompts can be

found in Appendix B. There was four parts to the interview. First, demographic

information of age, education, and country of birth was gathered. Second, participants

were invited to share their subjective definition of what the term “body image” meant to

them. This was to aid the communication between the interviewer and interviewee so that

terminology used during the interview was mutually understood. It was also to

understand whether body image was something that holistic and broad, such as

encompassing all facets of appearance and eating, or something more specific and

compartmentalised, such as satisfaction with one’s facial appearance. The third section

used the participants’ previously defined construct to ask how they would rate their own

body image, and level of body satisfaction. The final and longest section of the interview

asked what factors participants saw as influencing their body image. A number of

prompts were determined prior to the interview and included factors such as the media,

peers, family, specific situations such as going to the beach, romantic partners, and


exercise and eating habits. Participants were not specifically asked whether they had an

Eating Disorder either presently or in the past, but they were free to give this information

if they wished.

To increase the rigor of the findings, and to ensure that rapport was maintained,

reflection of content was used throughout the interview to make sure that the effective

and clear communication was occurring. In addition, a summary of key points was

provided by the interviewer at the end of each section and participants were given the

opportunity to resolve any discrepancies or clarify any ambiguities.

At the conclusion of the interview, participants were asked whether there was any

information they wished to add, or any important area that they felt wasn’t covered

adequately. If not, participants were then thanked for their time and left. The interviewer

then made any additional notes about themes arising from the interview.

Recruitment continued until data saturation occurred. That is, when no new

information was emerging from the interviews, it was deemed that an adequate coverage

of the topic had been made.

5.2.3 Data Analysis

All audio tapes, except for one, were transcribed by a professional transcription

company who were instructed to transcribe the interview verbatim. The other interview

was only half recorded due to technical errors. This was realised immediately after the

interview and detailed notes were taken. Analysis was based on the transcripts and

additional notes made at the time of the interview.

The data was analysed using an inductive, thematic analysis technique. This

technique is commonly used in research (e.g., Halliwell & Dittmar, 2003; Ziebland,

Robertson, & Neil, 2002) when the goal is to explore a previously untested idea, rather


than test an existing theory. As the existing theory is under-developed, and there is

relatively little existing research in this area, no pre-existing theories were tested. Using

thematic analysis, reoccurring ideas that are expressed by multiple participants are

grouped together into themes. Themes are originally based on the research questions, so

that individuals’ verbatim quotes are organised under the topics asked in each interview.

These themes contain verbatim quotes from participants as well as an interpretation of

their responses. Each new emerging theme is compared back to existing themes to

determine similarity. If the two themes are deemed sufficiently different, then a new

theme is developed. This process continued until all relevant information had been coded

into a theme and each theme developed represented a unique interpretation of the data.

Subthemes are then explored by examining the coded data under each theme. For

example, the role of the media was something that all participants discussed, and this

became a theme. Different opinions regarding the role of media became the subthemes –

some respondents saw the media as a motivating factor, while others saw it as a negative

influence. Coding was complete when the data was saturated, that is no new themes or

subthemes were apparent. To improve the reliability and validity of the coding, other

members of the supervisory team (both males), familiar with the study’s aims, were

consulted for feedback. This ensured that themes were clearly elucidated.

5.3 Results

A number of independent themes and subthemes emerged from the analysis. Each

theme is interpreted below and is illustrated by verbatim quotes from the interviews.

5.3.1 A Healthy Body Image Does Not Negatively Affect One’s Life

Most of the participants reported a healthy body image. When participants were

asked to rate their feelings about their appearance / body, most participants reported some


dissatisfaction with a particular feature, but stated that they were overall quite satisfied.

For example, a 29 year old male remarked “I’d say I guess I’m not a Brad Pitt but I’ll

give myself an eight out of ten”. A similar comment was also provided by a 20 year old

female “I’m quite happy with my body image. I know that there’s some areas that I could

fix up and make nicer, but I’m quite happy with it, yes”. All of the participants were able

to separate body satisfaction from general well-being and feelings of self worth, for

example “I mean I have days where I go, oh, I look really crap and I’m really fat and I

really need to go on a diet but I never get around to it” (18 year old female). This is

further expanded under the self esteem theme.

Some of the participants, mainly the women, reported their body image was quite

labile, and influenced by things like mood. For example, an 18 year old female

commented “I don’t know, I guess you have your days, like up and down days. Like some

days you’re like, oh okay, I feel really ugly or I feel really fat today”. A 23 year old male

also commented “It’s okay at the moment, it has been better”.

Many of the participants defined their body image in terms of general health. A

22 year old male comments “Yeah I’m fairly happy. I’d like to be a little bit more fit but

that’s not so much the image, that’s more health”. The importance of health in

determining satisfaction with appearance is identified by all participants, and is further

expanded under a separate theme.

Therefore, while participants were generally satisfied with their image, most

could identify some features with which they were not satisfied. More of the women

reported dissatisfaction than males, and the females also reported more history of body

dissatisfaction, and three participants a history of eating disorder-like behaviours /

attitudes. For example, a 29 year old female reported a history of symptoms similar to


anorexia (refusal to eat, cessation of menstruation, extreme weight loss), and reported

still turning to food for comfort. In addition, two men were identified that reported

symptoms of body dysmorphic disorder, and excessive exercise and dieting.

5.3.2 Having A Strong Self-Esteem / Self Image is A Protective Factor

All of the participants identified that having a strong sense of self was an

important factor in having a healthy body image. That is, the participants did not define

their self solely in terms of appearance. Many of the participants reported a time in their

lives when they did experience body image problems, and developing a strong self

esteem was important to overcoming these problems. Some example quotes are:

“Yeah, internal features are more important for myself and those other aspects that make

up a fulfilling life and a quality of life. And I don’t believe that whether I’ve got glowing

tanned skin or whether I don’t, is really going to affect the quality of my life” (22 year old

male).

“I mean life has taught me a lot of things. I mean I probably haven’t had the most ideal

life, so I’ve realised that things are not perfect, they’re not meant to be perfect, it’s just

the way of life. And it’s alright, you know, I’m just okay with it, you know, whatever

happens, happens. That’s sort of my way of thinking. I think that’s also the way with

how I relate about myself and, oh yeah, it happens so what am I going to do about it?

Can’t sit and cry about it, just get on with life.” (18 year old female).

“… you know, well I can lose all the weight in the world but unless I’m happy in myself

then it’s not going to make a difference.” (19 year old female)

“… but it’s important to me how I see myself” (56 year old male).


Participants regularly noted that there were much more important aspects to

themselves than appearance, for example a 50 year old woman noted “I see some other

things as being far more important to my self esteem than my appearance.” Similarly, a

22 year old commented, “I guess I got more brought up with the idea that intelligence

was the main thing.”.

When asked at the end of the interview what the most important factors in

influencing their body image were, all of the participants mentioned self esteem. Even

though participants could list a significant number of factors that influenced their body

image, their reactions to those situations were dependent upon their body image – “It has

to be myself (as most important factor in my body image) because it’s how I feel that

drives my behaviour in those situations.” (49 year old women). Therefore, it appears that

having a strong sense of self is an important protective factor against body dissatisfaction,

or at least not letting it influence general wellbeing.

5.3.3 Importance of Health and Fitness

All of the participants identified the importance of being fit and healthy regardless

of whether they were currently in a fitness regime. This was considered an important

component of body image, particularly by the males who tended to define their

satisfaction with themselves in terms of their fitness. This is illustrated in the following

comment by a 23 year old male about why he exercises: “To get fit, but also for that

feeling, because I’ve noticed the days I don’t exercise I’m sluggish and I’m pretty blah.

The days I do exercise is just a better feeling.…. Because appearance isn’t that

important to me at the moment. Health is more important.”


The women also reported engaging in exercise for health reasons, and many of the

women also defined their body image in terms of health and fitness. For example, a 50

year old female stated:

“I feel as if it (her body image) isn’t up to scratch in terms of health. I’m not that

concerned with appearance, but a little bit concerned with appearance, but it’s not all

encompassing. It’s more to do with the health, what I think could be healthier. So if I see

a muscle that’s not really firm, I think of it in terms of health and how that’s going to be

for me as I get older rather than in terms of how it might appear.”

Some of the women reporting exercising for appearance / weight loss reasons, and

typically did not enjoying it much. For example, a 20 year old female commented: “Yeah,

I try and do a little bit of exercise. I hate exercising. I hate it, but I do a little bit. I’ve

got a treadmill and I get on that sometimes. I’m trying to get back on track and to tone

up a bit at the moment, but it’s getting started is the main problem.”

The benefits of exercise were noted by all of the participants, and included things

like mood regulation, having more energy, being toned, to be social / bond with others, to

improve attractiveness, and reducing stress. The mood enhancing properties of exercise

were especially noted, for example, “I find it’s a stress relief myself, physical exertion.

Walking, to me, it’s great. It’s a good stress relief for myself” (22 year old male); “…

just want to play harder, go faster, just try and make myself feel a bit better..” (20 year

old male).

The importance of feeling healthy and fit was a consistent theme that was

emphasised by all participants. Being in good health was an important component of

body image.


5.3.4 Recognition of the Societal Ideal

All of the participants noted an ideal physique that is perpetuated in Australian

society, one that “for women it’s slim and trim, for males… buffed and bicep-ed” (22 year

old male). Participants were not clear where they thought this image came from, but

noted the media in general as one of the major conveyors.

“…on a larger scale even I guess if you want to say media” (22 year old male)

“…I guess female artists like J-Lo, Beyonce. Also film stars. You watch the award

shows and see all their fashions and all their dresses and you see that they all look tall

and skinny.” (18 year old female).

Another participant commented that this ideal image also reflects the current

concern with health and fitness, “I think the prevailing medical discourse in a way. The

prevailing medical discourse at the moment that goes really into health. That’s probably

really influencing my body image.” (50 year old female).

Participants differed in their response to these images. Some participants, all

female, said that they images made them feel worse about themselves, “…But this year

I’ve been watching a lot more TV and I actually feel bad.” (19 year old female), while

other participants reported the media had no impact on them, “The media doesn’t play a

large part for me. I think it plays a large part for other people though, particularly

women. I really don’t think it plays a large – I’m not very influenced by who and what I

see on television as far as body image goes.” (56 year old male).

Many of the participants noted that the media made them feel angry and worried

about the unrealistic nature of the images, “No, I hate that (music television). I have a

real aversion actually to that…. I feel sick thinking that adolescent girls are watching


that every weekend. That we’re still telling people that this is what we should all be

striving to be…. (I feel angry) That there are still teenage girls that have to grow up with

this shit, that will feel crap about themselves and will get into all sorts of problems. Then

I get angry that there are women that are prepared to portray themselves like this. That

if women refuse to do this, it wouldn’t happen.” (29 year old female).

Many of the participants reported dissatisfaction with the one type of image being

constantly presented, and a frustration with the focus on appearance and weight, for

example, “I just think because everybody’s focussed, like you see on TV and you see

everywhere that there’s the perfect image. Skinny, beautiful, but it’s all computerised

and touched up and everything. I think everybody in the world, in Australia have sort of

focused on that one image. There’s all sorts of different people and the different body

types as well.” (20 year old female).

A small number of participants reported that seeing idealised images was a

motivating factor to improve their own appearance, “I guess it helps me to gain an

understanding of what an idealised, or good looking, person would look like. And also,

that body image is achievable. You also hear a lot of history behind the actors, if they

had to loose weight, or put on body muscle… yeah, just those stories, that make it seem

more believable.” (20 year old male).

Many of the participants noted the unrealistic nature of the images, which served

as a protective factor, “Good for them” (22 year old male); “I sort of realised that they’re

basically selling a product, they’re selling themselves. So I’m like, okay, they are

supposed to look like that, they’re paid to look like that. I guess if I was paid to look like

a billion dollars then I probably would too.” (18 year old female).


Therefore, the idealised images in the media and the strong focus on looking good

were noted by all participants. This ideal was primarily seen as a negative influence.

5.3.5 Mood Can Affect How Events Are Interpreted

Most of the participants identified mood as influencing their body image, or how

they react to situations. The females in particular identified a strong link between feeling

depressed or stressed and consequently feeling unattractive. For example:

“if you’re in a really good mood and you try on something and it doesn’t fit, it’s like,

okay I’m in a depressed mood now, but if you’re in a real crap mood and you try

something on, you just get worse and you’re like, I want a chocolate. I can’t be bothered

anymore.” (female, 20 years old).

“Well stress a lot, if I’m tired a lot. Just things like if I’m generally feeling a bit lethargic

because I have some health issues and stuff, if that’s playing up then I’ll feel not as good

about myself as normal….. I actually feel fatter, yeah, I feel like I’ve put on weight and I

probably actually haven’t.” (19 year old female).

This was also reflected in some of the male participants, for example, “So when

I’m feeling down, I guess I do become more observant of how I feel and how my image is

being portrayed….So I think when I’m happier, I feel as though I probably do have more,

or I feel more attractive because of that.” (male 22 years old).

Therefore, mood is important to consider as a temporary factor to influencing

body image. Mood was also identified as important for exercise also (explored under the

exercise theme).


5.3.6 Other Factors

A number of other factors such as influences from family, friends, and romantic

partners were discussed, but mixed responses were gained. Although most participants

did at least mention influences from family, these were noted as more important during

childhood. As participants become older, they were able to discount any negative

influences, or use it as a motivating factor. A 50-year old women commented about her

father’s attitude towards women: “They always had to be really skinny, and there was

always talk about them eating like a bird and this type of thing.” She identified this as

something that angered her, and now she refuses to have this same attitude.

Partners were primarily seen as having a positive impact on participant’s body

image, but this was primarily through a reinforcement of their own self esteem. For

example, a 20-year old female commented, “Yeah, I suppose – I’ve had a few guys that

have treated me like crap and said you’ve got to do this and you’ve got to wear this and

you’ve got to do this. I’m like get stuffed, but the majority of the guys that I’ve been out

with, they’ve always complemented me on my body so that makes me feel good, of

course.”

Therefore, reactions from family, friends, and partners are important, but are

underscored by levels of self esteem. One women for example commented that “Even

though I don’t believe it (positive comments from family), it’s nice to hear it”, while

another women commented “It has to be myself (as most important factor) because it’s

how I feel that drives my behaviour in those situations.”


5.4 Discussion

5.4.1 Summary of Findings

The purpose of Phase One was to increase understanding of important

components of body image in men and women drawn from a non-clinical sample. The

important factors identified informed the development of the word stimuli in Phase Two,

and the selection of psycho-social variables in Phase Three. Individual interviews were

conducted with eight men and nine women, with six major themes emerging.

Interestingly, no substantial age or gender differences emerged in responses. The few

gender differences are discussed in each theme. These themes and their inter-relations are

summarised in Figure 5.1.

Figure 5.1. Interpretation of inter-relationship between key themes identified in the

qualitative analysis.

Self concept: Body image Health

Culture / Social ideal

Mood Self esteem

Healthy lifestyle

Risky weight management behaviours


This figure shows the important factor’s identified in conceptualising body image.

Cultural and societal norms are seen as having an overarching influence on what is

considered attractive which indirectly impact’s on one’s body image. The role of self-

esteem and mood are conceptualised as moderating factors in determining how these

societal ideals are interpreted. Body image and health are seen as part of one’s self

concept. The strength of one’s body image determines whether an individual engages in a

healthy lifestyle or engages in risky weight management behaviours. Each of these

factors is now explored in more detail.

The first theme to emerge was the distinction between evaluative and investment

components of body image. While most of the participants were generally satisfied with

their appearance, they could all identify parts of their body that produced dissatisfaction.

However, this dissatisfaction did not have a large impact on their life. This finding

suggests that typical measures of body dissatisfaction may be misleading if they are

simply asking about satisfaction with particular areas, and not considering the impact or

importance of this dissatisfaction. Therefore, in Phase Three of the research

questionnaires were selected that incorporate a measure of both level of dissatisfaction

and importance / impact of these feelings on general functioning.

While most of the participants reported satisfaction with their appearance, a small

group reported currently engaging in behaviours that would be considered ‘risky’ weight

management behaviours. Two of the men reported excessive levels of exercise, high

levels of dietary restraint, and an extreme preoccupation with weight loss, muscle gain

and general appearance. Many of the women reported dieting for both weight loss and

general health reasons either currently or recently, and one woman reported bulimia-like

symptoms (overeating, weight cycling). Three of the women reported experiencing an


eating disorder in the past, even though it was never formally diagnosed. These findings

suggest that even within a non-clinical sample, levels of body image disturbance are

moderate, and it may be useful to consider the prevalence of risky weight management

techniques as opposed to formal diagnosis.

The importance of being fit and healthy was noted by all participants, and was

therefore identified as a key theme. In fact, many participants defined their body image in

terms of satisfaction with their health and fitness levels. This was not restricted to males,

or even those who currently reported exercising. Many participants noted that the

increased cultural focus on eating healthy, being a healthy weight, and engaging in

regular exercise was important in shaping their beliefs. The majority of participants

reported that their exercising was primarily for health reasons, but that changes in

appearance were also important. Only three participants reported exercising for purely

appearance reasons. Many of the participants emphasised the importance of being fit in

their self concept and generally feeling good about themselves. Given this strong

emphasis on health and fitness, it was considered important to include exercise-related

stimuli in the Emotional Stroop task, and also to include attitudes towards fitness within

the psycho-social variables.

Another key theme to emerge was the importance of having a strong sense of self

that was not reliant on evaluations around appearance and weight. Many participants

commented on the number of other factors that were more important within their self

concept, such as achieving at university, securing a career, and having a supportive

family. However, the importance of feeling confident (which was related to looking

good) when achieving these goals was regularly highlighted. Having a strong self esteem

was rated by all participants as one of the most important factors in influencing their


body image. Believing in one’s self worth was identified as a protective factor when

exposed to things like comments from family or friends or being exposed to idealised

images in the media. Developing a strong self esteem was identified as the means through

which individuals could overcome times when they were experiencing body image

disturbance. Therefore it was considered important to include it as a psycho-social

variable in Phase three of the research.

All participants in the study identified the cultural obsession with being thin and

attractive, and the stigma against being overweight, so this was made into another key

theme. Participants could not clearly identify where this message come from, although

the media was regularly acknowledged. Most of the participants reported that they were

not negatively affected by these images in the media, but did acknowledge the images

were harmful in creating an unrealistic social idea. Most of the participants identified the

unrealistic nature of these images, but a few of the women reported that it was difficult

for them to completely ignore. Many of the female participants reported frustration at

only being exposed to the one type of image (i.e., thin, tall, attractive women). The men

seemed better able to ignore these images than the women, perhaps because some of the

men reported the images as motivating to improve their own appearance.

The media wasn’t identified as the main conveyor of this ideal, but it seemed that

participants did not know where else to attribute this message. Some participants talked

about ‘society’ and ‘culture’ as a whole, and that is was likely that this ideal was

conveyed in multiple ways, such as advertising, books, television, and social groups.

Where ever this ideal was ascribed to, all participants did report an awareness of it and

acknowledged that it was important in creating a society obsessed with appearance.

While this emerged as a strong theme, it was not clearly enough articulated by


participants to inform measurement selection in subsequent phases. Also, the reaction to

this cultural ideal appeared to be moderated by an individual’s body image, which was

further influenced by their self esteem. That is, everyone is exposed to the cultural ideal,

but only some people respond negatively to it.

Another key theme to emerge was the effect of mood on body image. Participants

regularly noted that their feelings of attractiveness fluctuated depending upon things like

mood. A general bias was noted such that feelings of stress or depression were associated

with viewing the self more negatively, while feelings of happiness or excitement resulted

in a more positive evaluation. As well as influencing body image, negative mood was

also the outcome of body dissatisfaction. Participants reported feeling depressed if they

could not find clothes to fit, or felt that they did not look nice. Some of the women

reported that they actually felt fatter when depressed. Therefore, the experience of

negative mood is important to consider as word stimuli in Phase Two, and also as a

psycho-social variable in Phase Three.

The final theme to emerge reflected the influence of family and friends on body

image. While all participants noted family and friends, not many reported this as a

negative influence. At least half of the participants reported some negative comments in

childhood about appearance, but none of the participants reported any long-term effects

of those comments. Friends and romantic partners were primarily seen as positive

influences, but this simply reinforced their own feelings of self worth. That is, by having

a strong self esteem participants were already confident in themselves. Therefore, the

influence of family and friends was not included in subsequent phases of the research as

it was not seen as a major influencing factor.


Therefore, the results of the qualitative analysis in Phase One identified important

components of male and female body image to be included in subsequent phases of the

research. A few limitations should be noted however with the research.

5.4.2 Limitations

One limitation of the study may be that only those interested in the topic of body

image volunteered, thereby reducing the generalisability of the findings. It’s also

conceded that for some people the topic of body image may be sensitive one, and

therefore chose not to participate. While interviews were chosen for this reason over

focus groups, it’s acknowledged that other, more anonymous methodologies, such as

mailed questionnaires, may have encouraged more respondents. However, given the

exploratory nature of the research questions, interviews were considered preferable.

A second limitation of the study reflects the incentives that some participants

received. Eligible first year students received course credit for taking part in the study,

while other participants received no incentives. Whilst it’s difficult to ascertain the exact

influence that this difference in incentives had on the results of the study, there was only

one participant who seemed ‘closed-off’ in the interview. This was one of the first year

students who received credit for participation, and her interview was excluded from

analysis. During the interview she answered questions very briefly, providing little detail

or elaboration. Additionally, she did not acknowledge many factors as influencing body

image, and seemed reluctant to provide any in-depth comments. It is acknowledged that

this may not have reflected her participating for gain only, but may have reflected a

communication or rapport breakdown with the interviewer. Either way, it was deemed

appropriate that this interview be excluded from analysis.


A third possible limitation of the study may be that the interviews were conducted

by a young female (aged 23 years). It is difficult to determine what effect this may have

had on respondents, and whether a male interviewer, or an older interviewer would have

elicited different information. Whilst the age and gender of the interviewer is a factor

acknowledged in other qualitative studies (e.g., Halliwell & Dittmar, 2003; Ziebland,

Robertson, & Neil, 2002), the precise nature of this influence is yet to be clearly

established.

The typical methodology used in body image research is standardised

questionnaires. While the use of questionnaires undoubtedly has advantages, they can at

times fail to capture the rich experiences, beliefs, and stories of participants. As issues

regarding body image may be sensitive for some people, one-on-one interviews were

chosen over group discussions. Interviews provide more privacy and intimacy, which

should facilitate open sharing of experiences. This study was useful in identifying the key

factors that are important in understanding the body image of males and females.


This chapter presented Phase One of the research program. Interviews conducted

with eight males and nine females highlighted a number of important influential factors

in body image, such as the importance of health and fitness, the distinction between

evaluation and investment in body image, the importance on being attractive in society,

the moderating effect of mood, and importance of having strong self esteem. These

factors are to be included as word stimuli in Phase Two of the research, and as psycho-

social vulnerability factors in Phase Three of the research.


Chapter Six: Selection of the Stroop Stimuli (Phase Two)


This chapter presents Phase Two of the research where studies were undertaken to

select the stimuli to be used for the cognitive measures. The importance of using

carefully selected word stimuli was highlighted in the literature review in Chapter Two.

To ensure the robustness of word stimuli to be used in the Emotional Stroop task, the aim

of this phase was to develop an empirically supported stimulus set that incorporated

words used in past research and the themes previously identified in the qualitative

analysis of Phase One. This chapter provides the Method, Results, and Discussion for

these studies.

Development of the Stroop stimuli occurred in four stages. Stage One involved

the generation of a list of word categories that would encompass multiple facets of body

image. These categories were developed based on frequently used categories in past

research as well as gaps identified from this research. Further, categories were added

based on the outcomes of the qualitative study in Phase One. After the selection of six

word categories relating to body image, Stage Two generated an initial list of words to fit

these categories. Independent ratings were then sought for these words in stage three to

determine appropriateness. Finally, in Stage Four, neutral words were matched to the

selected target words.

6.2 Stages One and Two: Initial Generation of Stroop Categories and Words

6.2.1 Method

6.2.1.1 Procedure. A literature review using EbscoHost was conducted to

determine the type of words and word categories used in past body image research

examining attentional bias. The types of Stroop categories used and the individual words


representing the categories were noted. Additionally, guidelines were sought for the

procedure used in the selection of individual words.

6.2.2 Results and Discussion

Based on procedures identified from the literature review in Chapter Three, four

criteria were developed for the selection of the Stroop stimuli to be used in the next phase

of the research. First, the Stroop categories should encompass a broad range of health and

body image concerns relevant to both males and females from a non-clinical sample.

Second, the individual words selected for each Stroop category should have high

semantic inter-relatedness, and be a good representation of their category. Third,

frequency data should be available for the words. Lastly, each category of experimental

words should be matched to a category of neutral words. The selected target words are

matched to the neutral words on length and frequency of occurrence in the English

language. The neutral words from each category should also be carefully selected to

ensure a high semantic relatedness. These guidelines ensure that the experimental and

neutral words are matched on relevant dimensions, with only the emotional salience of

the words being different.

Information on each word’s frequency of usage has typically been gained from

Kucera and Francis (1967). However, a newer source has also become available (Leech,

Rayson, & Wilson, 2001), based on British frequency data. Thus, both sources were used

in the current study to provide frequency data.

Based on word categories used in past body image research and themes identified

in the qualitative analysis, six experimental categories were developed. These categories

were: high- and low-calorie food words, positive- and negative-appearance words,

negative emotion words, and physical activity words.


Food- and appearance-related words were identified as the most commonly used

Stroop categories in past body image research. The inclusion of these categories is

important, as both food- and appearance-related concerns represent core pathologies in

body image disturbance.

Examination of the words used to represent appearance-related concerns showed

that both positive and negative concerns were commonly being represented in the one

category (e.g., “overweight” and “beautiful” in the same category of “body size / shape”).

It is likely that words signifying being overweight or unattractive represent a distinct set

of concerns to words signifying being thin or attractive. Positive appearance words such

as “thin” may signify a goal to achieve, or elicit negative affect if this goal is not

achieved; a finding that was supported in the qualitative analysis. Thus, to allow finer

discrimination between appearance words with a positive and negative connotation, two

categories representing appearance-related concerns were developed: negative

appearance words and positive appearance words. This distinction had not commonly

been made in past research.

While food-related words have been used frequently in past research, only a

small number of studies to date have directly examined the effects of both high calorie /

forbidden foods, and low calorie / non-forbidden foods (Sackville et al., 1998; Huon &

Brown, 1996). As these studies have reported that selective attention was different for

these two categories, combining a range of food words into the one category does not

allow for subtle distinctions to be made. Within the qualitative analysis both men and

women discussed the importance of eating health and avoiding fattening foods. Thus, two

categories were selected to represent high calorie / forbidden food words and, low calorie

/ healthy food words.


Food words and appearance words have been used frequently in past research,

however there is limited research examining other facets of body image disturbance.

Given the focus on engaging in a healthy lifestyle and increasing exercise levels

identified in the qualitative study, the category of physical activity / sport words was

developed. Exercise may also represent a means for body change or weight regulation.

No research to date has examined whether stimuli related to physical activity will elicit

attention and memory biases. Thus, this category of physical activity words was included

for exploratory purposes.

Only limited previous research has examined whether words related to negative

emotion would elicit selective attention and memory (Jones-Chesters et al., 1998;

Sackville et al., 1998; Seddon & Waller, 2000). Yet the results of the qualitative analysis

in Phase One highlighted the importance of mood in moderating body image. Past

research provides evidence of cognitive avoidance and attention for this information,

which appears to be linked with Bulimia Nervosa, or Bulimia Nervosa-like symptoms.

The inclusion of words related to negative affect allow for general psychopathology to be

assessed in addition to more specific body image concerns; an important distinction to

make considering the high comorbidity between mood disorders and eating disorders

(Cooper, 1995; Sebastian et al., 1996; Leon et al., 1993).

The aim of Stage One was to develop a list of Stroop categories representative of

a broad range of health and body image concerns reported in males and females from a

non-clinical sample. The six experimental categories developed in stage one

corresponded to concerns surrounding positive- and negative-appearance, high- and low-

calorie foods, negative affect, and physical activity. The aim of Stage Two was to

develop an initial list of words that represented the concerns of each of the six


experimental Stroop categories. These initial lists of words were generated based on

those used in past Stroop studies, in addition to the identification of core concerns that

emerged in the qualitative analysis in Phase One. Table 6.1 shows the preliminary list of

words generated along with the final selection, which is discussed next.

Table 6.1

Initial list of words generated and expected categorisation. Word proceeded with “*”

indicate final selection.

Positive

appearance

High calorie

foods

Physical

activity

Low calorie

foods

Negative

emotion

Negative

appearance

*Muscular *Chips *Jogging *Apple *Hate *Ugly

*Toned *Pizza *Aerobics *Carrot *Angry *Fat

*Slim *Lolly *Swimming *Vegetable *Stress *Obese

*Attractive *Hamburger *Sport *Celery *Depressed *Overweight

*Slender *Chocolate *Gym *Banana *Anxiety *Unattractive

Thin Cake Hiking Low fat Teasing Plump

Pretty Donut Work out Rice crackers Punishment Cellulite

Skinny Sugar Weights Diet Sad Chubby

Beautiful Ice cream Running Lettuce Alone Fat rolls

Petite Calories Panic Heavy

Biscuits Weight

Fudge


At the completion of Stages One and Two, the purpose of Stage Three was to

examine the appropriateness of the initial experimental words by seeking independent

raters. Stage Four then matched neutral words to the final selection of experimental

words. Stages Three and Four are now discussed in turn.

6.3 Stage Three: Rating and Final Selection of Experimental Stroop Words

6.3.1 Method

6.3.1.1 Participants. A total of thirty participants (12 males; 17 females) were

recruited from a university and offered course credit for participation. Ages ranged from

17 to 56 years, with an average age of 25.41 years (SD = 10.12). No exclusion criteria

were used.

6.3.1.2 Procedure. Ethical clearance was sought and granted from the University

Ethics Committee. Participants were recruited through the first year notice board and

offered course credit for their participation. Participants were given a brief explanation

of the study and given a booklet containing an information sheet, consent form, and a list

of the words. These documents can be found in Appendix C. Completion of the

questionnaire took approximately 15 minutes.

6.3.1.3 Materials. Sixty-one words were presented from the six experimental

categories previously developed in Stage One and Two (shown in Table 6.1). Participants

were given a rating booklet which asked two questions about each of these words. First,

participants were asked to indicate which category the word best represented from the six

alternatives provided. For example, if the word “thin” was presented, participants had to

circle what type of word “thin” was: positive / negative appearance, high / low calorie

food, negative emotion, or physical activity. Second, participants were asked to rate how

well the word fit the chosen category on a 1-5 scale, where 1 = “Not very representative”


and 5 = “Very representative. That is, if the participant thought that “thin” was a negative

appearance word, they might rate it as “4”.

6.3.1.4 Method of analysis. Data was examined to find which words were most

representative for each of the six experimental categories. For each word, frequencies

were examined for classification of the word and the representativeness rating. First, the

words were examined for the most common way they were classified. For example, the

word “thin” could be categorised as a positive or negative appearance word. Words that

were clearly placed into one category were then examined for how well they

characterised that category. Words were selected when there was clear agreement

regarding category membership, and the words were rated as being a good representation

(i.e., a rating of “4” or “5”) of that category. That is, a word like “attractive” could only

be included in the final selection if clear agreement was noted that “attractive” was a

good representation of a positive appearance word.


The aim of Stage Three was to gather empirical support for the final selection of

experimental words to be used in the Stroop task. Words that were not clearly categorised

as belonging to only one of the options available were excluded. This included the words

“thin”, “low fat”, “teasing”, “calories”, “punishment”, “diet”, “weight”, and “skinny”.

The remaining words were identified as belonging to the categories shown previously in

Table 6.1. The remaining words were examined for their representativeness ratings.

Words that received the highest ratings, and had data available on frequency of

occurrence in the English language, were selected as the final list of experimental words.

This process ensured that the final experimental words used in the Stroop task had

empirical support for their use, and that they were rated as being the most representative


of the category. The final selection of words to be used in the experimental condition of

the Stroop task is shown in Table 6.1.

6.4 Stage Four: Matching of Neutral and Experimental Words

6.4.1 Method

6.4.1.1 Procedure. Following the final selection of the experimental words

previously described, six categories of neutral words were to be selected. Various word

categories were brainstormed and frequency data for individual words were examined. If

a sufficient match could be found, this word was retained. As with the selection of the

target words, the neutral words had to form a clear semantic category, and be matched to

the target words on length and frequency of occurrence in the English language, as

provided by norms in Kucera and Francis (1967) and Leech et al. (2001).


Table 6.2 shows the final selection of words including information on frequency

of usage and word length. For each of the matched categories, a paired samples t-test was

run to ensure there were no significant differences in frequency of occurrence and word

length. Adequate matching was achieved, as no significant differences were noted for any

of the neutral and experimental word categories.


Table 6.2

Frequencies of Usage and Word Length Data used for Matching of Experimental and Neutral Categories.

Experimental category

Frequency Word length

Neutral category

Frequency Word length

Frequency Word Length

Negative emotion

Subject areas t p t p

Depressed 15 9 Mathematics 20 11 Anxiety 27 7 Medicine 34 8 Stress 38 6 Drama 38 5 Angry 42 5 Historian 30 11 Hate 27 4 Psychology 26 10 Mean (SD) 29.80 (10.61) 6.20 (1.92) Mean (SD) 29.60 (6.98)

8.6 (2.30) 0.58 0.59 -1.98 0.11

Negative appearance

Nationalities

Overweight 3 10 European 7 8 Obese 5 5 Asian 10 5 Fat 33 3 French 32 6 Ugly 21 4 Greek 20 5 Unattractive 3 12 Australian 4 10 Mean (SD) 13.00 (13.49) 6.80 (3.96) Mean (SD) 14.60 (11.43) 6.80 (2.10) -0.20 0.84 0.00 1.00 Low calorie foods

Astronomy

Celery 4 6 Saturn 3 6 Apple 15 5 Mars 19 4 Vegetable 26 9 Astronomy 24 9 Carrot 5 6 Telescope 4 9 Banana 5 6 Jupiter 9 7 Mean (SD) 11.00 (9.51) 6.4 (1.51) Mean (SD) 11.80 (9.31) 7.00 (2.12) 0.10 0.92 0.46 0.66


Physical activity

Gardening

Sport 45 5 Grass 55 5 Gym 4 3 Tulip 6 5 Aerobics 1 9 Gardening 2 9 Jogging 1 7 Palm 2 4 Swimming 18 8 Rose 18 4 Mean (SD) 13.80 (18.80) 6.40 (2.40) Mean (SD) 16.6 (22.44) 5.40 (2.07) 0.18 0.86 -1.18 0.32 High calorie foods

Animals

Hamburger 10 9 Seal 17 4 Chocolate 9 9 Dolphin 5 7 Lolly 3 5 Mosquito 2 8 Chips 2 5 Cockatoo 1 8 Pizza 3 5 Parrot 2 6 Mean (SD) 5.40 (3.78) 6.60 (2.19) Mean (SD) 5.40 (6.65) 6.60 (1.67) 0.00 1.00 0.00 1.00 Positive appearance

Transport

Attractive 52 10 Holiday 76 7 Slender 19 7 Flying 19 6 Slim 13 4 Transport 14 9 Muscular 16 8 Helicopter 16 10 Toned 1 5 Sedan 1 5 Mean (SD) 20.20 (19.04) 6.80 (2.38) Mean (SD) 25.20 (29.21) 7.40 (2.07) -0.38 0.72 0.33 0.75

Table 6.2 continued


6.5 Summary of Phase Two

The aim of Phase Two was to develop an empirically supported set of stimuli to

be used in the later Emotional Stroop task. A limitation noted in past research employing

the Stroop task has been the problematic manner in which the words are developed. A

broader set of word categories were developed to overcome the limited manner in which

past research has conceptualised body image. Words relating to both positive and

negative appearance were selected, in addition to a wide range of food words. The

inclusion of words representing negative affect allows for general psychopathology to be

assessed. Additionally, words relating to physical activity and sport were developed; a

category that has not previously been explored with a Stroop task. Words within each of

these six experimental categories were rated for appropriateness by independent raters to

ensure that only the most representative words were included.

Additional methodological refinements were made by ensuring the careful

selection and matching of neutral words. The lack of homogeneity between the neutral

words within a category as compared to the experimental word categories is problematic

given that selective attention is influenced by how highly related words are (Green and

Rogers, 1999). Therefore, in the current study, care was taken to ensure the semantic

homogeneity within each category of experimental and neutral words. Furthermore, the

neutral words were carefully matched to the experimental words on length and frequency

of usage in the English language. This process ensures that only the valance of the words

differed between the experimental and neutral categories, and that any differences in

reaction times are due to the emotional content of the words. The next chapter of the

thesis outlines the methodology the Stroop and memory task these words were in to

assess biased cognitive processing.


Chapter Seven: Cognitive Methodology Used to Assess Biased Processing (Phase Three).


The purpose of this chapter is to describe the methodology used for the main

study of the current project. The chapter describes the sample, design, and measures used.

7.1 Method

7.1.1 Participants

A National sample of 201 participants was recruited from the general community

and from two universities. Of the 144 women providing complete data sets, 30% were

recruited from the general community, while 70% were full time university students

studying psychology. Thirty-seven percent of these university students were recruited

from a university in Melbourne, while the remaining 33% were recruited from Brisbane

Universities. Most of the sample (63%) had at least completed 12 years of schooling, and

30% had completed some form of university degree, from a diploma to PhD. The

majority of the sample was Australian-born (86%), and 90% identified themselves as

Caucasian. When marital status was assessed, 30% were single, 32% were married or in a

de facto relationship, 31% were currently dating, and 6% were widowed / separated /

divorced. A wide age range was reported, with ages ranged from 17 to 70 years, with an

average age of 27.71 years (SD = 12.16), however 50% of the sample were aged 21 years

and under. Body Mass Index was calculated from self reported height and weight. Based

on guidelines from the World Health Organisation (2000), 8.3% of the women were

classified as underweight (BMI under 18.5), 62.5 were of a normal weight (BMI 18.5-

24.9), 16.7% were overweight (BMI 25-29.9), while 8.3% were classed as obese (BMI

greater than 30).


Of the 55 males in the study, just over half (58%) had been recruited from the

general community, and the remaining 42% recruited from Queensland (17%) and

Melbourne (24%) universities. Age ranged from 17 to79 years, with a mean age of 34.54

years (SD = 14.76), with just under half the sample (47%) were aged 29 years and under.

The average BMI of the men was 25.70, with none of the men being classified as

underweight, 50% falling within the normal weight range, 31% being classed as

overweight, and 14.5% as obese. The majority of the sample was well educated, with

only 14% not completing at least a high school education. The majority of the sample

(83%) was Australian born, and 92% identified themselves as Caucasian. A small

percentage described themselves as Asian, Aboriginal or Torres Straight Islander, or

European. When marital status was assessed, 41% of the men were married or in a de

facto relationship, 31% were single, 20% were dating, and 7% were divorced, separated,

or widowed.

Recruitment of participants from the general community was completed through a

series of media campaigns, including local radio and newspaper stories, and a small

segment on a National morning television program. These segments provided a brief

outline of the research and invited interested individuals to contact the researcher via

phone or email. These contacts were followed up and a mutually convenient testing time

was organised. These participants weren’t offered any incentives for participation, but

were given a nominal monetary amount to cover transport costs to and from the

university for testing.

The student participants were sourced from two local universities in Brisbane and

a university in Melbourne. The majority of these students were majoring in psychology,


and were offered course credit for participation. Testing either took place at the

respective university or at the participant’s home.

Normal colour vision was required to take part in the Stroop colour naming task.

No additional exclusion criteria were applied.

7.1.2 Design

The first Independent Variable was Stroop word category with six levels of the

experimental words. This was a repeated measures variable. The Dependent Variable was

the reaction time in milliseconds to respond to each of the Stroop categories. An

interference index was used wherein the reaction time of each of the experimental

categories was taken from the average of the matched neutral category, to gain an index

of how different the reaction times were for the experimental versus the neutral words.

The second Independent Variable was the body-image category participants were

assigned to during the cluster analysis. This was a between groups variable. Correlations

were also used to explore the relationship between the cognitive variables and a range of

additional psycho-social and demographic factors.

7.1.3 Materials

Two cognitive tasks were used to assess biased attention and memory, and a

battery of questionnaires assessed health and body image. Non-copyrighted

questionnaires are included in Appendix C.

7.1.3.1 Emotional Stroop task. A modified Stroop task was used to assess

selection attention for a range of body image related information. A computerised task

was developed by the associate supervisor, Dr. Doug Mahar, who had extensive

experience in programming psychological experiments. Testing took place on Windows

compatible computer with the screen resolution set to 800 X 600 pixels. A plain blue


background was used with a grey display box measuring 18 X 25 cm appearing in the

middle of the screen. Participants ran the experiment within this display box. The text in

the display box was presented in the appropriate colour (i.e., red, blue, green, or yellow).

Words in the display box were presented one at a time, were 1.5cm high, in lowercase,

and centred. If no response was given within two seconds, the word disappeared and a

reminder was given to participants to respond as quickly as possible. Participants

responded via four colour keys on the keyboard placed over the arrow buttons. Incorrect

or missed trials were not repeated.

Four colours were used for the words, red, green, yellow, and blue. The

presentation of the words was randomised for each participant, including the order of the

words, and the colour of the word.

Fourteen categories of words were used which consisted of six experimental

categories, six neutral categories, a colour consistent, and colour conflicting condition.

Each of the experimental and neutral categories consisted of five words per category,

while the colour conditions contained the four colour words. This resulted in a total of 68

words. For a full list of these words see Table 6.2 in Chapter Six.

As past research has shown rapid habituation effects (e.g., McNally, Riemann, &

Kim, 1990), each word was only presented twice in the Stroop task, giving a total of 136

trials. Pressing the space bar started each individual trial. Additionally, 10 practice trials

were given prior to the Stroop task, using a set of words revolving around office furniture

that did not appear in the actual task. Participants could repeat these practice trials if

needed. Testing took place individually, or in small groups, and took approximately four

minutes to complete.


7.1.3.2 Distracter task. Participants were asked to complete as many simple

multiple choice mathematics questions as they could within two minutes. This was to

reduce any effects of short term memory before the incidental memory task.

7.1.3.3 Incidental memory task. Participants were asked to list as many words as

they could remember from the Stroop task within three minutes on paper.

Following the cognitive tasks, a battery of psycho-social questionnaires was then

administered.

7.1.3.4 Demographic form. Participants were asked standard demographic

information such as their gender, age, height and weight (to calculate Body Mass Index),

ethnicity, and marital status. Information was also sought regarding any history of an

Eating Disorder, highest and lowest adult weight, and the type and frequency of exercise

in which they typically engage.

7.1.3.5 Hunger levels. Current level of hunger was assessed on a 1-10 scale, along

with an open ended question asking how long it had been since food had been consumed.

This was intended to rule out any effect hunger may have on performance in the Stroop

task.

7.1.3.6 Negative mood. The Depression, Anxiety, Stress Scale (Lovibond &

Lovibond, 1995) was used to measure self reported negative affect. Each of the three

subscales (Depression, Anxiety, and Stress) contains seven statements that respondents

indicate how much the statement has applied to them over the past week. Response

options range from 0 (did not apply to me at all) to 3 (applied to me very much, or most

of the time). Scores on the three subscales range from 0-42, with higher scores indicating

higher levels of negative affect. High alpha coefficients for all subscales have been

reported (Lovibond & Lovibond, 1995). All participants completed the Depression


subscale but due to a photocopy error only 30% of participants completed the Anxiety

and Stress subscales.

7.1.3.7 Reasons for Exercise. Reasons for exercise was measured using the Reasons

for Exercise Inventory (Silberstein et al., 1988). Twenty-four items cover seven general

domains of reasons for engaging in exercise, namely: exercising for weight control,

physical attractiveness, improving mood, body tone, health, and enjoyment. Each

subscale contains between three and four items that are answered on a 1 (not at all

important) to 7 (extremely important) semantic differential scale. Six out of seven scales

show acceptable internal consistency, with a range from .67 for enjoyment to .81 for

weight control.

7.1.3.8 Dysfunctional behaviours. Participants were asked to indicate “Yes” or “No”

as to whether they have engaged in the following behaviours to achieve weight loss in the

last six months: vomiting, laxative use, diuretic use, use of slimming tablets, fasting,

excessive exercising, dieting for health reasons, and dieting for weight loss. Additionally,

these were asked about whether they had binged on large amounts of food, and if they

felt a loss of control over that binging episode.

7.1.3.9 Eating Disorder Behaviours. The Eating Disorder Inventory (EDI; Garner,

Marion, Olmsted, & Polivy, 1983) is a 64-item questionnaire that assesses the

psychological and behavioural characteristics commonly found in eating disorders. Each

item is responded to on a 6 point Likert scale ranging from “always” to “never”. It

consists of eight subscales, namely Drive for Thinness, Bulimia, Body Dissatisfaction,

Ineffectiveness, Maturity Fears, Interpersonal Distrust, Interoceptive Awareness, and

Perfectionism. Research has shown this test to be reliable and valid, and scores are able

to differentiate eating disordered groups from other groups (Garner, et al., 1983).


7.1.3. 10 Body Mass Index (BMI). Self reported height and weight was obtained as

part of the demographic form. BMI was calculated by using the formula: kg/height in

meters². BMI is a general measure of body size.

7.1.3. 11 Self-esteem. The Rosenberg Self-Esteem Scale (Rosenberg, 1965) is the

most widely used questionnaire for measuring self-esteem. It is a short, 10-item measure

of general feelings of ones' self worth. Respondents rate the items on a 4 point Likert

scale from "Strongly Agree" to "Strongly Disagree". Half the items are reversed scored

so that higher scores indicate higher levels of self esteem. This scale shows adequate

psychometric properties with a test-retest reliability of .85, and it correlates satisfactorily

with other measures of self-esteem (Heinberg et al., 1995).

7.1.3.12 Social functioning. To measure general well-being, the Social

Functioning Questionnaire (Tyrer et al., 2005) was used. This brief 8-item measure is

based on the earlier Social Functioning Questionnaire which in turn was based on the

semi-structured interview protocol the Social Functioning Schedule. These two earlier

assessment tools were used extensively to measure well-being in a number of domains,

yet were quite lengthy. As such, the more recent version provides a brief, yet

psychometrically sound, assessment of social functioning (Tyrer et al., 2005).

Questions assess functioning in a range of life domains such as money problems,

relationships with others, and work satisfaction. Responses are made on a 4-point scale

and indicate how often problems in this arena occur. Normative data from a large non-

psychiatric sample are available. Scores range from 0-24, with higher scores indicative of

poorer social functioning.

7.1.3.13 Body attitudes. To assess a range of body image attitudes, two scales

were used, the full 69-item version of the Multidimensional Body-Self Relations


Questionnaire (MBSRQ; Cash, 2000), and The Body Attitudes Questionnaire (BAQ;

Ben-Tovim & Walker, 1991).

The MBSRQ consists of 10 subscales. The first six subscales measure satisfaction

with, and investment in appearance, fitness, and health (Appearance Evaluation; seven

items; Appearance Orientation; 12 items; Fitness Evaluation; three items; Fitness

Orientation; 13 items; Health Evaluation; six items; Health Orientation; eight items). The

Illness Orientation subscale measures reactivity to feelings of illness, while the final three

subscales measure satisfaction with particular body areas (Body Areas Satisfaction Scale;

nine items), self- and other-perceived weight status (Self-Classified Weight; nine items),

and fear of fatness / diet attempts (Overweight Preoccupation; four items). Some items

are reversed-scored, so that higher scores are more pathological.

Large-scale studies reveal the MBSRQ to be psychometrically sound, with high

Cronbach’s alphas (none less than .70). Sound test-re-test reliability for all subscales

(none less than .71) have also been noted across male and female samples (Cash, 2000).

To assess a range of attitudes women hold about their bodies, the BAQ was

deemed most appropriate. The BAQ contains six self-report subscales that will be used

namely Feeling Fat, Body Disparagement, Strength and Fitness, Salience of Weight and

Shape, Attractiveness, and Lower Body Fatness. Each of these subscales has been shown

to reflect body concerns that Australian women have, based on a community sample.

There are 44 items in total, with sub-scales ranging from four items (lower body fatness)

to 13 items (feeling fat).

This scale has sound psychometric properties: The full scale has a Cronbach’s

alpha of .87 indicative of high internal consistency. This scale is also reliable with a split-

half Kuder Richardson correlation coefficient of .92 and a test-retest reliability of .90 for


the feeling fat subscale (Ben-Tovim & Walker, 1993). Additionally, normative values are

available for Australian women.

7.1.3.14 Desire for muscularity. To assess pursuit of a muscular physique the

Drive for Muscularity scale (McCreary & Sasse, 2000) was used. This 15-item measure

assesses desire to improve tone and muscularity, and the means through which this is

achieved (such as weight lifting, drinking protein shakes). Each item is scored on a six

point scale ranging from always to never, with higher scores indicating greater drive for

muscularity. Adequate psychometric properties of this scale have been reported in both

males and females (McCreary & Sasse, 2000).

7.1.3.15 Dietary restraint. The Restraint Scale (Herman, Polivy, Pliner,

Threlheld, & Munic, 1978) is a widely used measure of dieting behaviours,

preoccupation with eating, and history of weight fluctuations. This scale consists of 10

items with higher scores indicating a higher level of dietary restraint, which appears to be

associated with chronic dieting (Ricciardelli & Williams, 1997). Adequate psychometric

properties have been reported (Ricciardelli & Williams, 1997).

7.1.4 Procedure

During recruitment, participants were not informed of the full nature of the study

to try and reduce demand characteristics. Instead, a cover story was given that the study

was examining speed of information processing and health behaviours, with a focus on

body image. Participants were told the study consisted of two components: some tasks

that measured reaction time, and then a set of questionnaires that measured a range of

health behaviours including questions about body image. A copy of the information and

consent forms can be found in Appendix D.


The Stroop was completed first. Participants were told they were completing a

measure of speed of information processing and to respond as quickly but as accurately

as possible, to words that would appear on the screen. They were instructed to ignore the

meaning of the words and just focus on the colours.

Upon completion of the Stroop task, the distracter task was given. Participants

were told this was also a measure of speed of information processing, and they needed to

answer as many questions as possible within two minutes.

The incidental memory task was then given. Participants were asked to recall as

many words as possible that they had seen on the screen during the colour naming task,

even though they had been previously instructed to ignore these words. Participants were

encouraged to use the full three minutes, even if they felt they could not recall any words.

Full debriefing was then provided, including the reasons for the use of mild

deception (e.g., that participants could not know about the memory task as it would

influence their responses). Participants were then asked to complete a questionnaire

package that assessed a range of health behaviours and attitudes, with a focus on body

image.


Chapter Eight: Results and Discussion for Biased Cognitive Processing

8.1 Preliminary Analyses

8.1.1 Overview of Analyses

The results are examined first for females, followed by the results for males, and

finally an examination of sex differences. Within each of these three main sections, the

results are further divided into the analyses examining group membership, selective

attention, selective memory, and the relationship between attention and memory.

Differences between groups were analysed using ANOVA’s, while correlations were run

to examine how the psycho-social variables were related to the cognitive data.

8.1.2 Data Screening

All analyses were conducted using The Statistical Package for the Social Sciences

(SPSS), and were evaluated against a 0.05 significance level for 2-tailed tests, unless

otherwise specified. Given the exploratory nature of this research, trends are also reported

(i.e., p values of 0.06 or 0.07). Prior to analyses, all variables were examined for accuracy

of entry and missing values. Missing data was coded when participants missed a

question, a page, or gave multiple responses. There was only a small amount of missing

data and was thus coded as such. As a cluster analysis cannot be conducted on variables

with missing values, a mean replacement was used on the BAQ subscales. Histograms of

all cognitive and psychosocial variables were examined for normality and outliers. All

data was reasonably normally distributed, no significant skewness or kurtosis was noted,

and no outliers were identified. Scores on the Depression, Anxiety, and Stress subscales

of the DASS showed some positive skewing, indicating low levels of these constructs

within the sample. Analyses involving these variables were run with both the original

scores and transformed scores, and both sets of results are given when relevant.


8.1.3 Scoring of the Cognitive Data

8.1.3.1 Scoring of the Stroop data. The average reaction time to respond to each

of the Stroop categories was analysed. Examination of error rates, which included the

selection of an incorrect colour, or a missed trial through responding too slowly / quickly

were manually examined. Overall, the number of errors participants made was small,

with the average being less than one error in each experimental and neutral category.

Two participants were excluded due to large error rates: one female participant who made

errors on 28% of trials and one male who made errors on 16% of trials. Responses greater

than two seconds were automatically excluded from the data by the Stroop program.

Given the large age range in the sample, and the recommendations made by

Dobson and Dozois (2004), interference indexes were calculated for the Stroop data to

allow for age differences in responding. Preliminary analyses revealed that age had very

strong significant correlations with raw reaction times, with older people taking longer to

respond to the all of the Stroop categories. To allow for age differences in response times,

interference indexes were calculated. These indexes are a measure of how much longer

each participant took to respond to the experimental words as compared to the neutral

words. Six interference indexes were calculated, corresponding to each of the six

experimental categories. For example, the interference index for the Negative Emotion

words was calculated by subtracting the reaction times for the Subjects category. A

negative score on the interference index indicates that response times were faster for the

experimental words than for the matched neutral words, while positive scores indicate

that response times are slower for the experimental words.

8.1.3.2 Scoring of the memory data. In addition to biased attention scores,

memory biases were also assessed. First, words in each category were scored as correct if


they had been previously presented on the Stroop task, or were a close variant of the

word (e.g., stress / stressed). Second, raw scores were converted into percentages to

account for individual differences in the total number of words recalled. Responses were

scored as the total number of correctly recalled words from each of the six experimental

categories divided by the total number of words recalled across all categories. It is

important to consider percentage scores as they give more information than raw scores.

For example, if a person correctly recalled ten words, but only three of those were

experimental words, they are responding quite differently from a second person who

correctly recalls only three words, but all three of these words are from experimental

categories.

8.1.4 Identification of Sub-Groups: The use of Cluster Analysis

8.1.4.1 Overview of the cluster analysis technique. As noted in the literature

review, past research using the Stroop task has typically compared a clinical group to a

presumed homogeneous control group, or has compared control groups on one

vulnerability factor. This rather simplistic analysis fails to acknowledge a wide range of

vulnerability factors, and the complex interaction of these factors. In order to examine

sub-groups of body image disturbance within a non-clinical sample, a cluster analysis

was conducted. The purpose of a cluster analysis was to examine whether participants

can be divided into homogenous sub-groups based on a similar profile of scores.

An overview of cluster analysis as a statistical technique is provided in Appendix

E. Briefly, cluster analysis is a statistical technique applied to multivariate data designed

to identify homogenous sub-groups (Aldenderfer & Blashfield, 1984). Conceptually, it is

similar to factor analysis in that it searches for underlying latent variables. However, in a


cluster analysis, this procedure is applied to variables rather than individual cases. The

outcome of a cluster analysis is set of homogenous groups.

Based on these recommendations from the literature (Aldenderder & Blashfield,

1984; Hair, Black, Babin, Anderson, & Tatham, 2006; Lange, Iverson, Senior, &

Chelune, 2002) a hierarchical cluster analysis using an average linkage method and

squared Euclidian distance measure was used. It should be noted that the cluster analysis

was used simply to provide some basic empirical support for the types of sub-groups

within the non-clinical sample. While it is recognised that cluster analysis has a number

of limitations, this procedure was deemed more useful than simply comparing individuals

on a median split, as past research has typically done. Instead of defining groups by one

variable, a cluster analysis allows for a more comprehensive understanding, as multiple

variables are taken into account.

The cluster analysis was completed separately for males and females for two

reasons. First, different factors were identified as influencing males and females body

image in the literature review and the outcomes of the qualitative analysis in Phase One.

Second, it was hypothesised that males and females would differ on their cognitive

processing therefore it was not meaningful to combine these two samples.

8.1.4.2 Results of cluster analysis for women. Core body image concerns, as

assessed by the BAQ were entered as six variables into the cluster for the 143 women.

Missing variables on the BAQ were replaced with the mean of the respective sub-scale.

In order to determine how many clusters were meaningful, a number of indicators were

examined. Based on the outcomes of the dendrogram, Icicle plot, and fusion coefficients,

a four cluster solution was deemed most appropriate. The decision processes is provided


in more detail in Appendix E along with the description of the cluster analytical

procedure.

Descriptive information for the four cluster solution is shown in Table 8.1. In the

four cluster solution, the emergence of a discrete highly symptomatic group is noted. This

was not evident within the two or three cluster solution.

Table 8.1

BAQ Sub-Scale Scores for the Four Cluster Solution in Women.

Cluster 1

(n = 62)

Cluster 2

(n = 28)

Cluster 3

(n = 36)

Cluster 4

(n = 17)

BAQ sub-

scale

M SD M SD M SD M SD

Attractiveness 14.08 2.18 15.57 1.39 11.52 2.13 13.76 2.25

Disparagement 13.84 2.70 11.07 1.58 20.67 3.14 20.82 3.76

Feeling fat 35.65 7.18 32.79 7.59 49.72 6.28 53.58 6.71

Salience 19.14 3.56 17.14 4.17 25.02 3.93 30.64 2.62

Lower body

fatness

11.05 3.16 8.92 2.52 14.30 2.77 16.29 3.07

Strength /

fitness

16.41 3.09 22.60 2.31 14.80 3.11 19.17 2.83

Examination of the profiles within the four cluster solution revealed four distinct

groups. Cluster one and two show similar means on most of the BAQ subscales. These

groups are characterised by higher feelings of attractiveness, little loathing of one’s body,


low feelings of fatness and low importance of appearance in one’s life. Cluster one and

two differ however on Lower Body Fatness and Strength / Fitness, with Cluster two

showing higher feelings of strength and fitness and less feelings of lower body fatness.

Cluster three and four appear to reflect groups of women with highly dysfunctional body

image attitudes. In particular, the means in Cluster four are similar to, or more

dysfunctional, than the scores found in Anorexic samples (Ben-Tovim & Walker, 1992).

Based on these scores, cluster one and two appear to reflect a group with very

little body image concerns. The distinguishing features between cluster one and two are

the high reports of strength and focus on fitness (and perhaps corresponding lower body

fatness) within cluster two. Hence these clusters were named “Normal” and “Athletic”

respectively. Cluster three appears to reflect a group that experiences moderate level of

dissatisfaction, and were therefore labelled “Dissatisfied”. Cluster four, having the most

dysfunctional profile closely representing scores of Eating Disordered samples, were

therefore labelled “Symptomatic”.

One criticism of cluster analysis has been that the use of different cluster

techniques can result in alternate group formations (Aldenderder & Blashfield, 1984;

Hair et al., 2006; Lange et al., 2002). In an attempt to overcome this limitation, the cluster

analysis was re-run using a different algorithm. A Wards’ method was used, as some

researchers recommend the use of this algorithm (e.g., Francis, 2004). Using this Ward’s

method, a similar result emerged to the previously described cluster solution.

According to Aldenderder and Blashfield (1984), additional support for the

validity of the clusters may be sought by comparing them on other key variables. The

four clusters were compared on relevant demographic variables of BMI and age, in


addition to other functionality measures of negative mood, self esteem, quality of life,

and number of risky weight management behaviours.

Cluster membership was entered as an Independent Variable in a one-way

Independent Groups ANOVA, with the functionality measures and demographic

variables as Dependent Variables. A breach of the homogeneity of variance assumption

was noted for BMI. Examination of the distributions within each cluster revealed no

outliers, nor any significant skewness or kurtosis. Log 10 and Square root transformations

of the data did not change the overall significance of the ANOVA. Additionally, the ratio

of smallest to largest variance did not exceed three, therefore the original scores were

maintained (Keppel, 1991). A significant breach of the homogeneity assumption was also

noted for depression scores. This appeared to be the result of small Standard Deviation in

the Athletic group compared to a large Standard Deviation in the Symptomatic group. A

square root transformation was applied to all depression scores which resulted in the

assumption being met. The transformed depression scores were retained for this analysis,

but did not change the outcome of the ANOVA, nor the pattern of significant post hoc

tests, therefore untransformed scores were retained for ease of interpretation.

A breach of the homogeneity of variance assumption was noted for age.

Examination of the distributions within each cluster revealed no outliers, nor any

significant skewness or kurtosis. Log 10 and Square root transformations of the data did

not change the overall significance of the ANOVA, or the pattern of significance with the

post hoc tests. Additionally, the ratio of smallest to largest variance did not exceed three,

therefore the original scores were maintained for ease of interpretation.

Scores on anxiety also showed a significant breach of the homogeneity of

variance assumption. Applying a square root transformation resulted in the assumption


being met, but the ANOVA was no longer significant. In this case, transformed scores

were retained.

Results of the ANOVA’s revealed that the four clusters differed significantly on

most of the functionality indicators. Significant differences were followed up with post

hoc tests with a Bonferonni adjustment (.05 / 4 = .012). Means are shown in Table 8.2.

Table 8.2

Differences in Demographic and Social Functioning Scores Between the Four Sub-

Groups Identified in Women (Standard deviations in brackets).

Normal

(n = 62)

Athletic

(n = 28)

Dissatisfied

(n = 36)

Symptomatic

(n = 17)

M (SD) M(SD) M(SD) M(SD)

Age 26.16 (10.73) 35.53 (15.27) 25.50 (9.73) 25.64 (12.15

BMI 22.06 (3.68) 22.44 (2.33) 25.07 (5.17) 25.62 (4.64)

Depression 6.16 (6.07) 2.89 (2.72) 9.94 (8.77) 10.05 (10.24)

Anxietyⁿ 1.78 (1.42) 1.59 (0.62) 2.83 (1.43) 2.62 (1.98)

Stress 10.52 (6.52) 9.57 (5.99) 16.36 (9.25) 14.50 (12.04)

Self esteem 30.63 (4.10) 33.55 (3.97) 27.11 (3.95) 27.52 (4.78)

Social

functioning

7.60 (3.19) 4.92 (2.20) 8.86 (2.97) 9.76 (4.54)

Dietary

restraint

10.96 (5.02) 10.84 (4.99) 16.62 (4.79) 20.06 (4.13)

Quality of life 0.98 (0.87) 1.59 (0.79) -0.04 (0.96) 0.28 (1.25)

ⁿ Square root transformed scores.


There was a significant difference between the clusters on depression scores, F (3,

138) = 6.68, p = .0001. The Athletic group reported much lower depression scores than

the other groups, although there was no significant difference between the Athletic and

Normal groups. Both of these groups had significantly lower depression scores than the

Dissatisfied, but only the Athletic group was significantly lower than the Symptomatic

group. There was no difference between the Dissatisfied and Symptomatic groups.

The groups did not significantly differ on stress, F (3, 37) = 1.56, p = .21 or

anxiety scores, F (3, 38) = 1.87, p = .15. Significant differences were noted in age, F (3,

139) = 5.14, p = .002, with the Athletic group being significantly older than all the other

groups. The groups could be significantly distinguished on BMI, F (3, 136) = 6.45, p =

.0001. The Athletic and Normal groups had the lowest BMI’s, and were not significantly

different from each other. Similarly, the Dissatisfied and Symptomatic groups were not

significantly different from each other on BMI. Both of these latter two groups were

significantly heavier than the former groups.

The four groups also differed significantly on self esteem, F (3, 135) = 14.89, p =

.0001, the impact that one’s body image has on quality of life, F (3, 132) = 18.39, p =

.0001, and social functioning, F (3, 139) = 6.42, p = .0001. Self esteem and quality of

life did not differ between the Dissatisfied and Symptomatic groups, but both groups

were significantly lower on self esteem and higher on quality of life, than the Athletic and

Normal groups. In addition, the Athletic group had significantly better self esteem and

better quality of life than the Normal group. The Athletic group had significantly better

social functioning than the other three groups. The Normal, Dissatisfied, and

Symptomatic groups did not differ. Finally, the groups differed significantly on dietary

restraint, F (3, 133) = 22.42, p < .001. The Normal and Athletic group reported a low


level of dietary restraint, and were not significantly different from one another. The

Dissatisfied and Symptomatic group reported levels indicative of high dietary restraint,

and were significantly higher than the Normal and Athletic groups.

Together, these results provide support for the existence of independent sub-

groups within a non-clinical sample. The women classified as Symptomatic in this

sample had similar scores on the body image variables that have been reported in

Anorexic samples. Compared to the other sub groups identified, the Symptomatic group

reported the poorest social functioning and self esteem, report more depression, and have

the highest BMI’s. The Dissatisfied group also report symptomatic scores on all body

image variables, and report moderate levels of the functionality measures. The women

classified as Normal and Athletic appear to not experience negative body image

concerns, and they also report better overall functioning.

8.1.4.3 Results of the cluster analysis for men. The measure used for the cluster

analysis in females, the BAQ, was developed specifically to encompass women’s body

image concerns, thus it was not deemed useful to categorise males. Instead, the MBSRQ

was used. A full description of the MBSRQ was given in the Stroop Materials section.

Briefly, the MBSRQ is one of the few measurement tools available that adequately assess

a wide variety of male body image concerns. Additionally, this scale reports excellent

psychometric properties, and normative data is available. Six subscales of the MBSRQ

were used in the cluster analysis for males: the Appearance Evaluation and Orientation

subscales, the Fitness Evaluation and Orientation subscales, the Health Orientation

subscale, and the Overweight Preoccupation Subscale. These subscales were selected to

cover feelings of attractiveness, general health and fitness, importance placed on

appearance and fitness, and dietary restraint; key variables that emerged in the qualitative


study of Phase One. The other four subscales of the MBSRQ were not used in the cluster

analysis for two reasons. First, those subscales did not emerge as core facets of men’s

body image concerns (e.g., illness preoccupation and perceived weight category).

Second, given the relatively small data set, only a limited number of clustering variables

could be used (Aldenderder & Blashfield, 1984). As cluster analysis requires a complete

data set, missing data was replaced with the mean of the subscale.

Core body image concerns, as measured by the six subscales of the MBSRQ,

were entered for the 54 men. A hierarchical cluster analysis using a Ward’s Method and

squared Euclidian distance measure was run on standardised scores. In order to determine

how many clusters were meaningful, a number of indicators were examined. Inspection

of the agglomeration schedule, fusion coefficients, and the dendrogram revealed that a

three cluster solution was most appropriate. A full description of this decision processing

is provided in Appendix E. The means for the MBSRQ subscales are shown in Table 8.3

for the three cluster solution.

This three cluster solution resulted in quite distinct groups that differed both

qualitatively and quantitatively. Group one was the largest group (n = 26), and reported

normative levels on most of the body image variables (sub-scales of the MBSRQ). This

group reported moderate feelings of attractiveness, high feelings of fitness, and low

overweight preoccupation. Little importance was placed on appearance, health, or fitness

in their lives. This group could perhaps be summarised as being happy with their body

image and placing low importance on body image. This group was therefore labelled

“Normal”.


Table 8.3

Descriptive Data for the MBSRQ Sub-Scales for the Three Cluster Solution in Males.

Cluster 1

(n = 26)

Cluster 2

(n = 19)

Cluster 3

(n = 9)

MBSRQ subscale M (SD) M (SD) M (SD)

Appearance Evaluation 3.69 (0.55) 2.52 (0.57) 3.97 (0.37)

Appearance Orientation 2.85 (0.51) 3.40 (0.64) 3.54 (0.78)

Fitness Evaluation 4.06 (0.55) 3.47 (0.78) 4.48 (0.50)

Fitness Orientation 3.22 (0.76) 3.16 (0.42) 4.35 (0.32)

Overweight Preoccupation 1.34 (0.33) 2.46 (0.80) 2.58 (0.53)

Health Orientation 3.33 (0.74) 3.12 (0.77) 4.18 (0.58)

Group two was a slightly smaller group of men (n = 19) with low feelings of

attractiveness and fitness, average overweight preoccupation, and a low importance

placed on appearance, fitness, and health. Compared to the Normal group, group two

reported lower levels of attractiveness, but placed more importance on appearance. Group

two also reported lower feelings of fitness, but were not motivated to do anything about

it. Therefore this group was labelled “Dissatisfied”.

A clear picture emerged with the characteristics of Group three (n = 9) who

represented a group of men who were driven by health and fitness. This group reported

the highest levels of feeling attractive, but placed only a moderate importance on

appearance. Additionally, high levels of fitness and a high importance on regular fitness

and maintaining a healthy lifestyle were noted. This group also reported very high levels


of fat anxiety / weight vigilance; higher levels than either Group one or Group two. Based

on these characteristics, this group was termed “Health Conscious”.

To gain additional support for the validity of the three cluster solution, the three

clusters were compared on additional variables not included in the cluster analysis.

Support for the distinctiveness of the three clusters should come from differences in

demographic (age and BMI) and functionality measures (mood, self esteem, quality of

life, number of risky behaviour in last six months, and social functioning).

Cluster membership was entered as an Independent Variable in a one way

Independent Groups ANOVA, with the functionality measures as Dependent Variables.

The results for anxiety and stress could not be used, given the small sample size of men

who actually completed these measures. The homogeneity of variance assumption was

breached for three of the variables: number of risky behaviours in the last six months,

quality of life, and BMI. Examination of the frequency distributions for the number of

risky behaviours in the last six months revealed that the Normal group was positively

skewed, in that most of the group reported none of risky behaviours. The Dissatisfied and

Health Conscious groups were more normally distributed. A log 10 transformation

resulted in the assumption being met, but the ANOVA was no longer significant. While

the transformed scores were retained, it is worthwhile noting the substantially lower

levels of risky weight management behaviours in the Normal group.

Examination of the histograms for BMI revealed no skewness, kurtosis, or

outliers. However, the standard deviation of the Dissatisfied group was twice as large as

the Normal and Health Conscious groups. Applying a square root transformation resulted

in the homogeneity of variance assumption being met, and did not change the significant

result of the ANOVA. Therefore, for aid of interpretation, the untransformed BMI scores


are retained. Table 8.4 provides a comparison of the demographic and functionality

scores across the three clusters.

Examination of the means revealed that the Dissatisfied had consistently poorer

functioning than the Normal and Health Conscious groups. On every one of these general

well-being variables, the Dissatisfied group had the most dysfunctional scores.

Additionally, they were older and heavier, and the only group to be classed as

overweight. Despite these mean differences, results of the ANOVA’s revealed significant

differences on only two of the functionality indicators. Significant group differences only

emerged for self esteem, F (2, 49) = 4.87, p = .01, and BMI, F (2, 48) = 3.39, p = .04.

These significant differences were followed up with post hoc tests applying a Bonferroni

adjustment (.05 / 4 = .012). The Normal group had significantly higher self esteem scores

than the Dissatisfied group, and a trend (p = .05) was noted for the Health Conscious

group to also have higher self esteem scores than the Dissatisfied group. There was no

difference between the self esteem of the Normal and Health Conscious groups.

Following up the significant BMI difference revealed that there was a trend for

the Dissatisfied group to have a higher BMI than the other groups No differences were

noted in the BMI of the Normal and Health Conscious groups.

Together, these results provide some support for the classification of body image

typologies in males. As a further check of the validity of the three cluster solution, the

ANOVA’s examining demographic and functionality measures were re-run using the two

cluster solution. Using the two cluster solution, significant differences were found on

most of the functionality indicators. It may therefore be argued that the two cluster

solution in fact has more external validity, given the consistent differences in other

variables not used in the original clustering process. Recall that the difference between


the two and three cluster solution was the emergence of the small health conscious group

in the three cluster solution. However, given the quite divergent nature of this smaller

group, it was still deemed appropriate to view it as a separate cluster. Group two and

three represent quite distinctive profiles, and therefore it was considered appropriate to

use the three cluster solution.

Table 8.4

Differences in Demographic and General Well-Being Scores Between the Three Clusters

Identified in Males.

Normal

(n = 26)

Dissatisfied

(n = 19)

Health Conscious

(n = 9)

M (SD) M (SD) M (SD)

Age 32.76 (14.16) 37.42 (16.43) 30.33 (8.84)

BMI 24.21 (3.13) 27.59 (6.26) 23.96 (3.24)

Depression 5.03 (6.50) 6.72 (8.40) 3.33 (6.63)

Risky behaviours ⁿ 0.19 (0.23) 0.34 (0.17) 0.35 (0.09)

Self esteem 33.36 (5.24) 28.84 (4.66) 32.87 (4.29)

Social functioning 5.58 (2.45) 7.44 (3.08) 5.33 (1.36)

ⁿ Log 10 transformed scores

8.2 Results and Discussion for Females

8.2.1 Descriptive Information

Table 8.5 and 8.6 shows the means, standard deviations, and Cronbach’s alphas

for all of the psycho-social questionnaires used in the female sample (N = 144). All sub-


scales of the Body Attitudes Questionnaire demonstrated adequate validity as shown by

the Cronbach alpha’s.

Table 8.5

Descriptive Statistics and Reliability Values for the Mood and General Well-Being

Measures in Women.

Scale M SD Min / Max value No. items Cronbach alpha’s

Depression

6.94 7.42 0-42 14 .96

Anxiety

6.66 7.18 0-42 14 .91

Stress

12.51 8.35 0-42 14 .94

Self esteem

29.93 4.70 10-40 10 .83

Social Functioning

7.85 3.45 0-24 8 .74

8.2.2 Biased Attention

Table 8.7 shows the mean reaction for each of the Stroop categories, in addition to

the interference indexes and t-tests. Minimal interference effects were found, indicating

that reaction times for neutral and experimental words were similar. Paired samples t-

tests were conducted to compare the average reaction time of each experimental category

to the matched neutral category. No significant differences were noted for any

comparison. However, high standard deviations were noted indicating substantial

variability in response times, hence the need to examine the role of vulnerability factors.


Table 8.6

Descriptive Statistics and Reliability Values for the Body Image Measures in Women.

Scale M SD Min / Max value

No. items Cronbach’s alpha

REI

Weight Control 4.75 1.37 1-7 3 .57

Fitness 4.89 1.24 1-7 4 .83

Mood 4.39 1.29 1-7 4 .77

Health 5.23 1.16 1-7 4 .85

Attractiveness 4.46 1.62 1-7 3 .80

Enjoyment 3.84 1.53 1-7 3 .84

Tone 4.73 1.51 1-7 3 .78

BAQ

Attractiveness 13.69 2.48 4-20 4 .78

Body Disparagement 15.86 4.82 8-40 8 .80

Feeling Fat 40.86 10.76 13-65 13 .89

Salience 21.67 5.75 8-40 8 .78

Lower Body Fatness 12.10 3.81 4-20 4 .70

Strength / Fitness 17.52 4.10 6-30 6 .82

MBSRQ

Appearance Evaluation 3.18 0.72 1-5 7 .89

Appearance Orientation 3.50 0.59 1-5 12 .80

Fitness Evaluation 3.37 0.86 1-5 3 .77

Fitness Orientation 3.03 0.78 1-5 13 .89

Health Evaluation 3.43 0.73 1-5 6 .84

Health Orientation 3.25 0.74 1-5 8 .80

Illness Orientation 3.04 0.69 1-5 5 .70

Overweight Preoccupation 2.78 0.96 1-5 4 .77

EDI

Drive for Thinness 27.25 8.96 7-42 7 .91

Interoceptive Awareness 44.25 8.31 10-60 10 .89

Bulimia 34.26 5.62 7-42 7 .88

Ineffectiveness 45.31 7.72 10-60 10 .94


Maturity Fears 35.33 6.14 8-48 8 .82

Interpersonal Distrust 29.40 5.82 7-42 7 .86

Perfectionism 22.36 5.65 6-36 6 .82

Drive for Muscularity 1.70 0.55 1-6 15 .80

Restraint Scale 13.45 5.90 0-35 10 .80

Table 8.7

Mean Reaction Time and Interference Indexes (in Milliseconds) for the Stroop Colour-

Naming Task in Females (N = 144).

M SD t p

Negative Emotion 899.60 123.89 -1.03 .30

Control 907.63 117.96

Interference index -8.02 93.11

Negative Appearance 911.84 119.11 1.29 .19

Control 902.13 119.35

Interference index 9.70 86.27

Positive Appearance 907.21 109.08 -0.27 .78

Control 902.18 115.38


Low Calorie foods 901.68 112.82 -0.91 .36

Control 908.97 117.74


High Calorie 903.52 109.69 0.21 .82

Control 906.69 111.33


Physical Activity 901.66 110.10 -0.56 .57

Control 905.51 113.47


Table 8.6 cont.


8.2.2.1 The role of demographic factors in biased attention. To examine the role

of demographic factors in Stroop interference, a series of one-way Independent Groups

ANOVA’s were run with relevant demographic variables as Independent Variables, and

interference scores as Dependant Variables. There was no significant difference between

the BMI weight categories (underweight, normal, overweight, and obese) on any of the

interference indexes: Negative Emotion, F (3, 134) = 0.73, p = .53; Negative Appearance,

F (3, 134) = 0.01, p = 0.99; Low Calorie Foods, F (3, 134) = 1.01, p = .35; Physical

Activity, F (3, 134) = 1.35, p = .26; High Calorie Foods, F (3, 134) = 0.12, p = .94, and

Positive Appearance, F (3, 134) = 0.23, p = .89. No significant differences were noted for

perceived weight status either: Negative Emotion, F (4, 100) = 0.32, p = .50; Negative

Appearance, F (4, 100) = 0.57, p = .59; Low Calorie Foods, F (4, 100) = 0.44, p = .77;

Physical Activity, F (4, 100) = 0.79, p = .53; High Calorie Foods, F (4, 100) = 1.17, p =

.32; Positive Appearance, F (4, 100) = 0.86, p = .48. These results indicate that neither

actual nor perceived weight status is related to biased attention.

As previous research has noted age differences in interference effects (e.g.,

Seddon & Waller, 2000), age was correlated with each index. There were no significant

correlations between age and any interference index (i.e., r’s < .10).

To examine whether hunger affected attention, correlations were run between the six

interference indexes and participants current level of hunger and time since last meal. As

can be seen in Table 8.8, current hunger levels did not seem to be consistently related to

interference effects. Only interference for positive appearance words showed a weak,

negative relationship with current hunger levels. Examination of the correlations between

amount of time since last meal consumption and interference indexes revealed that


positive appearance again was the only variable to show a significant correlation. Thus,

the demographic factors of age, BMI, and perceived weight were not related to any of the

six Stroop interference indexes.

Table 8.8

Correlations Between Hunger Levels and Interference Indexes in Women.

1. 2. 3. 4. 5. 6. 7. 8.

1. Hunger - -

2. Last meal .35** -

3. Negative

emotion

.04 .008 -

4. Negative

appearance

.008 -.05 .05 -

5. Low calorie

foods

.05 .14 .16 -.15ⁿ -

6. Physical

activity

-.13 -.02 .15 -.07 -.17* -

7. High calorie

foods

-.02 .06 .03 .15ⁿ -.08 .09 -

8. Positive

appearance

-.17* -.19* .07 .09 -.09 .09 .06 -

* p < .05. ** p < .01. ⁿ p = .06.

Also shown in Table 8.8 are the inter-correlations between the interference

indexes. Only a small number of these inter-correlations are significant, or show a trend

towards significance, indicating that these indexes are assessing distinct types of body

image concerns.


8.2.2.2 The role of vulnerability factors in biased attention. A series of bivariate

correlations were run in order to examine the influence of a range of psycho-social

variables on Stroop interference scores. Core body image attitudes, mood, and social

functioning variables were examined for their relationship with the six interference

indexes.

Interference for negative emotion words was significantly associated with lack of

anxiety, r = -.30, p = .05, and a trend for low levels of stress, r = -.29, p = .059. Higher

self esteem was associated with attention toward negative emotion information, r = .20, p

= .01. The relation between negative emotion interference and the body image variables

only approached significance, where avoidance was associated with higher feelings of

loathing one’s body (as assessed by BAQ Disparagement subscale; r = -.15, p = .06),

higher feelings of fatness (as assessed by BAQ Feeling Fat; r = -.14, p = .08 and BAQ

Lower Body Fatness; r = -.15, p = .06), more overweight preoccupation (as assessed by

MBSRQ; r = -.15, p = .07), and higher dietary restraint, r = -.15, p = .06. Thus, a quicker

reaction time for negative emotion words was consistently associated with poorer mood,

well-being, and poorer body image.

Attention toward negative appearance related words was moderately correlated

with higher anxiety scores, r = .38, p = .01, and stress, r = .36, p = .02. No significant

correlations were reported with social functioning, self esteem, or body image variables.

Thus, a quicker reaction time for negative appearance words signified better functioning.

Interference for low calorie food words was not significantly associated with any of the

mood variables, self esteem, or social functioning. Attention toward low calorie food

words was the only significant correlation with higher feelings of fitness (MBSRQ


Fitness Evaluation; r = .19, p = .02). Thus, a quicker reaction time for low calorie foods

was associated with poorer health and fitness. Interference for high calorie food words

however, was not significantly associated with any of the psychosocial variables.

Interference for physical activity words was only significantly correlated with two

of the body image measures. Attention toward these words was associated with higher

receptivity to feelings of illness (MBSRQ Illness Orientation; r = .17, p = .03), and a low

level of dietary restraint, r = -.18, p = .03.

Interference for positive appearance words showed a number of

significant, or near significant, correlations with body image variables. Attention toward

positive appearance words appears to be related to better functioning. Longer reaction

times for positive appearance words were associated with less feelings of body loathing

(BAQ Disparagement; r = -.16, p = .04), lower importance of appearance in daily life

(BIQLI; r = .16, p = .06), and a positive evaluation of one’s appearance (MBSRQ

Appearance Evaluation; r = .16, p = .05). Additionally, attention toward positive

appearance words was also associated with more importance placed on health (MBSRQ

Health Orientation; r = .16, p = .04), and lower levels of depression (DASS; r = -.16, p =

.05).

To summarise, it appears that more interference effects (i.e., a longer reaction

time) are associated with better functioning for Positive Appearance words, Low Calorie

food words and Negative Emotion words, while interference effects for Negative

Appearance words indicates poorer functioning. Reaction times for High Calorie food

words and Physical Activity words were not consistently related to any of the mood,

general well being, or body image variables.


Initially, it was planned to run regression analyses to determine the best predictors

of Stroop interference scores. However, given the small number of significant

correlations, and the weak correlation values of those that did approach significance,

regressions were not deemed appropriate.

As past research has not been able to ascertain whether attentional biases are

reliably found within non-clinical samples, and some research has suggested that these

biases are restricted to clinical groups, it may be that interference effects are only found

in those people who engage in highly dysfunctional, risky eating behaviours. Given that

the current research was targeting a non-clinical population, it was expected that the

prevalence of these risky behaviours would be low. Table 8.9 shows the number of

women who have engaged in a range of risk eating and weight loss behaviours in the last

six months. The results for laxative use and diuretic use are not reported, as only four

woman reported using laxatives in the last 6 months, and only two women reported

diuretic use.

These prevalence rates show that a range of dysfunctional eating and weight

management techniques are being used within non-clinical samples. The average number

of dysfunctional techniques was 1.81 (SD = 1.86), with a range from zero to nine. Just

over half of the women (56%) reported none or one risky behaviour, while 25% reported

between two and six risky behaviours, and only 2% engaged in more than six risky

behaviours. To examine whether women who engage in these behaviours show selective

attention for body image related information, independent groups t-tests were run. Given

the small number of women who engage in some of these behaviours, some of the results

should be viewed with caution.


Table 8.9

Percentages and Stroop interference scores for women who reported engaging in risky eating and weight management techniques in

the last six months (N = 144). Significant differences in interference scores are noted with“*”.

Vomited Slimming

tablets

Fasted Diet for health Diet for weight

loss

Excessive

exercise

Binge Binge no

control

Y N Y N Y N Y N Y N Y N Y N Y N

% 10 89 7 92 23 77 19 78 57 42 15 84 27 73 18 80

NE -36.69 -4.67 -38.45 -5.49 -38.08 0.98* 1.04 -10.02 -9.41 -6.16 -31.32 -4.01 -8.73 -7.77 -23.05 -6.19

NAP 61.43 3.63* 44.01 6.84 38.94 0.93* 7.66 9.62 20.69 -5.07# 1.34 11.14 -12.07 17.58# -13.99 14.11

LC 1.93 -8.36 -0.06 -7.88 -17.54 -4.21 1.06 -8.78 -14.07 1.83 11.15 -10.46 -17.66 -3.53 -1.35 -9.56

PAC 34.37 -8.32# 3.54 -4.46 -4.09 -3.77 -22.75 0.61 -7.12 0.55 1.75 -4.81 -1.53 -4.68 -17.96 -1.63

HC -1.21 -3.39 -12.66 -2.37 2.67 -4.92 -14.74 0.01 0.98 -8.75 13.75 -6.08 -21.75 3.55* -17.75 -0.95

PAP -24.89 8.53 -26.85 7.68 -10.23 9.60 -21.41 13.09 1.23 10.12 -23.02 9.85 -11.49 11.00 -22.98 12.19#

Note. NE = negative emotion words; NAP = negative appearance words; LC = low calorie food words; PAC = physical activity words; HC = high calorie food words; PAP = positive appearance words. * p < .05. # p = .06


At a descriptive level, those women who had engaged in risky weight

management behaviours in the last six months consistently showed enhanced interference

effects compared to those women who had not engaged in these behaviours. However

only a few differences reached significance. Women who reported purposely vomiting

after food consumption in the last six months exhibited significantly higher interference

for negative appearance words, t (142) = 2.52, p = .01, and a trend more interference for

physical activity words, t (142) = 1.88, p = .06, compared to women who did not report

vomiting recently. Those women who reported fasting recently to improve appearance

showed significantly quicker reaction times for negative emotion words, t (142) = -2.14,

p = .03, and significantly slower reaction times for negative appearance words, t (142) =

2.28, p = .02, compared to non-fasting women. Those women who reported dieting to

improve appearance showed a trend toward slower reaction times to negative appearance

words, t (142) = 1.72, p = .08. Binging was associated with a trend for faster reaction

times to negative appearance words, while non-bingers showed slower reaction times, t

(142) = -1.91, p = .05. Bingers also showed a quicker reaction to high calorie food words,

t (142) = -1.96, p = .05. Loss of control over these binging episodes was associated with a

trend for negative interference scores for positive appearance words, t (140) = -1.83, p =

.06. The women who reported use of slimming tablets and excessively exercising did not

differ in interference scores from women not engaging in these behaviours. Thus, it

appears that high interference scores may be associated with some risky weight

management techniques.

These results support the pattern of correlations reported previously between

Stroop interference indexes and the psycho-social variables. That is, slower reaction


times for Negative Appearance words was associated with poorer functioning (vomiting,

fasting, and dieting for appearance), while slower reaction times for Positive Appearance

words was associated with better functioning (dieting for health, non-binging, and not

loosing control over a binge).

One vulnerability factor used extensively in past research is restraint status. As

previously outlined, level of dietary restraint is typically measured using Herman and

Polivy’s Restraint Scale (1980). This scale measures restriction of food intake due to

weight concerns, history of weight fluctuations, and preoccupation with dieting. In

accordance with past research (e.g., Francis et al., 1997), the women were categorised

into restrained eaters if they scored 16 or above on the Restraint scale, or unrestrained

eaters if they scored below 16. Even though the sample was non-clinical in nature, a large

number of women were classified as restrained eaters (N = 56, or 41%). No significant

differences were noted between the women classified as restrained and unrestrained

eaters on any of the six interference indexes (all p’s > .05). Means are shown in Table

8.10.

Table 8.10

Mean interference scores for women classified as restrained or unrestrained eaters on

the Stroop categories.

Unrestrained Restrained t p

High calorie foods -1.00 .27 -0.89 .37

Low calorie foods -0.32 -1.02 0.44 .66

Physical activity -0.17 -0.67 0.33 .74

Negative appearance 0.97 1.26 -0.19 .84

Positive appearance 1.50 -0.33 1.19 .21

Negative emotion 0.05 -2.13 1.45 .14


8.2.2.3 Sub-group differences in biased attention. The primary function of the

cluster analysis was to identify sub-groups of women that differ in body image attitudes

so that their Stroop interference scores could be compared. To examine whether

interference for body image related information was a function of level of body image

disturbance, a series of ANOVA’s were conducted. To determine whether the groups

differed on interference scores for any of the categories, a MANOVA was run with group

membership as the Independent Variable and the six interference scores as multiple

Dependent Variables. Examination of histograms, skewness and kurtosis statistics, and

Kolmogorov-Smirnov tests revealed that univariate normality was not breached. As the

sample sizes were different between the groups, Box’s M was examined, but revealed no

significant breach of the assumption of equality of covariances matrices. The mean

interference scores for each sub-group across each of the six interference indexes are

shown in Table 8.11.

Table 8.11

Mean Stroop Interference (in Msec) Scores Across the Sub-Groups Identified in Women.

Cluster

Normal Athletic Dissatisfied Symptomatic

High calorie foods -2.118 -1.201 -1.695 -13.357

Low calorie foods -1.318 -18.430 -22.645 21.817

Physical activity 9.319 -18.140 -10.627 -13.984

Negative appearance 19.917 -.556 5.866 -2.525

Positive appearance 21.553 20.431 -30.176 -6.066

Negative emotion -4.786 15.162 -17.443 -38.115


Examination of the mean interference indexes for each of the Stroop categories

across the four clusters revealed some differences between the groups in interference

indexes. Using the Roy’s Largest Root statistic, there was a significant group difference,

F (6, 136) = 2.15, p = .05, in interference scores. Examination of the univariate tests

revealed a difference between the groups on interference for Positive Appearance words,

F (3, 139) = 3.18, p = .026. The post hoc tests revealed that the Dissatisfied group

showed significantly more negative interference (i.e., faster reaction times to the

experimental than neutral words) than the other three groups2.

To determine if there were any significant differences between the Stroop

categories within each of the groups, a Repeated Measures ANOVA’s was conducted for

each group. There was no difference between the six interference scores for the Normal

group, F (5, 57) = 1.12, p = .35; for the Athletic group, F (5, 23) = 1.99, p = .11; for the

Dissatisfied group, F (5, 31) = 0.68, p = .63; or the Symptomatic group, F (5, 12) = 1.02,

p = .44. These results indicate that women within each of these groups responded

similarly to each of the Stroop categories.

8.2.3 Biased Memory

8.2.3.1 Percentage of words recalled. The data was examined to see whether the

women recalled a higher percentage of experimental words compared to neutral words.

There was a higher amount of variability for recall of percentage of experimental words,

ranging from 0 (no experimental words recalled) to 100 (100% of words recalled were

2 It is noted that mixed design ANOVA could have been conducted instead of the MANOVA. This analysis was run and the same pattern of results emerged. There was no significant main effect of word type, F (5, 135) = 0.61, p = .69, and no significant interaction between word type and group, F (15, 411) = 0.91, p = .53. There was a significant main effect of group, F (1, 139) = 2.82, p = .04, with the Dissatisfied group showing the largest negative interference scores.


experimental). Comparatively, the variability for recall of neutral words ranged from 0%

to 62.5 %.

To determine whether the women showed an enhanced memory for experimental

words overall, a paired samples t-test was run on percentage values. Mean recall rates and

outcomes of the t-tests are shown in Table 8.12.

Table 8.12

Percentage of Words Recalled Across Each Category for Females.

M SD t p

Experimental 32.66 22.06 3.89 .001

Neutral 22.35 15.06

Negative Emotion 5.99 11.22 0.09 .92

Control 5.88 9.40


Control 3.36 6.90

Positive Appearance 3.89 7.09 0.01 .98

Control 3.87 7.58

Low Calorie Foods 5.89 10.57 4.62 .001

Control 1.27 5.25

High Calorie Foods 3.51 7.39 0.68 .49

Control 3.04 6.58


Control 5.19 7.66

A significantly higher percentage of experimental words was recalled compared

to neutral words. Examination of percentage rates in each of the six experimental

categories showed enhanced recall for three of the six categories. The women recalled


significantly more negative appearance words, low calorie food words, and physical

activity words, compared to the matched neutral categories. No significant differences

were noted for the recall of negative emotion words, positive appearance words, and high

calorie food words.

8.2.3.2 The role of vulnerability factors in biased memory. To examine whether

the percentage of experimental words recalled was related to any body image and

wellbeing variables, a series of bivariate correlations were run. Percentage of negative

appearance words recalled was associated with mood; higher recall was significantly

associated with higher anxiety, r = .32, p = .03, and a trend was noted for higher levels of

depression, r = .15, p = .06. Higher recall was also associated with higher levels of

dietary restraint, r = .18, p = .03. The recall of positive appearance words was not

associated with any of the psychosocial measure, although trends were noted for higher

recall to be associated with higher anxiety, r = .27, p = .08, N = 41, and higher stress r =

.27, p = .08, N = 40.

The percentage of negative emotion words recalled was negatively associated

with three of the EDI subscales, Interoceptive Awareness, r = -.19, p = .05,

Ineffectiveness, r = -.24, p = .01, and Perfectionism, r = -.19, p = .05. That is, inhibited

recall of negative emotion words appears to be related to eating disorder-like attitudes. A

trend was noted between higher recall of negative emotion words and poorer social

functioning, r = .17, p = .07.

The percentage of high calorie words recalled was only related to two body image

variables, the Maturity Fears subscale of the EDI, r = .19, p = .05, and a trend for

exercising for weight control, as assessed by the REI, r = .16, p = .07. Percentage of low


calorie food words recalled however, was significantly correlated with a large number of

body image variables. Higher recall of low calorie food words was associated with higher

pursuit of thinness (MBSRQ Overweight Preoccupation, r = -.20, p = .01; EDI Drive for

Thinness, r = .18, p = .06), and higher self esteem, r = .16, p = .05. Women who reported

exercising to improve attractiveness, r = -.17, p = .05, and to improve tone, r = -.22, p =

.01 (all assessed by REI), reported lower recall of low calorie food words. Poor recall of

low calorie food words was also associated with higher feelings of body loathing (BAQ

Disparagement, r = -.17, p = .03). Overall, these findings regarding recall of low calorie

food words showed mixed findings. Thus, high recall of low calorie food words is

generally associated with better body image.

Higher recall of physical activity words was significantly correlated with higher

feelings of fitness (BAQ Strength / Fitness, r = .18, p = .02 and MBSRQ Fitness

Evaluation, r = .16, p = .05), and higher importance placed on health and fitness

(MBSRQ Fitness Orientation, r = .25, p = .001, MBSRQ Health Orientation, r = .28, p =

.001, and MBSRQ Illness Orientation, r = .16, p = .04). Higher recall was also associated

with exercising for health and fitness reasons (REI Health, r = .29, p = .001, and REI

Fitness, r = .16, p = .06). Thus, a focus on health and fitness appeared to be consistently

related to a higher percentage of physical activity words recalled. Higher recall was also

associated with higher levels of Interoceptive Awareness (EDI, r = .20, p = .05), and

more negative mood. Higher recall was also significantly associated with higher levels of

anxiety (DASS, r = .30, p = .05).

To examine whether memory bias was associated with eating disorder symptoms,

comparisons were conducted between women who had, and had not, recently reporting


engaging in risky weight management techniques. Differences in the percentage of

experimental words recalled are shown in Table 8.13. Few significant differences

emerged. Women who reported excessively exercising showed enhanced recall of the

negative appearance words, t (140) = 2.41, p = .01, but lower recall of the low calorie

food words, t (139) = -1.44, p = .008, compared to women who did not report excessive

exercise. A trend was noted for the women who reported dieting for health reasons to

recall more negative appearance words than women who did not diet, t (139) = 1.83, p =

.06. Therefore, memory biases did not appear to be related to eating disorder symptoms.

8.2.3.3 Sub-group differences on biased memory. To examine whether the sub-

groups identified via the cluster analysis differed on their memory performance, a four

(cluster membership) by six (Stroop interference) Mixed Design ANOVA was conducted

with percentage of words correctly recalled as Dependent Variables. Table 8.14 shows

the average percentage of experimental words recalled across sub-groups.

Results of the Mixed Design ANOVA showed that there was a significant main

effect of word category, F (5, 134) = 9.06, p < .001, no significant effect for group, F (3,

138) = 0.19, p = .90, and no significant interaction between group and word category, F

(15, 408) = 0.76, p = .72. Examination of the follow up tests for the significant main

effect of word category revealed that regardless of group membership, the women

showed enhanced recall for negative appearance words.


Table 8.13

Percentage of Words Recalled for Women who Reported Engaging in Risky Eating and Weight Management Techniques in the Last

Six Months (N = 144). Significant Differences in Interference Scores are Noted with“*”.

Vomited Slimming

tablets

Fasted Diet for health Diet for weight

loss

Excessive

exercise

Binge Binge no

control

Y N Y N Y N Y N Y N Y N Y N Y N

% 10 89 7 92 23 77 19 78 57 42 15 84 27 73 18 80

NE 3.07 6.39 3.60 6.24 6.82 5.80 6.39 6.00 6.79 5.03 7.81 5.75 5.18 6.34 8.47 5.54

NAP 9.07 10.30 14.29 9.83 10.46 10.08 13.67 9.27# 9.56 10.98 15.80 9.25* 10.37 10.10 12.49 9.55

LC 4.17 6.73 11.61 6.02 4.04 7.19 5.19 6.83 4.84 8.60 2.35 7.13* 5.39 6.83 4.10 6.57

PAC 2.16 3.10 7.02 2.66 2.49 3.16 2.29 3.20 3.05 2.94 5.37 2.61 2.63 3.18 3.39 2.97

HC 4.59 3.46 4.50 3.50 3.02 3.75 3.45 3.65 2.97 4.39 2.80 3.71 2.67 3.90 2.52 3.87

PAP 4.46 3.85 4.58 3.86 4.57 3.72 2.40 4.20 3.66 4.25 3.09 4.05 3.82 3.95 2.78 4.01

Note. NE = negative emotion words; NAP = negative appearance words; LC = low calorie food words; PAC = physical activity words; HC = high calorie food words; PAP = positive appearance words. * p < .05; # trend noted (p = .06)


Table 8.14

Percentage of Words Recalled in Each of the Experimental Categories from the Stroop

Task Across Sub-Groups in Women.


Negative Emotion 5.15 (9.45) 6.05 (10.30) 6.89 (13.62) 7.53 (13.95)

High Calorie foods 3.30 (7.64) 3.87 (7.92) 2.76 (5.94) 6.04 (8.67)

Low Calorie foods 7.85 (17.95) 7.69 (11.50) 5.50 (8.43) 1.04 (4.16)

Positive Appearance 4.69 (7.42) 4.08 (7.42) 3.13 (6.11) 2.39 (7.64)

Negative Appearance 9.18 (9.20) 10.35 (12.46) 11.58 (14.10) 10.55 (11.37)

Physical Activity 2.46 (5.39) 4.19 (8.16) 2.26 (5.81) 4.69 (7.64)

8.2.3.4 Relationship between biased attention and memory. To examine the

relationship between attention and memory biases, interference and memory recall

percentages were correlated. Only the recall of negative appearance words was related to

interference for physical activity words, r = -.18, p = .02. That is, a quicker reaction time

for physical activity words was associated with enhanced recall of negative appearance

words. No other significant correlations were found between memory percentage scores

and interference indexes.

8.2.4 Error Rates

The number of errors made in each of the six experimental conditions of the

Stroop task was recorded. Overall, the number of errors made in each condition was very

low, with the average number of errors under one. In order to examine the trade-off

participants may have made between speed of responding and accuracy, correlations were

conducted between total number of errors, and raw reaction time scores for of the Stroop


categories. All of the correlations revealed that a trade-off occurred, such that faster

reaction times were associated with more errors.

To examine whether there was any differences in the number and types of errors

made between the sub groups, a MANOVA was conducted with group membership as

the Independent Variable, and the error rates for each of the six experimental categories

as multiple Dependent Variables. Means are shown in Table 8.15.

Table 8.15

Mean number of colour naming errors made on the Stroop task by females.


Negative Emotion 0.16 0.14 0.13 0.35

High Calorie foods 0.12 0.32 0.27 0.29

Low Calorie foods 0.30 0.14 0.11 0.29

Positive Appearance 0.24 0.14 0.25 0.17

Negative Appearance 0.12 0.03 0.05 0.29

Physical Activity 0.30 0.14 0.08 0.23

The MANOVA was significant, Roy’s Largest Root F (6, 136) = 2.57, p = .02.

Examination of the univariate tests revealed that only errors for Negative Appearance

related words showed significant group differences, F (3, 139) = 2.59, p = .05. The post

hoc tests, applying a Bonferroni adjustment (.05 / 4 = .012), showed that the

Symptomatic group made significantly more errors (M = .29) than the Healthy (M = .03)

and Dissatisfied (M = .05) group.


8.3 Results and Discussion for Males

8.3.1 Descriptive Information

A total of 55 male participants were recruited from the general community and

universities across Queensland and Melbourne. Tables 8.16 and 8.17 shows the

descriptive statistics for all of the psychosocial questionnaires used. All values are

consistent with normative values, and all questionnaires show acceptable validity.

Table 8.16

Descriptive Statistics and Reliability Values for the Mood and General Well-Being

Variables in the Male Sample

Scale M SD Min / Max value No. items Cronbach’s alpha

Depression

5.22 7.14 0-42 14 .95

Anxiety

2.87 2.41 0-42 14 .62

Stress

7.81 7.16 0-42 14 .93

Self esteem

31.62 5.21 10-40 10 .83

Social Functioning

6.06 2.57 0-24 8 .82

8.3.2 Biased Attention

Table 8.18 shows the mean raw reaction times for each of the Stroop categories,

in addition to the interference indexes. Paired samples t-tests were conducted to compare

the average reaction time of each experimental category to the matched neutral category.

A significant difference was noted between reaction times to Negative Emotion words


and the matched neutral category, such that the men were 20 milliseconds slower on

average to respond to the Negative Emotion words. Minimal interference effects were

noted for the five other Stroop categories, and none of these comparisons were

significant.

8.3.2.1 The role of demographic factors in biased attention. A series of one-way

Independent Groups ANOVA’s were conducted to examine the relationship between

demographic factors and Stroop interference scores. No differences in interference scores

was found between the normal weight, overweight and obese men for any of the six

interference indexes (all p’s > .05), although a trend was noted for the high calorie food

words, F (2, 47) = 2.78, p = .07, such that the men of normal weight showed attention

toward these words, while the overweight and obese men directed their attention away.

Age was not significantly correlated with any of the interference indexes (i.e., all r’s

<.20). Neither self rated hunger level, nor amount of time since last meal, were

significantly correlated to any of the Stroop interference indexes.

8.3.2.2 The role of vulnerability factors in biased attention. In order to examine

the relationship between psycho-social variables and biased attention, a series of bivariate

correlations were run. Each interference index was examined for it’s relationship with

body image, mood, and general well-being variables.

Quicker reaction times to colour name Negative Emotion words was significantly

correlated with lower appearance evaluation (MBSRQ; r = .31, p = .02). That is, a

facilitation effect for Negative Emotion words was associated with poorer body image,

but was not related to any of the mood or general well-being variables.


Table 8.17

Descriptive Statistics and Reliability Values for the Body Image Variables in the Male

Sample

Scale M SD Min / Max value

No. items

Cronbach’s alpha

REI

Weight Control 3.58 1.74 1-7 3 .85

Fitness 4.71 1.32 1-7 4 .77

Mood 3.86 1.57 1-7 4 .75

Health 5.40 1.16 1-7 4 .79

Attractiveness 4.24 1.70 1-7 3 .87

Enjoyment 3.47 1.46 1-7 3 .71

Tone 4.04 1.78 1-7 3 .88

MBSRQ

Appearance Evaluation 3.33 0.80 1-5 7 .84

Appearance Orientation 3.16 0.66 1-5 12 .83

Fitness Evaluation 3.92 0.72 1-5 3 .79

Fitness Orientation 3.36 0.75 1-5 13 .83

Health Evaluation 3.78 0.73 1-5 6 .83

Health Orientation 3.39 0.80 1-5 8 .83

Illness Orientation 3.05 0.89 1-5 5 .84

Overweight Preoccupation 1.96 0.81 1-5 4 .74

Drive for Muscularity 2.23 0.85 1-6 15 .89

Restraint Scale 9.83 5.15 0-35 10 .77


Table 8.18

Mean Reaction Times and Interference Indexes (in Milliseconds) for the Stroop Task in

Males (N = 54)

M SD t p

Negative Emotion 941.86 143.69 -2.16 .03*

Control 964.83 145.09



Control 964.92 148.35



Control 961.74 144.99


Low Calorie 963.73 156.56 1.36 .17

Control 953.29 159.97


High Calorie 957.2 150.79 1.24 .30

Control 963.49 156.29


Physical Activity 972.61 161.42 0.51 .61

Control 967.36 162.57


* p < .05

Quicker reaction times to colour name Negative Appearance words was only

significantly correlated with higher levels of stress (DASS; r = -.50, p = .04). Thus,


facilitation effects are only reliably related to poorer mood, although given the small

sample size for this correlation (N = 16), caution is warranted3.

Interference for Positive Appearance words was not associated with any of the

body image, mood, or general well-being variables. Interference for Physical Activity

words also did not show any correlations with the psycho-social variables.

Faster reaction times for Low Calorie food words were associated with lower

levels of stress (DASS; r = .51, p = .04, N = 16). That is, a facilitation effect for Low

Calorie food words was only associated with better mood, but not body image or general

well-being. Again, caution is warranted, as this finding is only based on 16 participants.

Faster reaction times for High Calorie food words was associated with exercising

for weight control reasons (REI; r = -.32, p = .02), and a trend for a lower drive for

muscularity (r = .25, p = .06). That is, a facilitation effect for High Calorie food words

was associated with a more positive body image.

To summarise, quicker reaction times to Negative Emotion and Positive

Appearance words appeared to be associated with poorer body image, while quicker

reaction times to High Calorie food words was associated with a better body image.

Response to Negative Appearance and Low Calorie food words was associated with

mood only, with quicker reaction times for Negative Appearance words related to poorer

mood, and Low Calorie food words associated with better mood. In this sample of men,

biased attention was not related to any of the general wellbeing or social functioning

variables.

3 The reader is reminded that due to a photocopying error, the full version of the DASS was only administered to a small proportion of the sample.


As with females, the number of risky weight management techniques was

examined. The prevalence rates of these behaviours in the last six months for men were

very low. None of the men reported vomiting on purpose after eating, use of laxatives or

diuretics. Only a small number of men reported using slimming tablets (n = 2),

excessively exercising (n = 4), binging (n = 6) or being unable to control their eating (n =

4). Thus, Table 8.19 only shows the number of men who reported dieting for appearance-

related reasons, and dieting for health reasons in the last six months. The average Stroop

interference scores are also shown for these groups.

Table 8.19

Percentage of Men who Reported Engaging in Risky Eating and Weight Management

Techniques in the Past Six Months and Stroop Interference Scores (N = 54).

Diet for health Diet for weight loss

Y N Y N

% 16 81 29 68

Negative Emotion -3.21 -2.62 -1.58 -3.21

Negative Appearance -0.19 -0.16 0.95 -0.65

Positive Appearance 0.47 0.67 1.82 0.13

Low Calorie Foods -0.64 2.09 1.08 1.86

High Calorie Foods -2.07 0.22 -1.41 0.37

Physical Activity 2.09 -0.28 0.01 -0.16


The average number of dysfunctional techniques was 0.87 (SD = 1.21), with a

range from zero to four. Most of the men (61%) had not engaged in any risky weight

management techniques in the last six months, only 5% of the men reported one risky

behaviour, 22% reported two, and 11% reported three or four behaviours.

No significant differences were noted in any of the Stroop interference scores

between the men who had, and had not, reported dieting for weight loss in the past six

months (i.e., all p’s > .05). Similarly, no significant differences were noted between the

men who reported dieting for health reasons, and those who did report dieting, in any of

the Stroop interference scores (all p’s > .05).

These prevalence rates indicate that this sample of men was not engaging in

unhealthy weight management techniques. A small proportion of the sample reported

dieting, but this was not related to biased attention for body image related information.

8.3.2.3 Sub-group differences in biased attention. The primary function of using a

cluster analysis was to provide an empirically-supported method for comparing the men

on Stroop interference. To examine whether the three groups identified differed on the six

Stroop interference indexes, a MANOVA was conducted with group membership as the

Independent Variable, and the six interference indexes are multiple Dependent Variables.

As recommended by Field (2005), the data was examined for univariate normality

of homogeneity of variance. Additionally, Box’s M was examined to test for

homogeneity of covariance matrices. No significant breaches were noted. The mean

interference indexes for each of the sub-groups are shown in Table 8.20.


Table 8.20

Mean Stroop Interference (in Milliseconds) by Sub-Group in men. Standard Deviations in

Parentheses

Cluster

Normal (n = 26) Dissatisfied (n = 19) Health Conscious (n = 9)

High Calorie 5.42 (99.69) -2.22 (110.61) -54.76 (189.64)

Low Calorie 24.57 (79.11) -13.40 (94.46) 52.56 (94.79)

Physical Activity 16.48 (88.26) -11.94 (100.45) 24.81 (97.41)

Negative Appearance -7.96 (66.73) -6.97 (82.29) 39.57 (88.31)

Positive Appearance -6.63 (85.20) -8.42 (83.53) 71.31 (63.52)

Negative Emotion -6.36 (69.93) -37.02 (96.57) -32.59 (70.14)

Examination of the mean interference indexes revealed that for all groups minimal

interference were evident. The overall MANOVA was non-significant, Pillai’s Trace F

(12, 94) = 0.97, p = .97, indicating no differences between the three groups on any of the

six Stroop interference indexes. Power levels however, were very low (.18), suggesting

that an increase in sample size was needed4.

Within each of the three groups, a Repeated Measures ANOVA was used to

assess for differences in responding between the six interference indexes. The Normal

group responded similarly to each of the interference indexes, F (5, 21) = 1.06, p = .41.

No differences emerged for the Dissatisfied group in response to the different word

4 It is noted that a mixed design ANOVA could have been conducted here instead of the MANOVA. This analysis was run, and the same pattern of results emerged. There was no significant main effect of word type, (F (5, 47) = 1.49, p = .20), group, (F (2, 51) = 0.33, p = .71), or interaction, (F (10, 96) = 1.03, p = .42).


categories, F (5, 14) = 0.46, p = .79, or for the Health Conscious group, F (5, 4) = .45, p =

.79.

To summarise, no evidence of interference effects were found within the current

male sample, even when a number of vulnerability factors were examined. That is, there

was no evidence of selective attention for body image related information.

8.3.3 Biased Memory

8.3.3.1 Percentage of words recalled. For each of the six experimental Stroop

categories, a percentage score was calculated that indicated the percentage of words

recalled that were related to the experimental categories. For example, a score of 10 for

Negative Emotion words would indicate that 10% of the total number of words recalled

was related to negative emotion. Table 8.21 provides descriptive information on the

percentage of words the men recalled across the Stroop categories.

To determine whether the men showed an enhanced memory for the experimental

words, paired samples t-tests were run and are shown in Table 8.21. The men recalled a

significantly higher percentage of experimental words compared to neutral words. The

men also showed enhanced memory for the Negative Emotion, Negative Appearance, and

Low Calorie food words, compared to the matched neutral categories. No significant

differences were noted in the recall rates for Positive Appearance, High Calorie, of

Physical Activity words and the matched neutral categories.


Table 8.21

Percentage of Words Recalled Across Stroop Categories for Males

M SD t p

Experimental 31.91 23.15 3.15 .003

Neutral 16.90 19.08

Negative Emotion 8.25 14.20 1.93 .05

Control 3.73 7.67


Control 3.30 9.32


Control 2.37 6.58

Low Calorie Foods 5.74 9.04 3.20 .002

Control 1.37 4.21

High Calorie Foods 1.83 4.68 0.43 .66

Control 2.28 5.30


Control 3.83 7.79

8.3.3.2 The role of vulnerability factors in biased attention. The percentage of

words recalled across the six experimental categories were correlated with the body

image, mood, and general well-being psycho-social variables. The percentage of Positive

Appearance and High Calorie foods words recalled were not related to any of the psycho-

social variables. Percentage of Negative Appearance words was not significantly

correlated with any of the variables, but a trend was noted such that higher recall of these

words was associated with lower importance placed on appearance (MBSRQ; r = -.25, p

= .06). Similarly, a trend was noted for higher recall of Physical Activity words and a

higher BMI, r = .25, p = .06.


Recall of Low Calorie food words was related to three of the body image

variables, but none of the mood or functionality variables. Higher recall was associated

with having a higher BMI, r = .36, p = .009, and reporting less satisfaction with body

areas (MBSRQ; r = -.28, p = .04). That is, recall of Low Calorie food words is associated

with being heavier and dissatisfaction with appearance.

Recall of the negative emotion words was associated with not significantly

associated with any of the variables, although two trends were noted for higher recall to

be associated with lower importance of appearance (BAQ; r = -.25, p = .06), and lower

anxiety (DASS; r = -.47, p = .07, N = 15).

To summarise, the percentage of experimental words recalled were not correlated

with any of the mood or general well-being measures. Only recall of Low Calorie food

words was significantly correlated to body image variables, with higher recall associated

with poorer body image.

8.3.3.3 Sub-group differences on biased memory. To examine whether memory

performance was related to different typologies of body image, a three (cluster) by six

(word category) mixed design ANOVA was conducted. Table 8.22 shows the average

percentage of words recalled across the six word categories for each of the sub-groups.

Results of the ANOVA revealed a significant main effect of word, Pillai’s F (5,

46) = 6.25, p = .0001, no significant main effect of group, Pillai’s F (2, 50) = 0.72, p =

.49, and no significant interaction between group and word category, Pillai’s F (10, 94) =

1.25, p = .26. Examination of the follow up tests for recall across word categories showed

that regardless of group membership, the men had high recall of Negative Appearance

and Negative Emotion words, coupled with very low recall of Low Calorie food words.


Table 8.22

Percentage of Words Recalled by Sub-Group in Men

Normal Dissatisfied Health Conscious

Negative Appearance 7.47 9.07 21.26

Positive Appearance 3.62 2.15 0.00

Low Calorie foods 4.46 9.09 2.38

High Calorie foods 2.29 1.47 1.11

Physical Activity 3.51 4.94 1.11

Negative Emotion 7.23 6.94 12.53

8.3.3.4 Relationship between biased attention and memory. To see whether biased

attention was related to biased memory, a series of bivariate correlations were conducted

between the six interference indexes and the six recall percentages. No significant

correlations emerged, although a trend was noted for higher interference effects for

Positive Appearance words related to more memory bias for these words, r = .23, p = .08.

8.3.4 Error Rates

The number of errors made across each of the six experimental categories in the

Stroop task was manually recorded. Overall, the number of errors made in each category

was very low, with an average of less than one error per category. In order to examine

whether the men focused on speed or accuracy when completing the Stroop task,

correlations were run between the number of errors made and the raw reaction times for

each of the experimental Stroop categories. The total number of errors made was

significantly and negatively correlated with each of the six experimental categories of


Stroop raw scores. This indicated that a trade-off occurred such that faster reaction times

were associated more errors.

To determine whether there was any difference in the number and types of errors

made between the sub-groups, a MANOVA was conducted with the Independent

Variable as group membership and the error rates for the six experimental categories as

the multiple Dependent Variables. Mean scores are shown in Table 8.23.

Table 8.23

Mean number of colour naming errors made on the Stroop task for males.

Normal Dissatisfied Health Conscious

Negative Appearance 0.11 0.23 0.66

Positive Appearance 0.40 0.14 0.33

Low Calorie foods 0.29 0.28 0.16

High Calorie foods 0.25 0.33 0.16

Physical Activity 0.22 0.23 0.16

Negative Emotion 0.29 0.19 0.50

The MANOVA effect was non-significant, F (12, 94) = 0.38, p = .96. This

demonstrated that there was no difference between the cluster groups on type of errors.

To summarise, the men only made a small number of errors and the type of errors was

not related to other body image measures.


8.4 Sex Differences in Biased Cognitive Processing

In order to determine if there were differences between males and females in their

response to the Emotional Stroop task, a number of Independent Samples t-tests were

conducted comparing their Stroop interference, incidental memory, and errors rates. As

can be seen in Table 8.24, all interference scores were close to zero, and no consistent

differences emerged between the sexes. However, at a descriptive level the men showed

much more attention toward the negative emotion words compared to the women. None

of differences approached significance though any of the six Stroop interference indexes.

Table 8.24

Means (and Standard Deviations) for Stroop Interference Scores Between Males (N =

55) and Females (N = 143)

Female Male t p

Negative Emotion -8.02 (93.11) -22.97 (81.60) 1.04 .29

Negative Appearance 9.70 (86.27) -0.40 (76.09) 0.76 .44

Positive Appearance 5.02 (87.78) 1.17 (84.06) -0.66 .50

Low Calorie Foods -7.28 (89.56) 10.43 (89.53) -1.24 .21

High Calorie Foods -3.16 (81.92) -6.24 (114.89) 0.21 .83

Physical Activity -3.84 (84.25) 5.25 (93.02) .28 .78

No significant differences in any of the six experimental categories were noted

between males and females in the percentage of the words recalled. As can be seen in

Table 8.25, males and females showed a similar pattern of memory performance.


Table 8.25

Means (and Standard Deviations) for the Percentage of Words Recalled Between Males

(N = 55) and Females (N = 143)

Female Male t p

Negative Emotion 6.04 (11.25) 8.10 (14.11) 0.29 .76

Negative Appearance 10.17 (11.42) 9.49 (17.02) 0.32 .74

Positive Appearance 3.92 (7.11) 2.84 (6.55) 0.96 .33

Low Calorie Foods 6.46 (13.75) 5.95 (9.07) 0.25 .80

High Calorie Foods 3.58 (7.41) 1.80 (4.64) 1.64 .10

Physical Activity 3.00 (6.39) 3.74 (7.06) -0.69 .48

Experimental 32.68 (22.13) 31.63 (23.02) -1.06 .28

Finally, an Independent Samples t-test was conducted to test whether males and

females differed in the number and type of errors made during the Stroop task. Again, no

significant differences emerged, suggested that males and females responded similarly to

the task. Means, standard deviations, and the results of the t-tests are shown in Table

8.26.

To summarise, no significant differences were noted between males and females

on the Emotional Stroop task. That is, sex does not appear to affect selective attention,

memory, or error rates. Results for males and females are still reported separately given

the different pattern of correlations between the cognitive and psycho-social variables

that were reported previously.


Table 8.26

Means (and Standard Deviations) for the Number of Errors Made During the Emotional

Stroop task for males and females.

Female

(N = 143)

Male

(N = 55)

t p

NE 0.17 (0.41) 0.27 (0.54) -1.37 .17

NA 0.11 (0.33) 0.21 (0.56) -1.61 .10

PA 0.21 (0.47) 0.29 (0.59) -0.91 .36

LC 0.22 (0.46) 0.27 (0.59) -0.61 .54

HC 0.22 (0.50) 0.27 (0.65) -0.55 .57

PAct 0.20 (0.50) 0.23 (0.54) -0.32 .74


Chapter Nine: General Discussion of Phase Three and Theoretical Integration

9.1 Overview

This final phase examined whether attention and memory biases exist for body-

image and health related information within a non-clinical sample. To achieve this aim,

empirically defined sub-groups were compared on an Emotional Stroop task and

incidental memory test. Correlations were also examined between the cognitive measure

and a range of psycho-social questionnaires. Research questions and hypotheses were

developed around three main areas of enquiry. The first aim was to examine the presence

of attentional biases for body image and health related information in a non-clinical

sample. Specifically, it was hypothesised that attentional biases, as evidenced by slower

reaction times to body image-related words, would be found in symptomatic participants

only, while no attentional biases will be found in asymptomatic individuals. The second

aim of the research was to examine whether memory biases for this body image and

health related information exist. Specifically, it was hypothesised that memory biases, as

evidenced by the recall of more target words, would only be found only in symptomatic

individuals. These first two areas of investigation were explored separately for males and

females. The final area of investigation was to examine whether gender differences would

emerge in attention to, and memory for, body image information. Specifically, it was

hypothesised that males and females would differ in their processing of sub-categories of

Stroop words, given the different emphasis on body image concerns. However, given the

lack of past research in this area, the nature of these specific differences could not be

developed.


There was some evidence of attention and memory biases within the sample

thereby providing partial support for all of the hypotheses. However some of the

processing biases were associated with having a more positive body image, rather than

being indicative of a more dysfunctional body image as identified in past research. These

findings suggest that some types of processing biases may be able to distinguish between

asymptomatic and symptomatic groups of women. These findings also suggest that there

may be qualitative differences between those diagnosed with an Eating Disorder, and

non-clinical, yet symptomatic women. Given the small number of men in the current

study who showed evidence of dysfunctional weight management behaviours,

conclusions regarding the association between processing bias and clinical status could

not be drawn. However, this is the first study that has explicitly looked at processing

biases in men thereby providing an important development in the literature.

The following section provides an in-depth discussion of the results for each word

category and integrates the findings from attention, memory and error rates. The results

are viewed within the context of past research and theoretical explanations are suggested.

The findings and implications from the male participants are presented in exploratory

terms only, given the limited number of past studies that have included male participants

in an Emotional Stoop task.

9.2 Integration of Past Research and Theories

The comparison of the current findings to past research is organised into six

sections, with each section focusing on a specific word category. Within each section, the

results from attention, memory, and errors rates are integrated to form a coherent picture


of the way in which each type of word is processed. Additionally, comments are made

about the differences in processing between males and females.

9.2.1 High- and Low-Calorie Food Words

Food words have been one of the most frequently used Stroop categories in past

research with women, yet little research exists on men’s reaction to these stimuli. Issues

surrounding food and eating represent a core factor in body image concerns. It is likely

that these concerns are found in a wide proportion of the population; from those who diet

for appearance-related concerns, to those who are health conscious, and those individuals

who continually obsess over caloric content. For example, hyper-attention to food words

may be found in women who are currently dieting (Boon et al., 2000). In the current

study, methodological refinements were made for the inclusion of food words so that the

effects of high calorie or forbidden foods could be disentangled from low calorie or non-

forbidden foods, a distinction that only a few past studies have made (i.e., Mahamedi &

Heatherton, 1993; Huon & Brown, 1996; Francis et al., 1997). This distinction between

high and low calorie food words is important as they represent different concerns. High

calorie foods for example are generally something to be avoided, as these words elicit

anxiety in chronic dieters (Huon & Brown, 1996). This study offered an additional

methodological refinement by ensuring the high semantic relatedness of the words within

these categories, rather than including general eating related words such as “meal” as in

Channon and Hayward’s (1990) study. That is words were selected on their caloric

content or how ‘forbidden’ they were, rather than just selecting general words related to

eating.


9.2.1.1 Integration of Findings with Past Research for Women. Past research

examining women’s reaction to food words has produced mixed results. Attentional

biases, as indicated by longer reaction times to food words compared to neutral words,

appears to be restricted to women with highly dysfunctional dieting behaviours and

attitudes, typically defined as highly restrained eaters (Cooper & Fairburn, 1992; Stewart

& Samoluk, 1997; Black et al., 1997; Green & Rogers, 1993). Other researchers however,

have failed to find this bias amongst restrained eaters (Lokken et al., 2006; Mahamedi &

Heatherton, 1993), or have observed biases in non-restrained eaters (Black et al., 1997).

Limited research does suggest that attentional bias for food words is not found in those

women with high drive for thinness scores (Perpina et al., 1993), or report normative

dieting (Cooper & Fairburn, 1992). Research does seem to consistently show that

interference for food words is not found in asymptomatic women (Cooper & Fairburn,

1992; Stewart & Samoluk, 1997; Mahamedi & Heatherton, 1993; Lokken et al., 2006;

Green & Rogers, 1993; Perpina et al., 1993).

Given the divergent nature of the current study to past research, a direct

comparison of the findings is problematic. The current study focused on and examined a

wider range of vulnerability factors than has been reported in past research, and also

made the distinction between high and low calorie foods. The current study did find some

evidence of interference effects within a non-clinical group for low calorie food words.

However, the pattern of interference supports the claim made by Huon and Brown (1996)

that interference effects for these words may represent engaging in a healthy lifestyle.

Although these researchers did not find selective attention to low calorie food words, the

results of the current study found that attention toward low calorie food words was


associated with higher feelings of fitness and more importance placed on health. Thus, for

non-clinical women, attention to low calorie healthy foods may be part of a healthy

lifestyle. However, it should be noted that these correlations were quite weak, and the

Athletic group that emerged from the cluster analysis did not show any interference

effects for low calorie food words. This result was surprising considering the

distinguishing feature of this group was the high focus on health and fitness. It may be

that these women focus more on exercise than food intake as a means to a healthy

lifestyle, or that food does not produce enough of an emotional response for schema

activation (Bower, 1981).

The results from the current study regarding interference effects for high and low

calorie foods can only be directly compared to the work of Francis et al. (1997) and

Sackville et al. (1998). The current results are somewhat consistent with Sackville et al.’s

finding of no differences between restrained and non-restrained eaters. Sackville et al.

compared women classified as Anorexic, high restraint, and low restraint on a number of

body image-related Stroop categories, two of which were high calorie food words and

low calorie food words. Using a blocked computerised task requiring a verbal response,

no significant differences were noted between the women classified as Anorexic, high

restraint or low restraint on colour naming low calorie food words, (although all groups

showed longer reaction times to the low calorie foods compared to the matched neutral

words). Similarly, the high and low restraint group did not differ in their response to high

calorie food words, although a trend was noted for the Anorexic group to show more

interference. Thus, these findings are consistent with the current results that interference


effects to low calorie food words and high calorie food words are not found within non-

clinical, yet symptomatic, women.

The current results however are inconsistent with Francis et al.’s findings of

interference effects in restrained eaters. The work of Francis et al. was the first to

distinguish between forbidden and non-forbidden food words in an Emotional Stroop

task. Francis et al. compared restrained and unrestrained eaters on a computerised Stroop

task and found that the women classified as restrained eaters responded significantly

slower to both types of food words (as compared to the matched neutral words), while the

unrestrained group showed longer response times to the neutral words. Francis et al.

(1997) interpreted these findings as an overall bias that restrained eaters show to all types

of food, and suggest that the distinction between forbidden and non-forbidden foods

occurs later in processing (i.e., that is not assessed by the Stroop task). The results of the

current study are not consistent with Francis et al. Despite a similar methodology,

restraint status did not significantly interact with response to the food words. At a mean

level, the women classified as non-restrained eaters showed a bigger negative

interference effect (i.e., quicker reaction times) to the high calorie food words, while the

women classified as restrained eaters showed a negative interference effect to the low

calorie food words. Hence, even the pattern of means between the current study and the

work of Francis et al are not similar.

The reason for these inconsistent findings remains unclear. Both studies used a

randomised Stroop task with comparable words. In both studies, the target and neutral

words were from one semantic category, had empirical support for their use, and were

carefully matched on length and frequency of occurrence. Small differences were noted


in the methodology between Francis et al and the current study. In the Francis et al study,

a total of 15 words were used in each category, making a total of 120 trials (where each

word is presented twice). In the current study only 5 words per category were used,

making only 40 trials of the food words. Thus, participants in Franics et al’s study were

exposed to far more presentations of the food words. This could potentially have lead to

habituation effects, as noted by Green and Rogers (1993). Habituation effects do not

appear to explain these discrepant findings however, as it would be expected that Francis

would have reported the non-significant findings, rather than the current study. Therefore,

habituation effects do not appear to explain these results. A second difference is the type

of response required. Francis et al used voice activated responses, while in the current

study button presses were used. However, previous research has demonstrated that

although button press is preferable, no differences emerge in interference indexes

between the two methods (Davidson & Wright, 2002; Johansson et al., 2005a).

Another potential factor to explain the differences in results may be that the

Francis study was more homogeneous in that only food and matched neutral words were

presented, while in the present study the food words were presented in amongst eight

other categories of target and neutral words. This explanation would also be consistent

with the findings of Sackville et al. (1998) who also used eight other categories of words

and found no differences between the high and low restraint women. That is, perhaps the

higher rates of exposure in Francis’ study lead to more interference effects. Given the

small number of studies to include multiple categories of Emotional Stroop words, this

argument warrants future research.


While the research specifically examining low calorie food words remains scarce,

high calorie food words have been more typically used. In the current study, some

evidence of interference effects was found for high calorie food words, but these effects

were limited to women who reported binging in the last six months. That is, the

hypothesis that interference effects would only be found in symptomatic women, was

supported for high calorie food words. No other evidence of interference for high calorie

food words was found, nor did any of the psychosocial variables show significant

correlations with the interference index for high calorie food words. The women did not

show enhanced recall for high calorie food words, a finding that is supported in previous

research with non-clinical samples (Boon et al., 2000; Lavy & van den Hout, 1993;

Mendlewicz et al., 2001).

This is the first study to look specifically at weight management behaviours and

Stroop interference in a non-clinical sample. Only one previous study was identified that

specifically examined frequency of eating disorder behaviours in relation to interference,

however this was conducted on women diagnosed with Bulimia (Cooper & Fairburn,

1993). Cooper and Fairburn (1993) found that interference effects on a target card

containing words related to negative appearance and food was only related to eating

disorder symptoms and frequency of purging. Frequency of purging in the last 28 days

was the best predictor of Stroop interference. Given that binging is one symptom of

Bulimia, comparisons could also be made to studies using Bulimic women. For example,

Lokken et al. (2006) found that interference effects for a combined category of high

calorie / eating words was associated with bulimic symptoms, but that no differences

emerged between the women classified as Bulimia, and the women classified as having


sub-clinical bulimic symptoms. However, given that other behaviours associated with

Bulimia did not show any relation to interference scores on high calorie food words in the

current study (e.g., purging, excessive exercising, and loss of control over binge), these

comparisons are problematic. Thus, the conclusion made by Cooper and Fairburn (1993)

that interference effects are related to symptom severity, rather than diagnosis per se, is

supported in the current study. More research is needed however to substantiate this

argument.

To summarise, the hypothesis that interference effects would only be found in

symptomatic women was only partially supported. No differences were noted between

the subgroups identified in the cluster analysis in colour naming either the high calorie or

low calorie food words. Attention toward low calorie foods words was associated with

more feelings of fitness and importance placed on health, while avoidance of high calorie

food words was only found in women who reported binging recently. The results from

the current study do converge with past research that forbidden and non-forbidden food

words represent different concerns, and therefore should not be included together into a

combined “eating” or “food” category (Placanica et al., 2002; Sackville et al., 1998;

Francis et al., 1997).

9.2.1.2 Integration of past findings with past research for males. In the current

study, little evidence of processing biases for food related stimuli were found within the

male sample. The men did not show any significant attentional bias for either high calorie

food words or low calorie food words. The men did however recall a significantly higher

percentage of low calorie food words. Also, a different pattern of correlations emerged

between the psycho-social variables and the low and high calorie food words, supporting


the distinct nature of these categories. Faster reaction times for high calorie food words

were associated with a more positive body image (exercising for weight control, lower

drive for muscularity), while quicker reaction times for low calorie food words were

associated with better mood (lower stress). No significant group differences emerged

between the sub-groups identified in the cluster analysis in response to either the high or

low calorie food words.

No evidence of biased memory for high calorie food words was found, and recall

was not associated with any of the psycho-social variables, sub-groups, or risky

behaviours. A memory bias was observed for low calorie food words, and higher recall

was associated with being heavier and reporting more dissatisfaction with appearance. No

significant differences were noted between the men who had, and had not, reported

dieting for appearance or dieting for weight loss purposes, in response to low calorie food

words. More colour naming errors for both high and low calorie food words was

associated with higher stress. However, only 16 men provided this data, so this result

should be viewed with caution. Together, these findings suggest that processing biases

were not consistently noted within this sample.

Previous research examining processing biases in males is limited. Channon and

Hayward (1990) included males in their sample, but as part of a larger group with

females. Although they note that no evidence was found of differences between males

and females in response to body shape and food words, the neutral words used were not

homogenous. The comparison of reaction times from a homogenous group of words to a

non-homogenous group of words is problematic, as the level of semantic association

between words is associated to interference effects (Green et al., 1999). That is, the


slower reaction times noted for the target words may have been due to the highly

associated nature of these words, compared to neutral words that did not form one

semantic category. Similarly, Fairburn et al. (1991) reported no differences between

males and females in response to a combined category of eating, weight, and shape

related words. However, given the card based methodology, non-homogeneous nature of

both the experimental and neutral words, and failure to use words specific to males

concerns with body image, these findings are problematic. Therefore, given the divergent

nature of the current study to these past studies, a comparison of results is not useful.

9.2.1.3 Theoretical Explanations. In theoretical terms, the results of the current

study suggest that food related words do not receive any preferential attention, but that

low calorie food words may receive increased elaboration in both men and women

(Williams et al., 1988a, 1997). The lack of either biased attention or memory for high

calorie food words suggests that, within this sample, food information is not important

enough to receive preferential attention, or threatening enough to interference with task

performance. Biased attention was found for high calorie food words for women who

recently reported binging. This finding supports the notion that women who engage in

unhealthy weight management behaviours have maladaptive schemas surrounding high

calorie food words (Vitousek & Hollon, 1990). Descriptive accounts show that binging

occurs with high calorie / forbidden foods (Woell, Fichter, Pirke, & Wolfram, 1989), but

it appears that this obsession is also represented by cognitive bias. It may be argued those

women who are prone to binging will find high calorie foods threatening; and this is

supported by the cognitive avoidance of such information. The distinction between

priming and elaboration identified in Williams et al. (1988a, 1997) theory means that


although this threatening material ‘grabs’ attention, further elaboration is avoided,

explaining the lack of memory bias for women who binge.

It is somewhat surprising that biased memory for low calorie food words was so

widespread in the sample. There was nothing unique about the sample that could explain

this bias (such as high level of dieting, restraint, or body dissatisfaction; or being

recruited from a gym or diet group). Therefore the widespread bias must stem from a

common factor within the sample. Perhaps the particular attention that Australian society

has paid to health-related issues such as weight loss, the high rates of obesity, and a focus

on healthy eating may explain these findings (Davis, Shapiro, Elliott, & Dionne, 1993).

Given the older age of the male participants (mean age = 34 years), concerns over health

in general and weight in particular, may result in a selective processing of low calorie /

healthy foods. It is interesting however that this biased processing was not found for

physical activity words in either the men or women (discussed later). Both engaging in

regular exercise and eating low calorie foods are means for engaging in a healthy lifestyle

and achieving weight loss, so it is not clear why only the one method received enhanced

elaboration. Future research could address this question.

The differential processing of high and low calorie food words suggest that

differentiation between these categories occurs early in processing, a conclusion that is

inconsistent with Francis et al. (1997). This finding suggests that the ‘pre-attentive

detection mechanism’ reported in anxiety and depression (Williams et al., 1988a, 1997)

may also be relevant within the body image research. Within anxiety, it is proposed that

state levels of anxiety can affect the allocation of resources, however it appears that

material associated with low calorie foods receive elaboration in most individuals.


It should be noted that the higher recall of low calorie food words may simply be

an artefact of the low recall for the matched neutral words. Recall for these neutral words

was very low compared to the other neutral categories. However, the matching process

ensured that there were no significant differences on key features of the experimental and

neutral words; hence this explanation is not favoured.

To summarise, both men and women showed a similar response to the food

words. However, given the different pattern of associations between the psycho-social

variables and processing of the food words, it is still useful to consider males and females

separately. In females, processing of the food words had stronger relations to the body

image variables than was observed in men.

9.2.2 Positive and Negative Appearance Words

The other most commonly used Stroop category is appearance related words.

Again, words related to body shape, weight, and general appearance have been used

frequently with women, while little research exists on men’s reaction to these words.

Given that dissatisfaction with weight and appearance are key factors in clinical and non-

clinical body image disturbance, and the high rates of body dissatisfaction reported in

males (Grogan & Richards, 2002) and females (Tiggemann & Lynch, 2001), it is

important to understand whether this disturbance is represented cognitively.

As outlined in the literature review, a number of significant limitations have been

noted in past research using appearance related words which warrant further

investigation. The current study aimed to address these limitations by separating positive

appearance / body shape words (e.g., “attractive”) from words with a more negative

connotation (e.g., “ugly”). Vitousek and Hollon (1990) note that our understanding of


processing biases for body image stimuli is limited by focusing on negative shape words

only. They argue that avoidance of counter-schematic information (such as words related

to thinness) is just as important to consider as attention for schematic information (such

as words related to fatness). To date, only limited research has used both categories of

words to examine selective attention (Sackville et al., 1998; Reiger et al., 1998), and

selective memory (Unterhalter et al., 2007). Additional methodological refinements were

made in the current study by ensuring the high semantic relatedness of the words in each

of these categories. Each word was carefully selected and independently rated to ensure a

good representation of the category. Past research has included more general words in

appearance categories (e.g., Lokken et al., 2006; Long et al., 1994). For example, the

“shape” words used by Lokken et al. (2006) contained only two words indicative of

negative shape (“fat” and “flabby”), while the remaining words were general body areas

(e.g., “thighs”). Additionally, the “weight” words primarily reflected being overweight

(e.g., “heavy”), so it’s not clear how this category is distinct from the “shape” category

used. The aim of the current study therefore was to address these limitations. Careful

selection of words ensured that both male and female body image concerns were

reflected. For example, words like “muscular”, “attractive” and “ugly” are expected to

equally relevant to males and females.

9.2.2.1 Integration of findings with past research for females. The results of the

current study clearly indicate that concerns surrounding positive appearance are distinct

from negative appearance. The positive appearance words were the only category to be

related to current hunger levels and time since last meal. Higher hunger levels and more


time since last food consumption were weakly, yet significantly, related to quicker

reaction times to colour name positive appearance words.

The pattern of correlations between the positive appearance interference index

and psychosocial variables revealed that longer reaction times (that is, attention toward

these words), was associated with better body image. Comparatively, longer reaction

times to colour name the negative appearance words was associated with poorer mood.

Both indexes were associated with risky weight management behaviours. Women who

reported vomiting after food consumption and fasting reported significantly longer

reaction times for negative appearance words, compared to women who did not engage in

these behaviours. Additionally, a trend was noted for women for dieted for weight loss

and binged to have longer reaction to negative appearance words than who didn’t report

these behaviours.

Although no significant differences emerged between the groups identified in the

cluster analysis, mean differences were noted. The Normal and Athletic group showed

high interference indexes indicating they spent longer colour naming the positive

appearance words, while the Dissatisfied group demonstrated quicker reaction times. The

Symptomatic group reported only a small interference index. In response to the negative

appearance words, only the Normal group showed a large interference index, indicating

attention toward the words. To summarise, longer reaction times to positive appearance

words was indicative of better body image and associated with less risky behaviours,

while longer reaction times for negative appearance words was associated with poorer

mood, and engaging in more risky weight management behaviours.


Only two studies to date have included positive body shape words when

examining biased attention (Reiger et al., 1998; Sackville et al., 1998). Both studies

compared women with eating disorders to a group of control women high and low on

dietary restraint, and found that interference effects increased with symptom severity.

Significant interference effects were only noted in the clinical groups, and both studies

found no evidence of interference effects in the non-clinical women. These results are

somewhat consistent with the current findings, as interference effects were limited to

women reporting more risky weight management behaviours, particularly for the negative

appearance words. It therefore appears that interference effects for appearance related

words are associated with increased symptom severity (Ben-Tovim & Walker, 1991;

Cooper et al., 1992; Green & Rogers, 1993; Lokken et al., 2006), and that interference is

associated with negative mood (Cooper et al., 1992; Leon et al., 1993).

No evidence was found of a memory bias for the positive appearance words in

this sample of women. As a group, the women did not recall many of the positive

appearance words, nor were the psycho-social variables related to recall. Additionally, no

group differences emerged when comparisons were made along the risky behaviours or

the cluster analysis groups. The women did however show a memory bias for the

negative appearance words. As a group, they recalled significantly more of the negative

appearance words compared to the matched neutral words. Higher percentage of negative

appearance words recalled was significantly related to poorer mood and higher restraint.

Additionally, women who reported excessive exercise and dieting for health showed

significantly higher recall than those women who did not report these behaviours.

Regardless of which group the women were classified into, they showed higher recall of


the negative appearance words. Finally, a small number of errors when colour naming

both the positive and negative appearance words was associated with poorer body image.

These findings suggest a widespread memory bias for words associated with being

overweight and unattractive.

These findings are consistent with previous research which has noted a general

memory bias for weight and appearance stimuli in women (Boon et al., 2000; Unterhalter

et al., 2007). Boon et al. (2000) found that all women (classified as restrained or

unrestrained eaters) showed a recognition bias for the body shape / size words, but no

details were provided regarding the valence of these words (whether negative or positive

appearance). Using a self referent encoding task where women were asked to rate how

relevant a set of words to them followed by an incidental memory task, Unterhalter et al.

(2007) found that women reported significantly higher recall of both positive and

negative weight / shape words compared to the neutral words. Results of the current study

suggest that memory bias is limited to negative appearance words. The high recall of

positive appearance words in Unterhalter et al’s study may be due to the use of a self

referent encoding task that required more cognitive processing of stimuli compared to an

Emotional Stroop task where participants to instructed to ignore the stimuli (Sharma &

McKenna, 2001). Also, the difference in time of the distracter task (20 seconds in

Unterhalter et al; three minutes in the current study) may mean that positive appearance

words are quickly forgotten, particularly if they are deemed not relevant.

Together, these results indicate that there is no link between encoding and

retrieval of positive or negative appearance words. Longer reaction times to colour name

positive appearance words was associated with better image, but no associations were


found for recall for these words. A higher number of errors made on this task were

associated with more body image dysfunction, indicating perhaps that more attention was

paid to the task of colour naming. Women with better image spend longer looking at

positive appearance words, but no further elaboration occurs. It appears that opposite

pattern of results emerges in response to negative appearance words. No evidence of

attentional bias was found, yet this was the only category of words that all women

showed increased recall for. Regardless of what sub-group women were classified as, the

percentage of negative appearance words recalled was high.

9.2.2.2 Integration of findings with past research for males. No evidence of

attention bias was found in this sample of men for either positive or negative appearance

words. Quicker reaction times for positive appearance words were associated with a

poorer body image (higher drive for thinness, trend for higher drive for muscularity),

while quicker reaction times for negative appearance words were related to higher stress.

Engaging in weight management behaviours was not significantly associated with biased

attention. No significant differences emerged the groups identified in the cluster analysis

on attention for either positive or negative appearance words.

The sample as a whole showed a memory bias for the negative appearance words,

but not positive appearance words. The percentage of words recalled was not related to

any of the psycho-social variables, engaging in risky weight management behaviours, or

sub-group. Errors when colour naming the negative appearance words were not

significantly associated with any of the measures, while a higher number of errors when

colour naming the positive appearance words was associated with exercising more for

fitness reasons, and having a lower BMI.


Based on these results, the hypothesis that processing biases would only be found

in symptomatic men was not supported for either positive or negative appearance words.

The men overall showed higher recall of negative appearance words; a relationship that

was not modified by any of the psycho-social variables.

Previous research examining processing biases in males is limited. As discussed

in the previous section on food words, only two studies to date have included male

participants when studying biased attention (Channon & Hayward, 1990; Fairburn et al.,

1991), and the designs of these projects were problematic (i.e., combined word

categories, non-homogeneous neutral words). Although both these studies did find some

evidence of interference effects, the homogeneous nature of the target words compared to

the neutral words could explain this difference (Green et al., 1991).

Processing biases in males may be limited to stimuli that focus more on

muscularity. Unterhalter et al. (2007) found that men recalled words related to

muscularity more frequently than general appearance related words. While the women

showed a memory bias for all appearance words, the men only showed enhanced recall

for the words related to high muscularity (e.g., “muscular”), as opposed to words

indicative of low muscularity (e.g., “scrawny”). That is, the men in Unterhalter et al’s

study reported memory bias for only positive muscular words, while in the current study

the men showed enhanced recall of a general category of negative appearance words. The

use of a self referent encoding task in Unterhalter et al’s study may have resulted in the

high recall of positive muscularity words, if the men were able to form rich and elaborate

memories of themselves as large and muscular (i.e., an ideal they would like to achieve).

After a short distracter task of 20 seconds, these memories could still have been


accessible. Self referent encoding tasks require cognitive processing of the stimuli as the

men were asked how strongly the word related to them. In an Emotional Stroop task,

participants are not required to actively engage with the stimuli; in fact they are instructed

to ignore it. As such the Emotional Stroop task is a better measure of shallow,

unintentional processing (Russo et al., 2006). The different task demands between the

two studies in addition to the longer distracter task used in the current study (two

minutes) may explain these discrepant findings. Future research should examine how

instructions to either engage with, or ignore the stimuli affects memory. This is the first

study to date that has examined processing biases in males using stimuli specifically

developed to represent male body image concerns. Clearly more research is needed to

further explore the generalisability of these findings.

9.2.2.3 Theoretical Explanations. In theoretical terms, the results of the current

study suggest that appearance related words do not receive any preferential attention, but

that negative appearance words may receive increased elaboration in both men and

women (Williams et al., 1988a, 1997). These findings may be interpreted in two ways.

The high levels of body image disturbance would implicate a highly developed schema

network surrounding negative appearance, which guides biased recall of negative

appearance information. This interpretation is not favoured in males however, given that

levels of body image disturbance were either low, or consistent with normative values.

This explanation is more plausible for women, and the findings may be interpreted within

the cognitive theories of Vitousek and Hollon (1990). Cognitive processing is directed

toward schema consistent information which in this case is negative appearance

information. The large number of women in the current sample who reported body image


disturbance reflects a ‘normative discontent’ (Rodin et al., 1984), which results in more

processing of negative appearance information. These findings suggest that more

elaboration (Williams et al., 1988a, 1997) occurs for self relevant information (Rogers et

al., 1977) consistent with one’s self schemata (Markus 1977, 1987).

The alternate explanation is that enhanced memory for negative appearance

information may reflect the increasing importance of being attractive, toned / muscular

for men, and thin for women (Davis et al., 1993). It can be argued that seeing one’s self

as not fitting this ideal (i.e., as fat and unattractive) has comparatively more negative

outcomes than viewing one’s self positively (i.e., as attractive and muscular) has positive

outcomes (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001). This may explain why a

memory bias was observed for negative, but not positive appearance words. The pressure

within society for women to look thin and attractive, and the negative connotations with

“fat” and “ugly” may result in this widespread processing bias. The recall of more

negative appearance information may serve to maintain body image concerns, as this

information is more readily accessible.

The widespread memory bias suggests that increased processing of negative

appearance stimuli is not restricted to clinical groups, or even symptomatic groups.

However a different pattern of processing the appearance words was found for males and

females. In males, the widespread bias suggests that although men may not be aware of,

or consciously effected by this pressure, it does influence cognitive processing. That is, it

may reflect a more general bias, rather than a bias that is specifically associated with

negative body image. None of the psycho-social variables were associated with increased

memory for negative appearance information in men, therefore the typically used


explanation in past research that bias is indicative of negative functioning must be viewed

with caution. Material that is selectively recalled is assumed to have some relevance to

the individual, however in this case it may simply reflect living within a society that is

obsessed with appearance. Men may have formed schemas surrounding appearance, but

these schemas are not linked to a wider concept of the self. That is, more generalised

schemas were developed, rather than self related schemas (Vitousek & Hollon, 1990).

This explanation would explain the higher recall of negative appearance material and the

lack of associations with the psycho-social variable, however clearly research is needed

to replicate this effect.

The different pattern of results for negative and positive appearance words

demonstrates the importance of considering these categories separately. These findings

challenge the notion that interference effects are always associated with poorer

functioning, as interference for positive appearance words was associated with better

body image in women. Therefore the hypothesis that interference effects would only be

found in symptomatic women was partially supported. Engaging in risky weight

management behaviours was associated with more interference effects in both positive

and negative appearance words in women. Additionally, positive appearance words were

the only category to show significant group differentiation, with the Dissatisfied female

group showing more interference. However, this study also indicated that longer reaction

times for positive appearance words were associated with better body image. This was

the first study to specifically examine both positive and negative appearance words in the

general community. Results indicated that these categories represent distinct concerns,

and highlight the need for more research examining these words separately.


9.2.3 Negative Emotion Words

Despite the growing recognition of the association between negative mood and

body image disturbance (Cooper, 1995; Leon et al., 1993), very little research has

included these words as stimuli in an Emotional Stroop in women, and no study to date

has used male participants. The present study included this category of words to explore

whether dysfunctional body image attitudes was associated with selective attention for

negative affect words (e.g., “anxiety”, “depressed”).

9.2.3.1 Integration of findings with past research for females. Findings of the

current study suggested that quicker reaction times for negative emotion words were

associated with poorer body image, mood, and general wellbeing. At a mean level,

women who recently engaged in vomiting, use of slimming tablets, fasting, excessive

exercise and reported loss of control over binging, showed quicker reaction times to

colour name negative emotion words compared to women who did not engage in these

behaviours. Significant differences were only noted for use of fasting though.

Examination of the groups identified in the cluster analysis revealed no significant

differences in interference effects for negative emotion words, although effects did

become progressively larger as symptomology increased. Therefore, the hypothesis that

interference effects for negative emotion words would only be found in symptomatic

individuals was partially supported.

To date, only three previous studies have examined attention bias for negative

emotion words in adults. Sackville et al. (1998) found no differences in interference

effects between women classified as Anorexic, or high and low dietary restraint, although

at a mean level interference increased with increasing symptomology. Jones-Chesters et


al. (1998) found interference effects in women with Bulimia, but not Anorexia, or control

groups, but only when the words were presented in a blocked format. Finally, Seddon

and Waller (2000) found that younger women (< 21 years) with high bulimic

symptomology showed a tendency for cognitive avoidance of the negative emotion

words, while the older women (>21 years) showed attention toward this information.

Based on this literature, it appears that interference for negative emotion words may be

related to bulimic symptoms. This is the first study to consider a wider range of psycho-

social vulnerability factors, and shows that cognitive avoidance of negative emotion

words has wider relations to poorer mood, well-being, and body image.

Higher recall of the negative emotion words was only associated with three body

image variables: lower interoceptive awareness, ineffectiveness, and perfectionism. No

significant differences in recall rates for negative emotion words were found between

women who had, and had not, engaged in risky weight management behaviours recently.

No significant differences emerged between the cluster analysis sub-groups, suggesting

that memory bias, or rather lack of memory bias, is only found in women with more

severe eating disorder-like symptoms. Therefore the hypothesis that a memory bias

would found in symptomatic women was not supported.

Fewer errors when colour naming the negative emotion words was associated

with poorer body image (high drive for thinness, bulimia symptoms, and body

dissatisfaction). That is, it appears that women with higher body image disturbance were

better able to ignore the valanced stimuli, and accurately complete the task. Jones-

Chesters et al. (1998) also found that the women classified as Bulimic made more errors

when colour naming negative emotion words.


9.2.3.2 Integration of findings with past research for males. In this sample of

men, increased processing of the negative emotion words was noted. The men overall

showed a facilitation effect when colour naming the negative emotion words. This was

the only category of words to show a significant interference effect. On average, the men

were 20 msec quicker to colour name the negative emotion words compared to the

matched neutral words. The men also showed a memory bias for these words, recalling a

significantly higher percentage of negative emotion words compared to the matched

neutral words. However, increased interference was not significantly related to increased

memory, which is consistent with Williams et al.’s (1988a, 1997) model that priming and

elaboration are separate processes. Examination of a wide range of psycho-social

variables revealed limited associations to biased attention and memory. Quicker reaction

times for negative emotion words was related to poorer body image (lower feelings of

attractiveness and higher bulimic symptoms), but no significant associations were noted

with memory scores. No significant differences were noted between the sub-groups on

attention, memory, or errors. Therefore, these results do not support the hypothesis that

interference effects would be found in symptomatic men only; biased attention and

memory for negative emotion words was found across the whole of sample of men.

9.2.3.3. Theoretical Explanations. Both males and females showed selective

recall of negative emotion words, and selective attention was noted in the men. However,

the pattern of correlations between the cognitive and psycho-social variables suggests that

the relationship is different for males and females. In males, limited associations between

the psycho-social variables and avoidance of the negative emotion words were noted,

while no associations were noted with biased memory. In women, avoidance of this


information was consistently associated with poorer functioning, but not associated with

biased memory.

In theoretical terms, words connoting negative emotions received preferential

processing in this sample. Affective material may have a lowered threshold for awareness

and be noticed much more rapidly than neutral material, particularly in women with

poorer body image. Examination of literature outside of the body image area reveals that

biased attention for negatively valanced information is not a new finding (Smith et al.,

2006). Baumeister et al. (2001) in their review of the literature examining attention for

negative information conclude that “We have found bad to be stronger than good in a

disappointingly relentless pattern” (p. 362). They suggest that people try and avoid

negative emotion in favour of more positive stimuli as a means of affect regulation. They

review evidence to shows that people tend to recall more negative information than

neutral or positive information. Smith et al. (2006) have found however that this effect

can be moderated by exposure to positive information. When participants were

subliminally primed using positive words (e.g., “friends”), or were treated very kindly by

the experimenter, the attentional bias toward negative information disappeared. It appears

that the positive prime does not create attention toward positively-valanced information,

but simply reduces the focus on negatively-valanced information.

This claim is also consistent the findings of MacLeod and Rutherford (1992) who

found that individuals low on trait anxiety showed an avoidance of threat information

when levels of state stress were high. It is possible that the high task demands and the

rapid presentation of the words in the Emotional Stroop task resulted in a high stress

condition for the participants (Sharma & McKenna, 2001). Research has noted that


shorter inter-stimulus intervals produce more interference effects (Sharma & McKenna,

2001). Under this pressure, the rapid detection of negative threat information is even

more important. By screening out threatening information, the emotional impact is

reduced. This is thought to be a strategic rather than automatic process, as MacLeod and

Rutherford (1992) found this avoidance only using a Stroop methodology wherein

participants were aware of the stimuli. The rapid detection of threat information is

consistent with the pre-attention decision mechanism proposed by Williams et al. (1988a,

1997). This mechanism makes a decision about the affective valence of material and

guides processing according. In non-emotionally disturbed individuals, as in the current

sample where levels of negative affect were low, attention is diverted away from

threatening information. Therefore, the results of the current study regarding biased

attention are consistent with the wider research on processing biases for negatively

valanced information. The men and asymptomatic women are showing the expected

attention bias toward negative emotion information, but symptomatic women showed

avoidance of this information.

The reason for this cognitive avoidance in only the symptomatic women is not

clear. One possible explanation is that the avoidance of negative material may serve to

maintain the women’s non-clinical status. For example, the negative emotion material

may be deemed too threatening, and thus is avoided. This suggestion is also consistent

with the decreased recall rates, and lower number of errors for negative emotion words

found in women with higher symptomology. These women may pre-attentively define

negatively valanced information as too threatening, and thus allocate more attention to

completing the task (i.e., correctly identifying the colour), hence the quicker reaction


times and less errors (Williams et al., 1988a, 1997). This information does not receive

any further elaboration, as evidenced by lower recall rates.

A similar explanation has been provided in past research wherein ‘low trait’

groups show a protective mechanism by selecting avoiding threatening information

(Johansson, Lundh, & Andersson, 2005; MacLeod & Rutherford, 1992). MacLeod and

Rutherford (1992), when examining high- and low-trait anxious individuals, found that

non-clinical individuals who score low on trait anxiety have almost a protective

mechanism wherein threatening information is avoided. When this group experiences

high state anxiety (i.e., stress from an upcoming exam), interference effects become more

pronounced, however they are avoiding rather than attending to the information. This

automatic avoidance of threatening information serves to maintain their low levels of

anxiety. Similarly, Johansson et al. (2005) found that when exposed to images of highly

attractive women before completing a Stroop task, body dissatisfied women reported an

interference effect while non-primed women showed a facilitation effect. These results

were explained as avoidance of threat related information, wherein body satisfied women

strategically divert processing resources away from threatening information. This in turn

may help to maintain their current levels of satisfaction by buffering against the any

negative impact of the media.

The progression from non-clinical yet symptomatic, to a full blown eating

disorder may shift attention back towards the negative emotion words. The increased

schema elaboration and increase in negative mood means that negatively valanced

information can longer be ignored. This suggestion is consistent with findings of Jones-

Chesters et al. (1998) who found that only women classified as Bulimic showed increased


attention toward negative emotion information, and also showed more errors in colour-

naming. Clearly, more research is needed to test this idea.

It is not clear though how this avoidance of information can be explained in

combination with the increased memory for negative emotion material. Although

processing of this negative information is avoided, it may still produce schema activation

and activation of emotional nodes (Bower, 1981). This activation, even though the

information is avoided, may still make the negative emotion information

disproportionately more available. That is, even though the information is successfully

avoided, the residual activation renders the information available a short period later

during the incidental memory task. More research is needed to test this explanation, and

to further investigate the association between encoding and retrieval bias. Mathews and

MacLeod (2005), in their review of cognitive biases associated with emotional disorders,

conclude that while attention bias is generally supported in anxiety and depression, there

is inconsistent research on memory bias. To date, there is no research that has examined

memory bias for negative emotion information within the body image literature.

9.2.4 Physical Activity Words

This is the first study to use the category of physical activity words (e.g., “sport”,

“gym”) in an Emotional Stroop task. The inclusion of physical activity is theoretically

important for several reasons. First, physical activity is part of engaging in a healthy

lifestyle. Given the high rates of obesity worldwide, and the current focus by the

Queensland Government on healthy eating and regular exercise, it is important to

understand the whether cognitive bias extends to these words. Second, excessive exercise

is found in clinical groups (e.g., eating disorders, body builders) a means for weight


control. Finally, the ideal body shape is becoming increasingly athletic and toned.

Therefore, an understanding of whether cognitive bias extends to physical activity words

is applicable to both clinical and non-clinical groups.

9.2.4.1 Integration of findings with past research for females. In the current study,

longer reaction times to colour name physical activity words was associated with a higher

recognition of any illness, and a trend for lower dietary restraint. Minimal interference

effects were noted for women who had, and had not, reported engaging in risky weight

management techniques recently. Comparisons of the interference indexes across the

groups identified in the cluster analysis revealed that the Athletic group showed the most

interference, although not significant. The other three groups also showed some evidence

of interference effects to the physical activity words, albeit to a smaller degree.

Therefore, the hypothesis that interference effects would only be found in symptomatic

individuals was not supported. However, it appears that interference effects for physical

activity words as not necessarily indicative of poorer functioning.

As a group, the women recalled significantly less physical activity words

compared to the matched neutral words. However, when the interference index was

correlated with the psycho-social variables, a higher percentage of physical activity

words recalled were consistently related to more focus on health and fitness. Significant

correlations revealed higher feelings of strength and fitness, a higher importance placed

on health and fitness, less illness, exercising to maintain a health lifestyle, but also high

anxiety. Both the Athletic group and the Symptomatic group showed a higher percentage

of physical activity words recalled, however the difference was non significant. Errors


when colour naming the physical activity words was not significantly associated with any

of the body image, mood, or general well-being variables.

9.2.4.2 Integration of findings with past research for males. No evidence was

found of biased cognitive processing for physical activity words in men. The men did not

show any selective attention or memory for these words, and no significant associations

were found with the psycho-social variables. More errors when colour naming these

words was associated with a higher level of stress, but no other associations were noted

with body image or general well-being variables. Similarly, no differences emerged

between the three sub-groups in response to these words. For men, exercising does not

appear to be related to mood, body image, or general well-being, a finding consistent with

past research using questionnaire-based methodology (Boroughs & Thompson, 2002;

Tiggemann & Williamson, 2000). For instance, Tiggemann and Williamson (2000) found

that men’s exercising behaviour was not linked to body satisfaction or self esteem, like it

was in women. Therefore the hypothesis that interference effects would be found in

symptomatic individuals only, was not supported for physical activity words.

9.2.4.3 Theoretical Explanations. No significant processing biases were noted for

physical activity words in males or females, although the women showed reduced

memory for physical activity words. Within the male sample, the psycho-social variables

showed a complete lack of association with cognitive processing of exercise words. In the

female sample, higher recall of exercise words was related to the health and fitness

variables.

The women as a whole group showed reduced memory for the exercise words, but

it’s interesting to note that higher recall was consistently associated with feeling fit and


healthy, and an importance of a healthy lifestyle. Alternatively, lower recall was

associated with poorer feelings of strength and fitness and lower importance on health.

Given the small number of women who were classified as Athletic, perhaps this reduced

recall of exercise words reflects that most women did not rate exercise as overly

important. Processing biases for physical activity words may are limited to women who

engage in regular exercise and report high levels of fitness. These women may have

highly developed schemas surrounding physical fitness, which allows increased

elaboration of this material. These findings are consistent with the “personal relevance”

hypothesis (Rogers et al., 1977) that only material that is personally relevant receives

biased processing. Thus, it appears that processing biases are found in some women, but

are limited to those who place increased importance on fitness. As this is the first study to

examine attention and memory bias for physical activity stimuli, further replication is

needed.

In theoretical terms, these findings suggested that males in the sample did not

have schemas developed around sporting activities / exercise, or these schemas are not

developed enough to influence cognitive processing. Even though a range of questions

were asked about exercising behaviours, reasons, and attitudes; and most of the sample

reported exercising in a typical week; this was not related to processing biases for

exercise stimuli. Men most frequently report exercising for health and fitness reasons

(Tiggemann & Williamson, 2000), but only a small proportion of men in the current

sample (n = 9) though were classified as “Health Conscious”. It may be that increased

attention and memory for physical activity words are only found in men who are more

athletic, engage in excessive exercise, or competitive sport; who have more highly


developed and elaborative schemas surround exercise. This claim is consistent with the

theoretical underpinnings of the research that biased processing is driven by schemas

(Beck 1967, 1991; Markus, 1977; Vitousek & Hollon, 1990), but that schemas need to

reach a certain level of activation before they influence processing (Bower, 1981). Future

research could examine whether words more specific to the type of exercise a person

does produces interference, as it has been noted that threat-specific words produce more

interference than general threat words (MacLeod & Rutherford, 1992). Alternatively,

exercise related words may only produce interference in elite athletes; a claim that

warrants further investigation.

9.2.5 Sex Differences in Cognition

Direct comparisons between males and females on attention, memory, and

number of errors revealed no significant differences. These findings suggest that body

image information is processed similarly between the sexes, a finding that is consistent

with some past research (Fairburn et al., 1981; Channon & Hayward, 1990), but

inconsistent with other findings (Unterhalter et al., 2007). In the current study, attentional

biases were not found in women, and were limited in men. Both groups recalled

significantly more experimental words, negative appearance words, and low calorie food

words, compared to matched neutral words. In addition, then men also showed increased

memory for negative emotion words, while the women showed poorer recall of physical

activity words.

Although no sex differences were observed, the pattern of findings was quite

different for men and women. For instance, in both groups facilitation effects for negative

emotion words were associated with poorer body image, but in women interference


effects were associated with a larger number of body image variables, and also mood and

general well-being variables. Similarly, the males showed an enhanced recall for the

negative emotion words which was not found within the female sample. Within the male

sample, psycho-social variables were not related to recall; while in the female sample

lower recall was associated with poorer body image. The hypothesis that sex differences

would emerge in response to the stimuli was therefore not supported, with the cravat that

different patterns of associations between cognitive and psycho-social variables are

found. Therefore, it appears useful to consider males and females separately in analyses,

even if they appear similar at a surface level.


The purpose of this chapter was to review the findings on processing biases in

men and women. Partial support was gained for the hypotheses that processing biases

would be found in symptomatic individuals only, across the six experimental categories

of words. In women, no evidence of attentional bias was found between groups; however

attentional bias was related to a range of body image and mood variables. Memory bias

was shown for negative appearance words, low calorie food words, and physical activity

words (lower recall). Again, memory bias was associated with a range of body image and

mood variables.

In men, attentional bias was found for negative emotion words and memory bias

was found for negative appearance words, negative emotion words, and low calorie food

words. However attention and memory bias was not consistently related the body image,

mood, and general well-being vulnerability factors. Although no differences emerged

between men and women for attention, memory, or number of errors, it is important to


consider the different pattern of correlations between the cognitive and psycho-social

variables within each sex.

These findings were compared to previous research, however given the unique

nature of the study, and the number of refinements made over past research, direct

comparisons of the findings was difficult. The results were interpreted within the

theoretical approaches of the cognitive approaches of Vitousek and Hollon (1990),

Markus’ self schema theory (1977, 1987), and the applicability of the depression and

anxiety models of Williams et al. (1988a, 1997) and Beck (1967, 1991) was shown. It is

therefore concluded that the processing of certain types of body image information that is

personally relevant (Rogers et al., 1977) is driven by schemas (Markus, 1977, 1987;

Williamson et al., 2002; Vitousek & Hollon, 1990) but that schemas need to reach a

certain level of activation before they influence processing (Bower, 1981). Additionally,

the distinction noted between priming and elaboration noted in the anxiety literature

(Williams et al., 1988a, 1997) was also demonstrated for body image information.


Chapter Ten: Overview, General Discussion, and Conclusions

10.1 Overview

The results from the current program of research have advanced our

understanding on the cognitive processing of body image and health related information

within the general community. Given the high rates of body dissatisfaction at both a

national and international level (Klemchuk, Hutchinson, & Frank, 1990), efforts to

understand factors associated in the maintenance of this problem are needed. The broad

aim of this project was to expand the literature on cognitive processes underlying body

image disturbance within males and females. Specifically, three phases of research were

undertaken to achieve this aim. In Phase One, a qualitative exploration was undertaken to

identify factors that males and females perceived as important components of their body

image. The outcome of this phase was an increased understanding of body image

concerns in non-clinical males and females. Factors identified as important determiners

of body image satisfaction were included as psycho-social variables in the Emotional

Stroop study (Phase Three). Phase Two involved the selection and careful matching of

an empirically supported set of stimuli to be used within an Emotional Stroop task. A

preliminary set of words was developed from a number of sources including past research

and based on the outcomes of Phase One. A study was undertaken to gather ratings on the

emotional valence, categorisation, and representativeness of the words. Words that

received the highest ratings on these categories were retained and matched to set of words

neutral in valance. This resulted in six categories of experimental words: positive

appearance / body shape words, negative appearance / body shape words, high calorie /

forbidden food words, low calorie food / non-forbidden words, negative emotion words,


and physical activity words. In Phase Three, an Emotional Stroop task and incidental

memory task were developed. A national sample of women (N = 144) and men (N = 55)

from universities and the general community was recruited. This study was unique in

providing a multifaceted understanding of processing biases in males and females.

Overall, the results indicated that processing biases are found within the general

community, but are not always associated with negative functioning.

Detailed analyses of the results from each of the three phases of research have

been provided in previous chapters (Chapters Five, Six, Eight and Nine). Therefore, the

purpose of this final chapter is to provide an integration of the findings from each phase,

explore the theoretical and practical implications of these findings, and make suggestions

for future research.

10.2 Integration of Key Findings

The results from all phases of the current research indicate that body image

disturbance was a significant problem within the sample. Even though a clinical sample

was not sought, a substantial proportion of both males and females reported a range of

dysfunctional attitudes and behaviours such as appearance dissatisfaction, risky weight

management behaviours, and dietary restraint. Substantial evidence was also found of

processing biases within a non-clinical sample, however not all biased processing was

associated with negative body image. The findings point to the importance of considering

both attention and memory biases, given the widespread evidence of the later, but not the

former in the sample. In females, more interference for positive appearance words, low

calorie food words, and negative emotion words were associated with better functioning

on the psycho-social variables, and lack of risky weight management behaviours.


Comparatively, quicker reaction times (or avoidance) of negative appearance words was

associated with poorer functioning on the psycho-social variables and engaging in

vomiting, fasting, and dieting to approve appearance. Interference on the food words was

not consistently associated with the psycho-social variables, and nor was attention for

physical activity words.

The women overall showed enhanced incidental memory for the negative

appearance words and low calorie food words, and lower recall of physical activity

words. Higher recall of the appearance words and low recall of negative emotion words

was associated with poorer mood and functioning. Higher recall of low calorie food

words was associated with better body image and self esteem, while higher recall of

physical activity words was associated with greater health and fitness. Recall of high

calorie food words was not consistently related to the psycho-social measures. This

relationship was not modified by sub-group, or engaging in risky behaviours.

Within the male sample, attentional bias was only noted for negative emotion

words, while memory bias was noted for negative emotion words, negative appearance

words, and low calorie food words. The general lack of associations between the psycho-

social variables, engaging in risky behaviours, and sub-group in association with the

cognitive variables suggests that processing biases in males are not linked to body image,

mood, or general well-being.

The sub-groups identified in both males and females support the non-

homogeneous nature of samples taken from the general community. While four distinct

groups emerged for females, and three groups for males, these groups did not consistently

differ on cognitive processing. At a mean level, a pattern emerged wherein increasing


body image disturbance was associated with increasing interference effects, but these

differences did not reach significance. It may that the Emotional Stroop task was not

sensitive enough to detect the subtle differences in processing between these groups.

Most likely though that given the small mean differences a larger sample size would be

needed to provide enough power to detect a significant difference. This argument is

supported by the lower power levels reported in many of the analyses using the sub-

groups. Given the much larger sample size in the current study compared to past research,

the conclusion that past research has made that processing biases are not found within

non-clinical samples is called into question.

In the current study, a range of mood (depression, anxiety, and stress) and general

well-being variables (self esteem and social functioning) were included to expand our

understanding of the impact of cognitive biases beyond body image variables. Limited

associations were noted however between the cognitive and these psycho-social

measures. This suggests that within males and females from a non-clinical sample, body

image concerns are separate from general well-being. This may reflect the non-clinical

nature of the group, that is, any body image disturbance is kept compartmentalised, and

does not influence general feelings of worthiness as a person. This suggestion is

consistent with the findings of Tiggemann and Williamson (2000) that found the relation

between body dissatisfaction and self esteem was much stronger in women than in men.

This also provides an important implication regarding the distinction between clinical

samples. Perhaps one of the key distinctions between these two groups is how ‘far-

reaching’ are feelings of body dissatisfaction.


These results were important is demonstrating that processing biases are found

within a non-clinical sample, and that not all biased attention and memory is indicative of

negative functioning. The theoretical implications of these findings are now explored.

10.3 Theoretical and Practical Implications

The results of this study were viewed within a number of theories that implicate

the role of schemas in biased processing. As there is much more information in the

environment available than can be processed at one point, a person must develop a way

of recognising and processing relevant information. It has been theorised that this

selective processing of information in one’s environment is guided by schemas. These

schemas guide processing based on information that is important to the person (Markus,

1977). That is, we notice in others what we hold important ourselves. For some

individual’s, increased importance placed on weight and appearance may serve to guide

their processing to this information when encountered. While all individuals’ hold

rudimentary schemas for appearance and weight, in some individual’s these schemas will

become highly associated with other facets of functioning (Markus, 1977, 1987;

Thompson et al., 1999; Vitousek & Hollon, 1990). Within this sample, evidence of the

general weight-related schemata was gained, that guides processing of information

relevant to cultural ideals and stereotypes associated with weight status (Vitousek &

Hollon, 1990). No evidence was found for weight-related self-schemata, however

Vitousek and Hollon (1990) note that this is restricted to those with clinical eating

disorders.

The lack of attentional bias within the current sample suggests that attentional

biases may not be stable within non-clinical groups, but are perhaps influenced by cues in


the environment. For example, exposure to an attractive and thin model may activate

appearance and weight schemas. For those women who place a high importance on body

size and shape, this temporary activation may result in biased processing. These findings

suggest that body-related schemas must reach a critical level of activation before they

begin to affect processing. Unless these schemas are activated, through the use of primes,

they remain dormant and do not consistently affect processing of body-image and health

related material. The use of a prime prior to an Emotional Stroop task is an important

future research direction.

Schemas also drive biased memory (Vitousek & Hollon, 1990; Williams et al.,

1988a, 1997). Exposure to schema consistent material selectively directs processing and

increased elaboration for this information, making it disproportionately more available.

The pattern of findings suggests that processing biases within non-clinical samples are a

bias at the retrieval stage, rather than the encoding stage. This conclusion is limited in

past research that has only examined attention or memory, but not both. The model of

Williams et al (1988a, 1997) has particular importance to understanding the current

results. This model made the distinction between priming (encoding) and elaboration

(retrieval) and noted that one may occur in the absence of the other. In the current

sample, only consistent evidence of increased elaboration of the stimuli was found. This

suggests that processing biases are not automatic, but occurring at a later stage of

information processing (Faunce, 2002; Williams et al., 1988a, 1997), such as the memory

or elaboration stage, wherein strategic processing is used. Women and men in the sample

were able to distinguish between high and low calorie food words (showing biased

memory for only the latter), and also positive and negative appearance (again showing


biased memory for only the latter). This supports the notion made by Williams et al.

(1988a, 1997) that a pre-attentive decision mechanism selectively guides processing. This

is also consistent with Beck’s content specificity hypothesis (Beck & Clark, 1988) that

biased processing is restricted to self relevant information (Rogers et al., 1977), rather

than being pervasive.

These findings are also consistent with the work of Bower (1981) by showing that

emotional material grabs attention which interferes with task performance, supporting the

distinct way in which emotional information is processed. This may be related to a

survival mechanism wherein threatening information must be responded to rapidly. As

such, this material has a lowered threshold for awareness and the ability to interrupt on-

going activity (Sharma & McKenna, 2001). In today’s society, this ‘survival mechanism’

is on the look out for stimuli that signify being overweight and unattractive, and reflects

the current push in society for healthy eating.

Outcomes of this project are primarily theoretical. Researchers are limited in their

understanding of ‘abnormal’ if normal cognitive processing is not yet understood. This is

the first study to focus explicitly on understanding cognitive bias within non-clinical

samples. Additionally, the applicability of any research is guided by the strength of its

theoretical approach. This study used a strong theoretical background, in addition to a

number of significant methodological refinements, to improve our knowledge of how

body image information is processed.

These results also have important implications for understanding the maintenance

of emotional and eating disorders. Research shows that attentional bias takes time to

develop and also to eliminate (Williams & Nulty, 1986). For instance, these researchers


found that interference times were more related to previous depression status, than

recovery a year later. Inconsistent evidence is found for whether treatment changes

interference effects in eating disorders (Carter, Bulik, McIntosh, & Joyce, 2000; Cooper

& Fairburn, 1994). Interventions could aim to make positive constructs more available,

and focus on affect regulation as current mood has been shown to moderate attention for

negative information (Smith et al., 2006). This may help to reduce the chronic

accessibility of eating and weight related schemata in individuals with high levels of body

image disturbance.

The identification of empirically defined sub-groups may be useful at both

theoretical and practical levels. The identification of the these groups has helped to

advance the body image literature by demonstrating that non-clinical groups are not

homogeneous in nature, as is sometimes assumed in past research. A multidimensional

approach was taken in formation of groups by considering general well-being measures

in addition to the more typically used body image variables. According to Garner et al.

(1983), “the focus on a single symptom may obscure meaningful idiographic or subgroup

differences” (p. 18). Meaningful group differences emerged in the current study

highlighting how a combination of variables interact, rather than simply comparing

individuals who score high or low on one measure. This is the first study to compare such

groups on selective attention and memory.

At a practical level, knowledge of these groups will help at a descriptive level by

understanding the range of body image disturbance within non-clinical samples. A

number of researchers conceptualise Eating Disorders as occurring along a continuum

(e.g., Garner, Olmsted, & Garfinkel, 1983), with symptoms ranging from mild, moderate,


to severe. A much larger proportion of the population displays some Eating Disorder-like

symptoms rather than a ‘pure’ Eating Disorder (Drewnowski, Yee, Kurth, & Krahn,

1994), yet comparatively little research has examined the psychological characteristics of

non-clinical groups. The groups identified in the current study suggest both qualitative

and quantitative differences between the groups. An understanding of the distinction

between these groups may help to identify individuals who are ‘at risk’ or in the early

stages of the disorder. Given that milder cases generally respond better to treatment

(Garner et al., 1983), early detection is vital. In both the male and female sample from the

current study, large groups emerged that were characterised by high levels of body image

disturbance. Understanding the psychological make up of this group is the first step in

early detection.

Future research could compare these ‘dissatisfied’ groups to individuals classified

with Eating Disorders to improve our understanding of the progression to this disorder.

For example, an important distinction may be in how ‘far reaching’ the negative effects

of body dissatisfaction are. It has been shown that basing one’s self esteem around

feelings of attractiveness is associated with more dysfunctional eating behaviours (e.g.,

Wilksch & Wade, 2004).

An understanding of these classification issues will be helpful within health

psychology. Knowledge of the dietary and exercise behaviours and attitudes of the

general community will help public health campaigns to target their message more

efficiently. Further discussions around classification will aid the field of clinical

psychology in the treatment of eating and weight disorders, and the classification and

diagnosis of Eating Disorders. Currently, approximately one third of individuals with an


Eating Disorder are classified as Eating Disorder Not Otherwise Specified (Crow, Agras,

Halmi, Mitchell, & Kraemer, 2002). Increased descriptive information will lead to an

improved understanding and refinement of classifications, which should lead to

improvements in the treatment of body image disturbance (Sloan, Mizes, & Epstein,

2005).

10.4 Strengths, Limitations, and Suggestions for Future Research

10.4.1 Strengths of the Research

The innovative design of this project substantially adds to the current research in a

number of ways. Significant developments over previous research were made in two

main areas: sample characteristics, and methodological refinements.

10.4.1.1 Sample characteristics. This is the first study to explore processing

biases in males with stimuli specifically designed to reflect male body image. Few studies

have included male participants, and these studies have tested attention bias in males for

stimuli that largely represent female concerns. In the current study, lists of words were

developed that would be equally relevant to both males and females (e.g., “attractive”,

“muscular”). Further, no study to date has examined memory bias for body image and

health related information in males. Given the high incidence of male body image

disturbance (Pope et al., 2000), this represents a significant gap in the current literature.

Thus, this study substantially contributes to the knowledge regarding processing biases in

males.

Additionally, this study has also contributed to the existing literature by collecting

data from a large representative sample. Much of the past research examining processing

biases has used a small convenience sample of university students. This study has


gathered data from a large number of participants (198 in total), half of whom were

recruited from the general community. This sample increases the generalisability of the

findings.

Finally, participants in the current study were examined on a large number of

vulnerability factors. Past research has been criticised for simply comparing participants

high and low on a particular measure (Faunce, 2002). The outcomes of the qualitative

study in Phase one clearly indicated that body image is influenced by a multitude of

factors, even within a non-clinical sample. This is the first study to compare empirically

derived sub-groups on attention and memory bias. Although few differences emerged

between these groups, the recognition of the heterogeneous nature of “normal” women

and men need to be noted.

10.4.1.2 Methodological refinements. A number of significant methodological

refinements were included in the current study, based on limitations identified in past

research. In the current study, a more rigorous set of words were developed. This is the

first study that has included a category of physical activity words, and adds to the limited

research that has separated positive appearance from negative appearance words.

Similarly, it adds to the limited research that has examined low calorie food words and

negative emotion words. The importance of separating these word categories is evidenced

by the distinct pattern of processing biases associated with each of these categories. The

outcome from this phase of the research is an empirically selected set of words that future

research can utilise. One frequent criticism of previous Stroop research is the poor

selection of word stimuli (e.g., Dobson & Dozois, 2004; Faunce, 2002; Lee & Shafran,

2004). Particular attention needs to be paid to the lexical characteristics of words, as


shorter and more common words are responded to more rapidly that longer or less

common words (Larsen et al., 2006). Recently attempts have been made to standardise

lists of body image words (Cassin & von Ranson, 2005). While this list is an important

first step, more research is needed to develop a set of words that encompass multiple

facets of body image disturbance, and are applicable for males and females. The

outcomes of Phase Two are important in addressing this limitation.

This study also makes significant contributions to the memory bias literature. Few

studies have examined memory bias for body image and health information in men and

women from the general community. Many cognitive theories identify memory bias as

important in the maintenance of pathology (e.g., Williams et al., 1988, 1997; Vitousek &

Hollon, 1990). This the first study to date that has examined both attention and memory

bias for males and females using extensive word stimuli. An examination of attention,

memory, and error rates allows for an increased understanding of the full range of

processing biases, and links between biased attention and memory. Additionally, an

explicit memory task was used based on the recommendations of Russo et al. (2006),

who suggest this is a more sensitive measure of biased memory than a recognition test.

Given the limited research that has examined either implicit of explicit memory bias and

body image disturbance, more research is needed.

Based on problems identified in past research regarding the presentation of the

Stroop task, a computerised version was used. This allows for greater randomisation of

the words, and is a more sensitive measure of response time to each individual word.

Additionally, a button press response was used, based on problems with inaccurate

recording identified with voice response.


The refinements listed have been identified by other researchers are important

advancements in the use of the Emotional Stroop task (Dobson & Dozois, 2004; Faunce,

2002; Johansson et al., 2005; Lee & Shafran, 2004). Therefore, this study significantly

contributes to the current knowledge by exploring a wider range of processing biases in a

larger, more representative sample, using a more robust Emotional Stroop task. Despite

the numerous strengths of this study, some limitations should be noted.

10.4.2 Limitations of the Research

As with any study on body image, the somewhat sensitive nature of the topic may

have influenced the type of person who participated. Perhaps women who were

particularly concerned with their appearance avoided the study. However, discussions

with the participants suggested the opposite, particularly from the general community.

Women especially, were interested in the study because of their own struggles with body

image problems. Most participants knew the study was examining some aspect of body

image, however a cover story was provided for the first part of the task (cognitive

measures) to try and reduce demand characteristics. Participants were instructed that the

first task was simply looking at speed of information processing and reaction time.

However, it is granted that the knowledge that the study was examining body image may

have influenced their responses. This is not an easy issue to resolve, particularly when

calling for participants from the general community who are not used to the use of

deception in research. Discussions with the participants afterwards however, revealed that

they were unaware of the true purpose of the cognitive task. If participants were aware of

the nature of the study it would be expected that more interference effects and memory

bias would be found rather than the selective biases found in the current results.


Despite attempts to collect data from a representative sample, almost three times

as many women than men participated in the study. It remains unclear why this

discrepancy emerged. Perhaps women are simply more likely to take part in

psychological research than men. Additionally, very few men take psychology units in

Queensland and these units are a major source of recruitment. It should be noted

however, that 55 men were still recruited; a sample size that far exceeds that used in

similar past research.

An additional limitation of the current study may be the use of the same set of

word stimuli for males and females. Although the words were carefully selected to reflect

concerns relevant to both sexes (e.g., “attractive”, “ugly”, “gym”), it is granted that a

separate set of stimuli developed specifically for each sex may have been more sensitive

to detect processing biases. As some research has concluded that interference effects

relate to how personally relevant the material is to the person (Rogers et al., 1977;

Unterhalter et al., 2007), this warrants further research. However, the categories of words

used in the current study did produce processing biases, which provides support for their

relevance.

A limitation relevant to all Emotion Stroop studies is the objective versus

subjective frequency of words (Faunce, 2002). Words in the current study were carefully

matched on objective frequency of occurrence within the English language; however it is

acknowledged that subjective frequencies of body image words may be higher in some

groups. For example, dieters are bound to be exposed more to food words, and

individuals highly dissatisfied with their appearance will think more about negative

appearance. This is a difficult limitation to overcome. Future research needs to develop


more creative ways to test processing biases, such as colour naming pictures instead of

words (Ben-Tovim, Paddick, & McNamara, 1995).

A further limitation relates to the lexical equivalence of the words used. Larsen et

al (2006) have outlined the importance of lexical equivalence of word lists in Emotional

Stroop tasks. Word frequency and word length are noted as key factors in lexical decision

time, with less frequent or longer words taking longer to name then more frequent or

shorter words. Therefore, word frequency and word length influence word recognition,

which in turn affects naming speed. In the current study, attempts were made to control

the lexical equivalence of word lists by ensuring that all lists were matched on word

frequency and word length. However, while these control measures were implemented,

the lexical access of the word lists was not directly tested. Therefore, differences in

reaction times between the word lists may have been due to differences in word

recognition rather than emotional impact. While researchers typically use length and

frequency as indicators of word recognition, more research is needed to examine the

lexical characteristics of word lists in the Emotional Stroop task, particularly the use of

body image words.

Finally, the use of the Emotional Stroop task has been criticised by some (Faunce

& Job, 2000; Placanica et al., 2002), yet praised by others (MacLeod, 1992; Williams et

al., 1996). Many researchers acknowledge a number of conceptual and methodological

issues still to be addressed when using the Emotional Stroop task (Dobson & Dozois,

2004; Lee & Shafran, 2004). These researchers argue that alternate methodologies, such

as dot probe task, are a more sensitive method for the detection of processing biases.

Future research needs to explore the utility of both the Emotional Stroop and dot probe


task in more detail within the body image literature. Both tasks have been used more

extensively within the depression and anxiety literature and future research could benefit

from the application of the methodological refinements noted in this literature.


The purpose of this final chapter was to understand the current research in a

broader sense by examining the theoretical and practical implications of the findings.

This research has substantially added to the existing literature on cognitive theories of

body image disturbance. Collectively, the results have expanded our understanding of

processing biases found in males and females from the general community. Using an

enhanced methodology it was demonstrated that some evidence of processing biases are

found within non-clinical samples, and that not all processing bias are associated with

negative functionality. This study is the first step in the development of a stronger

theoretical base from which intervention programs can be developed to reduce the

incidence of body image disturbance. A rigorous set of experimental procedures has been

developed that future research may utilise to help achieve this aim.


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Appendix A: Information and Consent forms used in the Qualitative Study of Phase One.

QUT Letterhead

Participant Information Sheet

“The application of a cognitive information processing approach to the understanding of body image and body image disturbance in non-clinical samples”

Ms Kate Mulgrew School of Psychology and Counselling Dr. Nathan Moss Queensland University of Technology Dr. Doug Mahar Beams Rd CARSELDINE 4034 Description

The purpose of this research is to asses what factors people think influence their body image, or physical attractiveness. This study is part of a larger project that is looking at the way people process information related to their bodies. We are interested in hearing from people from the general public. It doesn’t matter if you currently have, or have had, an eating or weight disorder of any kind. We would like to invite you to participate in this research. Your assistance would involve participating in two tasks. The first task is a semi-structured interview with the Chief Investigator (Kate Mulgrew). In the interview, you will be asked questions about what factors you think influence the way you view your body (for example, the media, friends, family etc.).This interview will take approximately 30-45 minutes. The second task involves rating a series of words. You will be provided with a list of words and asked to rate them on a few dimensions. This task is only expected to take about 20 minutes. Expected benefits

Although this project will not have any direct benefits for you personally, it is intended that the results will expand the current state of knowledge about body image in the general community. The incidence of body dissatisfaction is very high within Australia. It is hoped that the results from this study will help to inform treatment and intervention programs aimed at reducing the incidence of body dissatisfaction.

Risks

There are no risks associated with your participation in this project. This project has received ethical clearance from the Queensland University of Technology research office.

Confidentiality

You have a number of rights as a participant in this study. One of these is confidentiality. All responses will be kept completely confidential, and no one outside the research team will have access to the results. Any published material will be de-identified, meaning that there is no way that individual’s will be able to be identified from their responses.


Voluntary participation

Your participation in this project is completely voluntary. If you do agree to participate, you can withdraw from participation at any time during the project without comment or penalty, even if you have previously given your consent. Your decision as to whether to participate in this project will in no way impact upon your current or future relationship with QUT.

Questions / further information

If you should have any further questions about this project, or your participation in this project, the Chief Investigator (Kate Mulgrew) may be contacted via email: [email protected] or phone: 3864 4685.

Concerns / complaints

Please contact the Research Ethics Officer on 3864 2340 or [email protected] if you have any concerns or complaints about the ethical conduct of the project.

Thank you for your participation.


Participant Information Sheet

“The application of an information processing approach to the understanding of body image and body image disturbance in non-clinical samples”

Ms Kate Mulgrew School of Psychology and Counselling Dr. Nathan Moss Queensland University of Technology Dr. Doug Mahar Beams Rd CARSELDINE 4034

Statement of consent

By signing below, you are indicating that you:

• have read and understood the information sheet about this project; • have had any questions answered to your satisfaction; • understand that if you have any additional questions you can contact the research

team; • understand that you are free to withdraw at any time, without comment or penalty; • understand that you can contact the research team if you have any questions about

the project, or the Research Ethics Officer on 3864 2340 or [email protected] if you have concerns about the ethical conduct of the project;

• agree to participate in the project. Name

Signature

Date / /


Appendix B: List of Questions and Prompts for Interviews in Phase One.

Demographic information

Participants name:

Date of birth:

Highest level of education:

Country of Origin:

Topic 1: Participant’s definition of body image

Ask participants how they define body image.

Topic 2: Subjective assessment of their body image / body satisfaction

Ask how they would rate their body image / level of body satisfaction. Are they happy

with their bodies / appearance? How happy / unhappy?

Topic 3: Factors that influence their body image

Ask generally what factors they see as influencing their body image.

Ask what factors influence their physical appearance / weight satisfaction / body shape

satisfaction? (ie, make distinction between the various components of body image – link

to the components they mentioned in topic 1).

Prompts:

Media: television, magazines, advertising (billboards etc.)

Social support networks: friends, family, partner, children, siblings etc

Situations: e.g., tyring clothes on

Emotional states: e.g., when feeling sad about something else

Ask if there are times when these things don’t affect them. Or, are there times when they

affect them more.

Topic 4: Which factors are most influential in determining participant’s body image

Ask them to identify which of the previously mentioned factors are most influential in

affecting their body image. Identify top three.


Appendix C: Non-copyrighted questionnaires used in Phase Three.

Demographic form

1. Are you male or female (please circle)?

2. How old are you currently? ……………………………………………………

3. How tall are you? ………………………………………………………………

4. How much do you weigh? …………………………………………………….

5. Please indicate your highest and lowest adult weight:

Highest adult weight (excluding pregnancy)……………………………..

Lowest adult weight……………………………………………………….

6. What is the highest education that you have completed? ……………………..

7. What country were you born in? ………………………………………………

7a. If you weren’t born in Australia, how long have you been living here?

…………………………………………………………………………….

8. Which ethnic group would you most closely associate with? ………………………

9. Which best describes your marital status?

Single Married / de facto Dating

Divorced / Separated / Widowed Other (please specify) …………….

10. Have you ever been diagnosed with an eating disorder, or believed you had an eating

disorder? If yes, please give details on when this was, how old you were, the type of eating

disorder, treatment etc..

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………………………………


………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………..

11. Do you have any medical conditions that would impact on your eating or weight? If yes,

please provide a brief explanation.

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………………………………

……………………………………………………………

12. Do you currently engage in any form of physical exercise? If yes:

What type of exercise do you do? ……………………………………………….

How long, on average, do you spend exercising in a typical week? ……………..

13. How hungry are you currently? Please indicate from one to five, where one = not at all hungry

and five = extremely hungry:

…………………………………

14. Please indicate in hours in minutes how long it has been since you last ate a full meal.

…………………………………


Appendix D: Information and Consent forms used in Phase Three.

PARTICIPANT INFORMATION for QUT RESEARCH PROJECT

“Colour perception, health, and speed of information processing”

Research Team Contacts

Ms Kate Mulgrew Dr. Nathan Moss (07) 3138 4881 (07) 3138 4660

[email protected] [email protected]

Description

This project is being undertaken as part of PhD project for Kate Mulgrew. The purpose of this project is to examine the factors involved with the perception of colour and how this effects reaction times. We are also interested in how your current health affects this relationship. The research team requests your assistance to take part in this study. Participation

Your participation in this project is voluntary. If you do agree to participate, you can withdraw from participation at any time during the project without comment or penalty. Your decision to participate will in no way impact upon your current or future relationship with QUT (for example your grades).

Your participation will involve a once off, 45 minute session, at a time convenient to you. The first set of tasks looks at speed of information processing, where you will be asked to respond as quickly as possible to various stimuli, and your reaction time will be recorded. The second task involves completing some standardized questionnaires about your current state of health. All data will be completely confidential, and your name will not be attached to any of your data. As the data is non-identifiable, it should be noted that it will not be possible to withdraw your data once submitted. Expected benefits

While the project may not benefit you directly, it is expected that the results from this study will be able to help others, and help researchers identify important factors in understanding reaction time, colour perception, and health. Risks

There are no risks beyond normal day-to-day living associated with your participation in this project.

QUT provides for limited free counselling for research participants of QUT projects, who may experience some distress as a result of their participation in the research. Should you wish to access this service please contact the Clinic Receptionist of the QUT Psychology Clinic on (07) 3138 4578. Please indicate to the receptionist that you are a research participant. Confidentiality


All comments and responses are anonymous and will be treated confidentially. The names of individual persons are not required in any of the responses. Consent to Participate

We would like to ask you to sign a written consent form (enclosed) to confirm your agreement to participate.

Questions / further information about the project

Please contact the researcher team members named above to have any questions answered or if you require further information about the project. Concerns / complaints regarding the conduct of the project

QUT is committed to researcher integrity and the ethical conduct of research projects. However, if you do have any concerns or complaints about the ethical conduct of the project you may contact the QUT Research Ethics Officer on (07) 3138 2340 or [email protected]. The Research Ethics Officer is not connected with the research project and can facilitate a resolution to your concern in an impartial manner.


CONSENT FORM for QUT RESEARCH PROJECT

“Colour perception and speed of information processing”

Statement of consent

By signing below, you are indicating that you:

• have read and understood the information document regarding this project

• have had any questions answered to your satisfaction

• understand that if you have any additional questions you can contact the research team

• understand that you are free to withdraw at any time, without comment or penalty

• understand that you can contact the Research Ethics Officer on (07) 3138 2340 or [email protected] if you have concerns about the ethical conduct of the project

• agree to participate in the project

Name

Signature

Date / /


Appendix E: Overview of cluster analytical technique and sub-group formation.

Overview of Cluster Analysis

Cluster analysis is a statistical technique applied to multivariate data designed to

identify homogenous sub-groups (Aldenderfer & Blashfield, 1984). Conceptually, it is

similar to factor analysis in that it searches for underlying latent variables. However, in a

cluster analysis, this procedure is applied to variables rather than individual cases. The

outcome of a cluster analysis is set of homogenous groups.

There are many different variants of cluster analysis, and the exact type of

methodology employed depends on the nature of the data and questions being asked.

Only a brief discussion of cluster analysis is provided here, but for a full discussion of

cluster analytical techniques the interested reader is directed to Aldenderder and

Blashfield (1984), Hair, Black, Babin, Anderson, and Tatham (2006) or Lange, Iverson,

Senior, and Chelune (2002). Based on the recommendations of these sources, a

hierarchical cluster analysis using an average linkage method and squared Euclidian

distance measure was used on standardised scores. This selection is briefly explained

below.

Consideration of the type of cluster analytical technique to use revealed that a

hierarchical cluster was most appropriate. A hierarchical cluster analysis is the most

commonly used technique within psychological research, and is particularly useful for

exploratory data analysis (Aldenderder & Blashfield, 1984; Hair et al., 2006; Lange et al.,

2002). A non-hierarchical analysis may be used when there is existing theoretical advice

on what sub-groups may form. Given the exploratory nature of the current research, a

hierarchical cluster analysis was deemed more appropriate. A hierarchical cluster analysis


works by forming groups in successive steps. At the first stage, each individual forms

their own cluster, thus the number of clusters equals the number of participants. The next

step of a hierarchical cluster groups together the two most similar cases into a cluster.

This process continues until there are N-1 clusters available. The relative ease of

interpretation of this method (the user is not required to possess knowledge of advanced

multivariate statistics) is accompanied however, by two disadvantages. Despite a number

of recommendations from researchers (e.g., Aldenderder & Blashfield, 1984; Hair et al.,

2006; Lange et al., 2002), there is no clear method of determining how many meaningful

clusters exist. Second, only one pass of the data is made, meaning that an individual case

remains in the cluster it was first assigned to (some type of cluster analysis make multiple

passes of the data). However, despite these limitations, hierarchical cluster analysis has

been noted as a useful exploratory technique (Lange et al., 2002). Given the potential

problems with this method, Lange et al recommend a two-stage approach wherein a

hierarchical cluster is run to ascertain how many clusters exist in the data, followed by

another type of cluster analysis known as a k-means cluster, where the user is able to

specify the number of clusters to emerge, and goodness-of-fit measures can be examined.

A researcher must also decide upon an appropriate algorithm to assign the

individuals to their respective clusters. An average linkage method was deemed most

appropriate based on the recommendations of Lange et al. This method assigns new cases

based on the average distance from all individuals already in the cluster. The new case is

assigned to the cluster with the greatest similarity measure. The benefit of this method

over other clustering algorithms is that selection of cases into a cluster is not based on

extreme scores of any individual already within the cluster.


The selection of a proximity measure involves consideration of how the similarity

between cases and clusters is to be measured. A squared Euclidean distance measure was

used, which assesses how far points are away from each other. In addition, scores were

standardized to account for differences in magnitude between measures.

According to Aldenderder and Blashfield (1984) the use of external validation of

clusters serves to corroborate the cluster solution. If clusters differ significantly on other

key variables that were not originally used to generate the clusters, this adds support to

the distinction between sub-groups.

Based on these recommendations from the literature, a hierarchical cluster

analysis using an average linkage method and squared Euclidian distance measure was

used. It should be noted that the cluster analysis was used simply to provide some basic

empirical support for the types of sub-groups within the non-clinical sample. While it is

recognised that cluster analysis has a number of limitations, this procedure was deemed

more useful than simply comparing individuals on a median split, as past research has

typically done. Instead of defining groups by one variable, a cluster analysis allows for a

more comprehensive understanding, as multiple variables are taken into account.

Results of the Cluster Analysis for Women

Core body image concerns, as assessed by the BAQ were entered as six variables

into the cluster for the 143 women. Missing variables on the BAQ were replaced with the

mean of the respective sub-scale. In order to determine how many clusters were

meaningful, a number of indicators were examined. First, the dendrogram was examined

to determine where a ‘break’ in the clusters occur. This dendrogram shows the clustering

process from beginning to end. A large gap indicates that the distance between clusters


before merging is large (i.e., there is little similarity between them). Based on the

dendrogram, a two or three cluster solution appeared most applicable. The Icicle plot was

also examined to ensure that no clusters contained only a few individuals. Lange et al

recommend that any cluster containing fewer than 5% of the total cases should be

excluded. The two cluster solution split the sample into a large group (n = 90), and

slightly smaller group (n = 53). The three cluster solution further subdivided the latter

group, with a group with 36 cases, and a group with 17 cases. The four cluster solution

retained the groups with 36 cases and 17 cases, and then further subdivided the N = 90

group into a group with 62 cases and 28 cases. Thus, no groups contained less than 5% of

the sample.

The fusion coefficients in the agglomeration schedule were also examined. These

values reflect how similar the clusters are that have just been joined. A large increase in

the values indicates that two clusters have been merged that are relatively dissimilar (Hair

et al., 2006). Large increases were evident in the agglomeration coefficients between a 4

and a 3 cluster solution. Additionally, the fusion coefficients were plotted against the

number of clusters, which forms a graph similar to a scree plot found in a factor analysis.

As in factor analysis, the graph is examined for a ‘flattening’, which indicates that

clusters have been joined despite being dissimilar. Examination of the graph revealed a

flattening in the curve around the four cluster solution.

In order to examine the usefulness of a two, three or four cluster solution, scores

on key variables were examined for each cluster, within each cluster solution. A two

cluster solution appeared to split the sample into two groups that seemed to reflect a


‘dissatisfied’ and ‘non-dissatisfied’ group. Scores on each of the six sub-scales of the

BAQ are shown in Table A1 for the two cluster solution.

Table A.1

Means and Standard Deviations for BAQ Sub-Scales for a Two Cluster Solution in

Women.

Cluster 1 (n = 90) Cluster 2 (n = 53)

BAQ sub-scale M SD M SD

Attractiveness 14.54 2.08 12.24 2.39

Disparagement 12.98 2.72 20.72 3.31

Feeling fat 34.76 7.39 50.96 6.61

Salience 18.52 3.85 26.83 4.42

Lower body fatness 10.39 3.12 14.94 2.99

Strength / fitness 18.33 4.06 16.20 3.63

Compared to cluster one, cluster two contained women with lower feelings of

attractiveness, higher feelings of loathing of her body, and higher feelings of fatness and

lower body fatness. Additionally, these women in cluster two appeared to place more

importance on appearance in their lives, and report lower feelings of fitness. Thus, the

two cluster solution appears to identify those women with poorer body image attitudes.

The three cluster solution is shown in Table A2. This solution further divided the

previously identified cluster two into a further two sub-groups.


Table A.2

Means and Standard Deviations for BAQ Sub-Scales for the Three Cluster Solution in

Women

Cluster 1

(n = 90)

Cluster 2

(n = 36)

Cluster 3

(n = 17)

BAQ sub-scale M SD M SD M SD

Attractiveness 14.54 2.08 11.52 2.13 13.76 2.25

Disparagement 12.98 2.72 20.67 3.14 20.82 3.76

Feeling fat 34.76 7.39 49.72 6.28 53.58 6.71

Salience 18.52 3.85 25.02 3.93 30.64 2.62

Lower body fatness 10.39 3.12 14.30 2.77 16.29 3.07

Strength / fitness 18.33 4.06 14.80 3.11 19.17 2.83

The three cluster solution appeared to identify a smaller group of women with

more dysfunctional body image attitudes. The differences between the groups reflected a

dimensional, rather than categorical approach, wherein each group reported increasing

body image concerns. Cluster one shows the least symptomatic scores on all subscales,

while Cluster Two reports moderate levels of body image disturbance, while scores in

Cluster Three represent the most dysfunctional profile.

Descriptive information for the four cluster solution is shown in Table A3. This

solution further subdivides the largest, asymptomatic group seen in previous solutions.


Table A.3

Means and Standard Deviations for BAQ Sub-Scales for the Four Cluster Solution in

Women

Cluster 1

(n = 62)

Cluster 2

(n = 28)

Cluster 3

(n = 36)

Cluster 4

(n = 17)

BAQ sub-

scale

M SD M SD M SD M SD

Attractiveness 14.08 2.18 15.57 1.39 11.52 2.13 13.76 2.25

Disparagement 13.84 2.70 11.07 1.58 20.67 3.14 20.82 3.76

Feeling fat 35.65 7.18 32.79 7.59 49.72 6.28 53.58 6.71

Salience 19.14 3.56 17.14 4.17 25.02 3.93 30.64 2.62

Lower body

fatness

11.05 3.16 8.92 2.52 14.30 2.77 16.29 3.07

Strength /

fitness

16.41 3.09 22.60 2.31 14.80 3.11 19.17 2.83

Examination of the profiles within the four cluster solution revealed four distinct

groups. Cluster one and two show similar means on most of the BAQ subscales. These

groups are characterised by higher feelings of attractiveness, little loathing of one’s body,

low feelings of fatness and low importance of appearance in one’s life. Cluster one and

two differ however on Lower Body Fatness and Strength / Fitness, with Cluster two

showing higher feelings of strength and fitness and less feelings of lower body fatness.

Cluster three and four appear to reflect groups of women with highly dysfunctional body


image attitudes. In particular, the means in Cluster four are similar to, or more

dysfunctional, than the scores found in Anorexic samples (Ben-Tovim & Walker, 1992).

Based on the available evidence, the four cluster solution was deemed most

appropriate. The highly dysfunctional attitudes reported in cluster four appeared quite

distinct from the group of women with whom they were originally grouped with in the

two cluster solution. Within the three cluster solution, the scores of cluster four are

substantially higher than the other sub-groups, thus it appears important to consider this

group uniquely.

Based on these scores, cluster one and two appear to reflect a group with very

little body image concerns. The distinguishing features between cluster one and two are

the high reports of strength and focus on fitness (and perhaps corresponding lower body

fatness) within cluster two. Hence these clusters were named “normal” and “athletic”

respectively. Cluster three appears to reflect a group that experiences moderate level of

dissatisfaction, and were therefore labelled “dissatisfied”. Cluster four, having the most

dysfunctional profile closely representing scores of Eating Disordered samples, were

therefore labelled “symptomatic”.

One criticism of cluster analysis has been that the use of different cluster

techniques can result in alternate group formations (Aldenderder & Blashfield, 1984;

Hair et al., 2006; Lange et al., 2002). In an attempt to overcome this limitation, the cluster

analysis was re-run using a different algorithm. A Wards’ method was used, as some

researchers recommend the use of this algorithm (e.g., Francis, 2004). Using this Ward’s

method, a similar result emerged to the previously described cluster solution, supporting

the four cluster solution.


Results of the Cluster Analysis for Men

The measure used for the cluster analysis in females, the BAQ, was developed

specifically to encompass women’s body image concerns, thus it was not deemed useful

to categorise males. Instead, the MBSRQ was used. A full description of the MBSRQ

was given in the Stroop Method section. Briefly, the MBSRQ is one of the few

measurement tools available that adequately assess a wide variety of male body image

concerns. Additionally, this scale reports excellent psychometric properties, and

normative data is available. Six subscales of the MBSRQ were used in the cluster

analysis for males: the Appearance Evaluation and Orientation subscales, the Fitness

Evaluation and Orientation subscales, the Health Orientation subscale, and the

Overweight Preoccupation Subscale. These subscales were selected to cover feelings of

attractiveness, general health and fitness, importance placed on appearance and fitness,

and dietary restraint; key variables that emerged in the qualitative study of Phase One. As

cluster analysis requires a complete data set, missing data was replaced with the mean of

the subscale.

Core body image concerns, as measured by the six subscales of the MBSRQ, were

entered for the 54 men. A hierarchical cluster analysis using a Ward’s Method and

squared Euclidian distance measure was run on standardised scores. In order to determine

how many clusters were meaningful, a number of indicators were examined. Inspection

of the agglomeration schedule and the dendrogram revealed that a two or a three cluster

solution appeared most applicable, based on the change in the similarity scores between

the clusters. The Icicle plot was examined to ensure that no cluster contained fewer than


5% of the total cases. This started to occur with a four cluster solution, where only two

cases comprised a cluster.

In order to examine how meaningful the two and three cluster solutions were,

scores on the six subscales of the MBSRQ were examined. The two cluster solution

appeared to divide the sample into a larger group with better body image, and a smaller

group with poorer body image. Compared to cluster two, cluster one reported higher

satisfaction, but lower investment, with appearance; higher feelings of fitness and

importance placed on being fit; lower fat anxiety; and higher feelings of being healthy.

Scores on each of the MBSRQ subscales are shown below in Table A4.

Table A.4

Means and Standard Deviations for the MBSRQ Sub-Scales for a Two Cluster Solution in

Males

Cluster 1

(n = 35)

Cluster 2

(n = 19)

MBSRQ subscale M SD M SD

Appearance Evaluation 3.76 0.52 2.52 0.57

Appearance Orientation 3.03 0.66 3.40 0.64

Fitness Evaluation 4.17 0.56 3.47 0.78

Fitness Orientation 3.51 0.83 3.16 0.42

Overweight Preoccupation 1.66 0.67 2.46 0.80

Health Orientation 3.55 0.79 3.12 0.77


The means for the MBSRQ subscales are shown in Table A5 for the three cluster

solution. The three cluster solution kept the n = 19 group with poorer body image, but

further subdivided the larger group.

Table A.5

Means and Standard Deviations for the MBSRQ Sub-Scales for a Three Cluster Solution

in Males

Cluster 1

(n = 26)

Cluster 2

(n = 19)

Cluster 3

(n = 9)

MBSRQ subscale M SD M SD M SD

Appearance Evaluation 3.69 0.55 2.52 0.57 3.97 0.37

Appearance Orientation 2.85 0.51 3.40 0.64 3.54 0.78

Fitness Evaluation 4.06 0.55 3.47 0.78 4.48 0.50

Fitness Orientation 3.22 0.76 3.16 0.42 4.35 0.32

Overweight Preoccupation 1.34 0.33 2.46 0.80 2.58 0.53

Health Orientation 3.33 0.74 3.12 0.77 4.18 0.58

The three cluster solution appeared to identify a smaller group of men who could

be described as extremely driven by health and fitness. Compared to the n = 26 cluster

they were originally grouped with, this n = 9 cluster reported similar feelings of

attractiveness, but a much larger importance and investment placed on appearance. This

group also reported higher feelings of fitness, and placed more importance on regular

exercise. They report the highest levels of fat anxiety, and highest levels of being health


conscious. That is, this group appears to reflect men who are engaging in a health

lifestyle, and feel good because of it, but are also concerned about becoming overweight.

Given how distinct this small cluster was that emerged in the three cluster

solution, it was deemed useful to examine this group separately. To check the validity of

this cluster solution, the cluster analysis was re-run with an alternate clustering method

(between groups). The results of this cluster analysis revealed a similar solution, but the

group differentiation was not as clear compared to using the Ward’s method. Ultimately,

the purpose of a cluster analysis was to provide some empirical support for group

comparisons. Thus, the three cluster solution using the Ward’s method was used.

attention and memory bias for body image and health ... · body image and cognitive bias v abstract...

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