Masaryk University

Faculty of Arts

Department of Psychology

Field of Study: Psychology

Markéta Dohnalová

The Effects of Anxiety on Reading Efficiency An Eye-tracking Study

Master’s Thesis

Thesis supervisor: Mgr. Tatiana Malatincová, Ph.D.

Brno 2019

This master’s thesis was supported by the research infrastructure HUME Lab Experimental Humanities

Laboratory, Faculty of Arts, Masaryk University.

Declaration of Authorship

I hereby declare that this thesis is the result of my own original work and that it has not been submitted, in whole or in part, in any previous application for a degree. Due references have been made on all the used literature and resources.

In Brno, 30th April 2019

..................................................

Markéta Dohnalová

Acknowledgments

I would like to thank all my participants, especially those willing to spend over

two hours in the laboratory with me during my pilot studies.

My thanks also goes to Mgr. Čeněk Šašinka, PhD., for his goodwill and advice

concerning eye tracking; to Prof. Kenneth Holmqvist, for his consultation of my

design, his insightful input and the fastest existing course in the use of a high-speed

tower-mounted eye-tracker; and to Mgr. et Mgr. Eva Kundtová Klocová, PhD., for her

support and speedy replies to my many requests.

A special and heartfelt thanks goes to my friend Ing. Ondřej Takács, PhD., for

the incredible work he did in writing a software program that recognizes regressions

and perhaps more importantly for his support and generosity. To Bc. Jan Trtílek, for

his willingness to spend hours manually counting regressions and for his talent

in understanding what I mean.

A big thanks goes to my parents, for all the various ways they’ve supported me,

and to the many friends that took care of me, encouraged me, believed me and

consoled me in moments of crisis.

Last, but certainly not least, I would like to thank my supervisor Mgr. Tatiana

Malatincová, PhD. for her above standard guidance and support. I sincerely thank

her for all the e-mails she sent me at three a.m., all the consultations she squeezed

into her tight schedule, and all the priceless advice she so freely bestowed upon me.

I also thank her for encouraging me in all the moments that I felt like giving up.

Thank you for helping me carry this thesis through to the finish.

Abstract

In this thesis, the effects of anxiety on reading are examined within the framework of

the attentional control theory (ACT). The study is a 2 × 2 mixed experimental design.

High and low-anxious subjects read structurally difficult texts under ego-threatening

and neutral instructions. Eye tracking was used to measure their eye-movements

during reading and regressions were computed on the basis of single eye-movement

events using a tailored software program. There was no difference in readers’

comprehension, but high anxious individuals spent significantly more time on

regressions than low-anxious subjects in both conditions. An additional qualitative

exploration of regression scanpaths revealed significant variation in regressive reading

patterns and between subject differences indicated the use of various reading

strategies. Alternative explanations of the observed effect of anxiety on reading

regressions and implications for future research are discussed.

Key words: Test anxiety, ego threat, reading efficiency, reading regressions,

eye tracking, attentional control theory.

Contents

Abstract ............................................................................................................................... 5

Contents .............................................................................................................................. 5

Introduction ....................................................................................................................... 9

I. Theoretical background

1 Anxiety ......................................................................................................................... 11 Indicators and measures of anxiety ............................................................................ 12 1.1

1.1.1 Physiological/somatic indicators ................................................................................. 12 1.1.2 Behavioural indicators .................................................................................................. 13 1.1.3 Cognitive indicators ...................................................................................................... 13 1.1.4 Emotional/phenomenological indicators .................................................................... 14 State and trait anxiety ................................................................................................... 14 1.2

Test anxiety ..................................................................................................................... 14 1.3

1.3.1 Factors contributing to test anxiety ............................................................................. 16

2 Anxiety and cognition ............................................................................................. 18 A brief history of emotion and cognition ................................................................. 19 2.1

Anxiety from the view of cognitive psychology ....................................................... 21 2.2

Anxiety Disorders and Cognition ............................................................................... 21 2.3

Anxiety and Cognitive Processes ................................................................................ 23 2.4

2.4.1 Selective Attention ........................................................................................................ 24 2.4.2 Long-term memory ....................................................................................................... 24 2.4.3 Reasoning ...................................................................................................................... 25 2.4.4 Thought processes ......................................................................................................... 25 Worry ............................................................................................................................... 25 2.5

Attention bias ................................................................................................................. 27 2.6

3 Anxiety and cognitive performance ...................................................................... 30 Anxiety and academic performance ........................................................................... 30 3.1

Anxiety and sport performance ................................................................................... 31 3.2

Mediating and moderating factors of the relationship between anxiety and 3.3

performance .............................................................................................................................. 33 3.3.1 Subject Variables ........................................................................................................... 33 3.3.2 Task variables ................................................................................................................. 35

4 Explaining the relationship between anxiety and performance ...................... 37 The Development of the Attentional Control Theory (ACT) ............................... 38 4.1

4.1.1 Predecessors of ACT ..................................................................................................... 38 4.1.2 The Working Memory Construct and its Development .......................................... 39 4.1.3 Attentional Control Theory .......................................................................................... 43

5 Anxiety and reading ................................................................................................. 45 The reading process ....................................................................................................... 45 5.1

Reading and working memory, working memory capacity ................................... 47 5.2

The effect of anxiety on reading ................................................................................. 48 5.3

6 Eye-movements and eye tracking .......................................................................... 51 Short overview of the method and terminology ...................................................... 51 6.1

Studying eye-movements in reading .......................................................................... 53 6.2

6.2.1 Eye-movements in reading ........................................................................................... 53 6.2.2 Eye-mind hypothesis .................................................................................................... 54 6.2.3 Regressions .................................................................................................................... 56 6.2.4 Eye-tracking and regressions ...................................................................................... 58 6.2.5 Eye-movements in mindless reading ......................................................................... 59

II. The present study

1 Introduction to the present study .......................................................................... 61 Research questions and study aims ............................................................................ 63 1.1

Hypotheses ...................................................................................................................... 64 1.2

2 Method ........................................................................................................................ 65 Pilot studies and design modifications ...................................................................... 65 2.1

2.1.1 The first pilot study ...................................................................................................... 65 2.1.2 The second pilot study ................................................................................................. 66 Design of the current study ......................................................................................... 67 2.2

Participants ..................................................................................................................... 67 2.3

2.3.1 Targeted sample size and inclusion criteria ............................................................... 67 2.3.2 Recruitment ................................................................................................................... 68 2.3.3 Selection ......................................................................................................................... 68 Measures .......................................................................................................................... 70 2.4

2.4.1 Anxiety ............................................................................................................................ 70 2.4.2 Comprehension .............................................................................................................. 72 2.4.3 Reading time and eye-movement events .................................................................... 72 2.4.4 Regressions ..................................................................................................................... 73 2.4.5 Reading efficiency .......................................................................................................... 75 Stimuli .............................................................................................................................. 77 2.5

Setting and apparatus .................................................................................................... 77 2.6

Experiment procedure .................................................................................................. 78 2.7

2.7.1 Data processing and ethical aspects ............................................................................ 79 Defining and detecting regressions ............................................................................ 80 2.8

2.8.1 Specification for software detection ............................................................................ 81 2.8.2 Regression duration ...................................................................................................... 82 2.8.3 Manual data correction ................................................................................................ 83

3 Results ........................................................................................................................ 84 Statistical analysis ......................................................................................................... 84 3.1

Manipulation check ...................................................................................................... 84 3.2

3.2.1 The effect of anxiety on manipulation check scores ................................................ 86 3.2.2 Conclusion ...................................................................................................................... 87 Relations between reading variables ....................................................................................... 88 Descriptive statistics of reading measures ................................................................ 89 3.3

Testing hypotheses ........................................................................................................ 90 3.4

3.4.1 Statistic models and assumptions ............................................................................... 90 3.4.2 Comprehension .............................................................................................................. 91 3.4.3 Reading time .................................................................................................................. 91 3.4.4 Eye-movement events ................................................................................................... 92 3.4.5 Time spent on regressions ........................................................................................... 94 3.4.6 Reading efficiency ......................................................................................................... 96 Effects of task version ................................................................................................... 97 3.5

Summary of results ..................................................................................................... 100 3.6

4 Qualitative manual analysis of regressive scanpaths ...................................... 102

5 Discussion ................................................................................................................ 104 The effect of manipulation on low-anxious participants .................................... 105 5.1

Does an increase in time spent on regressions correspond to lower reading 5.2

efficiency? ................................................................................................................................ 106 A second look at regressions ...................................................................................... 107 5.3

Study limitations ......................................................................................................... 108 5.4

Implications for future research .............................................................................. 110 5.5

Conclusion ....................................................................................................................... 111

References ....................................................................................................................... 112

List of tables ................................................................................................................... 123

List of figures ................................................................................................................. 124

Appendices ...................................................................................................................... 125

9

Introduction Anxiety is known to have a negative influence on cognitive performance. It can

cause skilled and well-prepared individuals to fail in situations when they need

to perform their best. Reading is a complex cognitive task that is not exempt from the

undesirable effects of anxiety. Moreover, reading is a crucial skill that is closely tied to

education and codetermines success in the vast majority of scholarly tasks and tests.

Gaining a better understanding of how anxiety affects reading may have practical

implications for interventions aimed at helping highly test anxious individuals and

individuals suffering from anxiety disorders. It can also prove useful in helping

dyslectics by addressing accompanying emotional factors that make reading even more

difficult for them.

The main aim of this thesis is to examine how anxiety affects reading

in individuals with high and low trait anxiety under conditions of increased state

anxiety. The study specifically focuses on differences in reading efficiency and

in backward eye movements called regressions.

The thesis presents relevant research from the psychology of cognition and

emotion and describes the various ways in which anxiety has been found to affect

cognition. The theory central to the assumptions of the presented study is the

attentional control theory (Eysenck et al, 2007). This theory posits that anxiety

primarily affects the processing efficiency of cognitive tasks by reducing working

memory capacity. Task performance, on the other hand, often remains unaffected

by anxiety.

The empirical study presented is a modification of a previous experimental design

conducted by Calvo, Eysenck & Jiménez (1994b). Calvo and his colleagues examined

the effects of test anxiety on reading efficiency using a self-paced reading presentation

paradigm. The present study modifies the design by employing eye-tracking

technology to measure participants’ eye-movements online during reading. The

experimental design is complemented by a qualitative exploration of regressive

scanpaths. A final discussion considers possible interpretations of the observed data

and implications for future research.

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I. Theoretical background

11

The first theoretical part of this thesis is divided into 6 sections. It begins by

presenting anxiety and its manifestations (section 1), followed by an account of how

anxiety relates to cognition (section 2). Section 3 then focuses specifically on the

effects of anxiety on cognitive performance. The attentional control theory and its

assumptions are described in section 4. Section 5 is devoted to the reading process and

how it can be affected by anxiety. The last 6th section introduces the method of eye

tracking and presents an overview of eye movements in reading.

1 Anxiety Anxiety, in the most general sense, can be described as the emotional reaction that

occurs in response to a perceived unspecified threat. The term is often used as an

equivalent of fear, but there is an important recognised conceptual and physiological

difference between the two. The term anxiety is used to describe a “future-oriented,

long-acting response broadly focused on a diffuse threat,” unlike fear, which describes

“an appropriate, present-oriented, and short-lived response to a clearly identifiable

and specific threat.” (APA, 2015, p. 66)

Like other emotions, anxiety is a complex, heterogeneous phenomenon, burdened

with conceptual ambiguity and resulting in multiple and to various extents converging

or diverging definitions, theoretical constructs, and empirical paradigms. There are

three basic perspectives from which anxiety can be viewed, corresponding to three

separate response systems associated with anxiety - the behavioural, the physiological and

the verbal/cognitive (Lang, 1985). These response systems are interconnected but distinct

entities and can often diverge in their reaction to anxiety-inducing stimuli (see for

example Craske & Craig, 1984). Correspondingly, theories of anxiety may present

contradictory predictions depending on which system they consider primary and

within which response system they choose to assess anxiety.

An evolutionary perspective on anxiety may offer a more unified conceptual

framework for making sense of the concept. In the view of evolutionary psychology,

each emotion is a specific response pattern “shaped by natural selection to offer

selective advantages in certain situations” (Marks & Nesse, 1994, p. 248). The most

general definition of the purpose of anxiety is “to prepare the individual to detect and

deal with threats” (Bateson, Brilot, & Nettle, 2011, p. 707). According to Marks and

Nesse (1994), anxiety is aroused by “any cues that indicate a risk of loss” and different

subtypes of anxiety (e.g. general anxiety, social anxiety, etc.) evolved as specific

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responses to different kinds of resources being threatened (e.g. health, social status,

property, etc.).

Although this study’s main focus is on the link between test anxiety and cognitive

performance, given the complexity of the phenomenon of “anxiety” and how its

components are intertwined, one cannot study one aspect of it well without touching

upon the others. In the following chapter I will present a brief overview of the

principal indicators and measures of anxiety.

Indicators and measures of anxiety 1.1Given that anxiety, as mentioned above, is a complex state associated with

displays of cognitive, behavioural and physiological symptoms alike, there are many

possible indicators to its presence. A difficult question arises as to which symptoms

and to what degree must be present in order to establish a mental-physiological state

as truly anxious. Examples of questions that can arise are: Is increased heart rate and sweating in the presence of possibly threatening stimuli enough to conclude a person is experiencing anxiety? Can a person experience anxiety without being aware of it? Can a person be mistaken in saying they are feeling anxious? There is no consensus in the matter and the choice of

indicators is usually influenced by one’s preferred definition of anxiety and the

reactive system one chooses to focus on. Putting these problems aside for the moment,

a brief overview of the most commonly used indicators of anxiety follows.

1.1.1 Physiological/somatic indicators

Anxiety can manifest somatically in a number of ways. Common physiological

indicators of state anxiety associated with autonomic arousal include heart rate, blood

pressure, skin galvanic reaction, palmar sweat, and muscle activity (measured via

electromyography). Other methods of measurement include neuroimaging (e.g. EEG),

pupillometry (measuring changes in pupil diameter), or online measurement of ocular

events (e.g. blink duration and frequency, saccade speed and length, etc.).

The interpretation of somatic indicators is complicated by their ambiguity.

Different emotions can induce very similar physiological symptoms and the same

emotion may manifest itself differently depending on the individual and the situation.

Another complication is the absence of an already validated criterion for measuring

the examined emotion, which is necessary in order to correctly describe the link

between the emotion (anxiety) and somatic symptoms (e.g. blink frequency). See for

example Tichon, Wallis, Riek, & Mavin (2014).

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Initial attempts to describe an arousal pattern specific for anxiety (vs. fear

or anger, for example) had not met with success (see for example Martin, 1961),

although this may be due to the fact that anxiety is a not a homogenous phenomenon.

More recent research has shown that specific kinds of anxiety elicit distinct patterns

of brain activity (see Heller, Nitschke, Etienne, & Miller, 1997; Nitschke, Heller,

Palmieri, & Miller, 1999). The lack of a clear physiological pattern for anxiety make

self-report measures an indispensable part of anxiety research.

Individuals suffering from anxiety disorders can display a variety of other symptoms

caused by long-term anxiety and the somatization of anxiety, including but not limited

to insomnia, restlessness, muscle aches and headaches (see for example Gelenberg,

2000). Neuroimaging studies have shown anxiety disorders to be associated with

larger and more sensitive amygdalae (see for example Cannistraro & Rauch, 2003).

1.1.2 Behavioural indicators

Behavioural indicators of state anxiety include ocular events, facial expressions

and head movements. Other observable behaviours or specific reactions can

be interpreted as behavioural indicators of anxiety, either because they are recognized

as such by the scientific community or because they are chosen as indicators

of anxiety by a researcher within a specific experimental design. Performance on tasks

is also sometimes considered a behavioural indicator. Anxiety is a typically

behavioural phenomenon in that it can be taught and conditioned (see Martin, 1961),

and individuals high in trait anxiety tend to display specific behavioural patterns,

most typically those of avoidant behaviour.

1.1.3 Cognitive indicators

The cognitive component of anxiety consists mostly in anxious cognitive content (i.e. worry) and a change in attentional focus. Anxiety alters attention in a way that creates

an attention and interpretation bias, which consists mostly in an increased tendency

to evaluate stimuli as threatening and a difficulty shifting attention away from

potentially threatening stimuli (see section 2.6). Attentional bias is often assessed via

experimental manipulations, mostly by measuring reaction times after exposure

to threatening and non-threatening stimuli. The measurement of eye-movements has

proven useful in these designs. Anxious thoughts and content are usually determined

on the basis of self-report or implicit association tests. The cognitive aspects of anxiety

are described in greater detail below in Section 2. Anxiety and cognition.

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1.1.4 Emotional/phenomenological indicators

The subjectively experienced “feeling” of anxiety is of crucial importance, given

that self-report measures remain an indispensible tool for anxiety research.

A phenomenological view of anxiety can take us back to the common, intuitive

understanding of the term and also its linguistic history. Anxiety can be understood as

the unpleasant “narrowness” of being, or a “dark and distressing feeling of

expectation” (Joseph Lévy-Valensi, 1948, as cited in Crocq, 2015, p. 321). In the

17th century, anxiety was understood as “a state of agitation or depression with feelings

of distress in the praecordial region.” (Lazarus, & Averill, 1972, p. 245). The term

“anxiety” was historically often used to refer to distress linked to existential

and abstract fears (e.g. anxiety of fate, death, emptiness, guilt, etc.). Although

the definition of the term has since developed and shifted, anxiety remains

an indisputably unpleasant emotion, usually accompanied by worrying thoughts

and the presence of foreboding anticipation.

State and trait anxiety 1.2The distinction between state and trait anxiety is a common one and the two

terms today are generally accepted and used. Spielberger (1966), later to become the

leading author of the State-Trait Anxiety Inventory (STAI; Spielberger, Gorsuch,

Lushene, Vagg, & Jacobs, 1983), defines the two terms as follows: State anxiety is a “transitory state or condition of the organism that varies in intensity and

fluctuates over time” while trait anxiety refers to “individual differences in the extent to

which people are characterized by anxiety states and by prominent defenses against

such states.” (p. 12). The intensity of state anxiety experienced is then the consequence

of a combination of an individual's tendency to experience anxiety (i.e. trait anxiety)

and the strength of the stressor inducing it (i.e. the degree of threat), or, in other

words, the situational factor (see e.g. Eysenck, 1992).

Test anxiety 1.3Test anxiety can be defined as “…the proneness to worry about one’s own

performance (e.g., expectations of failure), and one’s own aptitude (e.g., self-

deprecatory thoughts) under evaluative or test conditions” (Calvo, Eysenck, Ramos &

Jimenéz, 1994, pp. 99-100).

Test anxiety can be considered a subtype of performance anxiety. Beilock,

Schaeeffer and Rozek (2017, p. 156) define performance anxiety as “fear and

15

apprehension connected to completion of a specific task (e.g., a test) or even

engagement with a specific domain (e.g., math).” Test anxiety is performance anxiety

experienced in the specific situation of being tested.

The construct of test anxiety can also be classified as a type of achievement

emotion. Pekrun (2006, 2017) described a variety of achievement emotions according

to whether they are (1) negative or positive and (2) activating or deactivating. Anxiety

is considered a negative activating emotion, whereas an example of a deactivating

positive emotion according to Pekrun would be relief (see Pekrun, 2017, p. 252).

The concept of test anxiety underwent significant development throughout the

years. Early on, it was studied as a single latent trait. In 1952, Mandler and Sarason

published a one-dimensional questionnaire called the Test Anxiety Questionnaire.

The items used in this questionnaire were chosen based on experiments studying the

affects of anxiety on learning. The criterion validity of the measure was assessed using

comparisons with MAT 1 , SAT 2 scores and predicted and actual grade average.

Individuals who tested high in test anxiety achieved significantly lower MAT and

SAT and predicted grade average scores than individuals low in anxiety. The scores

were also compared in groups of distinct social classes. The analysis confirmed that

test anxiety scores are higher for students who were on a scholarship and those whose

fathers had a middle class occupation (Mandler & Sarason, 1952).

In 1967, Liebert and Morris distinguished two main factors of test anxiety:

Worry and Emotionality. The distinction is still accepted by the most researchers in the

field today. The worry component describes cognitive content concerning one’s

performance, the risk of failure and comparing oneself with others. Emotionality describes the feelings of nervousness, tension and accompanying physiological

symptoms and arousal. Studies have shown that the worry component

is a significantly stronger predictor of performance impairment than the emotionality

component (see also section 3.1 Anxiety and academic performance). The distinction of

worry and emotionality became the basis of the Test Anxiety Inventory, published by

Spielberger in 1980. The questionnaire belongs among popular methods and has

presented extensive psychometric evidence of its reliability (see Zeidner & Matthews,

2003).

In 1983, Sarason published the Reactions to Tests questionnaire, which

described test anxiety on four separate dimensions. These dimensions were Worry,

1 Mathematical Aptitude Test. 2 Scholastic Aptitude Test.

16

Bodily Arousal, Tension and Test-irrelevant Thinking. The four sub-scales were chosen on the

basis of a factor analysis of 91 items taken from the Test Anxiety Scale (1978), a later

revision his first Test Anxiety Questionnaire (1952). The scales were compared

between each other according to how they were associated with intellectual

performance and cognitive interference. The main aim of this questionnaire was to

provide a tool that would better differentiate nuances in anxiety that most affect

cognitive performance.

Later on, models of test anxiety started to pay more attention to situational

factors and to describe the mechanisms underlying the emotion of test anxiety as a

dynamic process. Spielberger and Vagg (1995) introduced a transactional process

model of test anxiety. Their model presents test anxiety as a continuous process

determined by a dynamic interaction of personality and situation. Personality

influences an individual’s tendency to interpret the test situation in a certain way. The

resulting level of test anxiety is then a combination of this personality tendency and

the specific testing situation.

This transactional process model hints at the link between trait anxiety and

test anxiety. Individuals high in trait anxiety have a tendency to view a test situation

as more threatening, or to interpret an ambiguous situation more often as a test

situation, than individuals low in trait anxiety do. This is why trait-anxious

individuals are likely to experience a higher level of state anxiety in the given

situation. Correspondingly, individuals who score high in trait anxiety tend to score

high also in test anxiety measures. (See Sapp, 1999, for a description of the historical

evolution of the test-anxiety construct.)

1.3.1 Factors contributing to test anxiety

Appraisal and attribution

An individual’s evaluation of a situation and of his or her own capacity for

coping with it is known as appraisal (see also sections 2.1 to 2.3). The role appraisal

plays in the case of test anxiety has to do with a subject’s perceived control over their

performance. Weiner (1985) describes three dimensions of causal ascriptions

(appraisals) that play an important role in determining emotions associated with

success and failure. On the first dimension of locus, the appraisal assesses whether the

cause of achieved performance is internal (disposition, ability, etc.) or external (luck,

task specifics, characteristics of the evaluator, etc.). The second dimension Weiner

describes is the controllability of these causes. An internal appraisal of failure can be one

17

subjectively controllable (effort, motivation, amount of time invested in the task, etc.) or

one subjectively uncontrollable (e.g. ability, disposition). The final dimension is the

stability of attributed causes. Effort can be perceived both as a stable trait (e.g. laziness)

and as an unstable, context-dependent and willfully selected behavior. The third

dimension of stability has an important impact on the achievement goals individuals set

for themselves in future tasks. According to Weiner, all three causal attribution

dimensions have a direct influence on the achievement emotions one experiences.

Failure expectancy and achievement value, the control-value theory

Achievement emotions however are not influenced only by causal attributions

of the achieved result, but also by the subjective value of this achievement. Pekrun

(2006; 2017) describes this connection in the control-value theory of achievement

emotions. Pekrun’s theory conjoins the approaches of appraisal and expectancy-value

theories of emotion.

Expectancy-value theories explain achievement motivation as the result of an

individual’s assumptions about failure and success that can be described as functional

equations. In the equation, achievement motivation equals to the function of the

expectancy of success or failure and the value of this success or failure. (For a historical

overview of expectancy-value theories in achievement motivation research, see

Wigfield & Cambria, 2010.)

Pekrun uses this model to describe specific achievement emotions as

expectancy-value equations. Within his model, anxiety is a prospective (vs.

retrospective) failure (vs. success) emotion that can be described using the following

equation:

Anxiety = f [ EF × 1 – EF × VF],

where EF is the total expectancy of failure and VF is the total value of failure

(Pekrun, 2006, p. 321). According to this equation, the higher the expectancy of

failure in a test and the greater the value or subjective significance of this failure is,

the more test anxiety will an individual experience.

The control-value theory (Pekrun, 2006) then combines the expectancy-value

model with Weiner’s dimensions of causal attributions described above. Various

combinations lead to the experience of distinct achievement motivations. To

demonstrate on an example, expecting failure on a subjectively important test can

instigate both anxiety and hopelessness. Anxiety will be experienced when the cause

of the expected failure is attributed to at least partially controllable factors.

18

Hopelessness on the other hands will occur when the causes of failure are seen as

uncontrollable.

Social factors

A number of social factors have been found to influence the development

and/or maintenance of the tendency to experience performance anxiety. One of the

factors very significant for test anxiety is social evaluation and the associated fear of

being negatively judged. Dickerson, Gruenewald, & Kemeny (2004) conducted a series

of studies examining the effect of “social self” threats on physiological symptoms

associated with shame. Acute threats to the social self were found to increase cortisol

levels and proinflamamatory cytokine activity. Social evaluation is a powerful stressor

and plays an important role in test anxiety dynamics.

Social pressure can also take the form of negative stereotypes. These

stereotypes not only undermine an individual’s self-confidence, but can also lead to a

fear of confirming the negative stereotype if the individual performs poorly.

Stereotype threat, similarly to anxiety, can lead to impaired performance by loading

working memory (Schmader & Johns, 2003).

Performance anxiety can also be transmitted from one person to another.

Influential adults who partake in a child’s education progress have a significant

influence on their tendency to experience performance anxiety. Parents or teachers

with higher levels of a specific performance anxiety (e.g. math) can increase the

likelihood of their children also developing the same anxiety (see Beilock, Gunderson,

Ramirez, & Levine, 2010; Maloney, Ramirez, Gunderson, Levine, & Beilock, 2015).

See Beilock, Schaeffer, & Rozek, 2017, for an overview on performance anxiety.

2 Anxiety and cognition To comprehend how anxiety can affect the reading process, it is important to gain

an understanding of the broader effects of anxiety on cognition in general. The

relationship between anxiety and cognition is not a simple or straightforward one, nor

is there consensus about the key concepts and terms used to describe it (be it

“anxiety”, “cognition,” or “emotion”). Great variety can be found in research and

literature. Authors focus on different aspects of the problem, continue in different

theoretical traditions and adopt different terms to describe the same constructs, or

apply the same terms to different constructs, respectively. Yet despite these

divergences, they are in fact attempting to describe the same general but complex

phenomenon.

19

The aim of this chapter is not to decide between competing concepts and theories,

but rather to provide a broader understanding of the issue at hand. By presenting an

overview of the main theoretical issues concerning anxiety and cognition, I wish to

aid orientation in the terminology and create a wider theoretical framework within

which I can then introduce key concepts of the present study, described in the

subsequent chapters.

A brief history of emotion and cognition 2.1In 1962 Schachter and Singer conducted a now famous experiment regarding

the role of physiological arousal in emotional experience. They used epinephrine or

placebo injections in order to induce physiological arousal in their subjects. Subjects

also received one of several versions of (non-)information about what the effect of the

“Suproxin” they had allegedly received was, along with presentations of different cues

about what they may be experiencing. The study was said to demonstrate that the

same physiological arousal could lead to the experience of significantly different

emotions. The impact of the findings on the concept of emotion can be described by

the authors’ proposition that “emotional states may be considered a function of a state

of physiological arousal and of a cognition appropriate to this state of arousal.”

(Schachter, & Singer, 1962, p. 398) The authors also introduced the term “cognitive

label”, by which an individual interprets a state of arousal they are experiencing based

on cognitions and explanations available to them.

Although theories emphasizing cognitive aspects of emotions were by no means

absent in literature before, Schachter and Singer’s study had a large im on the

scientific community and opened the gateway for the development of more influential

cognitive theories of emotions.

The focal concept of the discussion became appraisal, or “the cognitive evaluation

of the nature and significance of a phenomenon or event.” (APA dict., 2015, p. 70) It

was clear that appraisal influences an individual’s experience of emotions, but how

this interaction works and whether it is necessary for the experience of emotion

remained unanswered.

A debate between Zajonc and Lazarus arose around the question of which comes

first, emotion or cognition. Zajonc (1980, 1984) argued for the primacy of emotion –

we first process stimuli automatically (assess its affective tone – positive or negative) and

only then cognitive processing follows. Lazarus (1982, 1984) argued for the primacy

20

of cognition, or, in his own words, that “thought is a necessary condition of emotion”

(1982, p. 1019). According to Lazarus emotion by definition requires cognitive appraisal.

To date, the debate is viewed by most as obsolete. First of all, Zajonc speaks

of affect whereas Lazarus mentions emotion. These terms however are not viewed

as equivalent. Affect is a term describing a more elementary unit of feeling, associated

primarily with the dimensions of pleasant-unpleasant and aroused-calm. Emotion on the

other hand is a “complex reaction pattern, involving experiential, behavioral, and

physiological elements…” (APA, 2015, p. 362). Second, it seems that Zajonc

and Lazarus’s approaches differ not only in their definition of emotion, but also in

their definition of cognition (Zajonc does not accept the term “cognitive” being used

for automatic processes). Finally, the empirical data collected throughout various

research studies since seem to imply that both affective and cognitive information by

itself (ambiguity of these terms aside) can initiate the process of emotion depending

on the situation and individual (see for example Lai, Hagoort, & Casasanto, 2012). See

Power & Dangleish, 2015, for an overview of the Zajonc-Lazarus debate, its impact

and attempts to resolve it.

The bond between emotion and cognition is more complex and interwined

than pioneer theorists like Lazarus and Zajonc anticipated. As Power and Dalgleish

(2015) summarize it: “the ‘emotion’ and the ‘cognition’ are integral and inseparable

parts of each other, and though it is useful to use different names for different aspects

of the generation of emotion, the parts are no more separable than are waves from the

water on which they occur” (p. 61).

Causality is now considered to exist in both directions between the “cognitive”

and the “affective”. Both conscious and unconscious appraisals, controlled and

automatic cognitive processes influence the experience of emotions. Interactions

between “cognition” and “emotion” occur not only in series, but also in parallel and

are present at all stages of emotional experience. Today, research focuses on describing

the specific mechanisms of the interaction of emotion and cognition and more

attention is devoted to studying its neurological foundations (see Power & Dalgleish,

2015).

The specific interactions of emotion and cognition will not be presented here,

but are exemplified in the following sections dedicated to the specific relationship

of anxiety and cognition.

21

Anxiety from the view of cognitive psychology 2.2

How do emotion and cognition interact specifically in the case of anxiety?

Lazarus and Averill (1972) presented a “cognitive” definition of anxiety as “an emotion

based on the appraisal of threat, an appraisal which entails symbolic, anticipatory, and uncertain elements” (p. 246, emphasis added). Lazarus and Averill go on to

describe the three basic characteristics of anxiety appraisals, which they view

as closely connected and interdependent.

Anxiety appraisals are symbolic in that they are often related to abstract ideas and

concepts (e.g. loss of identity, meaninglessness, and the like). They are anticipatory in that they are not confrontational reactions to recognizable present dangers

(as opposed to fear), but “expectation” reactions to yet unspecified threats. These

threats may be unspecified due to the fact that they pertain to the future

(i.e. foreboding), or because of a failure to comprehend the present. Finally, anxiety

appraisals are very strongly linked to uncertainty, be it the ambiguousness of a threat,

or motivational uncertainty as to what is to be done about it.

In view of these characteristics of anxiety and also the fact that defence

mechanisms (or defensive reappraisals) are very prominent with anxiety, the authors

go as far as to say that “anxiety involves a threat to the integrity of cognitive systems" themselves (p. 278, emphasis added). Lazarus and Averill view anxiety as an emotion

that has a specific and unique bond with cognition.

Anxiety Disorders and Cognition 2.3A clinical view of anxiety, focused mostly on specific anxiety disorders, can shed

light on some of the important aspects of the anxiety-cognition relationship. The

cognitivist approach to anxiety disorders has been influenced not only by general

cognitive theories of emotion, but largely also by the tradition of cognitive therapy,

especially the work of Albert Ellis and Aaron T. Beck (see Ehring, 2014, for an

overview). On the cognitive level, anxiety disorders are associated with altered cognitive appraisals, cognitive schemas and associated preferred coping strategies.

Pathological or excessive anxiety can be described as the consequence

of exaggerated threat appraisal relative to the “not-so-threatening” situation. All stages of

appraisal (primary appraisal, secondary appraisal, and reappraisal) are affected

in individuals suffering from anxiety disorders. Anxious individuals overestimate

harm and its consequences (primary appraisal) along with their own inability to cope

with it (secondary appraisal). They also suffer from biased safety estimates and tend

22

to show deficits in the employment of cognitive reappraisal. Cognitive reappraisal

is the updating of previously made appraisals or the reinterpretation of a situation or

threat. Reappraisal often serves to correct previous appraisal to correspond with newly

received information or to benefit the individual’s well-being and is a crucial

component of cognitive emotion regulation. See Beck, Emery, & Greenberg, 1985 and

Ehring, 2004, for reviews.

The tendency to make exaggerated threat appraisals may be the consequence of

maladaptive cognitive schemas. Beck and his colleagues (1985) view cognitive schemas as

“...‘cognitive structures’ that orient the individual to a situation and help him to select

relevant details from the environment and to recall relevant data” (p. 54). The

schemas of individuals with anxiety disorders tend to be more negative, rigid and less

flexible than those of healthy controls (see Ehring, 2014). The content of the

maladaptive schemas is disorder specific and the schematic “rules” can be described

by verbal statements such as: “Any strange situation should be regarded as dangerous.”, “It is always best to assume the worst.”, “They will attack at a sign of weakness.”, and other convictions

(Beck et al., 1985, p. 63).

Research suggests that these rigid cognitive schemas are maintained by several

mechanisms. One of these may be an underlying abnormality in lower-level or

automatic cognitive processes, for example an attention bias towards disorder-specific

threat (see more about attention bias in section 2.6).

Another important maintenance factor is behaviour, specifically avoidance and

safety-seeking behaviours, which are typical for individuals diagnosed with anxiety

disorders. Avoidance behaviour is behaviour aiming to avoid the source of perceived

threat. Socially anxious individuals for example try to stay away from situations such

as public speeches, eating in front of others, and the like. For individuals whose

anxieties are less specific though, it may be difficult or impossible to actively avoid

threats. Patients with GAD, whose fears tend to be rather general and future-oriented,

may instead resort to safety-seeking behaviours. These can include increased caution,

writing down lists, obsessive planning, or preparing for worst-case scenarios (see

Beesdo-Baum et al., 2012).

Finally, individuals with anxiety disorders tend to employ dysfunctional cognitive coping strategies, mainly in the forms of thought suppression and repetitive negative thinking (see Ehring, 2014). Thought suppression is the deliberate suppression of unwanted

negative thoughts (Purdon, 1999). In 1987, Wegner, Schneider and Carter described

thought suppression as leading to a paradoxical increase of the very same automatic

thoughts on the basis of their experiment with the famous “white bear”. Although

23

studies replicating this effect are inconsistent, thought suppression is still considered

an important factor in the development and maintenance of anxiety disorders (see

Purdon, 1999). Individuals with anxiety disorders also tend to concern themselves

with repeating thoughts about past, current or possible future problems, a behaviour

named repetitive negative thinking (see Ehring & Watkins, 2008). This phenomenon

is most clearly visible in the case of GAD’s excessive worry (see Ehring, 2014).

Cognitive therapy of anxiety disorders consists mostly in leading the client

to the realization and positive restructuring of maladaptive cognitive schemas and

supporting changes through tools of behavioral therapy, such as behavioral

experiments, aimed mostly at reducing maladaptive avoidance tendencies. A relatively

novel therapeutic method, focused on lower-level processes, is cognitive bias modification (CBM), consisting of exercises aimed at reducing either attentional bias or interpretation bias. CBM protocols often consist in computer exercises based on experimental

cognitive tasks such as the dot-probe task (see section 2.6). Individuals are,

for example, instructed to react as fast they can to a probe following the short

presentation of two faces – one neutral or happy and the other representing a negative

emotion. In CBM, the tasks are modified so that positive/neutral stimuli are more

often located in the position of the probe. Systematic repetition of trials is thought

to promote an implicitly acquired bias towards the positive/neutral stimuli (See Haim,

2010). The efficacy of this treatment is still disputed, with conflicting meta-analyses

finding non-existing to moderate overall effects (compare e.g. Hallion, & Ruscio, 2011;

Cristea, Kok, & Cuijpers, 2015; and Liu, Li, Han, & Liu, 2017).

Another relatively new approach in the therapy of anxiety disorders is that of

mindfulness based therapy. Its most notable representative is Acceptance and

Commitment Therapy (or ACT; Hayes, Strosahl & Wilson, 1999). This approach has

so far gained more consistent empirical support than CBM (see Hofman, Sawyer,

Witt, & Oh, 2010; Khoury et al., 2013), but can be viewed as only a variation of

cognitive therapy that emphasizes a specific phase of emotion regulation (i.e. the

acceptance of an emotion), one that is given less attention in traditional cognitive-

behavioral therapy (see Hofmann, & Asmundson, 2008).

Anxiety and Cognitive Processes 2.4As we’ve seen in the previous section, anxiety disorders are associated with

modified cognitive appraisals, schemas, limited coping strategies and also typical

behaviours such as avoidance, safety-seeking or thought suppression. In this chapter,

the focus shifts to the more specific effects anxiety can have on basic cognitive

24

processes. This topic was reviewed in detail by Mansell (2004) who describes the

effects of anxiety on 4 categories of cognitive processes: (1) selective attention, (2)

memory, (3) reasoning and (4) thought processes.

2.4.1 Selective Attention

Research has shown that anxiety affects selective attention in two distinct

ways. The first one is hypervigilance towards possible threats, that is, an attentional bias

towards threat. The second is, seemingly paradoxically, the avoidance of threatening stimuli. A possible evolutionary explanation of this double-effect is that anxiety can lead either

to an orienting mode (scanning for possible threat) or a defence mode (searching for

sources of safety, shift to flight-fright reaction) of the brain (see Mogg & Bradley,

1998). Whether the resulting response will be the former of the latter depends on

many factors, including trait anxiety, the intensity and type of the threat. Individuals

with anxiety disorders often often display a bias towards, or away from, disorder-

specific content (Mogg & Bradley, 1998). Studies have shown that threat-vigilant

processing biases can actually be induced. Participants who have been taught to be

vigilant to threat also report experiencing affective changes in the form of increased

state anxiety (Mathews and MacLeod, 2002).

Mogg, & Bradley (1998) argue that vulnerability to anxiety, characteristic

for individuals with anxiety disorders, has to do primarily with a lower threshold for

perceiving a stimulus as threatening. An attentional bias toward threatening stimuli

in itself is common to everyone including non-anxious individuals.

Since attention has an important role in the present study, a separate chapter

is dedicated to anxiety and attentional bias below in section 2.6.

2.4.2 Long-term memory

Anxiety can lead to preferred memorisation of threatening information,

or schema-appropriate information, alternatively. Although the generality of this

claim is disputed, studies have shown the effect of anxiety on memory for several

specific anxiety disorders. In a study by Radomsky & Rachman (1999), individuals

with OCD were better at remembering the location of touched (“contaminated”)

objects than participants in the control group. Mansell & Clark (1999) conducted

a study in which socially anxious individuals recalled less positive words from a

public feedback they received about themselves than low socially anxious participants.

25

Anxiety can also evoke “intrusive memories”, experienced as involuntary and

highly vivid imagery associated with traumatic or aversive autobiographical events.

Intrusive memories sometimes create the impression that they are happening in real

time (e.g. flashbacks in PTSD). The frequency of intrusive images has shown to be

greater in people with dissociative tendencies (see Clark, 1999, for a review).

2.4.3 Reasoning

Anxious individuals display biases in the interpretation of events, be it the

already mentioned overestimation of the probability and expected severity of

undesirable events taking place (see Mogg & Bradley, 1998) or the tendency to

interpret ambiguous stimuli (e.g. ambiguous sentences) as threatening (Eysenck,

Mogg, May, Richards, & Mathews, 1991). It has also been shown that the tendency to

make threatening interpretations can be trained in healthy individuals, leading to

their experiencing feelings of distress (Mathews & MacLeod, 2002).

Individuals with anxiety disorders may also display a tendency to use emotional reasoning, which is a particular heuristic of interpreting one’s (often somatic) feelings of

anxiety as evidence of real danger (see Mansell, 2004). Socially anxious individuals for

example may conclude that their public presentation was flawed on the basis of

feeling their hands shake (Mansell & Clark, 1999). See Clark & Wells (1995) for a

literature review of social anxiety and cognitive biases.

2.4.4 Thought processes

Anxious individuals tend to excessively think or ruminate about possible

future dangers in the form of worry, but also to suppress threatening thoughts

(see Ehring, 2004). This ambivalence seems analogous to the attentional

hypervigilance towards and avoidance away from threatening stimuli.

Worry is a crucial aspect of anxiety and it is sometimes viewed as

the equivalent of the cognitive component of anxiety (see Eysenck, Berkum 1992).

Given its importance, it deserves more attention and is further presented in the

following section.

Worry 2.5Worry is closely related to anxiety. The similarity of the two concepts is obvious

in the APA’s (2015) definition of worry as “a state of mental distress or agitation due

to concern about an impending or anticipated event, threat, or danger” (p. 1163). A

26

more informative and commonly used definition of anxiety was offered by Borkovec,

Robinson, Pruzinsky, & DePree in 1983 (p. 10):

Worry is a chain of thoughts and images, negatively affect-laden and relatively uncontrollable; it represents an attempt to engage in mental problem-solving on an issue whose outcome is uncertain but contains the possibility of one or more negative outcomes; consequently, worry relates closely to the fear process.

Although worry appears both in the form of thought and images, the form

of thoughts with a verbal component are predominant. This may distinguish worry

for example from obsessive thoughts experienced in OCD, which more often appear

as intrusive images (see Turner, Beidel, & Stanley, 1992). Worry is usually triggered

by an external or internal event, but can also appear spontaneously. Negative mood,

which correlates positively with worry, serves both as an onset and maintenance factor

of worry (see Borkovec et al., 1983).

Worry can become pathological due to increased intensity, frequency, and/or

limited controllability (experienced as automatic negative thoughts). Worry is a key

symptom of several anxiety disorders, most notably of generalized anxiety disorder, for

which it is an important diagnostic criterion (see DSM-V, p. 222). Individuals

suffering from GAD tend to experience excessive, uncontrollable worry with respect to

several commonly occurring domains (e.g. life circumstances, work, health, etc.;

see Turk & Mennin, 2011; Turner, Beidel, & Stanley, 1992).

In Borkovec et al.’s studies (1983) self-labelled worriers, as opposed to non-

worriers, scored higher in anxiety, depression and hostility, had greater difficulty

focusing on a monotonous attention task, and reported a higher frequency of negative

thought intrusions during the tasks.

Eysenck and Berkum (1992) identified two main components of worry: social

evaluation and physical threat. Trait anxiety correlated strongly with the component

of social evaluation, but not with worrying associated to physical threat. See also

Eysenck, 1984, for more about the relation of anxiety and worry.

Worry often leads to impaired cognitive performance, especially when present as

a component of test anxiety. Many studies have consistently shown worry to be more

closely related to cognitive and academic performance than the emotionality

component of test anxiety, the effect of worry being often sufficient for explaining the

overall effect of anxiety on cognitive performance (Deffenbacher, 1977, 1978; Zeidner,

1990; see Hembree, 1988 for a meta-analysis and Zeidner, 1988, for a review of the

27

differential aspects of the worry and emotionality components of test anxiety). Some of

these effects will be discussed further in the following chapters.

Attention bias 2.6Extensive literature has been devoted to the topic of attentional bias towards

threat in anxiety. Although the bias itself is a generally acknowledged and recognized

phenomenon, the specific mechanisms underlying it remain disputed. The study of

reactions to threatening stimuli is beyond the scope this thesis. However, there is a

close link between attentional bias research and the research of the effects of anxiety

on cognitive performance in tasks with neutral, non-threatening stimuli, and the two

approaches converge in their attempt to specify the exact cognitive processes

responsible for the observed differences between anxious and non-anxious individuals.

There is also an overlap between the paradigms used for exploring attentional bias

and the study of eye-movements in anxiety (see below).

Several theoretical models attempting to clarify the mechanisms underlying

attentional bias have been presented, and disagreements continue concerning: (1) the

specific processes and phases that constitute the bias (e.g. registration of stimulus,

activation of a “threat mode”, cognitive evaluation and re-evaluation, etc.); (2) the

order of these processes in time; (3) their localisation in the brain, including the

pathways that connect them; (4) the differentiation of automatic vs. controlled

processes, with some authors rejecting the idea of automatic attentional bias altogether

(see Wells & Matthews, 1994); (5) whether and which of the processes are

unconscious, preconscious, or conscious; and (6) how the identified component

processes interact and how they inform one another through feedback loops. Some

of the influential theoretical models include Beck & Clark’s (1997) cognitive model,

Öhman’s (1996) feature detection model, Mogg & Bradley’s (1998) cognitive-

motivational model and Bar-Haim et al.’s (2007) multidimensional model. Eysenck

and colleagues’ (2007) attentional control theory is also sometimes included, although

it is not primarily concerned with the processing of threat.

In their review of attentional bias mechanisms, Cisler and Koster (2010) present

the following experimental paradigms as the most commonly used for testing

conflicting predictions of the above mentioned theoretical models: (1) the modified

stroop task, (2) the dot-probe task, (3) the visual search task, (4) the spatial cueing task

and (5) the antisaccade task.

28

(1) In the modified stroop task, attentional bias in anxious individuals manifests as

a heightened response to reporting the colour of threatening words. (2) In the dot-probe task, two stimuli are briefly displayed on a screen at a small interval which

is sometimes below the limit of conscious processing. Following the display of these

stimuli, a probe appears in the location of one of them. Participants press a button

to indicate which stimulus was replaced (top or bottom). High-anxious individuals

demonstrate differences in response times for threatening and non-threatening

stimuli.

(3) The visual search task consists in participants searching for a target stimuli

amongst distractor stimuli. The task goal may for example to find a happy face amidst

angry faces or vice versa. (4) In the spatial cueing task participants focus on a point

between two rectangles. Before the presentation of a target in one of the rectangles,

an attention drawing cue is presented. The cue can either be valid, drawing attention

to the targeted rectangle, or invalid, drawing attention way from it. This cue can be

a threatening or neutral stimulus. The participants’ goal is to press a key as quickly as

they can indicating in which of the two rectangles the target appeared. Reaction times

between participants and conditions are compared.

(5) In the antisaccade task participants focus on a central fixation point on the

screen. They are then asked to look in the opposite direction of where a threatening

or neutral stimulus suddenly appears. The direction and duration of saccades are

compared. See Derakshan & Eysenck (2009), Derakshan, Ansari, Hansar, Shoker, &

Eysenck (2009) for more about the antisaccade task.

Cisler and Koster (2010) describe three separate effects of threat-related

attentional bias that can be recognized in the empirical data that has been obtained

using the presented paradigms. The first is a delayed disengagement from threat. This effect

has been demonstrated invariably in spatial cueing task designs and almost invariably

in the other paradigms. The second effect is that of facilitated attention towards threat. The

evidence for this effect is rather mixed, but the variance between studies seems to be

explained by the moderators of threat intensity and stimulus duration. The effect is

mostly present for sufficiently threatening stimuli displayed only for a short period

(see for example Koster, Crombez, Verschuere, Van Damme, & Wiersema, 2006). The

third and final effect described by Cisler and Koster is attentional avoidance. This,

contrary to facilitated attention towards threat, has been observed only for long

stimulus durations. Eye-tracking studies have observed attentional threat avoidance in

the form of eye-movements moving away from threatening stimuli. Pflugshaupt et al.

29

(2005) for example found that individuals suffering from arachnophobia first glance

very quickly at pictures of spiders and then look away from them.

Cisler and Koster (2010) also describe the main mediating mechanisms

of attentional bias effects in their integrative review. An important mediating

mechanism of attentional bias is attentional control. Difficulty disengaging from threat

was found to be more frequent in dispositionally anxious individuals with poor

attentional control. Anxious individuals with poor attentional control displayed

difficulty in disengaging also from stimuli presented for longer durations, contrary to

high anxious individuals without an attentional control deficit, who only displayed

this difficulty when the duration of stimulus presentation was short (Derryberry

& Reed, 2002). This and other findings about the mediating function of attentional

control in attentional bias are supportive of the main thesis of attentional control

theory (Eysenck et al., 2007), described further below. The theory posits that anxiety

impairs cognition primarily via the impairment of attentional control.

A second factor found to mediate the effect of anxiety on attentional bias is an

individual’s emotion regulation strategy. Gross (1998, p. 275) describes emotion regulation

as “the processes by which individuals influence which emotions they have, when

they have them, and how they experience and express these emotions.” Gross (1998,

Gross & Thompson, 2007) then presents 5 “families” of emotion regulation processes:

situation selection, situation modification, attentional deployment, cognitive change

and response modulation. The regulation process of attentional deployment is

associated with the willful allocation of attention. Individuals may relocate their

attention away from threatening, unpleasant or painful stimuli towards neutral

or pleasant stimuli as a method of emotion regulation (see Gross, 1998). Willful

attention allocation stands in contrast with the ability to control attention described

above and can be manipulated via the modification of task goals (see Cisler & Koster,

2010).

Studies support the existence of attentional bias in both automatic and

controlled stages of cognitive processing. Automatic attentional bias, which consists

mainly in facilitated attention towards threat, is an amygdala-centered process,

associated with enhanced amygdala activity. Top-down attentional bias, on the other

hand, manifests itself as various attentional control deficits, primarily a difficulty

disengaging from threatening stimuli, and is associated with reduced activity of the

prefrontal cortex (see Derakshan, & Eysenck, 2009; Cisler, & Koster, 2010 for

a review).

30

3 Anxiety and cognitive performance The intuitive assumption concerning anxiety and performance is that anxiety has

a detrimental effect on performance. It is a part of our general experience – forgetting

the speech we prepared when speaking in front of people, not remembering words on

an important interview, not being able to focus on a math test, are just a few

examples. When we are nervous and anxious, we tend to underachieve.

But when investigated empirically, the effect of anxiety on performance proved

very early on to be a complex one. Highly anxious individuals sometimes achieve

equivalent or even higher scores than their non-anxious peers. Stress sometimes

impairs performance, sometimes doesn’t. Several studies found surprising effects

of task type. Moldowsky and Moldowsky (1952) observed an effect of failure stress on

digit span items, but not vocabulary items. In a study by Aborn (1953), stress impaired

the recall of incidental learning, but not of explicitly instructed material. Some

studies even found stress to improve performance, for example in the case of simple

arithmetic problems (Steisel & Cohen, 1951) or the recall of words that were missed in

a previous memory task (Spence, 1957). For a review of these early findings, see

Martin, 1961.

Before I address this complex relationship between anxiety and cognitive

performance, I will briefly present findings from studies examining the effects of

anxiety on academic performance and performance in sports.

Anxiety and academic performance 3.1

The influence of anxiety on academic performance is hardly a direct one, and a

number of small-scale studies conducted throughout the years have found conflicting

results. Seipp (1991) conducted a meta-analysis of 126 studies examining the

relationship between anxiety and academic performance and found an overall negative

effect of r̅w = –0.21. A more modest estimate was found by Chappell et al. (2005), who

conducted a study investigating correlations between test anxiety and grade point

average in 5 414 undergraduate and graduate students. There was a significant

negative relationship between test anxiety and grade point average that corresponded

to approximately one third of a letter grade difference between high and low-anxious

individuals (i.e. the difference between B and B+).

31

Seipp’s (1991) analysis showed that the relationship between anxiety and

academic performance is closer for the subcomponent of worry than for that

of anxiety. In fact, the relationship between worry and academic performance was

large enough to explain the overall effect of anxiety. Anxiety type was another

significant moderator of the relation between anxiety and performance in Seipp’s

meta-analysis. The mean effect size for test anxiety was significantly larger than the

mean effect size of general anxiety (Δr̅ = 0.07). The effect sizes for state and trait

anxiety, however, were comparable.

A third moderator identified by Seipp (1991) was the method by which anxiety

was measured. The effect of anxiety on academic performance was larger in studies

where anxiety measures were more cognitive (as opposed to somatic, emotional, etc.)

and for studies where anxiety was measured after the task or performance.

Test anxiety, as could be expected, is reliably associated with poorer academic

performance (see Hembree, 1988, for a complex meta-analysis). In a study by Hopko,

Crittendon, & Wilson (2005), test anxiety was the only among a variety of measures

related to lower PIQ3 on standardized intelligence tests. The effect was significantly

larger for certain subtests compared to others. The authors interpreted this finding as

indicating that test anxiety may be linked to a more specific and less general cognitive

impairment.

Etiological studies show that individuals suffering from anxiety disorders also

struggle in the academic environment and are much more likely to drop out of or fail

to graduate from high school (e.g. Kessler, Foster, Saunders & Stang, 1995;

Van Ameringen, Mancini, & Farvolden, 2003). Subclinical social anxiety

(or “shyness”) in children (Crozier & Hostettler, 2003) and negative emotions in

students of middle school (Gumora & Arsenio, 2002) have also been found to have

a negative impact on academic performance.

Anxiety and sport performance 3.2

Sport psychology is another area of research interested in the possible negative

effects of anxiety on performance. The influence of anxiety on sport performance was

studied in many contexts, including golf (e.g. Clark, 2002), table tennis (e.g. Williams,

Vickers, & Rodrigues, 2002), rally driving (e.g. Wilson, Smith, Chattington, Ford, &

Marple-Horvat), climbing (e.g. Nieuwenhuys, Pijpers, Oudejans, & Bakker, 2008),

3 Performance intelligence quotient

32

karate (e.g. Williams & Elliot, 1999) and many more (see Wilson, 2008, for a review

of the anxiety-performance relationship in sport and an account of its mechanisms).

Sport psychology makes use of specific design paradigms, including simulations

with built-in eye tracking to measure gaze behaviour, and paradigms comparing

expert and novice players. These designs extend beyond sport and are also used to

study the behaviour of professionals in other areas, such as aviation (e.g. see Allsop &

Gray, 2014).

Sport psychology also coined the term competitive (competition) anxiety. Competitive

anxiety is defined as “a specific negative emotional response to competitive stressors”.

These stressors can be understood as “environmental demands (i.e., stimuli) associated

primarily and directly with competitive performance” (Mellalieu, Hanton, & Fletcher,

2009, p. 4).

The results yielded in research on anxiety and sport performance are analogous

to those in the domain of cognitive tasks – even in sport, anxiety can lead either to

detriments in performance or to enhanced performance, the latter brought about by

an anxiety-induced increase in motivation and arousal.

Conscious processing or self-focus theories, unique to the domain of sport psychology,

explain the detrimental effects of pressure-induced anxiety on sport performance as

the consequence of a shift from more efficient automated processes to less efficient

self-conscious and effortful step-by-step control of movements. Attentional control

theory (Eysenck, Derakshan, Santos, & Calvo, 2007) and its predecessor, the

processing efficiency theory (Eysenck & Calvo, 1992) also have a strong standing point

in sport psychology as models explaining the detrimental effect of anxiety on sport

performance. The results of studies testing the conflicting predictions of the conscious-

processing theories and the processing efficiency theory have primarily supported the

predictions of the latter (see Mullen & Hardy, 2000; Wilson, Smith, Chatington,

Ford, & Marple-Horvat, 2007; Wilson, Smith, & Holmes, 2007). Although the

conclusions of the studies were not unanimous, their results suggest that the

mechanism by which anxiety affects performance in sport is akin to the mechanism

underlying the relationship of anxiety and cognitive performance. See Wilson, 2008,

for a review of the attentional control theory in sport psychology.

33

Mediating and moderating factors of the 3.3relationship between anxiety and performance As mentioned above, the relationship between anxiety and cognitive

performance is a complex one. To answer the question of which factors explain some

of the variation in research findings, in the following text I am providing a list and a

brief description of the most important factors affecting the relationship between

anxiety and performance that have been identified through research.

3.3.1 Subject Variables

a. Type and intensity of anxiety, and level of arousal

Test anxiety, as already mentioned, is a stronger predictor of performance

decrements than general anxiety or anxiety manifested through somatic symptoms.

Some specific anxiety disorders can also lead to disorder-specific impairments (e.g.

social anxiety leads to longer reaction times in face-in-crowd task, see Mogg, Bradley,

& Philippot, 2004).

In general, the inverted U shape of the arousal-performance function defined

by the Yerkes-Dodson law (Yerkes, & Dodson, 1908) also applies for the relationship

of anxiety and performance. Moderate levels of arousal are optimal for cognitive

performance, while levels that are either too low or too high both result in poorer

performance (also see Stennett, 1957). Arousal is linked to motivation, which is

discussed next.

b. Motivation, effort, and compensation

Motivation tends to be increased in individuals high in trait anxiety, which

can explain why they often draw level with or sometimes even exceed their non-

anxious peers. Being afraid of failure, anxious individuals do their best to achieve

good scores, resulting in increased effort. This increased effort may be a method of

compensation for the impairment of cognition due to anxiety. Eysenck and colleagues

(e.g. Eysenck & Calvo, 1992; Calvo, Eysenck, Ramos, & Jimenez, 1994) have studied

various compensatory strategies highly anxious individuals use to sustain their level of

performance. These compensatory strategies use up a greater amount of cognitive

resources, so although performance effectiveness is preserved, processing efficiency

(see further below) is impaired.

The extreme ends of motivation, just like arousal, are detrimental to

performance. “Trying too hard” in situations of subjective importance (see Hardy,

34

Beattie, & Woodman, 2007), due either to high threat and/or high reward, leads to

performance impairment. On the other end of the scale, when anxious individuals are

not sufficiently motivated in a task, the effects of anxiety may affect not only their

processing efficiency, but also their performance effectiveness. In a study by

Broadbent, Cooper, Fitzgerald, & Parkes (1982), high trait anxiety correlated with

scores in the Cognitive Failures Questionnaire, which assesses cognitive slips and

errors in everyday life. This may be due to the fact that in such everyday situations

anxious individuals are not pressed to perform well and thus no longer resort to the

use of compensatory strategies, revealing the underlying cognitive impairment

associated with anxiety.

For a more detailed analysis of the effects of motivation on the anxiety-

performance relationship, see reviews by Eysenck, & Derakshan, 2011, and Berggren,

& Derakshan, 2013.

c. Self-confidence

Self-confidence, or “belief in success”, is considered an important factor

affecting the relationship between anxiety and performance, especially in the area of

sport psychology. Self-confidence constitutes one of the three scales of the Competitive State Anxiety Inventory-2 (CSAI-2; Martens, Vealey, Burton, Bump, & Smith, 1990), alongside

scales of cognitive and somatic responses, and represents the individual’s belief of how

capable they are of meeting the challenges of the task they face.

Within the framework of Hardy and Parfit’s (1990) cusp catastrophe model of

anxiety, Hardy, Beatty & Woodman (2007) expect a rapid drop in performance due to

the withdrawal of effort in the moment when the level of task requirements and

worry reach the point where the individual ceases to see themselves as capable of

completing the task. They argue for the inclusion of self-confidence in anxiety-

performance theories, noting that self-confidence is another moderating factor of the

effect of cognitive and somatic anxiety on performance (see Hardy, Woodman,

& Carrington, 2004).

The majority of studies conducted within the framework of the attentional

control theory do not use self-confidence as a measure in their designs. Eysenck (1982)

does however mention that a perceived low “subjective probability of performing the

task well” can cause a major drop in motivation despite a very high incentive (p. 88,

p. 109). The omission of the measure may also be due to the fact that the effect of self-

confidence on performance is mediated through effort and/or motivation.

35

3.3.2 Task variables

a. Task difficulty (demands) and importance (relevance)

When examining the effect of anxiety on performance, it is important to

consider the cognitive difficulty of the task examined. If no difference is found between

the cognitive performance of anxious and non-anxious individuals, it may be because

the level of task demands is too moderate (see Berggren & Derakshan, 2013) – high

enough to motivate and prompt anxious individuals to resort to the use of

compensatory strategies, but not sufficiently high to impair performance and manifest

an underlying anxiety-related deficit.

The interaction of cognitive task demands and perceived threat can lead to

rather surprising effects. A series of studies examined the effects of various stressors

on the performance of deep-sea divers (Baddeley, 1966d; Baddeley & Flemming, 1967;

Davis et al., 1972). Stressful vs. non-stressful conditions affected the divers’

performance in a manual dexterity task. However, this effect was absent when the

task was a cognitively demanding verbal task. The authors interpreted this effect as

the consequence of the limited attention capacity being fully used up by the

demanding task, leaving no room for worrying thoughts (about for example the

possible dangers of being 100 meters below the water surface). It therefore seems that

a reverse effect of cognitive performance on anxiety can be found, in that focusing on

a non-threatening cognitively demanding task can alleviate worrisome or intrusive

thoughts (see Baddeley, 2007). It is important to note, however, that the threat in this

particular set of studies was task-irrelevant and the authors were examining the effects

of state anxiety unassociated with elevated trait or test anxiety.

Task importance has to do with subjective relevance of the task and the degree to

which the tested subject perceives the test situation as threatening (i.e. level of ego-

threat). An increase in task importance leads to a parallel increase in the subject’s

level of state anxiety and worry (see Hardy, Beattie, & Woodman, 2007).

b. Temporal factors

Some of the effects of anxiety on cognition are time-dependent. For example,

the results of a study by Bar-Haim, Kerem, Lamy, & Zakay (2010) indicate that

anxious individuals (compared to non-anxious) subjectively experience time as moving

more slowly when exposed to threatening stimuli for only 2 seconds, but the group

differences disappear for longer exposure durations (4 and 8 seconds). This would

suggest that threat-related biases occur in early stages of cognitive processing.

36

Compensatory processing in anxious individuals has also been shown to

be affected by temporal factors. In a study by Ansari & Derakshan (2011), the length

of an inter-stimulus interval determined whether or not compensatory effort was

registered through CNV4 response. Differences were found only at longer inter-

stimulus intervals, suggesting that anxious participants “recruit more resources” when

given the benefit of a preparatory period.

These observations imply that various details of a study design that remain

unreported (e.g. waiting time, between-task intervals, the length of exposure to

a stimulus, etc.) might influence whether or not a difference between anxious and

non-anxious individuals is found, especially in the case attention bias paradigms

(described above).

c. Specific task characteristics

Besides temporal factors, there are other specific task characteristics that

moderate the effect of anxiety on performance. For example, anxious individuals seem

to struggle more with tasks involving more frequent and resembling distractor

responses, while their performance can sometimes be enhanced when the correct

response is very dominant (see Martin, 1961).

The type of cognitive processes required to successfully complete the task can

also be influential. The effect of anxiety on cognitive performance or efficiency can

be pronounced in tasks that require inhibition or shifting (see for example Eysenck,

Derkashan, Santos, & Calvo, 2007). Anxiety’s effect can be different for tasks assessing

automatic opposed to controlled processes. The specific verbal and motor components

of a task design will also lead to differences in the effect of anxiety on performance.

The effect of task specifics is one of the reasons why researchers in the field

have recently started to prefer simpler and more straightforward task paradigms over

complex tasks, although this comes with a certain cost to ecological validity.

d. Threatening vs. non-threatening stimuli

Threatening and neutral stimuli are processed differently, and this difference

is amplified in anxious individuals. Trait anxiety is associated with the already

mentioned attentional bias towards threat and an interpretation bias, which lead to

a preference of threatening interpretations of ambiguous stimuli.

4 Contingent negative variation.

37

Interestingly, studies have shown that emotionally biased processing can be

reduced by increasing an individual’s cognitive load, one study even finding a direct

reduction of the amygdala response to negatively affective images (Van Dillen &

Koole, 2009; see Berggren & Derakshan, 2013 for overview).

e. Working memory load

In dual-task paradigms, subjects are presented with a peripheral secondary task

while working on the primary task. Dual tasks create high demands on working

memory and lead more often to performance impairment in high-anxious individuals

than in their low-anxious peers (see for example Eysenck et al., 2007). The greatest

influence was found in tasks that create additional load on the central executive

(attentional load), compared to tasks that increase load either on the phonological loop

or the visuospatial sketchpad (see Eysenck, Payne, & Derakshan, 2005).

For a more detailed analysis of working memory load and anxiety

in performance tasks see Berggren, & Derakshan, 2013, and Moran, 2016 for

an extensive review and meta-analysis.

4 Explaining the relationship between anxiety and performance

The aim of this chapter is to present Eysenck and colleagues’ (2007) attentional

control theory (ACT), which constitutes the main theoretical framework of this study.

There are many advantages to the ACT.

For one, it is a theory that examines the anxiety-performance relationship in the

normal population and focuses on the effects of anxiety on cognitive tasks that do not

contain threatening stimuli. It is also a well-established theory that is continuously

developing in dialogue with empirical findings and offers valuable explanations of the

complex relation of anxiety and cognition. Last but not least, ACT offers a useful

paradigm for anxiety-performance research, mostly thanks to its distinction

of processing efficiency and performance effectiveness.

38

The Development of the Attentional Control Theory 4.1(ACT)

4.1.1 Predecessors of ACT

One of the early influential theories seeking to explain how anxiety leads to

cognitive impairment was Sarason’s (1988) cognitive interference theory (CIT). The main

hypothesis of CIT is that anxiety induces worry and self-preoccupation, emerging

in the form of task-irrelevant thoughts. These task-irrelevant thoughts interfere with

task-relevant cognitions and thus limit the amount of attention available for task-

related processes, leading to performance impairment. Since the worry component of

test anxiety has shown to best predict impairment of cognitive performance, Sarason’s

theory was a sensible attempt to explain observed phenomena. This early theory,

however, lacked precision and failed to explain (1) why anxiety (or more specifically

its worry component) does not always lead to performance impairment, and (2) which

specific cognitive mechanisms are involved in the interference process.

These points were addressed by Eysenck and Calvo’s (1992) processing efficiency theory (PET). Eysenck and Calvo introduced an important distinction using the terms

processing efficiency and performance effectiveness to explain inconsistencies in existing data

on anxiety and performance. While anxious individuals may achieve the same scores

as their non-anxious peers (equal performance effectiveness), they have to exert

greater effort or use up more of their resources in order to do so (lower processing

efficiency). According to PET, the reason why anxious individuals often meet (and

sometimes surpass) the standards of their non-anxious peers, is that anxiety, while

reducing efficiency, also enhances motivation. Increased motivation to succeed

(or avoid failure, respectively) prompts anxious individuals to exert greater effort and

to utilize various auxiliary compensation strategies. In Calvo et al.’s (1994b) study for

example, anxious subjects used subvocalization (articulatory rehearsal), made more

reading regressions and spend more time on the task than non-anxious subjects, but

achieved comparable performance in the tasks.

The second shortcoming of the previous cognitive interference theory was

addressed by PET by postulating that worry impairs processing efficiency by limiting

the capacity of working memory (WM). Designs using concurrent tasks that increase

load on WM led to a greater impairment in anxious individuals, supporting this

assumption. The effect was found only when the concurrent tasks placed demands on

the central executive, but not when placing load on the phonological loop or the

visuospatial sketchpad only. This lead the authors of PET to put forward a more

39

specific explanation, which posits that anxiety specifically disrupts the central

executive component of working memory.

Although this specification was an improvement over the initial vagueness

of CIT, it was still limited in that it did not specify which components of the central

executive in particular were influenced by anxiety. To specify these mechanisms

became the main purpose of the subsequently developed attentional control theory,

described further below. Parallel to the development of anxiety-performance theories,

the theoretical model of working memory also evolved with the same intention of

specifying working memory subcomponents and their functions. Since the working

memory model is a key component of the ACT’s theoretical framework, the following

section is dedicated to its development.

4.1.2 The Working Memory Construct and its Development

Working memory is an evolving conceptual construct that is generally acknowledged

and frequently applied in research. Despite the frequency of its use, it is still a

constract that lacks precision and many important questions remain unanswered. It

faces several conceptual difficulties, including the distinction of the working memory

construct from the construct of short term memory (STM) and the clarification of the

exact nature of WM and its components.

Miyake and Shah (1999, p. 450) presented an extensive definition of working

memory that unified contemporary views on the construct:

Working memory is those mechanisms or processes that are involved in the

control, regulation, and active maintenance of task-relevant information in the

service of complex cognition, including novel as well as familiar, skilled tasks.

It consists of a set of processes and mechanisms and is not a fixed "place" or

"box" in the cognitive architecture. It is not a completely unitary system in the

sense that it involves multiple representational codes and/or different

subsystems. Its capacity limits reflect multiple factors and may even be an

emergent property of the multiple processes and mechanisms involved.

Working memory is closely linked to LTM (long term memory), and its

contents consist primarily of currently activated LTM representations, but can

also extend to LTM memory representations that are closely linked to activated

retrieval cues and, hence, can be quickly reactivated (p. 450, note in

parentheses added).

40

Baddeley’s model of working memory

The first and most influential model of working memory to this day is that

of Baddeley (1986). It developed from the work of Baddeley and Hitch (1974), which

were the first authors to attempt a description of short-term memory as more than just

a short-term memory store. What led them to introduce the new construct was the

observation that limiting the capacity of STM leads to the impairment of cognitive

functioning. STM simply seemed to function like a multimodal working system.

The first challenge the authors faced was defining the model’s basic

components. Originally, three subsystems were described: the central executive and two

modality-based subsidiary storage systems, the phonological loop and the visuospatial sketchpad. Later a fourth component was added, labelled the episodic buffer (Baddeley,

2000).

The phonological loop is said to hold speech-based (perhaps purely acoustic)

information in a temporary store. It seems to depend on a temporary memory trace

that fades if not rehearsed or refreshed in some way and its rehearsal usually takes

the form of overt or covert vocalization. Similarly, the visuospatial sketchpad serves

as a temporary store of visual and spatial information and the rehearsal of the

information it holds, though less examined than the case of the phonological loop,

possibly involves eye-movements (see Baddeley, 2007).

The central executive (CE) is currently considered the most important component

of working memory, although in the first versions of the model it was only viewed

as a “limited pool” of general processing capacity. Later it was said to fill the function

of the attentional control of action, most probably associated with executive functions

of the frontal lobes (see Baddeley, 2007). Baddeley adopted Norman and Shallice’s

(1986) model of attentional control, which differentiates between behavioural control

at two levels - (1) the automatic, functioning on the basis of habits and schemas, and

(2) the intentional, which Norman & Shallice named the Supervisory Attentional System and which has the ability to override automatic and habitual responses.

Baddeley (2007) describes that since the role of the central executive

was redefined to serve mostly as an attentional and not a storage component,

the episodic buffer, taking up CE’s initial storage function, was added to the model.

The episodic buffer is understood as a temporary “work space” accessible through

conscious awareness that functions as the interface between separate WM subsystems

and long-term memory, as well as the interface between different codes of memory

(visual, verbal, episodic, etc.). Using various binding mechanisms, the episodic buffer

41

integrates information into a limited number of episodes. The limited capacity of the

episodic buffer is also used to explain individual differences in WM span.

Components of the Central Executive

The central executive has gradually attracted more and more attention from

researchers over the years and the question of what separate functions

or subcomponents constitute attentional control is now widely discussed.

Baddeley, in his more recent models of WM, identified four candidates

for separate executive component processes of the CE: (1) The general limited capacity

to focus attention. A relatively underspecified function and one that is difficult to study

due to the intervening variables of the automatisation of attentional processes and

varying individual attentional strategies. (2) The capacity to switch attention between tasks.

It seems that articulatory suppression impairs this capacity, suggesting that the

phonological loop may also serve an executive function. Auxiliary verbal control (“self-

talk”) has been observed in both verbal and non-verbal tasks. (3) The capacity to divide attention between concurrent tasks. This ability was found to be unusually well

preserved (in comparison with other functions) in the normal elderly population, but

impaired in patients with Alzheimer’s disease, supporting the assumption that is

an independent function (see for example Baddeley, Bressi, Della Sala, Logie, &

Spinnler, 1991). And finally, (4) the capacity to provide a link between WM and LTM.

See Baddeley, 2007, for a narrative review.

Perhaps with the exception of the fourth and final mentioned capacity of the

central executive, all of the proposed functions appear to be dependent on the frontal

lobes. Thus, functions of the CE and executive frontal lobe functions seem

to converge. This proposition was supported by studies that have found a link between

the dysexecutive syndrome and impaired dual task performance (see Baddeley, Della

Sala, Papagno, & Spinnler, 1997).

Miyake and colleagues (2000) examined executive functions in “frontal lobe

tasks” emloying a variety of common tasks used for the assessment executive

functions. These included, for example, the Wisconsin Card Sorting Test, the Tower

of Hanoi and others. Miyake and his colleagues used a latent variable analysis

to assess individual differences and evaluate the independence and separability of the

proposed executive functions. Miyake and his colleagues also link these functions to

the central executive of Baddeley’s WM system. On the basis of their study, the

authors presented three candidates for separate and relatively independent executive

functions. The first was the shifting function, which is responsible for shifting between

42

multiple tasks, operations or mental sets. The second was the updating function, which

serves to update and monitor working memory representations. Finally, the third

identified function was inhibition. This function enables the deliberate inhibition

of dominant, automatic or prepotent responses in situations that require it (Miyake et

al., 2000). Eysenck and his colleagues (2007) adopted this division of CE functions in

their attentional control theory.

Working Memory Span and Anxiety

Individual differences in working memory are usually described in terms

of differences in its capacity. Daneman & Carpenter (1980) proposed that working

memory involves a combination of the storage and processing of information and

designed a task combining these two functions, where the maximum number

of successfully recalled final words of sentences previously read out loud constituted

a measure now known as working memory span. Working memory span, to no surprise,

is closely linked to language comprehension, but complex span tasks also predict

a number of other abilities, including, for example, emotion regulation or resistance

to distraction (see Moran, 2016).

Moran (2016) conducted a meta-analysis of 177 correlational studies examining

the relationship between self-reported measures of anxiety and measures of

WM capacity and found anxiety to be significantly associated with lower scores

of WM capacity (g = .334), the effect being consistent for all types of working memory

span tasks (simple, complex and dynamic; see Moran, 2016). Experimental designs (in

which anxiety was manipulated) reviewed in his meta-analysis yielded mixed results,

which may possibly be due to design setbacks (it is often unclear whether or not

anxiety was successfully manipulated and/or lasted long enough).

There are several hypotheses explaining this rather robust link between

anxiety and WMC. Alongside various interference theories (e.g. Sarason’s and

Eysenck’s), there are other authors defending a different causal mechanism, consisting

in the competition of different functions localized in certain brain areas. These

theories are mostly a reflection of neurological findings, such as that the same areas

activated when experiencing anxiety are also associated with vigilance and spatial WM

processes (see for example Manoach et al., 2004). Since, from an evolutionary

perspective, anxiety is necessary for survival, anxiety is given priority and “wins” the

competition, leading to the impairment of the “defeated” function (see Shackman et

al., 2006). Other theories propose a domain-specific effect, i.e. distinct interactions

between different anxiety dimensions (worry vs. arousal) and different WM modalities

43

(phonological vs. spatial; see for example Robinson, Vytal, Cromwell, & Grillon,

2013). Worry has been found to lead to detriments on phonological performance only

for sufficiently demanding tasks, whereas arousal disrupts spatial working memory

across all levels of task difficulty (see Moran, 2016).

The link between anxiety and WMC has also been described in the opposite

direction. Working memory capacity may influence an individual’s tendency to

experience anxiety. A lower working memory capacity is associated with greater

distractor interference, a reduced ability to regulate one’s emotions, and a higher

frequency of intrusive thoughts. It may be that a limited WMC constitutes a risk

factor for the development of anxiety symptoms and disorders (see Moran, 2016).

Although there are still questions that need to be answered concerning the

relationship between anxiety and working memory, empirical research up till now

generally supports Eysenck et al.’s (2007) prediction that anxiety affects performance

via the working memory system.

4.1.3 Attentional Control Theory

The attentional control theory (Eysenck, Derakshan, Santos, & Calvo, 2007)

builds on the previous processing efficiency theory by proposing specific mechanisms

by which anxiety impairs attentional control pertaining to the central executive of the

WM system.

The main assumptions of the theory can be divided into three main premises.

1. Anxiety impairs the central executive

ACT posits that anxiety leads to impaired performance by disrupting the

functioning of the central executive component of working memory. Studies

comparing tasks involving the CE and other WM components, along with the fact

that the negative effect of anxiety on processing efficiency grows with increasing

demands on the CE, support this claim (see Eysenck et al, 2007, for a review).

2. Anxiety impairs attentional control by increasing the influence of the stimulus-driven attentional system

Eysenck and his colleagues posit that anxiety impairs central executive

functioning by disturbing the balance between top-down (goal directed) and bottom-up

(stimulus-driven) attentional processes (borrowing from Yantis’s distinction; Yantis,

1998). Anxiety shifts the scales in favour of stimulus-driven processing, which can

44

manifest as an attentional bias towards threatening stimuli and a failure to disengage

from it, but also as enhanced distractibility in the presence of any task-irrelevant

stimuli (though the effect remains stronger for threat-related material). Studies that

have found anxiety to enhance amygdala activation while reducing activity in

prefrontal cortical areas (particularly in the dorsal lateral and ventral lateral

prefrontal cortex areas) support this hypothesis (see Eysenck et al., 2007; Derakshan,

& Eysenck, 2009). ACT also states that the negative effect of anxiety on performance

can be reduced by use of compensatory strategies, most often in the forms of enhanced

effort and the use of additional processing resources.

3. Anxiety impairs the inhibition and shifting functions of the CE

According to ACT, anxiety primarily impairs the inhibition and shifting

functions of the CE, as defined by Miyake and colleagues (2000; see above).

Anxiety impairs processing efficiency (often also performance effectiveness)

in tasks requiring the inhibition function. This effect is pronounced for tasks that include

the inhibition of threatening stimuli. One of the paradigms used to test this

assumption is the antisaccade task (see section Attentional bias) where prolonged latencies

suggest that high anxious individuals take longer to disengage from threatening

(or irrelevant) stimuli (see Derakshan & Eysenck, 2009).

A similar effect of anxiety on performance was found for tasks requiring

the shifting function. This hypothesis was tested using task-switching paradigms like the

mixed antisaccade task, where anti- and pro-saccade trials are mixed within the same

block. Non-anxious individuals achieve higher scores when the task assignments

alternate than in monotonic blocks (consisting solely of either pro- or anti-saccade

trials) due to a phenomenon referred to as the “switch benefit”. More frequently

changing task goals may lead to an increase in performance by decreasing the negative

effect of goal neglect, which occurs when the same task is continually repeated. Anxious

individuals, however, show no improvement under the task-switching condition,

indicating they have a difficulty in managing the shifting mechanisms of attention

and benefiting from them (see Derakshan & Eysenck, 2009).

ACT views the updating function as the least affected by anxiety since it is not

directly connected to attentional control. Only when anxiety leads to an increase

of the overall demands on the central executive, i.e. under stressful conditions, does

the impairment of the function occur (see Eysenck et al., 2007).

The developments of ACT have brought with it many implications for research

on the anxiety-performance relationship. Along with a preference for simpler and

45

more direct designs testing specific cognitive functions and the effect of anxiety on

particular components of the central executive, the notion that anxiety may at times

only affect processing efficiency has given birth to new research paradigms. These

paradigms are based on the assumption that processing efficiency can be directly

expressed in terms of brain activity. They interpret increased activity in certain brain

areas as increased cognitive effort and, consequently, decreased processing efficiency.

Many of these studies have found a difference between anxious and non-anxious

individuals in tasks where no observable effect of anxiety on cognition was found in

behavioural data. This supports the assumption of ACT that although anxiety has no

visible effect on task performance itself, it does have an effect on processing efficiency.

(see Ansari, & Derakshan, 2011 for a more detailed analysis of the neural correlates of

cognitive effort).

5 Anxiety and reading

The reading process 5.1In this thesis, reading is understood as "the ability to extract visual information

from the page and comprehend the meaning of the text." (Rayner, Pollatsek, Ashby

& Clifton, 2012, p. 19.) It is a conservative view of the term that excludes

the skimming of texts or the “reading” of non-text material, such as maps.

There are many existing theoretical models of the reading process that attempt

to describe the various phases and levels of information processing that occur during

reading (see Alvermann, Unrau, & Ruddell, 2013, for an overview of influential

reading models). I have chosen to present an influential model of reading originally

described by Just and Carpenter in 1980. I have selected this model because it stresses

the function of working memory in reading and explores the reading process through

the observation of eye-movements.

The specific eye-movements generally discerned in reading are fixations (gaze rests on a certain place), saccades (eye-movements between fixations), and

regressions (backward saccades relative to the direction of the text). Eye-movements

during reading are described in detail further below.

Just and Carpenter (1980; 2013) attempted to create a model of reading

comprehension that would explain differences in fixation durations on separate words.

They assume that during a fixation, information processing occurs, i.e. the longer the

46

fixation, the more processing the word requires. Just and Carpenter assume that the

processing of words occurs within the working memory, which holds various

intermediate products (physical features of the text, words, meanings, etc.) of the

encoding and comprehension processes (extracting physical features, encoding words

on the basis of various codes, referring the word to an appropriate meaning, etc.).

According to their theory, working memory also directs the processing and

transformation of information, for example from physical features to sounds, from

words to representations, etc. This is how working memory creates new clusters to be

temporarily stored. Finally, working memory serves as the workplace where current

sensory input is processed via representations, schemas, and rules (e.g. orthographic,

syntactic, pragmatic etc.) from long-term memory.

Automatized reading processes occur rapidly and with a minimal expense to

attentional resources. Not yet automatized and more thoughtful processes take longer

and require more attention. Correspondingly, the longest fixations tend to be on new

and unfamiliar words, while words that are part of a well known or already

comprehended higher-order text unit (e.g. a phrase or a clause) may be skipped

entirely.

The role of working memory is analogous also for higher-order reading

processes. These include the determining of relations among words, clauses, and

finally entire units of text. Working memory enables the integration of

representations from previously viewed parts of the text with information from a new

sentence along with schemata from long term memory.

The pattern of eye movements in reading supports the immediacy assumption, which says that a reader attempts to relate each content word to its referent as soon

as possible from the moment it is viewed. If cues for the correct interpretation of a

word are found only later in the sentence or text, regressions commonly occur.

Just and Carpenter also include a computational episode in their account of the

reading process. The computational episode called “sentence wrap-up” occurs when

the reader reaches the end of a sentence. Consistent with the immediacy assumption,

when arriving at the end of a sentence a reader will try to complete its

comprehension. He will try to complete his understanding of the meaning by

searching for referents that have not yet been assigned, constructing remaining

interclause relations (in the form of mere assumptions if necessary) and dealing as

well as possible with any inconsistencies that remain. Only once the sentence is more-

or-less satisfactorily “wrapped-up” do the eyes move forward again, searching for the

next input. So the reader returns to the first phase of encoding in a new part of the

47

text and the reading process continues again from the beginning, as described. See

Just & Carpenter, 2013, for a detailed account of the model.

Reading and working memory, working memory 5.2capacity The two most important components of working memory for the cognitive task

of reading are the central executive and the phonological (or articulatory) loop. The

central executive is responsible for information processing and decision taking in

reading. The articulatory loop enables verbal material to be maintained longer in the

working memory, making it available for further processing.

The articulatory loop has shown to be more important as an auxiliary tool for

beginners learning to read than for fluent readers, and studies suggest it is possible to

read and understand statements without its use. The articulatory loop is important in

situations when the individual needs to judge phonological similarity, retain the

surface structure of a passage, when precise word order is necessary for

comprehension and perhaps also when the input rate of material exceeds the speed of

semantic processing. (See Baddeley, 1979.)

The central executive remains under investigation and specification, and its

precise role in reading is yet to be directly examined. Considering the CE is not a

singular component, it is highly probable that it will also play a multiple and complex

role in the reading process.

The specific function of inhibition, described as a component of the CE, may be

closely linked to reading comprehension. Inhibition differentiates well between good

and poor comprehenders (see Borella, Carretti, & Pelegrina, 2010). Good

comprehenders were found to better resist proactive interference (intrusion errors),

respond less to distractors and more successfully inhibit prepotent responses. Whitney,

Arnett, Driver, & Budd (2011) found two separate factors determining performance on

the reading span task: manipulation capacity and susceptibility to interference. Susceptibility

to interference, as in the study of Borella, Carretti, & Pelegrina (2010), is closely

related to inhibition. Robert, Borella, Fagot, Lecerf, & De Ribaupierre (2009)

investigated changes in WM and inhibitory control throughout the lifespan and found

that the efficiency of inhibition corresponds to the predicted level of attentional

resources available in distinct periods of life. Chiappe, Siegel, & Hasher (2000) have

gone as far as to posit that inhibitory processes determine WM capacity, although

48

according to Robert et al. (2009), this hypothesis can be reversed (i.e. WM capacity

may determine the ability to inhibit).

Individual differences in working memory and working memory span are

closely linked to reading comprehension. Working memory span was originally

assessed by a reading span measure (Daneman, & Carpenter, 1980). According to the

authors, reading span relies on both processing and storage functions of the WM.

Later studies have supported this prediction. WM deficit in poor comprehenders was

found mainly in tasks requiring both storage and processing of information. The effect

greater for tasks also including the inhibition of off-goal information and the updating

of memory content (see Carretti, Cornoldi, De Beni, & Romano, 2005; Carretti,

Borella, Cornoldi, & De Beni, 2009).

The effect of anxiety on reading 5.3The effects of anxiety on cognition, presented at length above, imply that

anxiety will also negatively affect reading comprehension. The fact that the reading

process relies heavily on working memory further supports this prediction.

In this section I will be focusing on the effects of anxiety on the reading

of neutral (i.e. non-threatening) material. For a complete picture, I will only briefly

mention studies exploring threat interpretation bias in reading. Calvo, Eysenck

& Estevez (1994a) studied differences between high and low-anxious individuals in

interpreting ambiguous sentences about ego-threat, physical-threat or non-threat

situations. High-anxious subjects showed facilitation towards drawing ego-threatening

inferences and made ego-threat inferences significantly faster than their low-anxious

peers. Low-anxious individuals on the other hand demonstrated the reversed effect

of facilitation for non-threatening inferences, but only when the probability of

a threatening interpretation of the ambiguous sentence was low. With high threat

probability, the differences between high and low anxious groups disappeared, with

low-anxious individuals equally showing facilitation for threat-affirmative

interpretations (see Calvo & Castillo, 2001). These findings correspond with theories

of attentional bias positing that bias towards threat is a common phenomenon and

that the difference between high and low anxious individuals simply resides in

a different threshold for perceiving stimuli as potentially threatening (Mogg

& Bradley, 1998).

How does anxiety affect the reading of neutral texts? According to Eysenck and

colleagues’ processing efficiency (Eysenck & Calvo, 1992) and attentional control

49

theories (Eysenck et al., 2007), anxiety should more often lead to an impairment

of reading efficiency than the impairment of reading performance itself, commonly

operationalized as reading comprehension. Studies support this prediction, including

a study conducted long before the existence of the processing efficiency theory itself.

In a study by Dizney, Rankin, & Johnston (1969), college females high in trait anxiety

scored comparably to their low-anxious peers in reading comprehension, but made

significantly more eye-movements during the process.

Within the framework of PET the effect was replicated several times. Calvo and

Carreiras (1993) conducted a study in which high test-anxious individuals showed

increased word-reading times in a moving-window reading task, but their

comprehension remained unimpaired. This study also found an interaction of anxiety

and specific linguistic variables on reading time, suggesting that anxiety selectively

impairs higher-order text level processes such as sentence integration, but not lower-

level processes such as encoding or lexical access. This may correspond with ACT’s

prediction of anxiety primarily impairing top-down attentional processes.

Also consistent with ACT, impaired reading performance was found in anxious

individuals only in test situations where they were prevented from using

compensatory strategies. The main auxiliary strategies high-anxious individuals resort

to in order to compensate for increased working memory load seem to be articulatory rehearsal, increased reading time and reading regressions (see Calvo, Eysenck, Ramos

& Jiménez, 1994b; Calvo, 1996).

Studies using articulatory suppression, especially in the form of irrelevant speech, found that overt articulation is more common in high-anxious individuals. This

suggests that the articulatory loop may play a special compensatory role for high

anxious subjects (Calvo, 1996). Articulatory rehearsal, however, actually serves

as something of a “last resort” for anxious individuals. When allowed to make

regressions or spend more time on reading, high anxious individuals used no more

articulation than their low-anxious peers (Calvo et al., 1994b).

Calvo et al.’s study (1994b) found that the most predominant and preferred

compensation strategy of high-anxious individuals reading under ego-threatening test

conditions was the use of reading regressions. That is to say, in order to achieve the same

reading comprehension scores as their low-anxious peers, high-anxious individuals

needed to return more often to already seen passages of the text. The interpretation of

this phenomenon within the framework of the then current PET explains these

regressions as ways of refreshing information that were not held in working memory

due to its reduced capacity caused by test anxiety. Not allowing regressions (subjects

50

could only move forward in the text) led to reduced reading speed (the 2nd choice

compensation strategy). When the pace of text presentation was fixed, subjects finally

resorted to articulatory rehearsal as their last available strategy of compensation.

Anxiety was also found to impair both reading efficiency and reading

comprehension in individuals suffering from PTSD. Sullivan, Griffiths, & Sohlberg

(2013, 2014) became interested in the subject because of their experience with PTSD

patients approaching them with similar problems. They said they were having

difficulties reading and comprehending texts and that their reading pace had

significantly decreased. PTSD is associated with several deficits of executive control,

including deficits in WM tasks, deficits in sustained attention and a decreased ability

to suppress distracting information. In their studies, Sullivan et al. (2014) found that

veterans suffering from PTSD took longer to read texts and had slower reaction times

than subjects in the control group. There was no effect found for paragraph

complexity or statement difficulty on the results of PTSD patients. However,

this effect may had been masked by the already mentioned prolonged reading times.

In self-report, readers with PTSD primarily ascribed their reading difficulties to

inattention to task. The authors interpret prolonged reading times in PTSD patients as

a result of impaired sustained attention due to intrusive thoughts associated with

PTSD. These attention-impairing intrusions may lead to “zoning out” or “mindless

reading”, in which the eyes continue to make forward saccades, but the text is

no longer being processed (see Reichle, Reineberg, & Schooler, 2010). Sullivan et al.

(2014) report that there was no effect of higher-order text charachteristics on patients’

reading times. Since syntactic and lexical effects are decreased in mindless reading

as opposed to mindful reading (Reichle, Reineberg, & Schooler, 2010; see below

section 6.2.5), the reported absence of higher-order text characteristics may speak in

favour of Sullivan et al.’s explanation.

It seems that intrusive thoughts associated with anxiety may impair reading

in two distinct ways. The first is the effect described Calvo et al. (2004). Anxiety loads

the working memory and leads readers to use compensatory strategies in order

to maintain their desired level of performance. The second way anxiety can affect

reading was described by Sullivan et al. (2013, 2014). According to their explanation,

anxiety impairs attention to the task through intrusive thoughts, which manifests

itself as mindless reading. Attention and working memory are very closely related

and intertwined concepts, and it is easily possible that these two demonstrations

of anxiety in reading are consequences of the same underlying process. A possible

factor influencing whether the mind chooses to wander or compensate may be

51

the degree of a subject’s motivation to achieve a certain level of performance. Another

factor is perhaps the intensity of anxiety, or specifically the intensity, frequency and

resistance of intrusive thoughts. Readers may also read mindlessly at varying depths

and with varying intensity for varying levels of anxiety. Schad, Nuthmann, & Engbert

(2012) proposed a levels of inattention hypothesis, describing mind wandering as occurring

in different degrees. “Weak” mind wandering was found to affect eye-movements

in reading only as a decrease of the sentence wrap-up effect, whereas in the case

of “deep” mind wandering, even low-level lexical effects of word length and frequency

were reduced.

Although both hypotheses explaining prolonged reading times in anxious

readers are linked to attention and executive processes, they may lead to distinct

predictions about the nature of eye movements in reading under anxiety. Are the

regressions anxious readers make mostly “refreshing” regressions or re-reading

regressions? Do the eye-movements on the text preceding a regression correspond

with normal, attentive reading, or can a reduction of the sentence-wrap up and/or

word frequency effects be seen? When readers regress, do they search for specific key

words essential for comprehension, or do they re-read entire passages? The analysis

of eye movements preceding regressions may hint at how well a sentence is processed

by a reader before they make a regression.

A more detailed description of eye movements and regressions in reading can be

found below in section 6.2.

6 Eye-movements and eye tracking

Short overview of the method and terminology 6.1Eye tracking is a technology that determines, with relatively high spatial and

temporal precision, where one’s visual gaze is located on an observed stimulus. The

most common eye-tracking technology works on the basis of measuring the angle

between the pupil centre and the first Purkinje image, which is the reflection of light

from the outer surface of the cornea (i.e. the pupil-corneal reflection system).

The most commonly used eye-trackers today emit infrared rays into the eye.

These are reflected from the cornea and then registered by a camera, which also

enables the recognition of the eye’s pupil centre. Eye-movements are measured

in angles, described in degrees and minutes. When combined with information about the

52

distance of the eye from the stimulus, gaze location on the stimulus surface can be

computed. This computation works only when the visual angel is sufficiently small, in

order to achieve necessary precision. Eye-trackers can be monocular or binocular,

depending on whether they measure movements of only one or both eyes. For a more

detailed description of eye-tracking technology and methodology, including historical

and alternative eye-tracking techniques, see Duchowski, 2007, and Holmqvist et al.,

2011.

There are several features of eye-trackers that influence the properties of the

output data from these devices. One of the features is the device’s sampling frequency, which designates how many times per second the position of the eye is registered. The

frequency of eye-trackers varies from 25 to 2000 Hz (number of recordings per

second), depending on the eye-tracker and its settings. Although high frequency may

be needed for a very detailed analysis of eye movements, a frequency of 250 Hz is

sufficient for most designs (see Holmqvist et al., 2011). A second characteristic is the

spatial accuracy of an eye-tracking device. Spatial accuracy corresponds to the average

difference between the actual gaze position and the position measured by the eye-

tracker. A related factor is spatial precision, which is the ability of the eye-tracker

to measure and reproduce the same gaze location in repeated measures. Temporal precision describes the delay of the eye-tracker compared to real time and

the consistency of this delay. Finally, eye-trackers vary in how sensitive they are

to the movement of the measured subject and in the method they use to filter data

and eliminate noise, that is all data unrelated to actual eye movements.

Eye-tracking methods provide scientists with many advantages. One of

the primary advantages of eye tracking is the possibility of recording precise

eye movement data online and in real time. Eye movements are extremely difficult

to control consciously, meaning that it is nearly impossible for subjects to pretend

or deceive by their eye movements without being “caught”. This holds the great

advantage of social desirability having a minimal effect on the collected data. Eye

tracking also offers the possibility of creating designs with relatively high ecological

validity. This is especially the case of eye-tracking portable glasses, which can be used

to measure eye-tracking data in authentic, non-laboratory environments.

The main disadvantages of eye tracking primarily concern data interpretation

and not the data itself. Eye tracking yields data that are sufficiently precise

and accurate for most research purposes, if both hardware and software are set up and

used correctly. With less robust or versatile eye-trackers, data loss may become

a significant problem. Some eye-trackers may lead to a loss of up to one third

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of participants (see Holmqvist et al., 2011). Eye-trackers differ in how well they scan

eyes of a certain colour or shape and eyes with glasses or contact lenses. Since

a relatively stable position is necessary for good measurement, working with children

presents an additional difficulty (see ibid.).

The problems concerning data interpretation are of greater import than

questions of data precision. As Holmqvist et al. (2011, p. 71) simply put it: “It is

impossible to tell from eye-tracking data alone what people think.” The greatest

current challenge facing eye-tracking research is reliably describing the link between

eye-movements and underlying cognitive processes. Longer fixations, for example, can

mean both increased interest in a stimulus and difficulties in processing a stimulus.

Individuals can also “zone out”, that is fixate on a point without processing it.

Triesch, Ballard, Hayhoe, & Sullivan (2003) found that individuals can look directly

at a task-related stimulus item, without at all having activated working memory.

Furthermore, the mind is often aware of that which is not fixated. Underwood,

Chapman, Berger, & Crundall (2003) found that subjects remembered about 20%

of un-fixated objects from a driving simulation video and were unable to recall

some of the fixated objects. The ambiguity of eye-movements increases the risk

of researchers succumbing to confirmation biases and using backward reasoning

to support their predictions with eye-movement data (see Holmqvist et al., 2011).

The obscurity of eye-movement data interpretation has also led to disputes about the

eye-mind hypothesis concerning eye-movements in reading research, which closer

relates to this study and is described below in section 6.2.2.

Studying eye-movements in reading 6.2

6.2.1 Eye-movements in reading

The movement of the eyes during the process of reading is discontinuous. The

eyes move forward in “jumps” called saccades and the gaze lingers at certain points

called fixations during which information is processed. No new visual information is

retrieved from the stimulus during saccadic movements, a phenomenon called saccadic suppression. The duration of a reading fixation can vary greatly and depends to a large

degree on the difficulty of cognitive processing (see above in Just & Carpenter’s model

of reading). The average fixation duration in reading is 200-300 ms. Saccade duration

primarily depends on the visual distance covered. On average, a distance of 2 visual

degrees lasts 30 ms, a 5° distance requires 40-50 ms, and so on. See Rayner, 1998, for

a complex review of eye movements in reading.

54

The visual field can be divided into the foveal, parafoveal and peripheral area.

Visual acuity is high only in the fovea, which constitutes a mere 2 degrees of the visual

field and corresponds roughly to 6-9 letters of standard text. Although visual acuity is

considerably decreased in the parafovea (approximately 5° of the visual field

surrounding the fovea), parafoveal processing plays an important role in reading.

Parafoveal processing enables the apprehension of certain characteristics of text

following the currently fixated word. The apprehension of the word length, word

spaces, and the like of neighbouring words aids the interpretation of the currently

fixated word (see Rayner, 1998).

Eye-movements in reading do not necessarily coincide with shifts in attention.

One can shift attention without changing the location fixated by the eyes. Attention

seems to precede saccades when moving on in a text, perhaps helping determine

where the next saccade is to land.

Eye-movement patterns in reading are substantially idiosyncratic. Fixation

durations, saccade lengths and frequency of regressions all vary significantly between

individuals. Rayner (1998) describes that these measures are influenced by many

factors including a subject’s reading level, speed and strategy, but also by

typographical characteristics and text difficulty or the nature of the reading task itself.

Novice, weak or dyslexic readers tend to make more frequent and longer fixations,

shorter saccades and more frequent regressions than average readers. These

differences primarily reflect difficulties on the level of cognitive processing, and not

problems with the execution of eye-movements as such. Correspondingly, the training

of oculomotor movements in reading has shown inefficient in increasing the speed of

reading comprehension.

6.2.2 Eye-mind hypothesis

The eye-mind hypothesis states, that “under certain circumstances, the eye

fixates the referent of the symbol currently being processed.” (Carpenter & Just, 1976,

p. 139). In the tradition of reading research, the eye-mind hypothesis constitutes a key

premise for interpreting eye-movements in reading. A key assumption of Just and

Carpenter’s (1980) model of the reading process (described above) is that longer gaze

durations on words indicate that a higher cognitive load is necessary for their

processing. A gaze duration is the sum of fixation durations falling within one

designated visual area, for example a specific word. As mentioned above, readers make

longer fixations on difficult or new words and words located in specific locations.

Longer fixations also occur on words located at the end of a clause or sentence.

55

These prolonged fixations are related to information integration and the making of

reading inferences. All of the mentioned moments associated with longer fixation

durations are also moments of increased cognitive demand. In brief, the eye-mind assumption in the context of reading posits that the eye stays fixated on a word for as

long as it takes to process it.

The relationship between gaze duration and processing has proved to be a very

close one, but the synchronisation of eye and mind is not absolute. Morris and Rayner

(1991) describe two main exceptions to Just and Carpenter’s immediacy assumption. The

first is the parafoveal preview effect, in which the mind is “ahead of” the eyes, processing

information that has not yet been fixated. The second is the spill-over effect, or the

transfer of fixation durations onto following words. This effect manifests itself for

example as shorter than average gaze durations on words immediately following low

frequency words. The eyes can also be “ahead” of the mind, when they fixate new

words while the mind is processing previous ones held in working memory (see

Radach & Kennedy, 2004). Despite these deviations, the eye-mind hypothesis remains

a central and, for the most part, valid premise of reading research. In the words

of Radach and Kennedy (2004):

The relation between fixation positions and durations and local processing is

strong enough to produce reliable effects when sampled over groups of

participants and items and in this sense eye movement measures provide an

extremely sensitive index of local processing load (p. 7).

Although the eye-mind hypothesis has shown to hold in the vast majority of

cases, there is still no guarantee it will be valid, especially in new and yet untested

paradigms. Just & Carpenter (1976a, b) also present limitations of their eye-mind

hypothesis and stress the importance of task selection for experimental designs

studying the eye-mind relation. They recommend the experimental tasks should have

a clear structure. This structure should be described by adequate theoretical models,

providing researchers with a general idea of what basic mental operation phases occur

during the given task. For example in three-term series problems (e.g. “If John

is leading Bill, and Tom is following Bill, then where is John?”) a succession of

mental operations necessary for the successful accomplishment of the task is quite

easily deducible (Just & Carpenter 1976b, p. 141). This enables a more reliable

interpretation of the observed eye-movements in analogous reading tasks (ibid.). Tasks

should also have clearly assigned goals, obvious to both the subject and the researcher,

56

and they should be designed so as to minimize off-task eye-movements (see Just

& Carpenter, 1976a).

6.2.3 Regressions

10% to 15% of all saccades are regressions, or backward movements of the eyes (Rayner, 1998). Since text direction can be relative, regressions can be also defined

as “movements of the eyes in which the reader refixates information that has already

been fixated.” (Duffy, 1992, p. 464) Research has shown regressions to be linked to

comprehension difficulties. The frequency of regressions increases with increasing

conceptual difficulty (see Jacobson & Dodwell, 1979) and with comprehension

failures, as observed in the case of ambiguous sentences (see Rayner, 1998).

Fast readers make fewer regressions (Rayner, 1998) and the inability to regress has

shown to negatively impact reading comprehension (Schotter, Tran, & Rayner, 2014).

Holmqvist (2011) summarizes that research has found a higher frequency of

regressions with less proficient readers, readers suffering from visual disorders,

dyslexia, Alzheimer’s disease and readers with Broca’s or Wernicke’s aphasia (up to 2

- 3 times as many regressions as in the normal population). Texts containing spelling

errors or non-text patterns also lead to an increase of regressions (ibid). Regressions

tend to decrease with age from the period children learn to read onward, but may

increase again in older adulthood (see Rayner, Reichle, Stroud, Williams,

& Pollatsek, 2006).

Regressions can be divided into several subtypes. Very short backward saccades usually occur after long forward saccades and are conducted in order to correct the

error by which the previous forward saccade “missed the mark”. Other extremely

short regressions are intra-word regressions. Approximately 7,2% of fixated words receive

two or more consecutive fixations during the first encounter of the word (Rayner,

1998).

In general, within line regressions to a word on the line that is being read are

more frequent than regressions that go back to previous lines. The average length of

these backward saccades is 5.4 letters, which is shorter than the average 9.5 letters of

forward saccades (see Vitu, & McConkie, 2000). Long saccades, 10 or more letter

spaces back or to another line, are usually caused by comprehension failures. These

long regressions are most often initiated as soon as the reader comes upon

disambiguating information in the text. Most of these saccades are fairly accurate in

regressing to the approximate region of the text where the key word for

comprehension is found (Rayner, 1998).

57

Why do readers regress in a text? Regressions may be automatically initiated

corrective saccades, as mentioned above. They may also be initiated when

encountering problems with word identification. Vitu and McConkie (2000) found

that the probability of regressing was higher for words of low perceptibility, low word

frequency, for longer words and for words with high retinal eccentricity (i.e. words

lying further away in the visual periphery). Although regressions occurred more

commonly to words that had previously been skipped, word frequency also affected

regressions to non-skipped words. Regressions also occur when the reader encounters

higher-level comprehension difficulties. Semantic and syntactic complexities have

repeatedly been shown to increase the probability of regressive eye movements (see

Rayner, 1998; Vitu, & McConkie, 2000). Longer, mostly interline regressions may also

have to do with a difficulty in retaining information from previously viewed parts of

the text in working memory.

Duffy (1992) introduces another type of reading regressions. She distinguishes

eye regressions and mental regressions. When readers in her study encountered

comprehension difficulties in a text, for example in an ambiguous sentence, they

reacted in one of two ways. Either the reader only paused (made a longer fixation)

when coming upon disambiguating information, or they initiated a regression

to previous parts of the text. According to Duffy, prolonged fixations in these

circumstances indicate that the reader is attempting to make a “mental regression”

in order to refresh information needed for the reprocessing of what they are currently

reading. When a mental regression is not possible, the reader will return to the

relevant part of previous text using an eye regression. According to Duffy, mental

regressions may be preferred because they take less time and aren’t as costly

as eye regressions. On the other hand, eye regressions are more certain of retrieving

the correct information compared to mental regressions, which must rely on a reader’s

memory and can therefore be inaccurate. Clifton (1992) found that readers recovering

from errors use these same strategies. Readers either used backward saccades

to previous parts of the texts or displayed longer fixations further on in the sentence,

which may have been moments during which mental regressions occured.

Duffy proposes two main factors that may decide whether a reader will opt for

a mental or physical regression. The first is the level of information needed – the more

complex the information, the greater the probability that the individual will prefer

returning to the problematic section of the text physically. Data from studies using

various paradigms correspond with this first assumption. Ehrlich and Rayner (1983)

found that subjects only rarely regress in situations when the disambiguation

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of pronouns is necessary. Subjects used eye-regressions only in 4% of the trials.

Frazier and Rayner (1982), on the other hand, found that subjects regressed in 43% of

garden-path sentences. The difference can be due to the fact that it is easier to retrieve

the antecedent of a pronoun from working memory than to reconstruct an entire

succession of words. Although there may be disagreement in how frequent regressions

are in reading in general, regressions seem to be common when the reader encounters

significant disruptions in comprehension.

The second factor influencing the choice between mental and eye regressions

is the distance of the targeted information. Readers will more willingly make eye-regressions

on the same line they are reading than to previous lines. This is perhaps due to the

costliness of distant regressions, where it becomes more difficult to precisely locate

and “land on” the desired part of the text.

6.2.4 Eye-tracking and regressions

With eye-tracking technology it is possible to define various regressive eye-

movement events. First of all, it is important to differentiate between backtracks,

regressions and look-backs. A backtrack is a saccade moving in the opposite direction

of the previous saccade. A regression is a saccade moving in the opposite direction of the

text. A regression can also be a backtrack, if following a forward saccade, but multiple

regressions can occur in sequence, in which case they are no longer backtracks. Look-backs (also called returns or refixations) are any saccades to areas of interest (e.g. specific

words) that had already been fixated. These need not necessarily be directed in the

opposite direction of the text, nor are all regressions look-backs, since the eyes often

regress to words that had been skipped during first-pass reading. (Holmqvist et al.,

2011)

Regressions can be assessed using various regression-related events. Amongst

the most common of these are measures describing the number of regressions. This can be

the total number of regressive saccades made when reading a text, or more specifically

the number of regressions from or to a certain area of interest (most often a specific

word). Another measure used is regression rate, or the number of regressions per a

chosen unit of measurement. Regression rate can be calculated for example as the

amount of regressions per second, per a certain number of words, per line, sentence,

paragraph, and so on. A related event is the regression scanpath, which is the rereading

scanpath that starts following a regression and ends when the eyes again reach the

point from which they initially departed. See Holmqvist et al., 2011, for a survey of

eye-movement events and measures.

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6.2.5 Eye-movements in mindless reading

Mindless reading is generally associated with longer fixations and a decrease

of the effect of lexical and linguistic variables on fixation durations, which is most

visible as a decrease of the word frequency effect (see for example Reichle, Reineberg,

& Schooler, 2010; Luke, & Henderson, 2013; Foulsham, Farley, & Kingstone, 2013).

Schad, Nuthmann and Engbert (2012) report that they sometimes observed shorter

and sometimes longer fixations in mindless reading, but this is relative to the

frequency of the fixated word. Immediately before readers caught themselves mind-

wandering, eye movements appeared to be especially erratic (Reichle, Reineberg, &

Schooler, 2010).

Nguyen, Binder, Nemier and Ardoin (2014) observed mindless reading in

children. When reading mindlessly, 2nd-graders skipped words more often and had

shorter gaze durations and total reading times than in the case of focused reading.

Word frequency effects were also decreased in mindless reading. When focusing on

the task, the eyes made more fixations and regressions, mostly intra-word regressions.

The research so far says only very little about the specifics of regressions in

mindless reading. Reichle, Reineberg, & Schooler (2010) found that readers were less

likely to make interword regressions prior to self-caught mind-wandering. Luke and

Henderson (2013) compared eye-movements for normal vs. unreadable texts,

constructed of pseudo-words made of blocks instead of letters. In their study, they

found that readers also regressed when reading senseless texts, but mostly to “words”

that had been skipped. In normal reading on the other hand, the eyes frequently

returned not only to skipped words, but also to those that had already been fixated.

Luke and Henderson conclude from these findings that oculomotor processes may

drive certain types of regressions, while others require cognitive control.

Unfortunately, there are no studies yet focusing on regressions following mindless

reading or comparing these with regressions that occur during focused reading.

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II. The present study

61

1 Introduction to the present study

The present study’s main purpose is to provide further insight into how

anxiety affects cognitive processes in during reading. Research investigating the

anxiety-performance relationship has repeatedly found significant effects of anxiety on

cognition, although this effect may not always be visible at first sight. The well-

established ACT theory holds that anxiety primarily affects processing efficiency. Although anxious participants often achieve comparable results to their non-anxious

peers, they do so at a greater cost to their cognitive resources. When cognitive load

increases beyond a certain limit, anxious individuals’ performance effectiveness can be

impaired along with their processing efficiency.

The present study is a modification of experiments presented in Calvo et al.’s

1994(b) paper, especially of the designs used in studies 3 and 4. Similarly to

Calvo et al.’s design, in the present study reading efficiencies of anxious and non-

anxious individuals were compared in neutral and anxiety-inducing conditions.

Reading efficiency measures were defined by reading time, time spent on regressions

and the proportion of regressions to total reading time. Ego-threatening instructions

were used to induce state anxiety in the experimental condition.

Ego threat, alternatively named evaluative stress, is commonly used in anxiety-

performance designs to manipulate state anxiety. Ego threat manipulations consist in

creating task conditions where an individual views potential task failure

as embarrassing, humiliating or otherwise negatively judged by others. Ego threat

designs often employ comparison with other participants, evaluation by professional or

expert evaluators, or associating performance with more stable personality traits

to induce anxiety. Individuals who score high in test anxiety tend to be more

susceptible to ego-threatening instructions. See Deffenbacher, 1978; Darke, 1988; Lee,

1999 for examples of designs using the ego-threat paradigm.

The results of Calvo et al.’s studies suggested that anxiety primarily affects

reading by increasing the number of reading regressions anxious participants make.

The time spent on regressions explained differences in reading time between anxious

and non-anxious participants. Increased time spent on regressions also resulted

in lower efficiency scores for high-anxious participants, although they achieved

comparable comprehension scores.

62

While the design of the present study resembles that of Calvo et al.’s

experiment, it focuses on different aspects of the problem. Calvo and his colleagues

were mostly interested in auxiliary strategies that anxious individuals use when they

read. The present study’s main aim is to explore the effects of anxiety on regressive eye movements. It seeks to gain insight into what kinds of regressions occur and what

specific mechanism can explain why anxious individuals use regressions more often

than non-anxious readers. A no less important aim of the study is to see if the effects

observed by Calvo et al. can be replicated.

The main modification to Calvo et al.’s design is the use of eye-tracking

technology to register online eye movements during reading. Eye-tracking provides

a more authentic method of detecting eye-movements than self-paced reading, thus

increasing the ecological validity of the measure of regressions. Moreover, eye-tracking

data allows for a precise count of eye-movements and subtypes of regressions can be

identified and differentiated. The specification of regressive movements can aid the

interpretation of the effect anxiety has on reading regressions.

A second modification is the deliberate selection of cognitively demanding

texts as task stimuli. Using cognitively demanding texts should increase participants’

working memory load. Several linguistic and syntactic text characteristics have been

found to increase working memory load. These include, for example, long compound

sentences, unusual word order, uncertain pronouns, pronouns located far from their

antecedents, frequent disjunctions and negations, or generally complex syntactic

structure (see Baddeley, 1979; Wright, 1980; Trabasso, Rollins, & Shaughnessy, 1971).

Texts rich in these characteristics were sought and preferred as stimuli material. It is

assumed that if anxiety affects reading primarily via the working memory system, this

effect will be pronounced for cognitively demanding texts.

Reading efficiency

The PET and ACT theories of the anxiety-performance relationship (see section 4

in part I) make a distinction between performance effectiveness and processing efficiency. The

efficiency of a task can be most basically defined as the ratio of performance to effort

or consumed resources (see Figure 1.1). Performance is usually understood as the

achieved score, or the direct result of a performance task.

Effort, on the other hand, can be operationalized using a variety of different

measures. Studies following the ACT model have used time invested in the task, the

use of auxiliary methods, or even increased activity in certain brain areas, among

others, as measures of effort (see section 4 in Part I).

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Efficiency = Performance

Effort

Figure 1.1. Efficiency described as the performance to effort ratio

Reading efficiency can then be defined as the ratio of reading performance to the

effort or resources necessary to achieve this level of performance (see Calvo et al.,

1994b). Reading performance is traditionally operationalized as the level of reading

comprehension achieved and is measured by means of comprehension questions

following the reading of a text. For specific reading efficiency measured used in the

present study, see section 2.4.5.

Reading efficiency = Reading comprehension

Effort

Figure 1.2. Reading efficiency described as the ratio of reading comprehension

to effort

Research questions and study aims 1.1How do anxious and non-anxious individuals differ in how much time they take

to read a text, how many fixations they make, and how much time they spend on

regressions? How do they differ when they read under normal vs. anxiety-inducing

conditions? Do high-anxious individuals make more reading regressions than low-

anxious subjects? What kinds of regressions are these? What is their purpose? It is

into these problems that the present study aims to provide new insight.

More specifically, the aim of this study is to compare how non-anxious and

anxious students read and understand difficult texts in ego-threatening and neutral

task conditions.

The measures in which anxious and non-anxious individuals are being

compared are:

• reading performance (measured as reading comprehension)

• reading time

• eye-movement events (fixations and saccades)

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• regressive eye movements (regressive fixations and saccades)

• time spent on regressions (or the rereading of the text)

• reading efficiency measures, operationalized as the ratio of

comprehension and (1) reading time, (2) reading regressions and (3) the

ratio of regressions to total reading time

A subsidiary goal of this study is to explore the regressive scanpaths of

participants and generate new hypotheses considering the various types and possible

functions of reading regressions in anxiety.

Hypotheses 1.2

On the basis of presented anxiety-cognition theories and the results of previous

empirical studies, the following hypotheses are postulated:

H1: There is a main effect of dispositional anxiety on participants’

a. reading time,

b. number of fixations,

c. time spent on regressions,

d. ratio of time spent on regressions to total reading time,

with anxious individuals spending more time / making more fixations / spending

more time on regressions / spending more time on regressions to total time than non-

anxious participants.

H2: There is an interaction effect of dispositional anxiety and experimental

manipulation on participants’

a. reading time

b. number of fixations

c. time spent on regressions

d. ratio of time spent on regressions to total reading time.

Specifically, the differences between anxious and non-anxious participants are greater

in the ego-threat condition as opposed to the control, with anxious readers achieving

higher scores in the selected reading measures than their non-anxious peers.

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H3: There is an interaction effect of dispositional anxiety and experimental

manipulation on the reading efficiency of participants a expressed by

a. total reading time,

b. time spent on regressions,

c. ratio of regressions to total reading time,

where the difference in reading efficiency between anxious and non-anxious readers is

greater for the ego-threat condition as opposed to the control, and anxious readers

achieve lower efficiency scores than non-anxious participants.

A difference between anxious and non-anxious participants in comprehension

scores is not expected.

2 Method

Pilot studies and design modifications 2.1Two pilot studies were conducted prior to the experiment. These pilot studies

served several purposes.

The first aim of the pilot studies was to test the intended manipulation

procedure, especially the functionality of the chosen manipulation and of the task

stimuli. The pilot studies also served to prevent possible technical difficulties that may

arise in eye-tracking studies.

The second aim was to gain preliminary eye-tracking data and establish the

best way to process and quantify reading regressions. During the pilot studies, an

estimate could be made of the ratio of data lost due to calibration failure or

measurement inaccuracy (this was estimated to be around 15 - 20%).

2.1.1 The first pilot study Preliminary reading tasks were administered to 6 participants. The

participants’ level of trait anxiety was assessed using anxiety questionnaires.

Participants were then presented with three reading tasks, each containing a single

continuous text of 400 - 500 words. In each task, the text was followed by eleven

comprehension questions. Comprehension questions were either multiple choice

or short open questions.

In the first pilot, three conditions were used: control, task-relevant threat and

task-irrelevant threat. The task-relevant threat corresponds to the ego-threat condition

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in the present study. The task irrelevant threat condition administered was meant

to induce anxiety unrelated to the current reading task. The task-irrelevant condition

was established by telling participants that after the experimental task a fourth

reading task would be presented. In the anticipated task, subjects would read a text

aloud and the experimenter would observe them and evaluate their performance and

diction. The participants of the study underwent a mock version of this task

to maintain credibility and enable task order counterbalancing.

The main conclusions of the first pilot study were the following. First of all,

the manipulations did not have a strong effect on participants according to their

subjective report. Second, due to the selection of rather long continuous texts,

calibration issues occurred more often, and eye-movement data precision decreased

toward the end of the trials. Third, a decision was made to leave out the third task-

irrelevant condition. One of the reasons for this decision was that the manipulation

did not show the desired effect on participants (they reported feeling anxious during

the mock-task, but not before it). Another reason was that administering more

conditions increased the likelihood that data loss would occur at least in one of the

trials, which would then lead to the exclusion all of the participant’s data. Moreover,

laboratory sessions with four reading tasks turned out to be very long and tiring.

Participants spent between an hour and half to two hours in the laboratory.

Likelihood of data loss and long experimental sessions would decrease the probability

of collecting a sufficiently large sample size.

2.1.2 The second pilot study Seven volunteers took part in the second pilot study. The texts and

comprehension questions remained the same as in the first pilot study, but the format

of the text was changed to improve eye-tracking data quality. Texts were presented

in smaller parts with larger spacing and font size and a validation of calibration was

added between the slides to increase precision. The third condition and the mock task

were omitted.

The main findings of the second pilot study concerned the text stimuli.

Participants reported that the correct response to comprehension questions required

good memorization of information from the text. It was concluded that under such

circumstances, the comprehension questions would be testing memory rather than

reading ability. To reduce the need for memorization, the choice was made to use

several shorter texts that would fit on a single slide, with 2 to 3 comprehension

questions following directly.

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A preliminary manual analysis of regressions in the texts also showed that the

variability in reading measures was very significant. In an attempt to unify as much

as possible participants’ reading strategies as much as possible and increase the

likelihood that the texts would be difficult to read under working memory load,

a second decision was made to choose more syntactically challenging texts (see below

in section 2.5). Texts with more difficult sentence structure would also allow for

comprehension questions that inquire about the main theses of the text and do not

require the memorisation of specific or detailed information.

The results of the second pilot study also led to a modification of the

presentation of ego-threatening instructions. The choice was made to present

instructions also in written form in the presentation and to add a countdown at the

beginning of each text in the ego-threatening condition (see more in section 2.7).

Design of the current study 2.2The presented study was a 2 × 2 mixed experimental design. The first factor was

a between-subject variable corresponding to the level of participants’ dispositional test

anxiety (low vs. high anxiety). The second factor was the within subject-variable

of experimental condition (control vs. ego-threat). Each participant completed two

reading tasks during the experiment, one where instructions were manipulated to

increase state anxiety and one with neutral instructions (control condition). The

dependent variables were a variety of reading measures, including comprehension,

reading time, measures describing the number of eye-movement events, measures

describing reading regressions and reading efficiency measures (see section 2.4).

Participants 2.3

2.3.1 Targeted sample size and inclusion criteria The target sample size was set to 20 participants per anxiety group. The target

size was chosen on the basis of the experimental design and previous studies. The

main study of reference was the study by Calvo, Eysenck and Jiménez (1994), which

employed a sample of 18 students per group. However, Calvo and his colleagues did

not report standard deviations of mean scores, which is why power analysis could not

be conducted to estimate sufficient sample size. Due to an increased likelihood of data

loss due to the limitations of eye-tracking technology, a larger initial sample needed to

be recruited.

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The only inclusion criteria for participants were fluency and literacy of the

Czech language. Participants with severe reading disorders or visual impairment were

excluded. All acquaintances of the experimenter were also excluded.

2.3.2 Recruitment Participants were primarily recruited from psychology courses at the Faculty of

Arts and the Faculty of Social studies and from the HUME lab participant pool at

Masaryk University. The experiment was presented in two psychology courses after

arranging with the respective teachers. The experiment was briefly introduced in

class, and the students received an invitation e-mail including the link to an online

screening and the anxiety questionnaire. In one of the courses, students could gain

bonus points for participating in a psychology experiment. E-mail invitations were

sent collectively to students in the HUME lab database. This participant pool

consisted of students enrolled in a subject concerning experimental research. Part of

the course requirements was participation in an experiment. An advertisement

appealing to volunteers was also published on social media groups (with the consent

of group administrators) and distributed as leaflets within the premises of Masaryk

University.

Since recruitment was still underway during first experimental sessions with

already recruited participants, participants who took part in the study were asked if

they could invite their acquaintances that might want to also participate by sending

them the link to the screening questionnaire.

2.3.3 Selection Participants were invited to participate in the experiment via a link to a Google

forms questionnaire. The online questionnaire included information about the

experiment, inclusion criteria and procedure, followed by informed consent and

consent to data processing (see Appendix 1). If the participant agreed to the

conditions, they could continue on to the second part of the questionnaire. The second

part served to assess the participant’s anxiety level. Participants responded to items of

3 separate anxiety questionnaires (see below in section 2.4 Measures).

Participants were sorted according to their anxiety scores. Respondents who

achieved scores corresponding to distinctly low or high anxiety were then invited to

participate in the laboratory part of the experiment. Another person than the

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experimenter scored participants’ anxiety, to ensure that the experimenter would not

be aware of the participants’ anxiety level.

Figure 2.1. Flow of participants from questionnaire to final sample.

Anxiety scores were compared with the top and bottom quartiles obtained from

previous psychological studies for the Reactions to Tests scale and the debilitating test anxiety subscore of the D-M-V questionnaire (see below in section 2.4 Measures).

Participants were invited continuously during subject recruitment, so it was not

possible to select participants based on descriptive statistics of the present sample from

the beginning. Every time new respondents completed the questionnaire, factor

Selection criteria: • Scores corresponded to low or high

anxiety

Exclusion criteria: • Extreme scores (n = 1)

Final sample (n = 33)

Invited to experiment (n = 57)

Consented and completed the questionnaire (n = 94)

Inclusion criteria: • Fluency in Czech language Exclusion criteria: • Reading disorders • Visual impairment

Participated in experiment (n = 41)

Excluded (n = 8): • Calibration failure • Data loss • Technical failure

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analyses were conducted for all three anxiety scales. New participants were sorted into

groups both according to the factor score and based on the comparison with quartiles.

Towards the end of recruitment, subjects were sorted solely based on the quartiles

of the anxiety factor. Participants whose scores fit within either the highest or the

lowest quartile were invited to participate in the experiment. One participant was

excluded due to extremely high anxiety scores, which indicated either an unreliable

response or pathological levels of anxiety, which were both grounds for exclusion.

Ninety-four students filled in the online questionnaire. Out of these, 57 were

invited to participate in the experiment. 46 responded to the invitation and scheduled

a session, out of which 41 actually came. Datasets of 8 participants were lost due

to calibration issues, data loss or other technical failures. The final sample consisted

of 33 students, 21 female and 12 male. 16 participants belonged to the high-anxiety

group, the other 17 to the low-anxiety group (see Figure 2.1).

Measures 2.4

2.4.1 Anxiety Three scales presented in the online questionnaire were used to assess

participants’ trait and test anxiety: the Taylor Manifest Anxiety Scale (Taylor, 1953);

Sarason’s (1984) Reactions to Tests questionnaire and the debilitating test anxiety

subscale of the D-M-V questionnaire (Dotazník motivace výkonu, Pardel, Maršálová,

a Hrabovská, 1984).

The Taylor Manifest anxiety scale is a personality scale of manifest anxiety.

It was based on studies examining the effect of manifest anxiety on performance. The

TMAS contains 38 items taken from the MMPI. Participants evaluate statements

about themselves formulated in the first person as either true or false. A sum of scores

of 16 - 38 points corresponds with high levels of manifest anxiety. The majority of

items concern frequently experienced somatic symptoms associated with anxiety, like

headaches, blushing, stomach problems, feelings of hunger, insomnia and others (e.g.

“I am very seldom troubled by constipation.”). Other items are related to worrying

(“I admit I have felt worried beyond reason over small things.”), experiencing feelings

of tension and nervousness (“I work under a great deal of tension.”) and beliefs about

the self (“I am more sensitive than most other people.”).

Odd-even test reliability was found to be r = 0.92. The test-retest reliability of

the original scale was shown to be r = 0.82 over 5 months and r = 0.81 for longer

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periods of time. The method’s criterion validity was tested by comparing scores with

103 neurotic and psychotic individuals. Following experimental designs have

supported the validity of the TMAS (see Holtzman, Calvin, & Bitterman, 1952). The

TMAS is usually used in experimental designs to select anxious and non-anxious

participants from two ends of the distribution of scores (ibid).

The Reactions to Tests (Sarason, 1983) questionnaire is a measure designed

to assess separate dimensions of test anxiety. Subscale items were selected on the basis

of factor analysis (see section 1.3 in part I).

The questionnaire contains 40 items pertaining to four subscales of tension, worry, test-irrelevant thinking and bodily symptoms. Tension items assess an individual’s

tendency to feel a level of general tension (e.g. “I feel distressed and uneasy before

tests.”). Worry items concern the tendency to entertain worrying thoughts about one’s

performance, comparison to others, anticipation of failure, and the like (“While taking

a test, I find myself thinking how much brighter the other people are.”) Items in the

test-irrelevant thinking subscale have to do with experiencing thoughts during the test

situation that are unrelated to the task itself (“Irrelevant bits of information pop into

my head during a test.”). Bodily symptoms items refer to the experience of specific body

reactions associated with test situations (“My stomach gets upst before tests.”). Items

were originally scored on a scale of (1) = not typical, (2) = somewhat typical, (3) = quite

typical, (4) = very typical. In the present study, a Czech translation of the method was

used and responses were scored on a 5-point scale.

The validity of the subscales was tested in experimental designs, where

individuals achieving high and low scores in the RTT were compared in their

performance on anagram tasks. There was an effect of scores in the worry subtest

on anagram performance (F (2, 162) = 3.25, p < 0.05) and was shown to be associated

with cognitive interference (Sarason, 1983).

The debilitating test anxiety items (anxieta brzdiaca výkon) were taken from

a subscale of the original Slovak method that can be translated as the “Questionnaire

of performance motivation” (Dotazník motivácie výkonu, Pardel, Maršálová, a Hrabovská,

1984). The questionnaire’s other two subscales are performance motivation (motivácia výkonu) and facilitating test anxiety (anxieta podporujúca výkon) The method is based

on Atkinson’s (1974) model of motivation factors (see Mitterová, 2014).

The subscale of debilitating test anxiety contains 17 items, scored on a 6-point

Likert scale. The items concern emotions and worries associated with difficulties

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experienced in test situations. Examples of items are: “When I feel tense, I am unable

to work as well as usual.”; “I feel exhausted after an exam.”; “When I feel afraid

during an exam, I experience memory lapses.”

The inner consistency of the D-M-V was assessed as a Cronbach’s alpha of

r = 0.81 to r = 0.87, test-retest reliability after a three week interval was rmedian = 0.84.

Correlations between subscales were weak but significant (rmedian = 0.11). Concurrent

validity was assessed by comparing scores with Ehlers’ and Merzse’s (1966)

questionnaire of performance motivation (r = 0.71, p < 0.01) and with Spielberger’s

STAI (r = 0.61, p < 0.01; Mitterová, 2014). Criterion validity was measured

by correlations with high school grade average, but yielded only small and often

insignificant results (ibid).

By way of the process described above in section 1.3.3, participants were

selected and sorted into groups according to their scores in the tree anxiety scales. The

final measure of anxiety used in analyses was a binominal variable, indicating either

low (0) or high (1) anxiety5.

The experimenter and all those involved in the processing of reading

regression data were blind to the participants’ anxiety levels to ensure the information

would not influence the experimental procedure or the manual analysis of the data.

The information about participants’ anxiety levels was added to the database only once

all participants’ reading measures were computed.

2.4.2 Comprehension Comprehension was operationalized using the sum score of correct responses

to the comprehension questions described in section 5.2. The maximum possible score

was 16, and was interpreted as 100% comprehension.

2.4.3 Reading time and eye-movement events The reading time for each participant and task was measured as the sum

of separate trial durations for each text of the task. Trial duration corresponds

to the time from the moment a text stimuli appeared on the screen to the moment the

participant ended the trial by pressing the spacebar and continuing on

5 Described also as non-anxious and anxious, respectively. The two terms are used interchangeably.

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to the comprehension questions. Thus reading time also includes the time an

individual spent scanning the screen or possibly searching for the next action.

Basic eye-movement events were calculated on the basis of single event statistics

generated by BeGaze eye-tracking software. The following variables were derived:

The number of fixations was measured as the sum of all fixations a participant

made in all of the trials of a reading task. It is a measure closely related to reading

time and the total number of saccades. The number of fixations may be altered

depending on the software event detection settings. For this study the default fixation

detection parameters were used.

The number of saccades is the sum of all a participant’s saccades made in each

trial of a reading task. Default saccade detection parameters were used.

There is a slight disproportionality between the number of saccades and the

number of fixations (rcontrol = 0.84, rego-threat = 0.86, p < 0.001) due to the fact that several

fixations may follow one another without a saccade being detected between them, or

conversely, several saccades may follow one another without any fixations occurring in

between.

2.4.4 Regressions Number of regressive fixations

The number of regressive fixations was measured as the sum of all

a participant’s regressive fixations in all 7 trials of a reading task. A regressive fixation

is defined as any fixation located to the left of the previous fixation according to its

position on the x-axis. The count of regressive fixations was generated automatically

from BeGaze single event statistics using tailored software. It is a measure that was

not corrected in any way and also includes fixations that are found to the left of the

previous after the eyes made a jump to a new line and other regressive fixations

excluded in some of the following regression measures.

Number of regressive saccades

Similarly to the number of regressive fixations, the number of regressive

saccades represents the sum of all regressive saccades that one participant made

within a reading task. A regressive saccade is any saccade whose end position is

located to the left of its start position according to the x-axis. This measure includes

right-to-left saccades that do not belong to actual regressions to previously viewed

parts of the text.

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Time spent on regressions

Regression. The regression of interest in this study is any meaningful return to

previously viewed parts of the text. In this sense a regression (or regressive event,

respectively) is not equivalent to a backward movement of the eyes, but includes all

eye-movement events within a location that has already been viewed once before. This

definition of regressions therefore excludes regressive saccades or fixations (defined

above) that are not associated with reading what has already been seen (e.g. corrective

regressive saccades, described below). Simultaneously, forward saccades are included if

they re-fixate already read passages of the text6.

For the specific operationalization and program definition of regressions, see

section 2.8.

Large regression. A large regression is any regression (as defined above) that covers

the distance of at least 15 letter spaces. 15 letter spaces is a distance that roughly

corresponds with smaller clauses or segments of text that express a single thought.

Large regressions correspond more closely to the regressions measured by Calvo et al.

(1994) in their study, where the text was presented sentence by sentence and it was

impossible to measure smaller regressions within these predefined segments.

Small regression. Any regression (as defined above) that covers a distance of between

5 to 15 letter spaces.

Rereading. Any regression (as defined above) that covers a distance of at least 5

letter spaces and includes at least three consecutive forward saccades. Most, but not

all, large regressions are also rereading regressions. Small regressions may in certain

situations be rereading regressions, but such situations are rare since 5 letter spaces

is a relatively short distance to fit 3 consecutive forward saccades.

Estimate of the number of regressive events. An additional measure was added that was

computed as the number of lines used in the tailored software to count regressions.

This number corresponds to a rough estimate of the number of regressions. This

measure, however, was not corrected by manual comparison with scanpaths (see

section 2.10 Manual data correction), and therefore contains errors stemming from

data loss and/or erratic eye movements.

6 Note: The word “regression” is used from here onward used in a specific sense, as described above. The word regression is traditionally used in eye-movement terminology to denote a backward movement of the eyes. In the empirical part of this thesis backward (right-to-left) movements of the eyes are named “regressive saccades” for better distinction of the two meanings.

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Ratios of time spent on regressions to total time

The measures of the time spent on regressions in proportion to total reading

times gives an idea of how much time a subject spent rereading the text as opposed to

first time reading. It equals to the time spent on regressions (or large, small and

reread regressions, respectively) divided by total reading time and multiplied by 100 to

yield a measure described in per cent.

2.4.5 Reading efficiency In this study, reading efficiency variables were computed as ratios of reading

comprehension and several reading measures.

Reading efficiency = Reading comprehension

Reading measure

Figure 2.2. Reading efficiency described as the ratio of reading comprehension and

reading time

Given that some of these measures could turn out to be equal to zero, the formula

presented in Figure 2.2 needed to be adjusted (see Figure 2.3). Since the efficiency of

someone who made no regressions should not exceed their comprehension scores (as

not to favour those who read fast but had very weak comprehension scores), the

following adjusted formula was used:

Reading efficiencyadjusted = Reading comprehension1 + Reading measure

Figure 2.3. Adjusted formula for the computation of reading efficiency described

using regressions.

Since reading variables were described un numbers of various digit spaces, the

numbers were divided by multiples of ten to achieve more easily legible and

comparable efficiency scores. For the precise formulas used to compute efficiency

variables, see Appendix 2.

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Reading efficiency measures were computed as ratios of comprehension and the

following measures:

1. Total reading time

This measure of reading efficiency belongs among the most intuitive. The more

time a subject spent reading the text, the more effort it required. The suitability

of this measure is amplified in this study, considering that achieving the best possible

comprehension/speed ratio was a part of the task assignment.

2. Number of fixations

The number of fixations is a measure closely related to reading time. The more

fixations, the more eye-movements the subject made and the more effort they exerted

into reading the task.

3. Number of regressive saccades/fixations

The more regressive saccades/fixations, the less direct a path did the subject follow

while reading. It can be assumed that less direct scanpaths take more effort, but it is

a question whether or not they are as such less efficient.

4. Time spent on regressions

Similarly to regressive saccades/fixations, time spent on regressions has to do with

the complexity of eye-movement patterns. This measure, however, also includes

rereading of certain passages. For example, in situations where the participant read

the text quickly once all the way through and then re-read it a second time from the

beginning, time on regressions may be large, yet the count of regressive fixations and

saccades will be small. It is also questionable whether an individual who reads a text

in this matter is reading less efficiently then someone who reads the text only once,

but makes more fixations and small regressive movements. It is also uncertain

whether a regressive reading pattern is more demanding of an individual’s cognitive

resources. Efficiencies were also computed for time spent on each type of regression

(large regressions, small regressions and rereading) and for ratios of time spent

on regressions to total time (see above in sections 2.4.3 and 2.4.4).

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Stimuli 2.5

Texts were selected according to two main criteria. The first was syntactic and

structural difficulty (see section 1). Texts that were cumbersome, contained long and

complex sentences, etc., were preferred. The second criterion was that the texts did

not present factual information like specific names or numbers. The texts were chosen

so that the general meaning of the text should be easily understood when read

correctly without requiring memorization of specific passages. The selected texts were

slightly modified. Some sentence structures were merged to yield more difficult

presentation and specific facts, names or references were deleted.

The texts were taken from various sources: instruction manuals, psychology

textbooks and monographs. Most of the topics were psychological, philosophical or

historically political, since the texts needed to be of an argumentative nature, rather

than a presentation of empirical facts. Pairs of texts by the same authors were

selected, one used in each of the two sets of texts, to balance the versions as much as

possible.

There were two sets of texts, A and B. Each set contained 7 texts of 25 to 100

words each. The total length of the each set was near to 500 words (493 in variety A,

496 in B).

The texts and comprehension questions were presented to 6 volunteers before

they were used in the experiment. Each volunteer read only a part of the texts, so that

each text was evaluated by at least two respondents. The volunteers were inquired

about text and question difficulty, the clarity of question formulation and the need for

memorization. The questions were then modified according to respondents’ feedback

and the texts in the tasks were sorted according to their reported difficulty from

easiest to most difficult.

Setting and apparatus 2.6The experiment took place in a laboratory setting. An SMI iView X Hi-Speed

tower-mounted eye-tracker was used to measure eye-movements during reading.

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Experiment procedure 2.7

1. Each participant came to the laboratory for an individual 1-hour long session.

The experimenter welcomed them, reminded them of the experimental procedure and

their rights as participants and then asked if they had any questions.

2. In the next step, the participant completed a short visual task to ascertain the

dominance of the eye. The SMI iView X Hi-Speed eye-tracker was set up to track the

participant’s dominant eye. The experimenter then introduced the eye-tracker to

the participant and adjusted it to fit the participant’s height and other parameters.

Once the eye-tracker successfully registered the eye’s gaze, the participant was

presented with a sample reading task. The task consisted of only one short text

followed by two comprehension questions. In the sample task, the correct responses

to the questions were shown to the participant after he responded to them himself.

The main purpose of the sample task was to acquaint the participant with the length

and difficulty of the task texts and the type of comprehension questions that would be

presented. It also helped decrease the effect of task order on reading.

3. After the sample task, the participant was presented with the first reading task

in either the control or the experimental condition. The order of the two tasks and

the text variety used for the experimental and control tasks were counterbalanced.

Both reading tasks consisted of 7 texts. Two to three multiple-choice

comprehension questions followed immediately after each of the texts. The task

instructions, texts and comprehension questions were all presented on the computer

screen in front of the participants. Participants used the spacebar to move forward to

the next slide and the computer mouse to select responses to comprehension

questions.

In both tasks participants were instructed to read as fast as possible while

achieving maximum comprehension. Further instructions varied depending on the

experimental condition.

Control task.

Instructions to the control task only asked participants to read the texts as fast as

they could while achieving the best comprehension possible. Participants were told

that the results from this task would be used to assess general reading tendencies of

anxious and non-anxious readers. For the exact formulation of the instructions, see

Appendix 4.

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Ego-threatening task.

In the ego-threatening task, participants were given the same basic assignment as

in the control task and asked to read as fast as they could while achieving maximum

comprehension. In addition, they were told that the results of the task would be used

to compare reading efficiency across participants, and they were informed that reading

efficiency is a measure that correlates with intelligence and academic performance.

Participants were told that their reading times would be measured in this task. The

commencement of the measurement was signalized by a countdown and a beep before

each of the texts, elements that were absent in the control condition. The instructions

also emphasized the necessity to focus fully on the task at hand. See Appendix 4 for

the exact formulation.

Apart from the written instruction presented on screen, the administrator orally

reiterated and stressed the instructions before each task. The difference between the

tasks was emphasized, so that if the ego-threat condition followed, the experimenter

would stress that this time efficiency was being compared and time was being

measured, whereas if the second task was the control task, the experimenter

emphasized that time was no longer measured as in the previous task.

The participants were given a short break between the two reading tasks and were

allowed to leave the eye-tracking tower, stretch, and the like. The intermission also

served to eliminate transfer effects from tasks, for example the transfer of anxiety

from the ego-threatening task onto the control task.

4. After completing both reading tasks, the participants filled out a manipulation

check questionnaire on another computer in the laboratory (see section 3.2). They

were then debriefed and the true design of the experiment was fully disclosed

to them. The participants were asked to not share information about the experiment

with anyone.

After debriefing and answering potential questions of the participants, the

experimenter sincerely thanked them for their participation and offered them

a symbolic reward in the form of a small snack.

2.7.1 Data processing and ethical aspects

The project for the present study was approved by the Research Ethics

Committee of Masaryk University. The personal data processed during the experiment

were the participants’ names and e-mail addresses. The data was protected using

pseudonymization. Each participant was assigned with a random number code and

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a meta-file was created that contained the codes, names and e-mail addresses of

participants. These were used during data collection for communication with the

participants. The meta-file was double password protected, stored on a safe device and

accessible only to the main experimenter. All experimental data was stored separately

in an anonymous form under the assigned codes. The meta-file was deleted after the

entire experimental process was enclosed and communication with participants was no

longer required.

Subjects were fully informed of the experimental procedure, participation

conditions and their rights as participants (see also Appendix 1). Each participant was

fully debriefed at the end of the session. The design of the experiment, including the

manipulation of state anxiety, was disclosed to them, and participants were assured

their results would not be compared individually. The experimenter explained that

anxiety may have caused a detriment in performance, asked the subject how they were

feeling and made sure any remnants of anxiety or unpleasant feelings were dispelled.

Considering that the manipulation used in the experiment was minimal, the levels of

anxiety induced were low and did not persist after the task was completed.

Defining and detecting regressions 2.8A software program was written in C# to aid the process of regression counting

on the basis of eye-movement event data generated automatically from BeGaze.

A regression was defined as all fixations and saccades from the beginning of a

backward saccade until a fixation that reached or surpassed the x position of the

initial starting fixation to the right (see Figure 2.4).

Eye-movement events had to satisfy the following conditions in order to be

counted as regressions:

1. The distance covered by the return of the gaze is at least 5 letter spaces.

Otherwise the gaze is considered to still fixate the same text unit (e.g. word) and not a

previous one.

The distance covered means the difference described in pixels between the location

of the 1st starting fixation of a regression and the left-most (or furthest back relative to

the text in case of between-line regressions) fixation pertaining to that same

regression. This is not necessarily the distance of the regressive saccade itself.

Participants often regressed using several subsequent smaller regressive saccades.

2. The regressive event is not a corrective saccade. Corrective saccades are right to

left saccades, which the eye makes when searching for the correct place to land the

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next fixation or when correcting a saccade that went further right than needed. The

following types of corrective saccades were identified:

a) Regressive saccades moving the gaze from the end of a line of text to the

beginning of a new line.

b) A single regressive saccade at the end of a text line to a previous part of that

same line, immediately before jumping to the beginning of a new line.

c) A regressive saccade at the beginning of a new line after a jump from the

previously read line.

d) A regressive saccade jumping back to a skipped unit of text after a long

forward saccade, followed by a second forward saccade landing to the right of

the first forward saccade (see Figure 2.4). Such corrective saccades were

characteristic for some participants where they were clearly a part of first pass

reading.

2.8.1 Specification for software detection

The conditions described above were specified for the software as follows:

1. The distance of 5 letter spaces was calculated in pixels for each text size

separately, regressions below this limit were excluded.

2. Limits were set for each text size and line spacing combination to determine

jumps between lines. These limits were determined on the basis of manual

exploration of the data (comparing real Y-axis differences in jumps between

lines with Y-axis differences in within-line movements).

3. Regressions that were simultaneously jumps to new lines were excluded.

4. Single regressions (only one saccade and fixation) at the end of a line before

jumping to a new one were excluded.

5. Single regressions at the beginning of a line after jumping from a previous

line were excluded.

6. Corrective in-line saccades were defined as regressive right-to-left saccades that

a) do not surpass another fixation to their left and simultaneously b) are

followed by a forward saccade that surpasses the initial starting fixation to the

right. (See sentence 1. in Image x1.) These regressive saccades were excluded.

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An additional exception was made to these conditions. When the fixation

durations were sufficiently long, such a situation was in fact considered a regression.

The limits were set to a minimum of 200 ms for the fixation duration of the fixation

preceding the regressive saccade (labeled 0 in the example, see Figure 2.4) and a

minimum of 250 ms for the fixation immediately following the regressive saccade

(labeled 1 in the example, see 1. in Figure 2.4). It was assumed that when fixations are

of a significant duration, re-reading does in fact occur. This assumption was made

based on manual exploration of scanpaths, where most corrective saccades were

of rather small fixation duration and borderline cases were often marked by longer

fixation durations.

Figure 2.4. Detecting corrective in-line regressive saccades

2.8.2 Regression duration

To measure the time that a regression took, the following time-marks were

used. To mark the beginning of a regression, the event end time of the fixation prior

to the first regressive saccade was chosen. The end time of the regression was decided

according to where the last fixation of the regression landed. If it landed within the

set radius of fourteen pixels around the starting fixation, the event end time of this

fixation was used (see 1. in Figure 2.5). It was supposed that in this case the fixation

belonged to the regressive event, since it was still reading the same part of the text. If

the last fixation exceeded the radius of the starting fixation to the right, the event

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start time of this fixation was used (see 2. in Figure 2.5). In such a situation, the

fixation itself was no longer considered to be a part of the regression, but belonged

to the first pass reading of new text.

Figure 2.5. Marking the end times of regressions

2.8.3 Manual data correction

Data obtained from the tailored software was corrected manually for possible

errors. This was done by comparing the software data with scanpath videos.

The majority of corrections concerned slanted data and irregular and erratic eye-

movements, associated with blinks or data loss. With certain scanpath offsets and

slants, the program thought that gaze returned to the beginning of the same line

when in fact this movement was a jump to a new line, or vice versa. This could lead

the program to mistakenly define 1st pass reading as a regression, or miss a regression

considering it to be 1st pass reading.

When an error was noted during the correction, the times of regressions in the

problematic area were recomputed manually based on scanpath videos and single

event statistics.

Data sets with more than 10% of data missing were excluded. For data loss within

the 10% limit, estimates of the missing parts were made. These were calculated using

average reading time (average time per word) of the given participant on the given

text. The number of words skipped due to data loss was multiplied by average reading

speed and the resulting time was subtracted from the actual time of the eye-tracking

fallout. The remaining time became the estimate of time spent on regressions.

To estimate how much of that time pertained to large, small and rereading

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regressions, ratios of these measures were adopted from other texts of the same

participant and applied to the estimated time of regressions.

3 Results

Statistical analysis 3.1All data analyses were conducted in IBM SPSS Statistics, version 23.0.0.

A series of 2 (low vs. high anxiety) × 2 (control vs. ego-threat) mixed factorial

ANOVAs were used to test the proposed hypotheses. 2 × 2 ANOVAs were also used to

check for the effect of the manipulation on manipulation check scores and the

possible effect of task order and text variety on reading measures.

Manipulation check 3.2A manipulation check was carried out to assess the effect of the anxiety

inducing condition on participants. Manipulation check measures were presented

to participants at the end of the experimental session in the form of an online

questionnaire. Participants responded to items concerning how they felt, what they

thought about during the task and also how difficult they found the texts and

questions. Combined scores were defined after data collection, based on related item

content and an exploratory factor analysis of participants’ scores. See Appendix 6 for

the original formulation of manipulation check items and items used for combined

scores.

The questionnaire contained 2 × 19 items, with identical questions for each

reading task. Items were scored on a 5-point Likert scale, with (1) corresponding to

“never” or “not at all”, (2) to “rather not”, “only rarely” etc., (3) corresponding to

“somewhat”, “sometimes” or “cannot determine”, (4) to “rather yes”, “yes, more often”

and (5) to “yes, very” or “very often”, depending on the scale in question (see

Appendix 6).

The manipulation check showed that the majority of participants did not feel

highly anxious during either of the reading tasks. The vast majority of score means

was below the average score of 3, which corresponds to mild agreement or neutral

response, and the distributions were generally skewed to the left in the direction

of disagreement with anxiety-related statements.

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With a range of 1 - 5, the mean of the general combined score of anxiety-

related items was M = 2.66 (SD = 0.87) for the control task and M = 2.92 (SD = 0.83)

for the ego-threat condition. For the most part they report never or only seldom

having experienced somatic symptoms corresponding to anxiety (M1 = 2.12, SD1 = 0.99, M2 = 2.46, SD2 = 1.037). Participants felt on average relatively comfortable or neutral

during reading tasks (M1 = 2.82, SD1 = 1.10, M2 = 2.76, SD2 = 0.97) and felt a bit more

tense in the ego-threat condition than in the control task (M1 = 2.58, SD1 = 1.20, M2 2.97, SD2 = 1.31). Participants reported having some worrying thoughts about

their performance in both conditions (M1 = 2.85, SD1 = 1.2, M2 = 3.33, SD2 = 1.24), but

they did not show a tendency of comparing themselves with others (M1 = 2.33,

SD1 = 1.16, M2 = 2.58, SD2 = 1.28) nor did they often experience self-deprecating

thoughts (M1 = 2.21, SD1 = 1.34, M2 = 2.36, SD2 = 1.37).

Participants admitted they often caught themselves reading mindlessly quite

often in both conditions (M1 = 3.73, SD1 = 0.94, M2 = 3.85, SD2 = 1.03) but they

evaluated their ability to focus on the task as moderate to good (M1 = 3.12, SD1 = 0.82, M2 = 3.15, SD2 = 0.91). They usually found the texts difficult (M1 = 3.33, SD1 = 0.92, M2

= 3.61, SD2 = 0.90), but not the comprehension questions (M1 = 2.58, SD1 = 0.94, M2 =

2.64, SD2 = 1.08). They reported that they used guessing to answer comprehension

questions only seldom or sometimes (M1 = 2.61, SD1 = 0.83, M2 = 2.67, SD2 = 0.82).

They judged on average that the comprehension questions required memorization

of specific parts of the text sometimes to rather often (M1 = 3.06, SD1 = 0.93, M2 = 3.24, SD2

= 0.87).

See Table 3.1 for means and standard deviations of combined scores and

Appendix 7 for descriptive statistics of manipulation check items.

Table 3.1.

Manipulation check – means of combined scores (N = 33)

Control

Ego-threat

M SD M SD

Emotion 2.50 0.93

2.72 0.91

Worry 2.46 1.03

2.76 1.02

Inability to focus 3.30 0.76

3.35 0.88

Task difficulty 2.99 0.54 3.16 0.55

Overall score 2.66 0.87 2.92 0.83

Note: The minimum possible score was 1, maximum 5. 7 Indexes denote experimental condition, 1 – control, 2 – ego-threat.

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Individual 2 × 2 ANOVAs were conducted to test the effects of manipulation

and anxiety on manipulation check scores. Ego-threatening instructions had a small

but significant effect on the Overall score (F(1, 31) = 8.24, p = 0.007, ηp² = 0.21, ηG² =

0.08, ΔM = 0.26) and also on the combined scores of Emotion (ΔM = 0.22) and Worry (ΔM = 0.30; see Table 3.2 for effect sizes).

Participants scored higher in anxiety in the ego-threat condition, as was

expected, but the differences were very small. Within emotion items, manipulation

had small significant effects on the scores of A4 – Tension (F(1, 31) = 6.62, p = 0.015, ηp²

= 0.18, ηG² = 0.05, ΔM = 0.39) and B – Somatic symptoms (F(1, 31) = 5.03, p = 0.032, ηp² =

0.14, ηG² = 0.06, ΔM = 0.34). From the individual worry items, manipulation had a

significant effect only on C1 – Worries about performance (F(1, 31) = 10.83, p = 0.003, ηp² =

0.26, ηG² = 0.08, ΔM = 0.48). See Appendix 7 for means and SDs of manipulation

check items.

As can be seen from mean differences and effect sizes, the manipulation had

only a very small effect on participants’ responses and did not induce a substantially

higher level of state anxiety than in the control condition.

Table 3.2.

Man. check – main effects of manipulation on combined scores (N = 33).

F(1, 31) p ηp² ηG²

Emotion 4.49* 0.042 0.13 0.04

Worry 8.74** 0.006 0.22 0.06

Focus 0.29 0.593 0.01 –

Difficulty 3.68 0.064 0.11 –

General effect 8.24** 0.007 0.21 0.08

** Effect was significant at the 0.01 level.

* Effect was significant at the 0.05 level.

3.2.1 The effect of anxiety on manipulation check scores

Anxiety had a significant effect on participants’ scores in the manipulation

check (see Table 3.3). Anxious participants experienced more anxiety during each task

than their low-anxious peers. This can serve to support the selection of participants

into the low and high anxiety groups, but says nothing about how successful the

manipulation check was itself. (see Table 3.3 for effect sizes, Appendix 8 for table

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of means). The main hypothesis concerning the manipulation check, if successful, was

that there would be an interaction of anxiety and experimental manipulation

on manipulation check scores, with anxious participants achieving higher scores than

non-anxious, and the difference being greater in the ego-threat condition. The

collected data do not support this assumption, indicating that the manipulation was

less successful than desired.

There was no statistically significant interaction of anxiety x manipulation

on manipulation check scores. The only item that produced at least an indicated effect

was C3 – Self-doubt (F(1, 31) = 3.44, p = 0.073, ηp² = 0.10). With the exception of the

item C3 – Self-doubt (F(1, 31) = 3.44, p = 0.073, ηp² = 0.10), interaction effect sizes were

trivial and non-significant.

Table 3.3.

Man. check – main effects of anxiety on combined scores

F (1, 31) p ηp²

Emotion 17.82* 0.042 0.37

Worry 22.21** <0.001 0.42

Focus 13.88** 0.001 0.31

Difficulty 0.04 0.839 –

General effect 26.84** <0.001 0.46

** Effect was significant at the 0.01 level.

* Effect was significant at the 0.05 level.

3.2.2 Conclusion

The results of the analyses conducted on manipulation check scores indicate

that the ego-threatening instructions had only a minor effect on participants.

According to the participants’ self-reports, the manipulation was not strong enough to

induce considerably higher levels of arousal or worry the ego-threat condition as

opposed to the control condition, or to increase the difference between anxious and

non-anxious participants in the ego-threat condition compared to the control. The

manipulation check did, however, confirm differences between the anxious and non-

anxious group.

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Table 3.4. Manipulation check - means of combined scores in low and high anxiety groups

Control

Ego-threat

LA

HA

LA

HA

M SD

M SD

M SD

M SD

Emotion 2.00 0.76

3.03 0.81

2.21 0.76

3.26 0.74

Worry 1.80 0.62

3.17 0.92

2.20 0.85

3.35 0.86

Focus 2.94 0.75

3.69 0.57

2.88 0.78

3.84 0.70

Difficulty 2.98 0.56

3.00 0.54

3.14 0.50

3.19 0.61

Gen. effect 2.11 0.62

3.24 0.72

2.41 0.70

3.46 0.59

Notes. LA – Low anxious group (N = 17). HA – High anxious group (N = 16).

Relations between reading variables To gain a better understanding of how closely interdependent the numerous

reading variables used in the study are, correlations between the variables were

compared. Appendix 9 presents a Pearson’s correlation matrix of all basic reading

variables for each experimental condition.

As expected, there were strong significant correlations between total reading

time and the number of reading events, i.e. the number of fixations (r1 = 0.93,

p1 < 0.001; r2 = 0.94, p2 < 0.001) and saccades (r1 = 0.83, p1 < 0.001; r2 = 0.77, p2 < 0.001)8.

The affinity with reading time is stronger for fixations than saccades. This effect is

also reflected in the somewhat weaker correlations of total time with regressive

fixations (r1 = 0.64, p1 < 0.001; r2 = 0.72, p2 < 0.001) and regressive saccades (r1 = 0.56,

p1 = 0.001; r2 = 0.53, p2 = 0.001). This difference is most probably due to the fact that

fixations are eye-movement events of generally longer duration than saccades.

Reading time also significantly correlated with time spent on regressions (r1, r2 =

0.92, p1 < 0.001, r2 = 0.92,, p2 < 0.001). The relation to reading time remained tight for

the measures of time spent on large regressions (r1 = 0.88, p1 < 0.001; r2 = 0.89,

p2 < 0.001) and time spent on rereading (r1 = 0.88, p1 < 0.001; r2 = 0.90, p2 < 0.001).

The exception to these close relationships was the measure of time spent on small

regressions. This measure did not significantly correlate with total reading time (r1 =

8 The indexes 1 and 2 refer to the experimental condition in question. Correlations r1 describe relations between variables in the control condition, correlations labeled as r2 pertain to the ego-threat task.

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0.31, p1 = 0.083; r2 = 0.26, p2 = 0.140) or with time spent on regressions, large

regressions or rereading (see Appendix 9 for all correlation coefficients).

The measure of small regressions did moderately correlate with the number of

fixations (r1 = 0.42, p1 = 0.015; r2 = 0.35, p2 = 0.047), regressive fixations (r1 = 0.60, p1 <

0.001; r2 = 0.51, p2 = 0.002) and regressive saccades (r1 = 0.43, p1 = 0.013; r2 = 0.52,

p2 = 0.002), but it’s strongest relationship was with that of the estimate number of

events (r1 = 0.86, p1 < 0.001; r2 = 0.87, p2 < 0.001).

The correlation matrices show that small regressions stand apart from large and

re-reading regressions, and they seem to serve a different purpose in reading and are

more closely related to the number of regressive eye movement events than to the

time spent on regressions.

The correlations of text comprehension with other variables were weak,

inconsistent and non-significant. This means that spending more time reading or

making more regressions was not generally associated with higher comprehension

scores.

The main conclusions of the correlation analysis served to aid the selection

of relevant measures for the following statistical analyses. As the primary regressive

measures, large and rereading regressions were subsequently preferred over small

regressions and regressive eye-movement events. From regressive eye-movement

events, regressive fixations will be selected before regressive saccades.

Descriptive statistics of reading measures 3.3Participants took 5 minutes on average to read all 7 texts of a reading task

(without counting the time spent on reading instructions or responding

to comprehension questions). Their average comprehension score was 12 points out of

sixteen, which corresponds to 75% success rate. The average fixation count was 1200

fixations per reading task, out of which 367 (31%) were regressive. The average time

spent on regressions was 2 minutes 51 seconds for the control and 2 minutes

21 seconds for the ego-threat condition. See Table 3.5 for an overview of the most

important descriptive measures and Appendix 10 for descriptive statistics of all

reading and efficiency measures.

The idiosyncratic nature of reading was reflected in large differences between

extremes in the sample. Participants achieved a comprehension level as low as 5

points out of sixteen (corresponding to a 31% success) whereas several others achieved

full score. The fastest reader read all 7 texts in 2 minutes 52 seconds, the slowest took

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over 9 minutes. Yet, the former participant achieved a comprehension score of 13,

whereas the latter struggled to achieve only 9. Both belonged to the low-anxiety

group.

Variation within subjects was also significant. The largest difference between

the reading time in the control and manipulation task for the same participant was

over four and a half minutes. There were three participants with a 5-point (out of

sixteen) difference in comprehension scores between the first and second reading task.

The variability was less pronounced within the 50 % around sample means.

The difference between the 25th and 75th percentile in comprehension was 3 score

points. For reading time this difference was 2 minutes and 10 seconds in the control

condition and 1 minute 17 seconds in the ego-threat task. The difference between the

25th and 75th percentile for time spent on regressions was around 1 minute and a half

in both conditions. For most reading variables, variability was greater in the control

condition compared to the ego-threatening task.

Table 3.5 Chosen descriptive statistics of reading and efficiency measures

Control

Ego-threat

min. max. M SD

min. max. M SD

Comprehension 9.00 16.00 12.46 1.92

5.00 15.00 11.79 2.58

Compr./total time 5.79 13.06 9.33 1.58

3.99 12.20 9.07 2.07

Total reading time (s) 196 554 340 83

172 473 302 72

Time regressions (s) 32 331 171 69

25 321 141 70

Regr. to total time (%) 16.3 65.4 48.5 11.4

10.0 68.2 44.4 14.0

N fixations 726 1954 1265 291

699 1762 1134 266

N regressive fix. 174 636 387 102

180 569 346 101

Testing hypotheses 3.4

3.4.1 Statistic models and assumptions Separate 2 (anxiety) × 2 (experimental condition) mixed ANOVAs were used to

assess the effect of anxiety on various reading measures.

The assumption of homogeneity of variance, tested using Levene’s test, was

violated in several of the analyses. Since the group sizes were nearly equal (NHA = 16,

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NLA = 17)9 and since the statistical software used to make the analyses did not offer

the possibility of robust statistics for mixed factorial ANOVAs, the original,

uncorrected effect sizes are reported. Violations of the assumption of homogeneity

according to Levene’s test are reported in the effect tables in Appendix 11.

3.4.2 Comprehension As expected, there was virtually no effect of anxiety on reading comprehension

(F(1, 31) = 0.01, p = 0.94, ηp² < 0.01). Anxious participants achieved comparable

performance effectiveness as their non-anxious peers, a finding consistent with ACT

theory. The effect of manipulation on reading comprehension was also non-significant

(F(1, 31) = 2.02, p = 0.17, ηp² = 0.06), and there was no significant effect of anxiety

× manipulation interaction (F(1, 31) = 0.56, p = 0.46, ηp² =0.02).

3.4.3 Reading time The effect of anxiety on total reading time was non-significant (F(1, 31) = 2.97,

p = 0.10, ηp² = 0.09), as was the interaction effect of anxiety and manipulation (F(1, 31)

= 3.51, p = 0.07, ηp² = 0.19). There was, however, a significant effect of manipulation

on total reading time (F (1, 31) = 7.29, p = 0.01, ηp² = 0.19, ηG² = 0.09). Both anxious

and non-anxious participants read texts faster in the ego-threatening task than in the

control task (for means see Table 3.6). This effect was far greater for the non-anxious

group, whereas in the anxious group, reading times between conditions were almost

identical (mean difference of only 11.35 seconds, SD1 = 73.40, SD2 = 77.50).

An additional t-test showed that the mean reading times were significantly

different between non-anxious and anxious groups when considering only the ego-

threatening task (t (31) = -2.80, p = 0.01, 95% CI [-17.41, -111.11], Hedges’s

gs = 0.96). In the manipulation condition, anxious participants (M2 = 335.41 s,

SD2 = 77.50 s) took on average 1 minute and 4 seconds longer to read the texts than

non-anxious participants (M2 = 271.15 s, SD2 = 50.87 s). In other words, under time

pressure in the ego-threat condition some and mostly non-anxious participants

increased their reading speeds, while others, mostly anxious, did not.

In conclusion, the main effect of anxiety and interaction effect of anxiety and

manipulation on reading time were non-significant (H1a, H2a). There was however a

significant difference of mean reading times between anxious and non-anxious

participants in the ego-threat condition.

9 LA – low-anxious, HA – high-anxious.

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3.4.4 Eye-movement events The main effect of anxiety across the majority of eye-movement measures was

non-significant, with the exception of the measure of saccades (F(1, 31) = 6.62, p =

0.015, ηp² = 0.18). This exception might have been accidental, considering that there

was no such effect for the other, closely related, measures (see section 3.3 Relations

between reading variables).

Anxiety did however have a significant effect in interaction with the

experimental condition on the number of fixations (F(1, 31) = 4.78, p = 0.036, ηp² =

0.13, ηG² = 0.06) and the number of regressive fixations (F(1, 31) = 6.12, p = 0.019,

ηp² = 0.17, ηG² = 0.06) participants made (see Figures 3.2 and 3.3.) Low anxious

individuals made fewer fixations in the ego-threat task than in the control condition

(ΔM = 233, SD1 = 334, SD2 = 183), creating a significant difference between themselves

and their high anxious peers in the ego-threat condition (ΔM = 209, SDLA2 = 183,

SDHA2 = 301). The direction of the interaction effect of manipulation and anxiety was

analogical for regressive fixations.

Table 3.6

Means of reading measures for anxious and non-anxious groups

Control

Ego-threat

LA HA

LA

HA

M SD M SD

M SD

M SD

Comprehension 12.65 2.00

12.25 1.88

11.65 2.80

11.94 2.41

Total reading time (s) 334 94

347 73

271 51

335 78

N fixations 1265 339

1264 242

1032 183

1242 301

N regressive fix. 391 131

383 63

313 94

381 99

N saccades* 962 289

1032 167

790 149

1000 185

N regr. saccades 272 101 290 59

217 73

275 52

Note. LA - Low anxious group, N = 17. HA - High anxious group, N = 16. ** Main effect of anxiety was significant at the 0.01 level. * Main effect of anxiety was significant at the 0.05 level.

In reference to the postulated hypotheses, it was not confirmed that anxious

individuals make significantly more fixations than non-anxious (H1b), but there was a

significant difference in the number of fixations between high and low anxious

individuals in the ego-threat condition (H2b).

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Figure 3.1. Interaction plot of the manipulation × anxiety effect on the number of

fixations

Figure 3.2. Interaction plot of the manipulation × anxiety effect on the number of

regressive fixations

A significant main effect of manipulation was present for all reading event

measures – the number of fixations (F(1, 31) = 7.10, p = 0.012, ηp² = 0.19, ηG² = 0.09),

regressive fixations (F(1, 31) = 6.90, p = 0.013, ηp² = 0.18, ηG² = 0.07), saccades (F(1, 31)

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= 4.78, p = 0.036, ηp² = 0.13, ηG² = 0.08) and regressive saccades (F(1, 31) = 6.07,

p = 0.019, ηp² = 0.16, ηG² = 0.08). Participants made less eye-movements on average

in the ego-threatening condition as opposed to the control. These differences are

larger for the non-anxious group (see Table 3.6 for means and SDs).

3.4.5 Time spent on regressions Anxiety had a significant main effect on all measures relating to large

regressions. Anxious participants spent significantly more time on regressions (F(1, 31)

= 7.38, p = 0.01, ηp² = 0.19), large regressions (F(1, 31) = 8.81, p = 0.006, ηp² = 0.22) and

rereading (F(1, 31) = 8.65, p = 0.006, ηp² = 0.22) than their low-anxious peers in both

conditions. In the ego-threat condition, the main effect was amplified, since time

on regressions decreased for low-anxious participants.

The difference in regressions between low and high anxious participants

appeared stronger when expressed as the ratio of time spent on regressions to total

time (F(1, 31) = 10.27, p < 0.01, ηp² = 0.25). The effect was greatest for the ratio of large

regressions to total time (F(1, 31) = 13.03, p < 0.01, ηp² = 0.30). On average, large

regressions made up about 34.4% (control) and 27.5% (ego-threat) of reading time for

low anxious participants, whereas the proportions reached 43.7 % (control) and 44.8%

(ego-threat) for the high-anxious group. See table 3.7 for means and SDs.

Table 3.7

Means of regression measures for anxious and non-anxious groups

Control

Ego-threat

LA HA

LA HA

M SD M SD M SD M SD

Time regressions (s)* 158 82

184 52

104 45

180 72

Time large regr. (s)** 125 80

153 50

78 41

156 70

Time rereading (s)** 119 80

151 50

73 43

149 71

Time small regr. (s) 33 16

30 12

26 13

24 10

N regr. estimate 72 33 70 14 62 27 66 20

Regr. to total time (%)** 44.7 13.6

52.5 6.9

37.2 12.4

52.1 11.6

Large regr. to total t (%)** 34.4 14.4

43.7 8.7

27.5 11.7

44.8 13.1

Rereading to total t (%)** 32.7 14.8

44.0 8.9

25.3 12.5

42.5 13.6

Small regr. to total t (%) 10.3 5.3 8.8 3.0 9.7 4.7 7.3 2.6

Note. LA - Low anxious group. N = 17. HA - High anxious group. N = 16. ** Main effect of anxiety was significant at the 0.01 level.

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Anxious and non-anxious participants did not significantly differ in any

measures concerning small regressions. The main effect of anxiety was trivial and

non-significant for both the time spent on small regressions (F(1, 31) = 0.35, p = 0.558,

ηp² = 0.01) and the estimated number of regressions (F(1, 31) = 0.02, p = 0.888,

ηp² < 0.01). The effect was also absent for the measures of regressive fixations and

regressive saccades mentioned above. See Appendix 11 for a table of effect sizes of

anxiety on all reading measures.

Significant effects of manipulation appeared for the time spent on regressions

(F(1, 31) = 5.31, p = 0.028, ηp² = 0.15, ηG² = 0.07), and its sister variable the ratio of

regressions to total time (F(1, 31) = 4.60, p = 0.04, ηp² = 0.13, ηG² = 0.05). The effects of

manipulation on large and rereading regressions were non-significant. The largest

effect of manipulation appeared for time spent on small regressions (F(1, 31) = 11.90, p = 0.002, ηp² = 0.28, ηG² = 0.10). This seems to correspond with the present effects of

manipulation on eye-movement event measures, seeing that small regressions

correlate with these measures (see section 3.4). Participants spent less time on

regressions in the ego-threat condition, a difference for which low-anxious

participants are mostly responsible, although for small regressions the difference is

also present in the high-anxious groups (see table 3.7).

The interaction effects of anxiety and manipulation on regression variables

were near to significant. For time spent on regressions the interaction effect was F(1,

31) = 4.14 (p = 0.051, ηp² = 0.12). See Appendix 11.

Conclusion concerning time spent on regressions

As expected, there was a significant effect of anxiety on the time spent on

regressions (H1c). A significant effect was only present for measures involving large

regressions (of at least 15 letter spaces) and was non-significant for small regressions

and regressive eye-movements.

The mean difference of time spent on regressions between high and low-

anxious participants was larger in the ego-threat condition (ΔM = 76 s) than in the

control condition (ΔM = 26 s). However, the interaction of anxiety and manipulation

(H2c) was non-significant at the 0.95 confidence level (F(1, 31) = 4.14, p = 0.051, ηp² =

0.12).

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3.4.6 Reading efficiency Anxious individuals were less efficient than their low anxious peers (see table

3.8.). However, this effect was statistically significant only for efficiency variables

concerning regressions (see Appendix 11 for all effect sizes). There was no effect

of anxiety on the most intuitive measures of reading efficiency. Anxious and non-

anxious participants did not significantly differ in efficiency operationalized as the

ratio of comprehension to total time (F (1, 31) = 0.40, p = 0.558, ηp² = 0.01) or to the

number of fixations (F (1, 31) = 0.35, p = 0.558, ηp² = 0.03).

Anxious individuals achieved significantly lower efficiency scores when

efficiency was computed using time spent on regressions (F(1, 31) = 7.87, p < 0.01,

ηp² = 0.20), time spent on large regressions (F(1, 31) = 9.57, p < 0.01, ηp² = 0.24)

and time spent on rereading (F(1, 31) = 9.56, p < 0.01, ηp² = 0.24).

When efficiency was defined using time spent on large regressions, high

anxious individuals achieved a score 1.75 points lower than that of low anxious

individuals in the ego-threat condition. This difference corresponds to roughly the size

of 1 standard deviation in either group (SDLA2 = 1.92, SDHA2 = 1.64). These results

indicate that spending more time on regressions did not significantly aid

comprehension, leading to an altogether lower efficiency score for high-anxious

participants.

Table 3.8

Means of efficiency measures for anxious and non-anxious groups

Control

Ego-threat

LA HA

LA HA

M SD M SD M SD M SD

Compr./total time 9.56 1.81

9.10 1.32

9.17 2.25

8.97 1.92

Compr./N fixations 5.74 1.37

5.44 0.83

5.76 1.45

5.41 1.31

Compr./N reg. fix. 9.19 1.78

8.85 1.26

8.87 2.13

8.68 1.89

Compr./N reg. saccades 10.03 1.89 9.47 1.18 9.56 2.21 9.40 2.02

Compr./time regr.** 5.43 1.98

4.43 0.92

5.85 1.61

4.51 1.43

Compr./time large reg.** 6.33 2.38

4.99 1.16

6.73 1.92

4.98 1.64

Compr./rereading time** 6.53 2.56

5.03 1.12

6.99 2.13

5.14 1.70

Compr./time small reg. 9.62 1.93 9.46 1.59 9.26 2.15 9.68 2.19

Note. LA: Low anxious group, N = 17. HA: High anxious group, N = 16.

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The effects of anxiety on efficiency measures computed using the proportion

of total time that was spent on regressions were non-significant and aren’t reported

here (see Appendix 11 for their effect sizes).

None of the effects of manipulation or the interaction effects of manipulation

and anxiety on efficiency variables were significant. This may reflect the failure of the

manipulation to induce state anxiety in the subjects. See Appendix 11 for effect sizes.

Conclusion concerning efficiency

The interaction effects of manipulation and anxiety were trivial and non-

significant for all of the reading efficiency measures. Hypothesis 3 was not supported.

There was, however, a main effect of anxiety on efficiency measures when

described using time spent on regressions, large regressions and rereading. Anxious

individuals were less efficient than non-anxious participants.

Effects of task version 3.5As mentioned in section 2.7, the sets of text stimuli (A or B) used in the ego-

threatening and control condition along with the order in which the 2 tasks were

presented were counterbalanced. Due to a loss of participants however, low and high

anxious participants were unevenly distributed within the text version groups (see

Table 3.9).

A series of 2 × 2 mixed factorial ANOVAs was computed to check for the

possible effects of task version (task version × manipulation) and condition order

(order × manipulation) on reading and comprehension measures.

Table 3.9

Table of N for task versions, task order and sex according to anxiety

LA HA N

Text in ego-threat A 8 7 15

B 9 9 18

Order of ego-threat 1 8 8 16

2 9 8 17

Sex F 10 11 21

M 7 5 12

Total 17 16 33

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There was no significant effect of task order on any of the variables.

A significant effect of task version was however present for several variables, whereas

other variables were significantly affected by the interaction of text type and

manipulation.

The significant effect of task version used in the ego-threat task was found for

reading time (F(1, 31) = 6.56, p = 0.016, ηp² = 0.18), time spent on regressions (F(1, 31)

= 9.22, p < 0.01, ηp² = 0.23) and the number of fixations (F(1, 31), p = 0.014, ηp² = 0.18).

The effect of task version on comprehension was small and non-significant, but the

interaction effect of text version and manipulation on comprehension scores was large

and significant (F (1, 31) = 30.63, p < 0.001, ηp² = 0.50, ηG² = 0.26). There were also

significant interaction effects of task version and manipulation on most efficiency

variables (see Table 3.11).

Table 3.10

Reading and efficiency measures according to task version (A, B) and experimental condition (1, 2)

Control

Ego- threat

Text M SD Text M SD

Comprehension Gr. A1 B1 12.87 2.20

A2 10.20 2.76

Gr. B2 A1 12.11 1.64

B2 13.11 1.49

Altogether 12.46 1.92 11.79 2.58

Reading time (s) Gr. A B1 298 77

A2 284 69

Gr. B A1 375 73

B2 317 72

Altogether 340 83 302 72

Time regressions (s) Gr. A B1 132 65

A2 119 69

Gr. B A1 203 56

B2 160 67

Altogether 171 69 141 70

Compr./time Gr. A B1 9.92 1.68

A2 7.95 2.20

Gr. B A1 8.85 1.36

B2 10.00 1.41

Altogether 9.33 1.58 9.07 2.07

Compr./N fixations Gr. A B1 6.14 1.10

A2 4.96 1.44

Gr. B A1 5.13 0.96

B2 6.11 1.12

Altogether 5.59 1.13 5.59 1.38 1 Group A – participants who read set A of texts in the ego-threat condition, N = 15.

2 Group B – participants who read set B of texts in the ego-threat condition, N = 18.

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When comparing scores between groups (see Table 3.10), the differences

showed that participants found set A more difficult than set B. Altogether, they

achieved lower comprehension scores in version A and also spent more time reading

it, made more regressions and achieved lower reading efficiency than in task version

B.

The most important question to answer is whether this effect of text version

may have biased the effects of anxiety on reading variables found in the analyses

described above. This is especially important considering that the groups were uneven.

The group with task version A in the control condition consisted of 15 participants,

out of which only 7 belonged to the anxious group. Task version B in the ego-threat

condition was presented to 18 participants, with even low and high anxiety subgroups

(9 participants in each group). See table 3.9.

Seeing the direction of the effects and taking into consideration that group B

is altogether “more anxious” than group A, it seems improbable that the effects of task

version have confounded the effect of anxiety detected in analyses reported above.

Additional 2 × 2 × 2 (task version × anxiety × manipulation) ANOVAs

confirmed that anxiety effects on primary reading variables prevailed when task

version was entered into the model10 . The interaction of task version × anxiety

× manipulation was significant for reading time (F (1, 29) = 4.74, p = 0.038,

ηp² = 0.23), the number of fixations (F (1, 29) = 5.84, p = 0.022, ηp² = 0.17) and time

spent on large regressions (F (1, 29) = 4.87, p = 0.035, ηp² = 0,14). The interaction was

also significant for reading efficiency expressed using time spent on regressions

(F(1, 29) = 4.31, p = 0.047, ηp² = 0.23) and time spent rereading (F(1, 29) = 4.74,

p = 0.038, ηp² = 0.23).

Table 3.11

Text variety × manipulation interaction effects for variables of efficiency

Efficiency defined as: F p ηp² ηG²

1. Compr./time

35.63 < 0.001 0.54 0.28

2. Compr./regress.

15.21 < 0.001 0.33 0.17

3. Compr./rereading

14.39 0.001 0.32 0.16

4. Compr./N fixations 34.02 < 0.001 0.52 0.27

5. Compr./regr. sacc. 31.55 < 0.001 0.50 0.28

10 2 × 2 × 2 ANOVAs were calculated only for selected variables, see table 3.12.

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The main effects of anxiety on reading variables presented in the previous

analysis not only remained significant, but were enhanced when text version was

included in the model. The main effects of anxiety were F(1, 29) = 9.54 (p = 0.004,

ηp² = 0.25) for time spent on regressions; F(1, 29) = 11.25 (p = 0.002, ηp² = 0.28) for

time spent on large regressions and F(1, 29) = 10.74 (p = 0.003, ηp² = 0.27) for time

spent rereading. There where the main effect of anxiety was absent in the initial

analysis, it remained absent also in the model correcting for text version.

In conclusion, text version influenced the way participants read in both

conditions, and their comprehension scores in the ego-threat condition. However,

additional analyses introducing text version into the model have shown that this effect

did not confound the previously observed anxiety effects, but was more likely

to weaken them.

Table 3.12

Text variety × anxiety × manipulation interaction effects

F p ηp²

Comprehension 0.03 0.872 < 0.01

Reading time 4.74* 0.038 0.23

Time regressions 4.05 0.053 0.12

Time large fixations 4.87* 0.035 0.14

Time rereading 4.70* 0.038 0.14

N fixations 5.84* 0.022 0.17

Compr./time 0.46 0.503 0.02

Compr./regressions 4.31* 0.047 0.13

Compr./rereading 5.52* 0.026 0.16

Compr./N fixations 1.98 0.170 0.06

Compr./regr. saccades 0.46 0.501 0.02

*Interaction effect was significant at the 0.05 level.

Summary of results 3.6H1. Main effects of anxiety

a. The effect of anxiety on reading time was non-significant (F(1, 31) = 2.97,

p = 0.10, ηp² = 0.09). The hypothesis was not sufficiently supported.

b. The effect of anxiety on number of fixations was non-significant (F(31, 1) = 1.62,

p = 0.212, ηp² = 0.05). The hypothesis was not supported.

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c. There was a significant main effect of anxiety on time spent on regressions (F(1, 31)

= 7.87, p < 0.01, ηp² = 0.20). Anxious individuals spent more time returning to

previously read parts of the text in the control (ΔM = 26 s) and the ego-threat

condition (ΔM = 76 s).

d. There was a significant main effect of anxiety on the ratio of time spent on regressions to total time (F(1, 31) = 10.27, p < 0.01, ηp² = 0.25). Anxious participants

dedicated a greater proportion of their reading time to regressions than non-

anxious participants.

H2. Interaction effects of anxiety and manipulation on reading measures

a. The interaction effect of anxiety and manipulation on total reading time was non

significant (F(1, 31) = 3.51, p = 0.07, ηp² = 0.19). However, non-anxious

participants spent significantly less time on average than anxious participants

in the ego-threatening condition (ΔM = 64.26 s, t (31) = -2.80, p = 0.01, 95%

CI [-17.41, -111.11], Hedges’s gs = 0.96). The data therefore indicated the

expected effect but the effect was not strong enough to reject the null

hypothesis.

b. There was a significant interaction of manipulation and anxiety on the number of fixations (F (1, 31) = 4.78, p = 0.036, ηp² = 0.13, ηG² = 0.06). Anxious

individuals made more fixations in the ego-threat condition than non-anxious

participants.

c. The interaction effect of anxiety and manipulation on the time spent on regressions was non-significant (F (1, 31) = 4.14, p = 0.051, ηp² = 0.12).

d. The interaction effect of anxiety and manipulation on the ratio of time spent on regressions to total time was also non-significant (F (1, 31) = 3.69, p = 0.064,

ηp² = 0.11).

H3. Interaction effects of anxiety and manipulation on reading efficiency

H3 a. – c.: Interaction effects of anxiety and manipulation on all of the reading

efficiency measures were small and non-significant.

There was a significant main effect (F(1, 31) = 7.87, p < 0.01, ηp² = 0.20)

of anxiety on efficiency computed as the ratio of comprehension to time spent on regressions, with anxious individuals achieving lower efficiency scores than non-anxious

participants. The effect was, however, comparable between the two conditions.

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The effect of anxiety on reading efficiency computed as the ratio

of comprehension to time spent on regressions, the most intuitive of the measures

of efficiency, was very small and non-significant (F(1, 31) = 0.40, p = 0.531, ηp² = 0.20).

Comprehension

In agreement with our expectations, the effect of anxiety on comprehension

scores was small and non-significant (F(1, 31) = 0.01, p = 0.94, ηp² < 0.01).

Task version effects

There were significant effects of task version on reading variables. Task version

A was more difficult for participants, and this effect was pronounced for the ego-

threat condition. This effect should not have confounded the previously observed

main effects of anxiety, but may have weakened them.

4 Qualitative manual analysis of regressive scanpaths The manual analysis of a subsample of regressive scanpaths was conducted as

a part of data exploration and also a necessary step in the development of regression-

computing software.

Small, large and rereading regressions were recognized as distinct reading events.

Small regressions had the length of 5 to 15 letter spaces and were usually made

immediately following the first pass reading of the given word or segment. A subtype

of small regressions was recognized, which we called “clusters”. Unlike simple small

regressions that returned to a word once and continued forward again, the eyes shifted

forward and back again multiple times in a cluster. Cluster regressions were often

located either around difficult or unusual words, or at the end of sentences and

paragraphs, indicating integration processes and sentence wrap-up (see Just

& Carpenter, 1980, also section 5.1 in part I). Large regressions (longer than 15 letter

spaces) usually occurred either as jumps to words on another line or as rereading

regressions. Within lines jumps without rereading were rather rare.

Other distinctions between regressions could potentially be made. There were

visible differences in the density of fixations in rereading regressions between readers.

This offered a possibility of differentiating between rereading (usual density of fixations)

and rescanning (less dense and shorter fixations). Correspondingly, first pass reading

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could sometimes be considered first pass scanning. Whether reading the text again after

such scanning should be considered as a regression is disputable.

Regressive patterns

During the manual analysis of the scanpaths, distinct “regressive styles” could be

recognized. Our observations more or less corresponded with Goldman and Saul’s

(1990) global reading approaches. Goldman and Saul compared how students read

short structured texts in various signalling conditions. They identified three global

and local reading approaches. Similar global approaches could be recognized also

in our data.

The first global approach described by Goldman and Saul is Once Through. This

corresponds to a straightforward, direct reading of the entire text from start to finish.

The second approach, Review, was used when the subject read the entire passage once

through to the end and then returned to reread either part or all of the text. The

third approach was Regress. In this approach, rereading occurred before the reader

reached the end of the text.

Hyöna, Lorch, & Kaakinen (2002) explored individual differences in reading in

a task where the respondents’ aim was to make a summary of the expository text.

Hyöna and his colleagues identified four different types of readers. The first were fast linear readers. Their reading was characterized by the absence of regressions.

The second group were slow linear readers, which made many forward fixations and

reviewed each sentence before continuing onward. The third group were nonselective readers. These often looked back during their reading to previously viewed sentences.

The final group was named topic structure processors. Topic structure processors paid

increased attention to headings, and were found to have the largest working-memory

capacity and write the most accurate text summaries. The distinction between fast linear readers and slow linear readers could be also applied to scanpaths in our data.

In the analysed regressive scanpaths, we also recognized a difference in how

erratic or confused participants’ regressions seemed. Considering that erratic eye-

movements are associated with mindless reading (see section 6.2.5 in part I), it may be

that anxiety leads to an increase of this type of “chaotic” regressions. See Appendix 12

for selected images of various regressive scanpaths.

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Conclusion

Regressions observed in participants’ scanpaths were diverse in a number

of characteristics. Regressions do not constitute a unified measure because they

correspond to many distinct events, strategies, intentions, etc. In order to gain a more

precise understanding of how anxiety affects anxious readers, causing them to spend

more time on reading regressions, further analysis and operationalization of the types

of regressive events is needed. To truly understand how anxiety influences regressions,

measures including the function of regressions, and not only their amount and

durations, would be helpful.

Many questions arise from the present analysis: Which kind of regressions

is most responsible for the differences between low and high anxious subjects? How

do regressions change under state anxiety? Do individuals high in trait anxiety tend to

use different reading strategies than low-anxious individuals, regardless of the current

level of state anxiety? Is anxiety associated with how erratic regressions are?

Are regressions more often the result of strategic choice, or rather the result

of comprehension difficulties or mindless reading? Future studies may lead to a better

understanding of why anxious individuals make more regressions when they read.

5 Discussion The present study successfully replicated some of the effects from previous studies,

which are in agreement with the ACT theory. The effect of anxiety on comprehension

was non significant. Anxious and non-anxious participants achieved comparable

comprehension scores, which is in line with ACT’s premise that anxiety does not

primarily affect performance effectiveness.

There was a main effect of anxiety on time spent on regressions. Anxious

participants spent more time on regressions, large regressions and rereading than

their non-anxious peers in both conditions. The difference was stronger when

regressions were described in proportion to total reading time. Moreover, anxious

participants’ reading efficiency was lower than that of non-anxious subjects when

efficiency was calculated using these regressive measures. This can be interpreted as

the negative effect of anxiety on processing efficiency, as Calvo and his colleagues

interpret it in their study (1994b).

Several expected effects were non-significant in this study, namely the effect

of anxiety on total reading time and on the number of fixations, observed in previous

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studies (Dizney, Rankin, & Johnston, 1969; Calvo et al., 1994), were both non-

significant. Even in Calvo et al.’s study however, these effects were significant only

in some of the experiments. The effect may perhaps be weakened by the variability

of reading time scores and fixation counts in non-threatening conditions, and/or there

may be some yet undescribed moderating variables of the effect of anxiety on reading

time. While the main effects of dispositional anxiety on total reading time and the

number of fixations were non-significant, the main effects of anxiety on efficiency computed as the ratio of comprehension to total reading time and to the time spent on

fixations were close to zero. This indicates that anxious individuals were no less

efficient than non-anxious in the most intuitive of efficiency measures.

The absence of significant effects of anxiety x manipulation interactions in this

study are most likely the result of manipulation failure. As indicated by the results

of the manipulation check and that of the main statistical analysis, ego-threatening

instructions had only a minor effect on the participants. The scores of the high-

anxious group indicate that anxious individuals may have felt increased anxiety

in both the control and ego-threatening condition. The experimental procedure itself

can be anxiety inducing, considering it occurs in a laboratory settings and participants

have to read with their head fixed in an eye-tracking tower. Whichever the case, the

difference between the two experimental conditions was not sufficiently large in order

to examine the difference in how anxious and non-anxious participants read under

considerably different levels of state anxiety.

The effect of manipulation on low-anxious 5.1participants Although it was hypothesized that the difference between anxious and non-

anxious individuals would be greater in the ego-threat condition than in the control

condition, it was, surprisingly, non-anxious participant who were responsible for the

difference where it was present. In the ego-threatening condition, low-anxious subjects

read faster, made less fixations and regressions, while achieving only slightly lower

comprehension scores than in the control.

There are several possible explanations of this effect. One of the explanations

may be the already mentioned possibility that the ego-threat condition did not have

an effect on high-anxious individuals in comparison to the control because anxious

participants experienced state anxiety in both tasks. Non-anxious individuals, on the

other hand, were not anxious in the control task, and therefore the difference between

the two tasks was greater.

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The second possible explanation is that the difference between the control and

experimental condition for low-anxious participants may not have been the result of

increased anxiety in the ego-threat condition, but rather of increased motivation. The

ego-threatening instructions stressed the importance of speed in the task while

achieving the best possible comprehension score, and the inclusion of a countdown

before each text created time pressure. For non-anxious participants, it is easy to

interpret these instructions as challenging rather than threatening. Non-anxious

participants may have felt more motivated in the ego-threat condition, resulting in

faster reading and a decrease in regressions.

A third factor that may play a part in this observed effect is that of task strategy. It is possible that the differences between low and high anxious participants reflect

a preference for risk in the non-anxious participants and a preference of certainty in

anxious subjects. Applied in the study’s context, it may be that low-anxious

individuals decided to opt for a reading strategy that prioritizes speed, whereas high-

anxious individuals chose a strategy favouring precision. The failure of anxious

subjects to increase their speed in the ego-threatening condition may reflect

an inability of high-anxious individuals to flexibly adapt their reading strategy. This

would correspond with ACT’s postulate that anxiety has a negative effect on the

shifting functions of the central executive. The differences on which these

assumptions are based however are small, and the effects would need to be replicated

in order for the assumptions to gain importance. For the present moment, they may

serve as suggestions for future research.

The proposed explanations are not antagonistic. The anxiety-performance

relationship is known to have many moderator (see section 3.3 in part I.), and all

of the suggested factors may play a role

Does an increase in time spent on regressions 5.2correspond to lower reading efficiency? The only measures of reading efficiency significantly affected by anxiety were

those comparing comprehension to the time spent on regressions. The effects

of anxiety on efficiency adjusted to total reading time and to the number of fixations

were non-significant and negligible in size.

It is possible that real effects were masked in this study due to a small sample

size, unintended effects of text variety and the unsuccessful manipulation of state

anxiety. Moreover, the effects of anxiety on reading time and the number of fixations

were nearly significant. On the other hand, Calvo and his colleagues (1994) yielded

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similarly ambivalent results concerning the differences of anxious and non-anxious

participants in total reading time.

However, it remains a question whether the amount of time spent on

regressions is a valid indicator of processing efficiency. Since individuals that regress

may spend an equal amount of time and make an equal amount of fixations during

their reading in order to achieve the same comprehension scores as non-regressors,

spending more time on regressions may just be an alternative, equally efficient

reading strategy. It is also possible that making regressions does, in fact, use

up a greater amount of cognitive resources than more direct reading patterns, but

there is no empirical evidence at the moment to support this assumption.

Moreover, distinct reading patterns were recognized during manual scanpath

exploration even within individuals who spent more time on regressions. Some

regressive paths were direct, while others were erratic and chaotic. Let us consider

two readers. One reads the text once through at a quick pace and then returns to the

beginning and reads it a second time, in a similarly direct fashion as the first time.

The second reader reads the text only once at a much slower pace, with a greater

amount of fixations and small regressions. If their reading times and comprehension

scores are equivalent, is it justifiable to regard the first reader as less efficient than

the second?

Further research is needed to explore differences in reading style, reading

strategy and situation-specific reading events. Due to significant inter- and intra-

individual differences in reading, comparing the eye-movements of a large sample of

readers in a sufficient amount of texts would yield the most informative results.

To differentiate between the effects of a more stable reading style or strategy and those

of state anxiety on eye movements, achieving a significant difference in state anxiety

between conditions is necessary.

A second look at regressions 5.3One of the aims of this study was to examine the reading regressions that

anxious readers make. Several types of regressions were identified on the basis of

manual data exploration, enabling the creation of separate measures of time spent on

regressions. These separate measures were large regressions, small regressions and re-

reading regressions. Eye tracking also allowed for measures of the number of

regressive eye-movements, i.e regressive saccades and fixations (described in section

2.4.4).

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Data analysis revealed that large regressions and rereading regressions make

up the majority of time spent on regressions. Small regressions, on the other hand,

are related to regressive eye-movements. Anxiety significantly affected the amount

of large regressions, most of which were also re-reading regressions. Smaller

regressive events were not affected by anxiety.

These findings suggest that the difference between anxious and non-anxious

participants is not associated with eye-movement control or lower word-level cognitive

processes. The data indicate it is more likely that anxiety affects higher-level cognitive

processes, associated with sentence integration and the making of inferences.

The prevalence of long rereading regressions does not seem to reflect a process

of returning to specific words that were lost from working memory due

to an increased working memory load in anxious participants. It is possible, however,

that increased working memory load leads to comprehension failures, inducing

participants to reread entire passages. But there are also alternative explanations as

to what causes the difference of regressive behaviour in anxious and non-anxious

participants. Rereading could also result from mindless reading or from a difference

in reading strategy.

The proposition that the difference between anxious and non-anxious readers

may have to do with distinct reading strategies was already mentioned above. Support

for this assumption comes from the manual exploration of regressive scanpaths

(see section 4). Reading a text twice just as quickly from the beginning to the end does

not seem like the result of comprehension difficulties or increased working memory

load. Regressions have also been known to increase comprehension (see Schottter,

Tran, & Rayner, 2014). A more regressive strategy may be used to make preliminary

inferences in first pass reading, and confirming these first assumptions during second

pass reading.

Study limitations 5.4The results reported in this study may have been biased in several ways. The

effect of task version has been explained and may have resulted in the masking of

existing effects. Unequal numbers of anxious and non-anxious participants within the

task version groups due to data loss may have been the source of additional bias.

The main limitation of this study is the already mentioned insufficiency of the

experimental manipulation. Attaining a sufficient difference of state anxiety between

the control and manipulation is necessary for understanding the direct effects of state

109

anxiety on reading measures for anxious and non-anxious participants. The fact that

the experimental manipulation of anxiety did not succeed in inducing a significant

level of state anxiety also complicates the interpretation of results. Both of the reading

tasks may have been anxiety-inducing for high-anxious individuals.

For manipulation to be more successful, participants needed for the risk

of failure to be of greater importance than it was in the experimental situation.

To successfully induce ego-threat, telling the participants they would see their results

in comparison with others at the end of the task and giving them false feedback,

would most probably achieve better results. Participants could also be told that their

results were part of a group score, or that their performance might effect the success

of the research itself, which would then lead them to feel under greater pressure.

A set time limit could also be employed to increase time pressure. The task goal for

the participant would then be to read as many short texts and correctly respond to

as many comprehension questions as possible.

The results may not be generalizable to “normal” reading due to the unnatural

reading conditions. The participants read with their heads fixed in an eye-tracking

device and the experiment took place in a laboratory setting in the presence of an

experimenter. This may have mostly influenced anxious participants. The increased

difficulty of the chosen texts may have lead participants to make more regressions

than they would in regular reading. A more extensive pilot study of the selected texts

could have achieved higher task version comparability.

Several effects were indicated in the expected direction but were non-significant.

This could have been prevented by analysing data of a larger sample

size. The intended sample size was not achieved due to invited participants not

coming to the second experimental part of the study and due to data loss caused by

calibration and other technical failures.

Another limitation of the study is that only summed scores for all seven texts

of a task were analysed. A text-level analysis would have reduced potential effects of

text differences across tasks and increased statistical power of the analyses. Also, the

texts used as stimuli were difficult not only in structure, but also in their topics and

the vocabulary used, as was mentioned by several participants in their feedback.

It may be a better design strategy to artificially create structurally difficult texts

concerning familiar and simple topics, and thus minimize potential between-subject

differences in topic familiarity.

110

Implications for future research 5.5

As already mentioned above, future research examining the effect of anxiety on

reading should focus more on assessing participants’ reading strategies. A more

functional analysis of regressions, describing regressions according to their purpose in

reading, would help clarify what leads anxious participants to regress. A word level

analysis of eye-movements preceding regressions could indicate to what degree

mindless reading is the cause of regressions. To better assess the direct influence of

state anxiety on reading measures, it may be helpful to use online indicators of

anxiety simultaneously with online eye tracking.

111

Conclusion The results of the present study successfully replicated the effect of anxiety on

time spent on regressions, observed in a previous study by Calvo et al. (1994b). Eye-

tracking technology enabled us to specify that the observed regressions mostly served

to reread text passages. Anxious participants spent more time on regressions, large

regressions and rereading regressions than their low-anxious peers. There was

no difference in comprehension between anxious and non-anxious participants.

The effects of anxiety on reading time were non-significant and they were

negligible for reading efficiency computed as the ratio of comprehension to total

reading time. This has called into question the adequacy of using time spent on

regressions as an indicator of reading efficiency. A manual exploration of reading

scanpaths indicated that there are many distinct processes causing regressions

in reading and the amount of time spent on regressions may also be the consequence

of a participant’s reading strategy.

The study represents only a very small step towards describing and clarifying the

relationship of anxiety and reading. It does, however, offer a framework for future

designs that aim to explore the effects of anxiety on reading regressions. Its principal

implications for future research are the need to focus on reading strategies and

to investigate the possible link of regressions and mindless reading.

The differentiation of various types of regressions and the development of software for

computing time spent on rereading may prove useful for future researchers interested

in this topic. It is the author’s hope that this thesis will incite this interest and bring

attention to a small area of research that lay dormant for nearly two decades.

112

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List of tables Table 3.1. Manipulation check – means of combined scores p. 86

Table 3.2. Man. check – main effects of manipulation on combined scores

p. 87

Table 3.3. Man. check – main effects of anxiety on combined scores p- 88

Table 3.4. Manipulation check - means of combined scores in low and high anxiety groups

p. 89

Table 3.5. Chosen descriptive statistics of reading and efficiency measures

p. 91

Table 3.6. Means of reading measures for anxious and non-anxious groups

p. 93

Table 3.7. Means of regression measures for anxious and non-anxious groups

p. 95

Table 3.8. Means of efficiency measures for anxious and non-anxious groups

p. 97

Table 3.9. Table of N for task versions, task order and sex according to anxiety.

p. 98

Table 3.10. Reading and efficiency measures according to task version (A, B) and experimental condition (1, 2)

p. 99

Table 3.11. Text variety × manipulation interaction effects for variables of efficiency

p. 100

Table 3.12. Text variety × anxiety × manipulation interaction effects p. 101

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List of figures Figure 2.1. Flow of participants from questionnaire to final sample.

p. 70

Figure 2.2. Reading efficiency described as the ratio of reading comprehension and reading time

p. 76

Figure 2.3. Adjusted formula for the computation of reading efficiency described using regressions.

p. 76

Figure 2.4. Detecting corrective in-line regressive saccades

p. 83

Figure 2.5. Marking the end times of regressions

p. 84

Figure 3.1. Interaction plot of the manipulation × anxiety effect on the number of fixations

p. 94

Figure 3.2. Interaction plot of the manipulation × anxiety effect on the number of regressive fixations

p. 94

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Appendices

Appendix 1. Study information and informed consent

Appendix 2. Transformed efficiency formulas

Appendix 3. Texts and comprehension questions

Appendix 4. Task instructions

Appendix 5. Link to tailored software for computing regressions

Appendix 6. Manipulation check items and combined scores

Appendix 7. Descriptive statistics of manipulation check items

Appendix 8. Table of mean scores for individual items of the manipulation check

according to dispositional anxiety

Appendix 9. Correlation matrices of reading measures

Appendix 10. Descriptive statistics of reading and efficiency measures

Appendix 11. Effects of anxiety on reading and efficiency measures

Appendix 12. Examples of regressive scanpaths

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Appendix 1. Study information and informed consent

Adapted to text from Google forms format.

1) Introduction

VÁŽENÁ ÚČASTNICE, VÁŽENÝ ÚČASTNÍKU,

děkuji za to, že máte zájem o účast na výzkumu 'Vliv úzkosti na efektivitu čtení: eye-trackingová studie' a že jste si udělal/a čas na vyplnění tohoto dotazníku.

Jmenuji se Markéta Dohnalová a tuto studii provádím jako součást své diplomové práce z psychologie na Filozofické fakultě Masarykovy univerzity.

Studie zkoumá vztah mezi úzkostí, myšlenkovými procesy a očními pohyby které probíhají během čtení. Výsledky studie tak pomohou k hlubšímu a podrobnějšímu porozumění procesům, kterými může úzkost způsobovat obtíže při čtení. Tyto poznatky mohou pomoct lidem trpícím nadměrnou úzkostí, a také přispět k lepšímu porozumění emočním faktorům, které mohou komplikovat fungování lidí s poruchami učení, zejména s dyslexií.

Před vyplněním samotného dotazníku Vám budou předloženy informace o studii a požádám Vás o vyplnění informovaného souhlasu. Celkem 95 položek dotazníku slouží k posouzení Vaší tendence k prožívání úzkosti. Celý dotazník by Vám měl zabrat přibližně 20-30 minut, ačkoli se čas může lišit dle Vašeho individuálního tempa. Nejedná se o diagnostickou metodu, tedy nemusíte se bát, že by Vám někdo na základě Vašich odpovědí přiřadil diagnózu nebo hodnotil, do jaké míry jste "normální". Vyplnění dotazníku je předpokladem pro účast na druhé části výzkumu, která probíhá v laboratoři.

Dotazník, prosím, NEVYPLŇUJTE, POKUD: a) nejste rodilý mluvčí českého jazyka, b) nemáte zájem účastnit se experimentální části studie v laboratoři, nebo c) trpíte závažnou oční vadou nebo poruchou učení (např. dyslexií), která by Vám bránila v plnění úkolů v experimentální části.

V případě, že budete vybráni i do druhé fáze experimentu, obdržíte pozvánku na Vaši e mailovou adresu. Druhá fáze experimentu pak bude probíhat v týdnech od 25. 2. do 1. 3. 2019 a od 11. 3. do 15. 3. 2019. Budete si moct zvolit čas (bloky budou hodinové), který se Vám hodí.

Děkuji Vám za Váš zájem a čas.

S úctou

Markéta Dohnalová

Studentka psychologie FF MU

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2) Informace o výzkumu

Název studie

The Effect of Anxiety on Reading Efficiency – An Eye-Tracking Study (Vliv úzkosti na efektivitu čtení – eye-trackingová studie)

Základní informace o studii

KDY STUDIE PROBÍHÁ?

Studie probíhá od Listopadu 2018 do Dubna 2019

KDO TUTO STUDII ORGANIZUJE A PROVÁDÍ?

Hlavní výzkumník Markéta Dohnalová (1,2) (studentka oboru Psychologie na Filosofické fakultě Masarykovy univerzity) za podpory vedoucí práce Mgr. Tatiany Malatincové, Ph.D. (2) a Laboratoře pro experimentální humanitní vědy (HUME lab) Veveří 28 (budova K — 1. nadzemní podlaží) 602 00 Brno

(1 - HUME lab, Filosofická fakulta, Masarykova Univerzita, Brno) (2 - Psychologický ústav Filosofické fakulty Masarykovy univerzity v Brně) O čem je tato studie? Jak bude probíhat?

O ČEM JE TATO STUDIE:

Studie je součástí diplomové práce a jejím cílem je blíže porozumět vlivu úzkostnosti na efektivitu čtení.

JAK A KDE BUDE STUDIE PROBÍHAT?

Studie probíhá ve dvou částech. První částí je vyplnění online dotazníku týkajícího se úzkostnosti, druhou částí je následné měření očních pohybů při čtení, které probíhá v laboratoři a na které obdrží vybraní jedinci pozvání. Vyplnění dotazníku nezaručuje Vaše pozvání na druhou část výzkumu v laboratoři, je však jeho nutnou podmínkou, stejně jako Vaše udělení souhlasu s účastí ve studii.

První část

První částí studie je dotazník úzkostnosti. Jedná se o soubor celkem 95 otázek, na které odpovídáte buď na 5 či 6bodové škále, nebo pomocí ano/ne. Otázky jsou zaměřené na Vaše prožívání každodenních a testových situací. Vyplnění dotazníku trvá přibližně 20-25 minut. Dotazník není součástí diagnostického nástroje, nezjišťuje tedy patologii, pouze posuzuje míru běžné úzkostnosti.

Druhá část

Druhá část studie bude probíhat ve výzkumné laboratoři HUME lab II. Jedná se o dvojici čtecích úloh, při kterých budou měřeny pohyby Vašich očí. V laboratoři byste neměli strávit více než hodinu, přesný čas se však může různit podle Vaší individuální rychlosti a též podle úspěšnosti nastavení přístroje.

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Průběh druhé části:

Pozvánku na druhou část experimentu s konkrétním časem, kdy se máte dostavit do laboratoře, obdržíte e-mailem. Budete požádáni, abyste pokud možno přicházeli bez očního líčení či umělých řas, a abyste, pokud trpíte refrakční vadou, dali přednost brýlím bez silných obrouček nebo kontaktním čočkám.

V domluveném čase se poté dostavíte na určené místo a budete uvedeni do laboratoře, kde nejprve obdržíte informace o tom, co Vás čeká, a bude Vám dán prostor pro dotazy.

Poté se posadíte se k eye-trackingovému přístroji, kterým budou měřeny Vaše pohyby očí. Eye-tracking je metoda, která měří pohyby očí za pomocí odrazu infračerveného záření z Purkyňových obrazců oka. Běžně se využívá ke studiu zrakové pozornosti. (Viz fotku vysokorychlostního eye-trackingového přístroje níže.) Po nastavení přístroje Vám nejprve bude na obrazovce předložena příkladová čtecí úloha, kde si ve zkrácené verzi vyzkoušíte, jak budou vypadat následující úlohy. Proběhne kalibrace - nastavování přístroje, aby správně registroval Vaše oči, a případná dodatečná nastavování pro co nejpřesnější měření očních pohybů.

Následovat budou dvě čtecí úlohy, během kterých budou měřeny pohyby Vašich očí. Na obrazovce Vám budou předkládány konkrétní instrukce k úloze, texty ke čtení i následné otázky na porozumění textu. Mezi dílčími úlohami bude též probíhat rekalibrace. Po dokončení úloh vyplníte krátký dotazník, ve kterém odpovíte na otázky týkající se toho, jak se Vám na úlohách pracovalo.

Studií Vás bude provázet výzkumný/á asistent/ka, který/á bude zajišťovat kalibraci a na kterého se budete moct kdykoli během experimentu obrátit.

Jaké jsou podmínky pro účast na studii?

Vzhledem k povaze úlohy je nutné, aby byli respondenti rodilými mluvčími českého jazyka. Studie se dále nemůžete účastnit, pokud trpíte závažnými očními vadami nebo dyslexií. Refrakční oční vady (např. krátkozrakost) nejsou sami o sobě problémem, ale brýle, zejména v případě cylindrických vad (astigmatismus), snižují pravděpodobnost úspěšné registrace Vašich očí eye-trackingovým přístrojem. V případě neúspěšné registrace pohybů nebo nedostatečné přesnosti naměřených dat nemohou být Vaše data ve výzkumu použita.

Jaký je přínos studie?

Vaší účastí přispějete k porozumění vlivu úzkosti na poznávací procesy, konkrétně na čtení. Tyto poznatky mohou být dále využity pro vyvinutí metod a programů pro podporu jedinců s úzkostnými poruchami či s poruchami učení.

Co když budu chtít od studie odstoupit?

Vaše účast ve studii je dobrovolná. Můžete odmítnout účast v této studii a můžete kdykoli do uzavření sběru dat bez udání důvodů odstoupit od této studie a to bez postihů nebo ztráty výhod, k nimž byste byli jinak oprávněni. Vaše data budou v tomto případě z databáze odstraněna, po uzavření sběru dat však budou data zcela anonymizována (bude odstraněn dokument párující výzkumná data k osobním a identifikačním údajům) a nebude tedy již možné zpětně dohledat, která data patří Vám.

Jak budou zpracovány a chráněny mé osobní údaje?

Odpovědi z dotazníku v první části studie a v případě účasti na druhé části studie též výsledky měření budou ukládány pod číselným kódem, který Vám bude přiřazen. Vaše identifikační údaje (jméno a e-mailová adresa) potřebné pro komunikaci s Vámi (a též pro spárování dat z dotazníku s daty z experimentu) budou společně s číselným kódem uloženy ve zvláštním

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souboru. K tomuto souboru bude mít přístup pouze hlavní výzkumnice, bude chráněný heslem a uložen odděleně od ostatních dat na bezpečném úložišti, odděleně od ostatních dat, které bude též chráněno heslem. Laboratoř HUME Lab a vedoucí diplomové práce budou mít přístup pouze ke kódovaným datům a nebudou mít k dispozici Vaše osobní údaje.

Po ukončení sběru dat bude soubor obsahující Vaše osobní údaje a kódy zničen a data budou uchována pouze v anonymní podobě. Tato data již bez osobních a identifikačních údajů (tj. anonymizovaná) mohou být pak v rámci otevřené vědy přístupná i dalším výzkumníkům.

Na koho se mám obrátit v případě dotazu?

Máte právo kdykoli v průběhu výzkumu klást otázky.

Pokud máte jakýkoliv dotaz týkající se této studie, obraťte se na:

Hlavního výzkumníka:

Markéta Dohnalová

438070@mail.muni.cz

Vedoucí diplomové práce:

Mgr. Tatiana Malatincová, PhD.

malatincovie@mail.muni.cz

nebo na

hume.lab@phil.muni.cz

Laboratoř pro experimentální humanitní vědy (HUME lab)

Veveří 28 (budova K — 1. nadzemní podlaží), 602 00 Brno

Tel. číslo: 549 49 8991

Tento projekt byl schválen Etickou komisí pro výzkum Masarykovy univerzity. V případě dotazů, nejasností či připomínek k průběhu výzkumu můžete kontaktovat vedení komise na adrese ekv@muni.cz.

3) Informovaný souhlas

Souhlas s účastí ve výzkumném projektu a se zpracováním osobních údajů

UDĚLENÍ SOUHLASU S ÚČASTÍ NA VÝZKUMU

Prohlašuji, že jsem četl/a text Informace o výzkumu uvedený v předchozím bloku a porozuměl/a jsem jeho smyslu.

Souhlasím s mojí účastí v uvedeném výzkumném projektu a rozumím, že mohu souhlas odmítnout, případně svobodně a bez udání důvodů z účasti odstoupit.

Zároveň souhlasím s poskytnutím svých osobních údajů v rozsahu svého jména, e-mailové adresy, odpovědí z dotazníku a naměřených dat a jsem srozuměn/a s tím, že mé jméno a adresa NEbudou uloženy spolu s výzkumnými daty a poslouží výhradně pro komunikaci s výzkumníkem během sběru dat a pro spárování dat z první a druhé části výzkumu za pomocí kódu.

o Souhlasím

o Nesouhlasím

(účastník zaškrtl odpovídající)

130

Souhlas s využitím výzkumných dat

S možným budoucím použitím získaných dat v anonymizované podobě (bez osobních a identifikačních údajů) pro další výzkumné účely

o Souhlasím

o Nesouhlasím

(účastník zaškrtl odpovídající)

Byl/a jsem informován/a, že

• mám právo požadovat přístup k osobním údajům týkajícím se mé osoby, jejich opravu nebo výmaz, popřípadě omezení zpracování, mám právo vznést námitku proti zpracování osobních údajů týkajících se mé osoby,

• mám právo podat stížnost dozorovému orgánu (Úřad pro ochranu osobních údajů) v případě, že se domnívám, že zpracování mých osobních údajů probíhá v rozporu s právními předpisy;

• mám právo tento souhlas se zpracováním osobních údajů kdykoliv odvolat, aniž by mi za to hrozila jakákoliv sankce či znevýhodnění, a to oznámením na správci osobních údajů. Zákonnost zpracování údajů před odvoláním souhlasu tím není dotčena.

Správce osobních údajů:

Markéta Dohnalová studentka oboru Psychologie Filozofická fakulta Masarykovy univerzity email: 438070@mail.muni.cz

Udělení souhlasu

Vyjádřením tohoto souhlasu též potvrzuji, že jsem RODILÝM MLUVČÍM ČESKÉHO JAZYKA a že NETRPÍM ZÁVAŽNOU OČNÍ VADOU ČI PORUCHOU UČENÍ, která by bránila mé účasti na výzkumu.

Na základě výše uvedených informací uděluji tímto Markétě Dohnalové, jako správci osobních údajů, SOUHLAS s uvedeným zpracováním osobních údajů za účelem vědeckého výzkumu.

Jméno Příjmení

_________________________ (prázdné pole pro doplnění)

E-mailová adresa

__________________________ (prázdné pole pro doplnění)

131

Appendix 2. Adjusted efficiency formulas

Measure used to compute

efficiency Adjusted formula

Reading

measures

Total reading time Compr. score

1+(reading time(ms) × 10-6)

Number of fixations Compr. score

1+(N fixations × 10-3)

Number of regressive saccades Compr. score

1+(N regressive saccades × 10-3)

Regression

measures

Time spent on regressions Compr. score

1+(time regressions(ms) × 10-5)

Time large regressions Compr. score

1+(time large regressions(ms) × 10-5)

Time rereading Compr. score

1+(time rereading(ms) × 10-5)

Time small regressions Compr. score

1+(time small regressions(ms) × 10-5)

Ratios

Regressions/total Compr. score1+( time regressions(ms) total time(ms) )

Large reg./total Compr. score1+( time large regressions(ms) total time(ms) )

Rereading/total Compr. score1+( time small regressions(ms) total time(ms) )

Small reg./total Compr. score

1+( time rereading(ms) total time(ms) )

132

Appendix 3. Texts and comprehension questions

The original sources of the texts are cited, texts were modified for the purpose of the experiment. The modified versions are presented. Correct responses are highlighted in italic.

Version A

Text 1 (Návod k obsluze Zetor 5211-7245)

Při parkování traktoru (soupravy) přes noc, mimo parkoviště, na neosvětlené komunikaci, nezapomeňte jej osvětlit nejméně jedním světlem viditelným zepředu i zezadu, umístěným na boku traktoru (soupravy) ke středu komunikace.

(29 slov)

1. Text je popisem toho, a. jak bezpečně parkovat traktor na neosvětlené komunikaci. b. jak zapnout světla umístěná na boku traktoru. c. jak řídit traktor na neosvětlené komunikaci. d. jaké světla zapnout při jízdě na neosvětlené komunikaci.

2. Autor textu uvádí, na které straně traktoru vzhledem k vozovce by světla měla být

rozsvícena. a. Ano. b. Ne.

Text 2 (Freud, 1937)

Za nepopřený poznatek smíme považovat, že veškerý materiál, tvořící obsah snu, nějakým způsobem pramení z prožitku, tedy je ve snu reprodukován, vybavován. Ale mylná by byla domněnka, že se taková souvislost snového obsahu s životem za bdění nutně a bez námahy jeví zřetelným výsledkem srovnání. Naopak souvislost musí být bedlivě hledána a v mnoha případech se dovede dlouhou dobu skrývat. Příčina vězí v některých zvláštnostech, které má vzpomínací schopnost ve snu a které se, třebas všeobecně pozorovány, dosud nedaly nijak objasnit.

(49 slov)

1. Autor v textu tvrdí, že všechen materiál tvořící sen nějak pramení z toho, co zažíváme. a. Ano. b. Ne.

2. Autor v textu tvrdí, že a. na základě snů nelze předvídat lidské chování. b. analýza snů nám pomáhá pochopit naše pravé touhy. c. vztah mezi snovým obsahem a reálným životem není zjevný. d. neexistuje kauzální vztah mezi snovým obsahem a reálným životem.

Text 3 (Russell, 1993)

I když připustíme, že Slunce, hvězdy a hmotný svět obecně nejsou výtvorem naší představivosti nebo množinou vhodných koeficientů v našich rovnicích, je to, co lze o nich říci, neobyčejně abstraktní, mnohem více, než jak se to projevuje z jazyka, který používají fyzikové, snaží-li se být srozumitelnými. Prostor a čas, jimiž se zabývají, nejsou prostorem a časem naší zkušenosti. Oběžné dráhy planet se až na jisté zcela abstraktní vlastnosti nepodobají ilustrovaným elipsám, které můžeme spatřit zaznamenány na mapách sluneční soustavy.

(79 slov)

133

1. Autor v textu uvádí, že Slunce, hvězdy a hmotný svět jsou jen výtvorem naší představivosti.

a. Ano. b. Ne.

2. Autor v textu tvrdí, že prostor a čas jsou pouhé iluze. a. Ano. b. Ne.

3. Dle autora oběžné dráhy planet a. by neměly být ilustrovány za pomocí elips. b. se elipsám podobají jen v několika málo aspektech. c. v realitě nejsou v ničem podobné elipsám zakresleným na mapách sluneční

soustavy. d. jsou jen výtvorem naší představivosti.

Text 4 (Nakonečný, 1995)

Motiv lze vymezit jako „faktor aktivace a řízení způsobů chování“, nutno poznamenat, že jako takový znamená buď objekt ve vnějším světě, který u individua vyvolává určitou pohotovost k chování, nebo vyjadřuje tuto pohotovost samu: v prvním případě se jím označuje např. jídlo, v druhém případě hlad. V obou případech však finálním efektem je dosažení určitého psychického stavu, uspokojení (v našem případě nasycení), dosažení objektu je pouze prostředkem dosažení tohoto vnitřního uspokojení.

(71 slov)

1. Autor využívá příkladu jídla a hladu, aby ukázal, že a. existují motivy společné všem lidem. b. existují motivy, které jsou pro lidský druh vždy žádoucí. c. chování vyplývá z interakce objektu a vnitřního stavu. d. motivy určují směr a intenzitu lidského chování.

2. Text obsahuje definici motivu. a. Ano. b. Ne.

Text 5 (Kant, 2016)

Ať už si děláme jakýkoli pojem o svobodě vůle v metafyzickém smyslu, přece jsou její projevy, lidská jednání, stejně jako každá jiná přírodní událost, určeny podle všeobecných přírodních zákonů. Dějiny, které se vyprávěním těchto jevů zabývají, jakkoli hluboko mohou být ukryty její příčiny, nechávají nás přesto o sobě doufat, že když pozorují hru svobody lidské vůle ve velkém, mohou odhalit její pravidelný běh; a tímto způsobem to, co u jednotlivých subjektů vypadá jako spletitá a prostá pravidla, bude moci být poznáno na celém rodu přesto jako neustále pokračující, i když pozvolný vývoj jeho původních vloh.

(95 slov)

1. Projevy svobodné vůle jsou dle autora určeny a. na základě rozhodnutí jednotlivých lidí. b. na základě toho, jak si lidská společnost interpretuje dějiny. c. na základě obecných přírodních zákonů. d. různě, záleží na tom, jak rozumíme pojmu “svobodná vůle”.

2. Podle autora nám nahlédnutí do dějin lidstva pomůže a. definovat pojem svobodné vůle. b. získat skutečnou svobodu v našem rozhodování, c. odhalit skryté příčiny lidského jednání. d. odhalit pravidelnost zdánlivě zcela nepravidelných jevů.

134

Text 6 (Havel, 1990)

Východní Evropou obchází strašidlo, kterému na Západě říkají „disidentství“. Toto strašidlo nespadlo z nebe. Je přirozeným projevem a nevyhnutelným důsledkem současné historické fáze systému, jímž obchází. Zrodila ho totiž situace, kdy tento systém už dávno není a z tisícerých důvodů už nemůže být založen na čisté a brutální mocenské svévoli, vylučující jakýkoliv nekonformní projev, kdy je ale na druhé straně už do té míry politicky statický, že téměř znemožňuje, aby se takový projev natrvalo uplatňoval v prostředí jeho oficiálních struktur.

(80 slov)

1. „Strašidlo disidentství“ přichází dle textu právě ve chvíli, kdy systém mimo jiné a. vylučuje jakýkoli nekonformní projev. b. je z důvodu změn doby výrazně nestabilní. c. již nemůže stavět na nekompromisním prosazování mocenských záměrů. d. je v kontextu světového obchodu ekonomicky nevýhodný.

2. Nekonformní projev se podle textu nemůže oficiálně v popisovaném systému trvale uplatnit proto, že

a. se politický systém stal příliš statickým. b. systém nekompromisně uplatňuje mocenskou svévoli. c. ještě pro něj nenastala v rámci historického vývoje správná chvíle. d. jsou nekonformní projevy vnímané jako protiústavní.

Text 7 (Masaryk, 1924)

Veliká francouzská revoluce a následující reakce a restaurace, pak revoluce menší, jakožto pokračování revoluce veliké, obracely pozornost nejširších vrstev na protivy a boj starého a nového režimu a živily teorie a pokusy o lepší, co možná definitivní reorganisaci států, národů Evropy a lidstva; v té době vzniká uvědomělý socialismus. Teoreticky doba nalézá svůj výraz v nové, vědecké historiografii. Filosofie dějin pěstuje se u všech národů, kvete historie, ekonomika, a vůbec všechny společenské vědy; ustaluje se sociologie jako soubor všech těchto speciálních pokusů, jako věda o lidské společnosti a jejím vývoji.

(90 slov)

1. Veliká francouzská revoluce dle textu vedla a. k novému způsobu politického myšlení. b. k silnějšímu národnímu cítění. c. ke krizi společenských věd. d. ke kritice socialismu.

2. Text uvádí vznik sociologie jako příčinu rozvoje přemýšlení nad lepší možnou organizací Evropských států.

a. Ano. b. Ne.

3. Autor v textu otevřeně hodnotí Velikou francouzskou revoluci jako pozitivní událost. a. Ano, protože podnítila rozvoj politického myšlení. b. Ne, autor ji hodnotí jako negativní událost. c. Ne, autor ji nehodnotí, pouze popisuje její historický vliv.

Součet slov textů varianty A: 493 slov

135

Version B

Text 1 (Návod k obsluze Zetor 5211 – 7245)

Při zahřívání motoru elektrickým ohřívačem chladicí kapaliny je nutno překontrolovat přívod elektrického proudu, zasunout zástrčku do ohřívače a potom připojit na síť. Po ukončení zahřívání odpojte od elektrické sítě. Až potom vytáhněte zástrčku z ohřívače.

(35 slov)

1. Text je a. popisem, jak správně postupovat v případě přehřívání motoru. b. popisem, jak správně a bezpečně zapojit elektrickou síť. c. popisem bezpečného postupu zahřívání motoru pomocí elektrického ohřívače. d. doporučením toho, jak lze efektivně využít elektrického ohřívače.

2. Autor v textu uvádí konkrétní rizika, která má špatné zacházení s elektrickým ohřívačem.

a. Ano. b. Ne.

Text 2 (Freud, 1937)

Rozbor příčin, vyvolávajících sen, zaujímá v pojednání spisovatelů velmi mnoho místa. Že se tento problém vynořil teprve, když se sen stal předmětem biologického bádání, je samozřejmé. Staří, kteří považovali sen za seslaný bohy, nepotřebovali hledat zdroj podnětů, vyvolávajících sen; sen pramenil z vůle božské nebo démonické síly, obsah snu z jejího vědění nebo záměru. Ve vědě se brzy vynořila otázka, zda podnět k snění je vždycky týž, anebo může být mnohonásobný, a s otázkou úvaha, zda příčinné vysvětlování snu patří do psychologie nebo spíše fyziologie.

(85 slov)

1. Uvedený text lze popsat jako a. přehled historického rozvoje zájmu o zkoumání příčin snu. b. popis správného postupu výkladu snů. c. kritiku nevědeckých vysvětlení původu snů. d. vysvětlení toho, jak může mnoho různých podnětů vést ke stejnému snovému

obsahu.

2. Text uvádí, že si ve starověku lidé sen vysvětlovali na základě pohybu tělních tekutin. a. Ano. b. Ne.

Text 3 (Russell, 1993)

Začněme slovem “skutečný”. Rozhodně existují objekty vnímání a tedy, má-li být otázka, zda tyto objekty jsou skutečné, otázkou relativní, musí ve světě existovat druhy objektů, totiž skutečné a neskutečné, a o neskutečných se předpokládá, že to jsou v podstatě takové objekty, které neexistují. Otázka, které vlastnosti musí mít nějaký objekt, aby to byl skutečný objekt, je otázkou, na kterou existuje málokdy, existuje-li vůbec nějaká, přiměřená odpověď.

(66 slov)

1. Lze říci, že hlavním cílem textu je… a. popsat typy neskutečných objektů. b. poukázat na problematičnost pojmu „skutečný“. c. popsat, jak funguje zkreslení lidského vnímání. d. popsat vlastnosti, které by měl mít skutečný objekt.

2. Text přímo vyjadřuje pochybnost o tom, zda jsou objekty vnímání skutečné. a. Ano. b. Ne.

136

3. Autor v textu řeší, zda vůbec lze definovat podmínky pro „skutečnost“ objektů.

a. Ano. b. Ne.

Text 4 (Nakonečný, 1995)

Co vyjadřují takové pojmy jako: vřelý, uzavřený, agresivní, bázlivý, prudký apod.? Vyjadřují obvykle více či méně oprávněné zobecnění pozorovaných projevů osobnosti, tedy jakési popisné charakteristiky osobnosti pozorovaného jedince. Existuje několikerý způsob charakterizování osobnosti jedince, které vyjadřují různý stupeň zobecněného pozorování, které má dvě východiska: prvním je pozorování projevů jedince v různých, ale srovnatelných situacích, a druhým je pozorování opakovaných projevů v takových situacích. Za těchto podmínek je možno dospět k obvyklé adjektivní psychologické charakteristice jedince, tj. označit jej určitou vlastností osobnosti, jako je např. bázlivost, označit jej za bázlivého.

(89 slov)

1. Autor mluví mimo jiné o podmínkách, které je potřeba naplnit, pokud a. chceme člověka označit nějakou vlastností. b. chceme nezaujatě pozorovat chování jedince. c. chceme aplikovat poznatky psychologie osobnosti v praxi. d. chceme efektivně zkoumat procesy jako paměť nebo vnímání.

2. Autor v textu poukazuje na slabé stránky adjektivní psychologie. a. Ano. b. Ne.

Text 5 (Kant, 2016)

U člověka (jako jediného rozumného tvora na zemi) by se měly ty přírodní vlohy, které jsou zaměřeny na užití jeho rozumu, plně rozvinout jen v rodu, nikoli však v jednotlivci. Rozum u nějakého tvora je schopnost rozšířit pravidla a záměry použití všech jeho sil daleko za přírodní instinkt a rozum nezná žádné hranice svých rozvrhů. Sám však nepůsobí instinktivně, nýbrž vyžaduje pokusy, cvičení a vyučování, aby pozvolna postupoval od jednoho stupně nahlédnutí k druhému.

(74 slov)

1. Autor v textu tvrdí, že: a. Žádný jednotlivec nedisponuje rozumem, disponuje jím pouze rod. b. Vlohy k užívání rozumu se plně nerozvíjí v jednotlivci, ale pouze v rodu. c. Člověk, na rozdíl od ostatních tvorů, disponuje přírodními vlohami i na

úrovni jednotlivce, nikoli pouze rodu. d. Člověk se liší od ostatních tvorů v tom, že jako jediný své přírodní vlohy

rozvíjí pomocí pokusů, cvičení a vyučování.

2. V textu autor popisuje rozum jako a. schopnost využít svých sil za hranice přírodního instinktu. b. schopnost využít svých sil k jednání proti svému přírodnímu instinktu. c. instinktivní schopnost umožňující využít co nejvhodněji své přírodní vlohy. d. schopnost pochopit důsledky, ke kterým vede jednání na základě přírodního

instinktu.

137

Text 6 (Havel, 1990)

Jedna zvláštnost, kterou se náš systém liší od různých jiných moderních diktatur, vychází z dědictví původního “dobrého pochopení” některých problémů společnosti. Náš systém disponuje nepoměrně konciznější, logicky strukturovanou, obecně srozumitelnou a ze své podstaty velice pružnou ideologií, která při své komplexnosti a uzavřenosti nabývá až povahy jakéhosi sekularizovaného náboženství: nabízí člověku hotovou odpověď na jakoukoli otázku, nelze ji dost dobře přijmout jen částečně a její přijetí zasahuje hluboko do lidské existence.

(71 slov)

1. Hlavní argument textu je, že se „náš“ systém od jiných moderních diktatur liší a. v povaze jeho ideologie. b. v tom, jak rozumí hodnotě lidské existence. c. v tom, jak přistupuje k náboženství. d. v způsobu rozdělení moci.

2. Autor tvrdí, že ideologie “našeho systému” a. má i pozitivní přínos pro společnost. b. je lepší než ideologie moderních diktatur. c. je díky své logické strukturovanosti lepší než tradiční náboženství. d. je založena na „dobrém pochopení“ některých problémů, se kterými se společnost potýká.

Text 7 (Masaryk, 1924)

Všecky tyto snahy buditelské, všecko to nadšené úsilí o samostatnou a samobytnou kulturu národního obrození přirozeně musilo býti založeno na nějakém jednotném názoru na svět a na život. Nemá-li život lidí myslících býti řetězem jednotlivých episod – a takového života člověk jen poněkud myslivější a opravdovější prostě nesnese, – musí všecka práce myšlenková, i praktická, založena býti na jistém a pevném základě filosofickém. Buď si základ ten jakýkoli, ale každý, kdo skutečně myslí, jej má, míti jej musí.

(76 slov)

1. Uvedený text lze popsat jako a. kritiku nedostatečného filosofického základu obrozeneckých snah. b. vysvětlení nutnosti jednotného filosofického názoru pro obrozenecké úsilí. c. popis vlivu epizod lidského života na rozvoj potřeby filosofického základu a

přirovnání k obrozeneckému úsilí. d. vysvětlení příčin rozdílu ve filosofických názorech obrozeneckých myslitelů.

2. Autor v textu tvrdí, že každý, kdo o věcech přemýšlí, staví své myšlení na nějakém filosofickém základě.

a. Ano. b. Ne.

3. Autor popisuje hlavní argumenty vytvářející jednotný filosofický základ obrozeneckého úsilí.

a. Ano. b. Ne.

Součet slov textů varianty B: 496 slov

138

Appendix 4. Task instructions

A. Control task

Slide 1:

Čtěte prosím pozorně následující instrukce.

Bude Vám prezentováno 7 krtákých textů o délce 25 – 100 slov. Za každým z textů následují 2-3 otázky na prozoumění textu s nucenou volbou. Na otázky odpovídejte vždy pouze na základě informací, které se nachází v textu.

Jednotlivé úlohy (texty + obrázky) jsou obdobné cvičné úloze, kteoru jste si vyzkoušeli na začátku experimentu. Texty jsou seřazeny dle vzrůstající náročnosti.

Data z této úlohy budou využita pro zkoumání obecných tendencí ve čtení u úzkostných a neúzkostných jedinců.

B. Ego-threatening instructions

Slide 1:

Čtěte prosím pozorně následující instrukce.

Slide 2:

Bude Vám prezentováno 7 krtákých textů o délce 25 – 100 slov. Za každým z textů následují 2-3 otázky na prozoumění textu s nucenou volbou. Na otázky odpovídejte vždy pouze na základě informací, které se nachází v textu.

Jednotlivé úlohy (texty + obrázky) jsou obdobné cvičné úloze, kteoru jste si vyzkoušeli na začátku experimentu. Texty jsou seřazeny dle vzrůstající náročnosti.

Slide 3:

Vaším úkolem je přečíst text co nejrychleji a ZÁROVEŇ dosáhnout co nejlepšího možného porozumění textu.

Vybrané texty jsou náročné, proto je třeba Vaší úplné pozornosti.

Jakmile je text zobrazen, můžete u něj setrvat jak dlouho potřebujete, ale po stisknutí mezerníku už se k němu nelze vracet.

Slide 4:

V této úloze se měří čas. Čas se měří pouze během čtení textů, během odpovídání na otázky se čas neměří.

Slide 5:

Začátek měření času oznámí zvykový signál.

Slide 6:

Data z této úlohy budou využita k porovnání individuálních rozdílů v čtecí efektivitě, včetně srovnání efektivity u úzkostných a neúzkostných jedinců.

Čtecí efektivita je poměr mezi rychlostí četby a porozuměním textu. Schopnost efektivně číst koreluje dle výzkumů s inteligencí a s akademickým úspěchem.

U ego-ohrožující úlohy dále před každým textem předcházelo odpočítávání. Na obrazovce se obrazila po sobě čísla 3, 2, 1 a poté obrázek se stopkami zároveň se zvukem pípnutí. Poté se účastnikovi zobrazil text.

139

Appendix 5. Link to tailored software for computing regressions Software for computing time spent on regressions on the basis of BeGaze single event

statistics is freely accessible at:

https://gitlab.com/ondrej.takacs/regrese?fbclid=IwAR3zWVt5zB4xWxHqnRbXGF2y0oJMk1C4jTSyQa8UXHfVwGoximd07ed6NQw

The program works best for fairly precisely measured gaze data. For data with larger

offsets or slanted scanpaths, comparing the software output with participants’

scanpaths manually is recommended to correct for possible errors, especially in the

detection of between-line jumps.

QR code to link:

140

Appendix 6. Manipulation check items and combined scores

All items were answered on a 5 point likert scale, with 5 denoting maximum agreemetg and/or frequency. Items

A3 and C5 are reversed.

Individual items:

A. Jak jste se během úlohy cítili? 1. Během úlohy jsem se cítil/a stísněný/á a neklidný/á. 2. Během úlohy jsem byl/a nervózní. 3. Během úlohy jsem se cítil/a příjemně. 4. Během úlohy jsem se cítil/a napjatý/á, v tenzi.

Skórování: (1) = vůbec ne, (2) = spíše ne, pouze nepatrně, (3) = mírně/nelze určit, (4) = spíše ano, (5) = ano, velmi

B. Somatické příznaky úzkosti. 1. Během úlohy, ve které se ne/odpočítával čas, jsem si u sebe všiml/a příznaků

úzkosti v těle (např. zrychlený tep, studené ruce, pot, třes, nevolnost,...).

Skórování: (1) = ne, nikdy, (2) = spíše ne, jen vzácně, (3) = občas, (4) = ano, častěji, (5) = ano, velmi často

C. Co Vás během úlohy napadalo? 1. Během úlohy jsem si dělal/a starost, abych podal/a dostatečně dobrý výkon. 2. Během úlohy mě napadalo, jak se mi oproti ostatním daří. 3. Během úlohy mě napadaly o sobě pochybující myšlenky typu: „Určitě to

nezvládnu.“ „Dělám to celé špatně.“ „Jsem horší než ostatní.“ 4. Během úlohy se mi stávalo, že jsem „četl/a očima“, aniž bych vnímal/a text. 5. Během úlohy se mi dařilo soustředit se na daný úkol.

Skórování: (1) = ne, nikdy, (2) = spíše ne, jen vzácně, (3) = občas, (4) = ano, častěji, (5) = ano, velmi často

D. Jak na Vás působily úlohy? 1. Texty v úloze byly náročné na porozumění. 2. Otázky v úloze byly náročné na porozumění. 3. K zodpovězení otázek bylo třeba zapamatování si konkrétních pasáží textu. 4. Během zodpovídání otázek jsem tipoval/a.

Skórování: (1) = vůbec ne, nikdy, (2) = spíše ne / málo kdy, (3) = něco mezi / jak kdy, (4) = spíše ano / častěji, (5) = ano, velmi / velmi často

Combined scores:

1. Emotion: mean of the sum of scores in items: A1, A2, A3 (reversed), A4 and B.

2. Worry: mean of the sum of scores in items: C1, C2 and C3.

3. Inability to focus: mean of the sum of scores in items: C4 and C5.

4. Difficulty: mean of the sum of scores in items: D1, D2 and D3.

5. General effect: mean of the sum of scores in items: A1, A2, A3 (reversed), A4, B, C1, C3 and C4. (Items that most loaded the main factor in both condition according factor analysis.)

Appe

ndix

7. D

escr

iptiv

e st

atis

tics

of m

anip

ulat

ion

chec

k ite

ms

Tab

le A

7

D

escrip

tive s

tatis

tics o

f man

ipula

tion

chec

k item

s (N

=33)

C

ontr

ol

E

go-th

reat

M

M

D

mod

e (n)

SD

M

MD

m

ode (

n)

SD

A1.

Une

asy,

res

tless

. 2.

21

2 2

(15)

1.

11

2.

36

2 2

(10)

1.

03

A2.

Ner

vous

. 2.

39

2 1

(9)

1.12

2.

58

3 4

(10)

1.

17

A3.

Fel

t com

fort

able

. (R

ever

sed.

) 2.

82

3 3

(11)

1.

10

2.

76

3 3

(13)

0.

97

A4.

Ten

sion

. 2.

58

2 2

(10)

1.

20

2.

97

3 4

(12)

1.

31

B. S

omat

ic s

ympt

oms

of a

nxie

ty.

2.12

2

1 (1

1)

0.99

2.46

2

2 (1

0)

1.03

C1.

Wor

ries

abo

ut p

erfo

rman

ce.

2.85

3

4 (1

0)

1.20

3.

33

4 4

(14)

1.

24

C2.

Com

pari

ng o

nese

lf w

ith o

ther

s. 2.

33

2 1

(10)

1.

16

2.

58

2 2

(9)

1.28

C3.

Sel

f-do

ubts

. 2.

21

2 1

(13)

1.

34

2.

36

2 2

(11)

1.

27

C4.

Min

dles

s re

adin

g.

3.73

4

4 (1

4)

0.94

3.

85

4 4

(13)

1.

03

C5.

Abi

lity

to fo

cus.

(Rev

erse

d.)

2.88

3

3 (1

7)

0.82

2.84

3

2 (1

2)

0.91

D1.

Tex

t diff

icul

ty.

3.33

3

4 (1

4)

0.92

3.

61

4 4

(14)

0.

90

D2.

Que

stio

n di

fficu

lty.

2.58

2

2 (1

7)

0.94

2.

64

3 2

(11)

1.

08

D3.

Nee

d to

mem

oriz

e te

xt.

3.06

3

3 (1

4)

0.93

3.

24

3 3

(13)

0.

87

D4.

Res

pons

e gu

essi

ng.

2.61

3

3 (1

5)

0.83

2.67

3

2 (1

4)

0.82

Appe

ndix

8. M

ean

scor

es fo

r ind

ivid

ual m

anip

ulat

ion

item

s ac

cord

ing

to a

nxie

ty

Tab

le A

8

M

anip

ulatio

n ch

eck -

mea

ns o

f ind

ividu

al sco

res a

ccor

ding

to lo

w an

d hig

h an

xiety

grou

ps.

C

ontr

ol

E

go-th

reat

LA

H

A

LA

H

A

M

SD

M

SD

M

SD

M

SD

A1.

Une

asy,

res

tless

. 1.

77

0.83

2.

69

1.20

1.94

0.

9 2.

81

0.98

A2.

Ner

vous

. 1.

88

1.05

2.

94

0.93

1.94

1.

03

3.25

0.

93

A3.

Fel

t com

fort

able

. (R

ever

sed.

) 2.

71

0.99

3.

69

1.01

2.82

0.

95

3.69

0.

79

A4.

Ten

sion

. 1.

94

0.97

3.

25

1.06

2.41

1.

23

3.56

1.

15

B. S

omat

ic s

ympt

oms

of a

nxie

ty.

1.71

0.

99

2.56

0.

81

1.

94

0.9

3.00

0.

89

C1.

Wor

ries

abo

ut p

erfo

rman

ce.

2.29

1.

16

3.44

0.

96

2.

77

1.2

3.94

1.

00

C2.

Com

pari

ng o

ne-s

elf w

ith o

ther

s. 1.

82

1.01

2.

88

1.09

2.18

1.

13

3.00

1.

32

C3.

Sel

f-do

ubts

. 1.

29

0.47

3.

19

1.28

1.65

0.

86

3.13

1.

20

C4.

Min

dles

s re

adin

g.

3.29

0.

92

4.19

0.

75

3.

29

0.92

4.

44

0.81

C5.

Abi

lity

to fo

cus.

(Rev

erse

d.)

2.59

0.

71

3.19

0.

83

2.

47

0.72

3.

25

0.93

D1.

Tex

t diff

icul

ty.

3.12

1.

05

3.56

0.

73

3.

41

0.8

3.81

0.

98

D2.

Que

stio

n di

fficu

lty.

2.65

0.

93

2.5

0.97

2.65

0.

93

2.63

1.

26

D3.

Nee

d to

mem

oriz

e te

xt.

3.18

0.

95

2.94

0.

93

3.

35

0.93

3.

13

0.81

D4.

Res

pons

e gu

essi

ng.

2.53

0.

8 2.

69

0.87

2.65

0.

79

2.69

0.

87

Not

e. LA

- L

ow a

nxio

us g

roup

, N =

17.

HA

- H

igh

anxi

ous

grou

p, N

= 1

6.

Appe

ndix

9. C

orre

latio

n m

atric

es o

f rea

ding

mea

sure

s

Tab

le A

9.1

Cor

relat

ions

betw

een

read

ing v

aria

bles (

cont

rol c

ondi

tion)

.

Rea

ding

var

iabl

es

1

2 3

4 5

6 7

8 9

10

11

1 C

ompr

ehen

sion

–

2

Tota

l rea

ding

tim

e

-0.0

88

–

3 Ti

me

regr

essi

ons

-0

.079

0.

921*

* –

4 Ti

me

larg

e re

gr.

-0

.091

0.

879*

* 0.

979*

* –

5

Tim

e sm

all r

egr.

0.0

39

0.30

6

0.21

5 0.

011

–

6

Tim

e re

read

ing

-0

.083

0.

879*

* 0.

973*

* 0.

995*

* 0.

003

–

7 N

fixa

tions

-0.0

86

0.93

1**

0.90

5**

0.83

9**

0.4

19*

0.84

2**

–

8

N r

egr.

fixa

tions

-0.0

44

0.64

2**

0.68

4**

0.57

4**

0.60

0**

0.55

8**

0.81

3**

–

9 N

sac

cade

s

-0.0

85

0.82

6**

0.89

0**

0.85

5**

0.26

8 0.

853*

* 0.

842*

* 0.

698*

* –

10

N r

egr.

sac

cade

s

0.0

29

0.55

8**

0.64

8**

0.57

3**

0.42

8*

0.56

3**

0.68

0**

0.80

7**

0.84

4**

–

11

N r

egr.

eve

nts

0

.030

0.

296

0.32

9 0

.157

0.

859*

* 0.

118

0.44

3**

0.70

4**

0.40

0*

0.60

7**

–

** C

orre

latio

n is

sig

nific

ant a

t the

0.0

1 le

vel (

2-ta

iled)

.

*

Cor

rela

tion

is s

igni

fican

t at t

he 0

.05

leve

l (2-

taile

d).

Appe

ndix

9. C

orre

latio

n m

atric

es o

f rea

ding

mea

sure

s T

able

A9.

2

Co

rrelat

ions

betw

een

read

ing v

aria

bles (

ego-

thre

at).

R

eadi

ng v

aria

bles

1 2

3 4

5 6

7 8

9 10

11

1 C

ompr

ehen

sion

–

2

Tota

l rea

ding

tim

e

0.05

9 –

3

Tim

e re

gres

sion

s

0.18

0 0.

920*

* –

4 Ti

me

larg

e re

gr.

0.

156

0.89

4**

0.98

6**

–

5 Ti

me

smal

l reg

r.

0.

157

0.26

2 0.

203

0.03

5 –

6 Ti

me

rere

adin

g

0.15

6 0.

902*

* 0.

981*

* 0.

997*

* 0

.028

–

7

N fi

xatio

ns

0.

094

0.93

7**

0.92

5**

0.88

4**

0.3

48*

0.89

1**

–

8

N r

egr.

fixa

tions

0.15

5 0.

722*

* 0.

809*

* 0.

738*

* 0

.513

**

0.73

1**

0.84

3**

–

9 N

sac

cade

s

0.07

0 0.

772*

* 0.

874*

* 0.

860*

* 0

.184

0.

851*

* 0.

856*

* 0.

781*

* –

10

N r

egr.

sac

cade

s

0.17

0 0.

532*

* 0.

686*

* 0.

611*

* 0

.515

**

0.58

6**

0.68

5**

0.85

9**

0.79

8**

–

11

N r

egr.

eve

nts

0.

174

0.28

2

0.3

41

0.19

9 0

.865

**

0.16

0 0

.406

* 0.

598*

* 0.

364*

0.

675*

* –

** C

orre

latio

n is

sig

nific

ant a

t the

0.0

1 le

vel (

2-ta

iled)

.

*

Cor

rela

tion

is s

igni

fican

t at t

he 0

.05

leve

l (2-

taile

d).

Appe

ndix

10.

Des

crip

tive

stat

istic

s of

read

ing

and

effic

ienc

y m

easu

res

Tab

le A

10.1

Des

crip

tive s

tatis

tics o

f rea

ding

mea

sure

s (N

= 3

3)

C

ontr

ol

E

go-th

reat

m

in.

max

.

M

SD

m

in.

max

.

M

SD

Com

preh

ensi

on

9.00

16

.00

12

.46

1.92

5.00

15

.00

11

.79

2.58

Tota

l rea

ding

tim

e (s

)*

196

554

34

0 83

172

473

30

2 72

N fi

xatio

ns*

726

1954

1265

29

1

699

1762

1134

26

6

N r

egre

ssiv

e fix

.*

174

636

38

7 10

2

180

569

34

6 10

1

N s

acca

des*

49

2 16

54

99

6 23

7

523

1326

892

196

N r

egr.

sac

cade

s*

93

460

28

0 83

111

386

24

5 67

Tim

e re

gres

sion

s (s

)*

32

331

17

1 69

25

321

14

1 70

Tim

e la

rge

regr

. (s)

21

30

0

139

68

3

304

11

6 69

Tim

e re

read

ing

(s)

21

292

13

5 68

0 30

3

110

69

Tim

e sm

all r

egre

ssio

ns (s

)**

7 70

32

14

10

61

25

12

N r

egr.

est

imat

e*

20

143

71

25

27

117

64

24

Reg

r. to

tota

l tim

e (%

) 16

.3

65.4

48.5

11

.4

10.0

68

.2

44

.4

14.0

Larg

e re

gr. t

o to

tal t

ime

(%)

6.6

57.5

38.9

12

.7

1.

0 65

.1

35

.8

15.0

Rer

eadi

ng to

tota

l tim

e (%

) 8.

4 56

.6

37

.7

13.2

0.0

64.1

33.7

15

.6

Smal

l reg

r. to

tota

l tim

e (%

) 3.

7 21

.8

9.

5 4.

4

3.0

20.3

8.5

4.0

** M

ain

effe

ct o

f man

ipul

atio

n w

as s

igni

fican

t at t

he 0

.01

leve

l.

* M

ain

effe

ct o

f man

ipul

atio

n w

as s

igni

fican

t at t

he 0

.05

leve

l.

Appe

ndix

10.

Des

crip

tive

stat

istic

s of

read

ing

and

effic

ienc

y m

easu

res

T

able

A10

.2

D

escr

iptiv

e sta

tistic

s of e

fficie

ncy m

easu

res (

N =

33)

C

ontr

ol

E

go-th

reat

m

in.

max

.

M

SD

m

in.

max

.

M

SD

Com

pr./t

otal

tim

e 5.

79

13.0

6

9.33

1.

58

3.

99

12.2

0

9.07

2.

07

Com

pr./N

fixa

tions

3.

05

8.18

5.59

1.

13

2.

56

8.22

5.59

1.

38

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148

Appendix 12. Examples of regressive scanpaths

1. Length of text reread.

a. Rereading of a clause or small sentence segment

The reader reads the text segment once (fixations 9 to 12), then returns (fixations 12 to 15) and reads it again (fixations 15 to 19) and continues (fix. 20).

Figure A13.1. Clause reread 1

Figure A13.2. Clause reread 2

b. Rereading of a sentence

The reader reads the entire sentence up to the last word before the comma (fixations 3 to 22) then returns to the beginning (fixations 22 to 24) and reads the segment a second time (fixations 24 to 38).

Figure A13.3 Sentence reread 1

Figure A13.4 Sentence reread 2

149

c. Rereading of the entire text

The participant reads the text once through (fixations 2 to 56) then returns to the beginning (fixations 56 and 57) and rereads the entire text again (fixations 58 to 80).

Figure A13.5. Text reread 1

Figure A13.6. Text reread 2

d. Multiple regressions

The participant makes many regressions and rereads passages multiple times.

Figure A13.7. Multiple regressions

150

2. Various fixation densities and small regression counts in first pass reading

a. Longer and less frequent fixations, minimum of small regressions

The participant read the text once through quickly with almost no regressions. Fixations are relatively thin, fixation durations are quite long on several words.

Figure A13.8 “Thin” first pass reading

b. Frequent fixations and small regressions

The participant read the text through with a greater amount of small saccades (circled in black). Fixations are dense.

Figure A13.9 “Dense” first pass reading