PETER N.ROSENWEG BA, Grad Dip Couns

DISSERTATION

Evaluation of a self-regulating skill

for rapid attention recovery (rART).

Submitted to Swinburne Higher Degree Research

In fulfillment of the requirements for the degree of

Doctor of Philosophy

Swinburne University of Technology

Faculty of Science, Engineering and Technology

August 2018

Men ought to know that from the brain and from

the brain only arise our pleasures, joys,

laughter, and jests as well as our sorrows pains

griefs and tears.

It is the same thing which makes us mad or

delirious, inspires us with dread and fear,

whether by night or by day, brings

sleeplessness, inopportune mistakes, aimless

anxieties, absent mindedness and acts that are

contrary to habit.

Hippocrates (c. 400 B.C) The Sacred Disease

Declaration

I declare that no material has been included herein that has been accepted for the award of any

other degree or diploma, except where due reference is made in the text of the examinable

outcome.

To the best of my knowledge the submitted thesis contains no material previously published or

written by another person except where due reference is made in the text of the examinable

outcome.

This thesis is not work based on joint research or publications or that of other authors.

Peter N.Rosenweg

Acknowledgements

With much appreciation for their endless effort and insight by

Dr Mark Schier

Dr Peter Higgins

Group Captain Doug Edwards RAAF (Ret)

Dr Janice Langan-Fox

Table of Contents

Declaration ................................................................................................... iii

Acknowledgements .......................................................................................iv

Tables: .............................................................................................................i

Abbreviations & Glossary ........................................................................... vii

ABSTRACT ..................................................................................................................... I

OUTLINE OF THE THESIS ......................................................................................... I

LIST OF CHAPTERS ....................................................................................................II

1. INTRODUCTION ....................................................................................................... 1

Where is the risk? ........................................................................................... 1

1.2 The extent of human error ........................................................................ 2

2. ATTENTION & SELF REGULATION ................................................................. 10

2.1 Introduction to Attention ........................................................................ 10

2.2 Models and limitations of attention ........................................................ 10

2.3 Capacity Models: .................................................................................... 11

2.4 Bottleneck models: ................................................................................. 12

2.5 Attentional Networks. ............................................................................. 13

2.6 Arousal and performance........................................................................ 14

2.7 Attention switching and multitasking ..................................................... 18

2.8 Brain State and Mental State .................................................................. 19

2.9 Mental State and Unsafe Behaviour ....................................................... 21

2.10 Cognitive resilience and mental state ................................................... 23

2.11 Cognitive resilience and performance .................................................. 24

2.12 Cognitive resilience and prospective memory...................................... 25

2.13 Attention in situation awareness ........................................................... 26

2.14 Self-regulation Absent in NOTECH Training ...................................... 28

2.15 Self-regulation Absent in Simulator Training ...................................... 31

2.16 Self-regulation Absent in Profiling ...................................................... 31

2.17 Is it Possible to Measure the Right Stuff? ............................................ 32

2.18 NEO Personality Profile & the PCI ...................................................... 34

2.19 Capability and mental state .................................................................. 35

2.20 The Pilot’s Perception of cognitive demand......................................... 37

2.21 Cognitive Load and Experience ........................................................... 39

2.22 Complacency and awareness ................................................................ 40

2.23 Automation a Reverse Problem ............................................................ 41

2.24 Necessity to manage the self ................................................................ 43

2.25 Summary of Need for self-regulation training ..................................... 46

2.26 Aim of the Study .................................................................................. 47

3. SELF-REGULATION TRAINING ......................................................................... 48

3.1 Self-Regulation and Self Control ........................................................... 48

3.2 Behavioural markers of Self-Regulation ................................................ 51

3.3 Methodological Issues in Evaluating Self-Regulation ........................... 51

3.4 Comparison of Effect Size by Training Methods ................................... 53

3.5 Conventional Training Approaches ........................................................ 53

3.6 Behavioural Operant and Cued Techniques ........................................... 55

3.7 Metacognitive Self-Regulation Techniques ........................................... 56

3.8 Cognitive Behavioural methods ............................................................. 57

3.9 Self-regulation through Mindfulness ...................................................... 59

3.10 CB Methods and Counselling ............................................................... 60

3.11 Self-Regulation Techniques ................................................................. 61

4. PSYCHOPHYSIOLOGY OF ATTENTION .......................................................... 63

4.1 Introduction ............................................................................................ 63

4.2 Energy and Mental Effort ....................................................................... 64

4.3 Mental Energy & Fatigue ....................................................................... 66

4.4 Methods of Arousal ................................................................................ 67

4.5 Nasal olfactory action and the sniff ........................................................ 70

4.6 The Sniff as an Analogy of the Startle ................................................... 71

4.7 The Role of the Amygdala in Rapidly Cued Attention .......................... 72

4.8 The Breathing mechanism ...................................................................... 74

4.9 Measured Breathing and Mental State ................................................... 75

4.10 Operant Learning .................................................................................. 77

5.0 METHODOLOGY .................................................................................................. 79

5.1 Introduction ............................................................................................ 79

5.2 Research Design ..................................................................................... 79

5.3 A Technique to Achieve Attention Recovery......................................... 80

5.4 Issues in Delivering Online Training...................................................... 81

5.5 Unsupervised Baseline Testing .............................................................. 83

5.6 The Research Hypothesis ....................................................................... 85

Hypothesis 1 ................................................................................................. 85

Hypothesis 2 ................................................................................................. 85

Hypothesis 3 ................................................................................................. 85

Hypothesis 4 ................................................................................................. 85

5.7 Overall Structure of the Study ................................................................ 85

5.8 The Study Development Group .............................................................. 86

5.9 Criteria and Development of the Test Instruments ................................. 87

5.10 Baseline test development .................................................................... 87

5.11 Development of the baseline test .......................................................... 90

5.12 The Attention Recovery Demonstration Video .................................... 91

5.13 The Aviation Student Groups ............................................................... 91

5.14 The Rapid Attention Recovery Instruction Set..................................... 93

5.15 Data Collection ..................................................................................... 94

5.16 Statistical procedures. ........................................................................... 95

6. RESULTS ................................................................................................................... 97

6.1 Introduction ............................................................................................ 97

6.2 Hypothesis .............................................................................................. 98

6.3 Demographics of the baseline test development group .......................... 98

6.4 Overview of Experimental Group Study Participation......................... 100

6.5 Experimental Group - Enrolment, Retention & Performance .............. 103

6.6 Experimental Group Compliance with Instructions ............................. 104

6.7 Perception of Workload Strain ............................................................. 108

6.8 Experimental group baseline test performance by Gender ................... 111

6.9 Sample Bias and Generalisability of results ......................................... 113

6.10 Results for Hypothesis 1. Fatigue affects Behaviour ........................ 113

6.11 Results for Hypothesis 2 - Self-regulation predicts rART usage ...... 116

6.12 Results for Hypothesis 3. Self-regulation predicts course completion .................................................................................................................... 120

6.13 Results for Hypothesis 4 - More aviation students complete the course .................................................................................................................... 122

Analysis of variance for mental alertness. .................................................. 124

6.14 Contribution of Gender to the study. .................................................. 127

6.15 Gender bias in task completion .......................................................... 132

6.16 Efficacy of the rapid ART Technique by gender ............................... 132

7. DISCUSSION ........................................................................................................... 133

Introduction ................................................................................................ 133

Study Objectives ......................................................................................... 133

The Study Background ............................................................................... 134

What difference was sought ....................................................................... 137

Study Outcomes .......................................................................................... 138

Control Group ............................................................................................. 138

Participant behaviour .................................................................................. 141

Study methodology and issues ................................................................... 143

Future Directions ........................................................................................ 145

8.0.0 REFERENCES .................................................................................................. 146

APPENDICES .............................................................................................................. 164

Appendix A: Test Development Statistics ................................................ 164

Appendix B. Detail of the Online Questionnaire ....................................... 167

Appendix C: Rapid ART Instruction ......................................................... 175

Appendix D. Ethics Clearance................................................................. 176

Appendix E. Call for Volunteers ................................................................ 177

Tables:

Table 1.1. Ratio of fatalities per incidents by industry ........................................................ 12

Table 2.1 Executive functions and self-regulatory mechanisms .......................................... 21

Table 2.2 Incident types classified in the SA model ............................................................ 27

Table 3.1 Comparison of mean effect size by training methods .......................................... 48

Table 3.2 Utility of four self-regulation training methods ................................................... 55

Table 5.1 Scale Measures Associated with Behavioural markers ........................................ 78

Table 5.1 Scale Measures Derived for Behavioural markers ........................................... 79.

Table 5.3 Development group baseline test statistics .......................................................... 80

Table 5.2 Baseline test scale definitions ..................................................................................

Table 6.1 Aviation experience development group benchmark test .................................... 88

Table 6.2 Initial development group baseline test statistics ............................................... 89

Table 6.3 Re-test performance development group baseline test ........................................ 89

Table. 6.4. Demographics by study completion and departure rates ................................... 90

Table 6.5 Aviation experience by age and gender completing the study ............................. 91

Table 6.6 Demographics of early terminations experimental group .................................... 92

Table 6. 7 Time and days on course by age and completion status ..................................... 93

Table. 6.8 Hours elapsed between log-ins and sessions - all participants ........................... 94

Table 6.9 Source of Perceived Workload Strain (PWS) by total sample ............................. 96

Table 6.10 Perceived workload strain for the completion group ......................................... 96

Table 6.11 Perceived Workload Strain one-sample test ...................................................... 97

Table 6.12 Experimental group baseline test means by Gender .......................................... 99

Table 6.13 VIF for Mental Alertness and Fatigue Management ....................................... 100

Table 6.14 Regression predicting Fatigue Management on Mental Alertness and Perceived

Workload Strain ......................................................................................................... 101

Table 6.15 VIF for Mental Alertness and Management of Fatigue ................................... 102

Table 6.16 Regression predicting Perceived rART Efficacy on Mental Alertness and Fatigue

Management ............................................................................................................... 103

Table 6.17 VIF for Mental Alertness and Fatigue Management ....................................... 104

Table 6.18 Regression predicting Perceived Ease of rART Method on Mental Alertness and

Fatigue Management .................................................................................................. 105

Table 6.19 VIF for Mental Alertness and Fatigue Management ....................................... 106

Table 6.20 Regression predicting Completion by Mental Alertness and Fatigue Management

.................................................................................................................................... 107

Table 6.20 Independent Samples t-Test for pilot experience by completion ..................... 108

Table 6.21 Analysis of Variance for Fatigue Management by Flying Experience ............ 110

Table 6.22 Descriptive statistics for Mental Alertness by Pilot Experience ...................... 110

Table 6.23 Analysis of variance for Fatigue Management by Pilot Experience ................ 111

Table 6.24 Descriptive statistics Fatigue Management by Pilot Experience .................. 111

Table 6.24 VIF for Gender, Mental Alertness, Workload Strain and Fatigue Management

.................................................................................................................................... 112

Table 6.25 Regression predicting Completion on Gender, Mental Alertness, and Fatigue

Management ....................................................................................................................... 113

Table 6.26 VIF for Gender, Mental Alertness, Fatigue Management, and Perceived

Workload Strain ......................................................................................................... 114

Table 6.27 Regression predicting Cognitive Performance on Gender, Mental Alertness,

Fatigue Management, and Perceived Workload Strain. ............................................. 115

Table A1. Statistics - Baseline test-retest administration ................................................. 145

Table A2. Comparison of development group baseline retest scores time 2 with time 1 . 145

Table A3 Summary - Development group baseline test scale reliabilities and item statistics

.................................................................................................................................... 145

Table. A6. Study completion rates, age & gender by session for 221 enrolled participants

.................................................................................................................................... 146

Table B1. Online questionnaire and presentation order ..................................................... 147

Table B.2 Preliminary literacy Test conducted at start of the baseline survey .................. 152

Table B2. Study Survey Reading Grade .......................................................................... 152

Table B3. Flesch-Kincaid Formulae Grade Levels ........................................................... 152

Table B3 Perception of stress and coping with the TLX ................................................... 153

Table B.4 Fatigue Management & Alertness Baseline Questions ..................................... 154

Table B.5 Functional performance test questions. ............................................................. 155

Table of Figures

Figure 1.1 The Swiss Cheese Model by Reason .................................................................. 10

Figure: 1.2 From technical to human causes Source: .......................................................... 11

Figure 2.1 Yerkes-Dodson Law of arousal and performance .............................................. 14

Figure 2.2 Capacity model for attention .............................................................................. 15

Figure 2.3 Wickens model of attention. ............................................................................... 16

Figure 2.3 Tang et al, 2015 process of mindfulness meditation .......................................... 23

Figure 2.4 Relationship of cognitive failure to incident rates .............................................. 24

Figure 3.1 Hierarchy of Performance dependencies and behaviours ................................... 47

Figure 4.1 Behavioural dependencies and identified attributes ........................................... 56

Figure 4.2 The Olfactory Pathway ....................................................................................... 62

Figure 4.3 Path of Nasal Air ................................................................................................ 64

Figure 4.4 Proposed Cued recall of mental state to improve attention ................................ 68

Figure 5.1 The Rapid Attention Recovery Instruction Set ................................................... 82

Figure 6.1 Study login times by course completion status. .................................................. 93

Figure 6.2 Comparison of elapsed log-in times between sessions. ...................................... 94

Figure 6.3 Perceived source of workload strain – completion group .................................. 97

Figure 6.4 Gender by coping with perceived workload strain ............................................. 98

Figure 6.5 Experimental Group total test results by gender ................................................. 99

Figure 6.6 Q-Q scatterplot for normality for Mental Alertness and Perceived Workload

Strain predicting Fatigue Management. ..................................................................... 101

Figure 6.7 Residuals scatterplot for homoscedasticity of Mental Alertness and Perceived

Workload Strain predicting Fatigue Management. .................................................... 101

Figure 6.8 Q-Q scatterplot for normality for Mental Alertness and Fatigue Management

predicting Perceived rART Efficacy .......................................................................... 103

Figure 6.9. Residuals scatterplot for homoscedasticity for Mental Alertness and Management

of Fatigue predicting Perceived rART Efficacy ......................................................... 103

Figure 6.10 Q-Q scatterplot for normality for Mental Alertness and Fatigue Management

predicting Perceived Ease of rART Method .............................................................. 105

Figure 6.11 Residuals scatterplot for homoscedasticity for Mental Alertness and Fatigue

Management predicting Perceived Ease of rART Method ........................................ 105

Figure 6.12 Q-Q scatterplot for normality for Mental Alertness and Fatigue Management

predicting Rate of Completion ................................................................................... 107

Figure 6.13 Residuals scatterplot for homoscedasticity for Mental Alertness and Fatigue

Management predicting Rate of Completion ............................................................. 107

Figure 6.14 Means of Rate of Completion by Pilot Experience. ....................................... 109

Figure 6.15 Q-Q scatterplot for normality for Gender, Mental Alertness, Perceived Workload

Strain, and Fatigue Management predicting Rate of Completion .............................. 112

Figure 6.16 Residuals scatterplot for homoscedasticity for Gender, Mental Alertness,

Perceived Workload Strain, and Fatigue Management predicting Rate of Completion

.................................................................................................................................... 112

Figure 6.17. Q-Q scatterplot for normality for Gender, Mental Alertness, Fatigue

Management, and Perceived Workload Strain predicting Overall Cognitive

Performance ............................................................................................................... 114

Figure 6.18. Residuals scatterplot for homoscedasticity for Gender, Mental Alertness,

Fatigue Management, and Perceived Workload Strain predicting Overall Cognitive

Performance ............................................................................................................... 114

Figure: 6.19 Total Group Report of RART Efficacy ......................................................... 115

Figure: 6.20 Efficacy of rART by Gender ......................................................................... 116

Figure C0.1. Video: Actor with voice over demonstrating the 4-step rapid ART technique

.................................................................................................................................... 156

Figure C1. Online demonstration of the 4-step procedure ................................................. 156

Abbreviations & Glossary

:

ABS Australian Bureau of Statistics ART Attention Restoration Technique AST Attention state training AT Attention training CB Cognitive Behavioural methods (therapy & training) CPL Commercial pilot license CR Cognitive Resilience CRM Crew Resource Management DSMIV Diagnostic & Statistical Manual of Mental Disorders 4th Ed GA General aviation HF Human Factors ICAO International Civil Aviation Organisation IMT Integrated mindfulness training IRR Inter rater reliability IST MBSR

Item Sum Total method Mindfulness Based Stress Reduction

MPL Multicrew Pilot license MT Mindfulness training NOTECH Non-technical skills PPL Private pilot license PTSD Post-traumatic stress disorder PWS Perceived workload strain rART Rapid attention recovery technique SA Situational awareness SAGAT Situation Awareness Global Assessment Technique SR Self-regulation STOM Strategic task overload model TCIF Applied Task Analysis and Task Complexity in Flight’ TEM Threat and error management TLX Task Load Index NASA TLX

ABSTRACT

Loss of attention, perception and judgement is frequently cited as contributing to deficient

performance and adverse incidents in safety-critical industries, such as aviation, transport,

healthcare and in the formative domains of education and training. External cueing,

instructional, chemical, cognitive behavioural and mindfulness techniques have been

extensively researched to improve performance of attention through self-regulation of mental

state. However, there has been no research to date into a context independent, self-initiated,

rapid attention recovery technique (rART), to augment self-regulating skills that can be

invoked to recover attention at the instant of demand. Undergraduate students from an

Australian University volunteered to learn an efficient mental enhancement technique in an

unsupervised online tuition of a self-regulating skill. After completing the self-regulation

baseline test participants were instructed to log in online and complete a 10-minute video-

assisted exercise each night immediately prior to sleep, for ten sessions. Outcome measures

of retention and compliance were the evidence for success. A final self-report of the

utilisation of the rART technique outside of the study provided a qualitative dimension to the

data. Attrition of the 220 enrolled participants resulted in 32% (n=70) completing the

benchmark measures with 46 completing the whole course. Results included Self reports of

the efficacy of the rART for those completing the course with a mean of 65%, slightly higher

for females than males, but found to be overall independent of the baseline test in predicting

success. The contrasting groups of aviation and general students were near equal on course

retention and on self-reports of perceived efficacy of the rART method. A significant

difference was found in measured indicators of self-regulating behaviours between aviation

and general student groups with respect to mental-state (alertness) and fatigue management.

Results showed that participant self-regulation of mental alertness and management of

fatigue correlated with reports of coping with workload strain. However, neither measure

predicted completion of the online course. Completion of the course together with reported

efficacy outside of the nightly course suggests the completion group had effectively utilised

the rART in the study. Compliance with study protocols was found to be consistent with

requirements whereas the partially completed respondents were widely inconsistent in their

application. Due to the negation of the control group the rART training and effect was not

clearly established in this study.

Keywords: Attention, resilience, self-regulation, situation awareness, performance, cognitive failure, respiration, human

factors, cued recall, fatigue countermeasures, startle reflex, mental state, coping skills, aviation, healthcare.

i

Outline of the thesis

This research explores a self-regulating skill involving a rapid attention recovery

mechanism evolving from the compelling need for the maintenance of alertness and

responsiveness that confront individuals in safety critical industries. Secondly, this

experiment involved the online delivery and assessment of the rART technique as a self-

regulating skill for attention recovery with a sample of aviation student pilots and non-

aviation students.

The pursuit of an understanding of how to improve cognition under stress continues to

generate vigorous research and debate into almost all facets of attention and self-regulation.

Tang and Posner (2009) had emphasised the differences between attention training (AT)

obtained with mental exercises and attention state training (AST) achieved by mindfulness,

which addresses and coordinates a much wider range of self-regulating elements involving

both the central and autonomic nervous system. However, the utility of present AST

methods is problematic in occupations in which action and response are time critical and

subject to uncertainty of retrieval of the preferred mental-state under competing demands.

There is an absence of research focused on self-initiated, context independent mechanisms to

instantly recover and free mental resources when overloaded, disturbed or emotionally sensitive

for those with a pre-existing condition, when under threat, fatigue or increased mental load. The

speed of automatic behaviours surrounding natural defensive responses such as the ‘startle

response’ prompted the adaptation of an existing ‘normal psychophysiological mechanism’ as

a technique to recover attention and full cognitive functioning at the instant of demand when no

anticipation or preparation time is possible.

ii

List of Chapters

Chapter 1 INTRODUCTION

details the contribution and size of the risk that human errors and non-compliance have in

perpetuating adverse events in safety critical industries and driving the search for a rapid

universal remedy. Student pilots were chosen as the critical occupational model.

Chapter 2 ATTENTION & SELF-REGULATION discusses attention in the context of self-

regulation, differentiates mental-state and brain-state on mental and task switching, fatigue

and resilience. Assembles and synthesises behavioural markers that address hazardous

performance and mental states,

Chapter 3 SELF-REGULATION TRAINING Discusses models of attention as the basis for

self-regulation. Compares the efficacy of conventional and metacognitive methods for the

temporary recovery from fatigue and degrade of cognitive performance through four

mainstream options to training in the achievement of self-regulating behaviours.

Chapter 4 PSYCHOPHYSIOLOGY OF ATTENTION Discusses the psychophysiological

connection between the olfactory sniff and attention. Outlines the respiratory process and

probable neurological sequences that activate changes in mental state.

Chapter 5 METHODOLOGY discusses the recruitment of participants, research design, and

online training delivery methodology. The construction of the initial benchmark test with

an independent development depicting behavioural self-regulation measures for the study

baseline test. The scripting and filming of the training component is discussed, and the

resulting statistical analysis of results outlined.

Chapter 6 RESULTS An analysis is presented of the stated hypothesis, the statistical results and

participant compliance with the study protocols. A comparison is made of the usability and

efficacy of the online process against participant results and demographics.

Chapter 7 DISCUSSION reviews the study objectives, background of the study and participant

controls designed for the unsupervised delivery. The hypothesis are discussed in the light

of limitations and participant behaviour. Further suggestion is made of research to expand

the concept and neurophysiological basis of the action

1

1. INTRODUCTION

Where is the risk?

Individual performance is sensitive to mental state and the ability to continue to function when

fatigued, overloaded or distracted. Attention to the task is the essential criterion for

performance, reinforced by adaptive and learned skills embedded in self-regulating behaviours.

This study identifies and evaluates an attention recovery technique as a self-regulating skill

utilising an online delivery method with a student sample. Chapter one illustrates the magnitude

of the risk that makes this an imperative study from both scientific and social perspectives.

Pilots are a group that have a repeated ‘attention critical task’ at both take-off and landing.

Their tasks require extensive technical training and the development of self-regulating skills to

maintain their flying skills; attributes which are not necessarily present in the novice pilot but

developed over time. To accommodate the large numbers of new pilots needed, governments

and aviation regulators worldwide have modified training standards to expedite the more rapid

placement of novice pilots into airlines. (ICAO, 2007; (CASA, 2015). Demand for pilots has

increased with Boeing and Airbus as well as some 1700 other jet airliner manufacturers and

major airlines globally projecting a continued 6% growth annually in air travel over the next

15 years. Growth due to fleet expansion and attrition due to retirements require an estimated

558,000 new pilots to be trained to ensure maximum utilisation of more than 70,850 aircraft

on the order books presently (Tinseth, 2015) (Leahy, 2015). The training and development of

large numbers of young pilots to operate in a hazardous environment makes the evaluation and

training of self-regulating behaviours in trainee’s imperative. The outcome of poor attention,

anticipation and the likelihood of cognitive failure when stressed would inevitably escalate the

risk of human error on the flight deck.

2

Human error continues to dominate incidents in safety critical industries such as aviation,

transport and healthcare. Human error resulting from loss of attention and situation awareness

(SA) (Endsley, 1995) has been widely researched. It is apparent that when the quality of

attention as the essential interface between human perception, cognition and response is

affected, then cognitive functioning is also affected (Kahneman, 1973)(Wickens, 1992)

(Posner, Rothbart, Sheese, & Voelker, 2014). Increased stress has been shown to vary attention,

narrowing and slowing thought processes to increase errors (Eysenck, Derakshan, Santos, &

Calvo, 2007; Eysenck, Payne, S., & Derakshan, N., 2005). The need for the ability to return to

a fully functioning mental state after disruption has been viewed as enabled by the individual’s

cognitive resilience (CR) (Staal, Boulton, Yaroush, & Bourne Jr, 2008), or ‘mental toughness’

in being able to bounce back after disruption.

1.2 The extent of human error Endsley’s (1995) widely-used Situational Awareness (SA) construct, views the potential for

error management and competent functioning, through the cognitive elements of perception,

comprehension and projection (judgement) about a current situation. SA is thus fundamentally

dependent on attention state training to facilitate perception in the model (Tang & Posner,

2009). The SA construct has been extensively used in describing and measuring the necessary

cognitive attributes and performance of pilots (Campbell, Castaneda, & Pulos, 2009; Carretta

et al., 2014) and air traffic controllers (Chappelle, Thompson, Goodman, Bryan, & Reardon,

2015; Pecena et al., 2013). Attributes similarly required for mobile equipment operators,

surgeons and nurses, chemical plant operators and police officers. Incident investigations

across industries as detailed in chapter two (Table 2.2) have shown that human error has

featured in 80% or more of incidents (ATSB, 2004; Endsley, 1995) with overall loss of

attention and perception directly associated in 76% of cases (Endsley & Garland, 2000; Jones

& Endsley, 1996; Shook, Bandiero, Coello, Garland, & Endsley, 2000) (O’Hare, 2006;

3

Sneddon, Mearns & Flin, 2006). The universal nature of human error and specifically loss of

SA has also been implicated as a key factor in investigations of preventable adverse events in

ospitals due to incorrect medication, misidentified patients, poor hygiene compliance and

deferred or forgotten intentions (Levin et al., 2011).

The magnitude of the problem of human error encompasses all industries. Pilot error rates

continue to be successively reported at 75-80% for Australia (CASA 2014; ATSB 2007; ATSB

2014) and 80% for the US (FAA, 2010) and similarly for global aviation (ICAO, 2015). The

figures and overall approach to pilot error and human error in general, have been challenged

by several authors, generally critical of the non-inclusion of antecedent organisational factors

(Dekker, 2011; van Winsen, Henriqson, Schuler, & Dekker, 2014). The authors arguing that

organisational factors in the culture, system of work and even non-work related events have a

greater role and impact on safe behaviour. Equipment usability studies have been employed by

combining behavioural and engineering concepts, to minimise risk and improve fail-safe

operation at the human-system interface. For example, to program pre-failure warning systems

and automatic stops, or as in one historical example, a simple change to the shape of an aircraft

undercarriage lever. The solution being replacement of the grip with a tyre-like knob to prevent

inadvertent error in selection during the highest workload, the last 20% of the landing phase

by the pilot. Typically, when the pilot’s vision and concentration is out of the window and less

so looking down to find the lever in the gloom of a buttons and instrument rich cockpit.

Similarly, the 'Positive Deviance Program' was implemented in some hospitals, by engineering

interactive behaviours through training to achieve compliance with infection control. (Lloyd,

Buscell, & Lindberg, 2008), the person being recognised as the last line of defence with the

ultimate discretion to prevent adverse incidents.

The global healthcare industry is the source of most preventable fatalities and adverse incidents

in the workplace. Progressive quarterly figures are published (HHA, 2015) on compliance rates

4

with protocols show the difficulties in the measurement of non-compliance, pointing to the

problematic nature of subjectively influenced and inevitably biased self-reporting assessments

(Cohen & Sherman, 2014; O'Brien & O'Hare, 2007). The measures have tended to display

inflated rates, masking the true situation when compared with covert observation methods

(Skinner, 1953, van de Mortel, Apostolopoulou, & Petrikkos, 2010). However, even when

using more objective electronic methods of assessing compliance to avoid observer bias, true

measures were confounded by what has been mistakenly understood to be the classic

‘Hawthorne effect’ (Adair, 1984); insofar that nurses mindful of being observed increased their

compliance then later decreased it when unobserved (Edmonds, Brown, White, & Kirk, 2013).

Observed non-compliance with hygiene protocols, as a key behavioural marker to infection

and fatality rates, vary worldwide, reportedly hovering at around 20-25% in Australia (HHA,

2015), but when covertly observed, non-compliance rose to more than 60% of the time. The

standard hospital responses to non-compliance have been more training, raising consciousness

through abundant signage and placement of hand gel bottles within reach of every work site.

An implicit recognition of the human factor operating through fatigue and inevitable

distractions in the hospital system of work.

Education and simulated practice will raise the threshold of vulnerability at which an individual

is likely to lose situation awareness and responsiveness to circumstances (Koglbauer, Kallus,

Braunsting, & Boucsein, 2011). However, conventional training does not necessarily address

the intrinsic attributes or mental state of the nurse, pilot, driver or operator and have proved

insufficient to ensure compliance under pressure or defend against expedient decisions under

stress (Goodman, 1999; Orasanu & Martin, 1998). Reinforcing conclusions derived from the

global research on safety that the unaddressed human factor provides the most potential for

loss. Dismukes, investigating the extent of problems due to remembering intentions

(prospective memory) at NASA discussed the inherent variability of human performance and

5

in the frailty of prospective memory amongst medical and aviation personnel concluding that

trained and conscientious people routinely make mistakes and that just trying hard would not

eliminate errors. A theory that was further expanded by him in his reference to the statistical

inevitability of failure in remembering intentions or prospective memory. By contrast James

Reason, in his ‘Swiss Cheese’ model (fig 1.1) had advanced the argument that even if the

preconditions for a failure were present in a situation, failure would not result until or unless

all conditions had lined up to deliver a domino-like effect, but that the human in the sequence

could intervene and prevent a problem developing in timely manner (Reason, 2000).

(Adapted from Reason, J. Human Error: Models and Measurement 2000)

Fault free performance in this context thus suggests the need for constant alertness and attention

to the task and an active anticipation of threat and error. In addressing that issue (Dodhia &

Dismukes, 2009) had proposed a cognitive solution involving pausing at an interruption to

firmly re-encode an intention, utilise salient cues as reminders and pause further to mentally

restate both the intention and the task objective when changes have occurred. While this

unquestionably decreases prospective memory lapses and unintentional error due to forgotten

and interrupted intentions, in a less time urgent context where a pattern or ‘primed’ response,

based on previous learning could be given (Ross, Klein, Thunholm, Schmitt, & Baxter, 2004)),

rapid temporal changes in a stressful situation would likely mean an increase in cognitive error

Figure 1.1 The Swiss Cheese Model by Reason

6

rates as decision times shorten. Starcke et al in a review had shown that both decision speed

and risk taking increased with stress (Starcke and Brand, 2012). Mechanisms such as

checklists, cues, memory techniques and speed of recall, are themselves vulnerable to the same

human factors of distraction, deferments, interruptions, violations and fatigue.

It is just as easy to forget to use the reminder as it is to forget to execute an intention. so then

the solution must lie more closely to cognitive task switching and the capacity to ‘unload’ any

mental preoccupation or stress (Wickens, 2014), to function as required at the instant of

demand. From technical to human causes, the contribution of pilot error in incidents recorded

by the transport regulator for the Australian aviation sample has hovered around 75% or more

over the last ten years despite the vastly increased emphasis on human error, in training

programs (ATSB, 2007; ATSB, 2014).

While Australian regular public transport airline operations represent a volume that is

magnitudes less than those of the USA, Middle East, Asia and Europe, the rate of incidents due

to pilot error is reported as proportionally similar across regions and airlines, at around 80-90%

of all fatal incidents.The figures indicate that the human factor risks amongst highly trained

pilots and maintenance engineers have not been fully addressed (Fig 1.2).

Figure 1.2 From technical to human causes Source: (Boeing, 2007)

7

The question arose as to whether the aviation case was the same as that of other industries. A

simple comparison of the severity of risk and the crucial need for solutions was assessed for

various industries (table 1.1). The ratios of fatalities to incidents were calculated as indicated

in (Table 1.1) from fatalities per ‘serious’ investigated workplace incidents, across aviation,

Industrial, hospital and transport industries.

No industries were found where incidents did not occur. Analysing the rates of fatalities and

survivability for each industry revealed that while there were inevitable differences in skills,

motivation and culture, the increase in severity of risk was relative to the system of work, the

task and the impact of human error. That is, whether the risk just affected the nurse or operator

or flowed on to deliver harm to another person, the patient, the bystander, or another driver

(Wiegmann & Shappell, 2001). At the higher severity ratios (Table 1.1), the predominant

technology in the workplace is likely to be more sophisticated, the complexity of defensive

skills and the training needed to manage the risk and the impact on others may be more

complex, task errors may impact others more than the self. For example, in nursing, patients

are more at risk than nurses from the procedures and equipment used to heal them. Similarly,

in aviation, aircraft systems and weather collectively increase passengers and crew risk but

Table 1.1. Ratio of fatalities per incidents by industry

Industry Ratio Frequency of adverse events Source

Hospital patients 1:57 200,000 infections & 3480

fatalities (ACSQHC, 2014. p44)

Transport Drivers 1:100 6500 crashes & 65 fatalities ABS, 2015. BITRE,

2015

Aviation 1:150 5683 serious incidents, 38

fatalities ATSB, 2015 Quick

Counts

General industrial 1:2859 531,800 incidents, 186 fatalities Safe Work Australia,

2013, p22

8

leave the maintenance technician unscathed. Inspection of the calculated severity ratios

suggests that the risk level increases as the combination of the complexity of operator tasks,

the urgency and the potential for distraction and inattention increases (Yu, 2016). Nurses

working shifts in a hospital operating rooms and emergency departments are expected to be

able to deal with a succession of rapid and frequently interrupted events while involved in

complex tasks (Antoniadis, Passauer-Baierl, Baschnegger, & Weigl, 2014). The hospital or

nursing context in a public hospital represents a systematic process of rendering attention to

patients, the management of materials and equipment and the often voluminous and detailed

documentation of patient medical notes and reports, overlain by sudden demands and ad hoc

interruptions (Cohen & Sherman, 2014). Access to patients, pateient records and equipment

means that nurses need to constantly walk from one location to another in a ward. Contributing

to daily fatigue, accumulated further over the span of the shift (Barker & Nussbaum, 2011) to

erode mental energy and attention (Linden, Keijsers, Eling, & Schaijk, 2005), The increase in

physical fatigue potentially inhibits motivation to comply with the moment-by-moment

hygiene controls (Huis et al., 2013). The heavy road transport statistics reflect the

circumstances of road transport drivers and mobile equipment operators. Truck drivers are

often at the mercy of other road users in cars, occasionally needing to defensively manage

heavy articulated vehicles, road trains or tankers to stay on the road or stop suddenly. Rail and

long-range road transport includes extended periods of drivers sitting in a cab, subject to the

monotony of long unrelieved distances, risking complacency (Prinzel III, DeVries, Freeman,

& Mikulka, 2001). Low task stimulus, spasmodic cognitive demands, coupled with fatigue

induced by noise, heat and vibration have a performance and safety effect on drivers. Critical

competencies such as visual perception and motor control (Conway, Szalma, & Hancock, 2007;

Frissen & Guastavino, 2014) are affected and combine to lessen driver alertness and

responsiveness. Evidence from claims-based incidents show that mobile equipment operators

9

of all kinds (forklift, cranes, trucks) are more than four times at greater risk of traumatic injuries

and fatalities than all other types of industrial work in Australia (Safe_Work., 2015). The

aviation groups in the risk ratio comparison table (Table 1.1.) mainly represent pilots but the

risk of ‘deficient performance’ equally extends to maintenance personnel because of their

critical role in ensuring that aircraft can fly safely. The general industrial context (Table 1.1)

represents the greatest number of incidents in the cross-industry comparison of severity, mostly

of a high frequency and low severity such as ‘slips, trips and falls, made more serious when

being struck by falling objects, falls from height and exposure to uncontained energy. The

largest employer is the general industrial sector which is also lesser supervised by comparison.

The large numbers of small to medium companies as employers - provide the greatest

opportunity for incidents covering more than half of the Australian workforce (Australian

Bureau of Statistics, 2015). Improved techniques and analysis in the last 50 years have

progressively shifted the emphasis from technical factors as the cause of adverse events to be

largely replaced by human factors (fig 1.2), serving to bring error management and

performance through self-regulation (SR) into sharper focus. Research into attention recovery

as a key aspect of SR has become imperative given the rate of growth in the need for services

that are subject to the variability in the human factor. The emphasis on traditional education

and training now increasingly includes consideration of performance and the way people self-

regulate to achieve their objectives. While fatigue and the systems of work that impact SR and

performance, continue to be widely researched topics, the means and mechanisms to enhance

SR has had a consistent but slower growth, dependent as it is on a better understanding of the

way brain-state and mental-state influence each other. The path to reducing the risk and

decreasing incidents is through a better understanding of the human factors in workplaces.

10

2. ATTENTION & SELF REGULATION

2.1 Introduction to Attention Attention and error are intertwined in human performance, a failure in one frequently leading

to the other. The need for an understanding of attention and the means to recover it when lost

is universal and critical in all roles associated with cognitively challenging, time-critical or

hazard-prone decisions. Attention to the task is one of the most frequently addressed topics in

psychology attracting a wide range of theoretical, functional and metaphysical contributions.

Relevant to attention recovery are perspectives that have evolved from capacity models by

Kahneman (1973) (fig 2.2), Structural models by Wickens (1992) (fig 2.3) and Posner’s neural

network theory (Petersen & Posner, 2012) (Fan, McCandliss, Fossella, Flombaum, & Posner,

2005; Posner & Boies, 1971; Posner et al., 2014).

2.2 Models and limitations of attention That limitations exist in both structural and capacity models of attention is evidence of the

complexity in the relationship linking attention and performance. The decline of performance,

as is commonly attributed with arousal, in the Yerkes, R. M & Dodson, J. D 1908 law, suggests

that performance quality beyond an optimal point becomes inversely related to arousal (fig

2.1). The apparent reversal in performance has been central in several perspectives and theories

to suggest either capacity or structural limitations in attentional models. In comparison, in both

capacity and structural models, as load, contention, time pressure and arousal increase beyond

individual ability, error rates increase, effort decreases and performance, diminishes.

Kahneman had pointed out that an important aspect of effort and attention was the dependency

n the task-load and the time pressure imposed on short-term memory (Kahneman, 1973, pp27).

11

2.3 Capacity Models: A major difference in the models is in the way the they are said to handle specific contention

and overload, for parallel events. In Kahneman’s model (fig 2.2) that task falls to management

of capacity and the allocation policy in which arousal has the potential to reallocate and activate

a temporary reserve increasing capacity or to re-prioritise concurrent tasks. Whereas in the

Figure 2.1 Yerkes-Dodson Law of arousal and performance

(Adapted from Kahneman 1973)

Figure 2.2 Kahneman’s Capacity model for attention (Adapted from Kahneman 1973

12

structural model as described in Wickens model (fig 2.3), concurrent processing is not available

and similar demands made on the facility face the problem of being blocked as overload.

Human activity in general and safety critical roles often operate with a need to manage

concurrent streams of attention. How the streams are allocated and apportioned when in

conflict or overload is important in identifying functional constraints. Kahneman’s (1973)

model (fig 2.2) depicts a linear process from arousal to response. The model see attention as

having a limited capacity necessarily managed by a feedback and allocation policy.

For example in ‘capacity’ models (Kahneman, 1973), attention is more discretionary or

competitive in allocating capacity for various needs but remains a limited commodity, highly

dependent on mental effort (Shenhav et al., 2017), while allowing for some simultaneous

processing, easily disrupted or reduced. The capacity model of attention implies limitations but

helps, in part, to explain degraded attention with task overload. In the capacity model, attention

is difficult to maintain over extended periods and easily degraded by normal competing

distractors and influences such as age, task load and emotional states ( Eysenck et al., 2007).

2.4 Bottleneck models: Bottleneck models as in the Wickens, (1992) (Fig.2.3), extend the capacity model to

accommodate the number of processing channels available. Commencing with attention as the

input, the structural or ‘bottleneck’ approach as depicted in Wickens model. Perception,

judgement, memory and response, as well as sensory stimuli, have a direct input. A position at

the person-stimulus or signal interface gives the attention receptor a preeminent position as it

directly accesses other cognitive functions in Wickens model. However, it is not sufficient to

just have receptors, the capacity to engage, absorb and respond to external needs and

circumstances depends on the person’s ability to select, adapt or switch attention. A change in

13

mental-state can be a common human response in coping with stress. Avoiding thinking about

an issue, taking a deep breath to relax, or taking direct action to eliminate the problem

Figure 2.3 Wickens model of attention. (Adapted from Wickens (1992)

is often automatic and unconscious in reducing discomfort (Monet & Lazarus 1977. p145).

While discomfort serves as immediate motivation to seek change, the intensity of the need may

compel more conscious and deliberate action or drugs to effect change. Recovery when

clinically or chronically stressed is commonly facilitated by cognitive behavioural counselling

or specific training in changing mental-states as in meditation and mindfulness training (MT).

2.5 Attentional Networks. Posner & Boies (1971) advanced a network theory of attention, moving away from attention

as a uniform concept encapsulated by the linear models of Kahneman and Wickens. Posner’s

Attention Network Theory (ANT) provided an anatomical as well as a functional picture of

attention, defined as anatomically separate from other processing systems that are affected by

14

attention. (Petersen & Posner, 2012). The ANT theory distinguishes between attention and

processing networks in a potentially duplicated three-network model comprising the

components of alerting as the arousal system, orienting focused on prioritising sensory inputs

and the executive managing conflict in the network. While functional networks are named their

action is distributed across brain regions. A significant aspect of the theory has been the

practical use of the model for attention training (AT), with application in the improvement of

cognitive test results and specific clinical interventions (Raz & Buhle, 2006). Development of

the attention model has seen the utilisation of (MT) which Tang & Posner (2009) refer to as

‘attention state training’ (AST) to effect change through specific networks. The meditation-like

practice in the concept is the means to act on the network to change mental-state. The effect of

which generalises across the cognitive domain to influence other domains of behaviour and

contrasts with attention training, a task specific skill for a procedure requiring direct executive

control.

2.6 Arousal and performance How attention is initiated must necessarily look to arousal and orientation. Kahneman (1973)

had viewed arousal and effort as the same thing. Arousal for attention is generally associated

with being a more energised state, different from just paying attention which represents a more

passive state. Posner et al, (1971) had suggested that attention, was not limited to just meaning

‘awareness’ but also had the properties of selectivity - as focused and divided attention, and

intensity - as alertness and sustained attention (Posner & Boies, 1971). Sturm et al, (2001)

added to more emphasis to arousal by dividing attention into components of intrinsic and phasic

alertness, where intrinsic alertness represented arousal and phasic alertness, representing the

ability to modulate the level of receptivity to an arousing stimulus (Sturm & Willmes, 2001).

The phrase, “a maximum state of arousal”, suggests there are no remaining resources available

to deal with other demands. The Yerkes-Dodson 1908 law (fig 2.1) states that performance is

15

an inverse function of arousal level at a point (Kahneman, 1973, pp.49). The ability to switch

attention from divided to being focused and to sustain alertness for a greater period infers the

supply of energy for cognitive effort. At the extremes, where arousal is lowest or absent, a lack

of mental energy and inclination to cognitive effort, can present as apathy as seen in various

mood disorders (DSMIV, 1994). While physical exercise has been seen to increase mental

arousal and effort up to a state of exhaustion, a subsequent attempt to increase it further results

in a conscious inability to stimulate or increase it further, despite the desire to do so.

The bi-directional relationship between exercise and mental effort had shown that sustained

physical activity decreased with increased mental fatigue (Marcora, Staiano, & Manning,

2009), while a decrease in mental effort was reflected in poorer cognitive test performance

(Storbeck et al. 2015; Cockshell & Mathias, 2012; Wright, 2014; Jackson et al. 1999). Mental

state was a found to be a further limiting factor, with cognitive-affective variables such as

anxiety highly predictive of disruption of mental effort and performance in sports (Allen, Jones,

McCarthy, Sheehan-Mansfield & Sheffield, 2013). An anxious arousal resulting in a decline in

performance, consistent with the Yerkes-Dodson law (fig 2.1).

Kahneman’s suggested that optimal performance could be most easily achieved where the

perceived threat was low and where the energy required to manage it was low. Also, where

arousal was not excessively high, where a broad attention state was experienced and where

attentional stability prevailed together with improved memory recall and physical coordination.

In summary, a prescription that closely resembles a stable low anxiety mental state.

The allocation of mental effort is most often seen in the context of task demands, for example,

as in driving in traffic, but can also be discretionary and strongly mediated by trait-based

persistence and conscientiousness in determining the time on task. After which it could be

relinquished for something more comfortable and desirable as an alternative (Wickens et al.,

16

2015). The ANT by Posner et al, sees allocation of resources as a network task managed by the

executive in the model.

At the behavioural level, mental-switching could also be due to a ‘seductive attractor’.

Described as the situation where the target attractor represents a particular interest with a

hedonic valence, that is likely to result in gratification and a lower mental workload (Bearman,

Paletz, & Orasanu, 2009).

Kahneman’s (1973) model (Fig 2.3) articulated the arousal-attention relationship by expanding

on outcomes of low and high arousal to reveal dependencies in the model. Arousal was depicted

early in the model and fatigue later accounting for the erosion of attention. The inclusion of an

‘evaluation and allocation’ policy in the model suggested executive based cognitions and

decisions that also involved motivation and intentions; an aspect mentioned later in this section

by Wickens et al (2015) in discussing task retention and attention switching using the Strategic

task overload model (STOM).

At the psychological level, Lazarus and Folkman (1984), in differentiating the two mental

states in their model of coping as ‘intrapsychic’ and ‘direct action' implied that effort was

required for coping and mental energy. They used the two terms when referring to mental state

to mean to adjust, confront, regulate feelings, to detach the self, and make the effort to ‘make

things work’ (Lazarus & Folkman, 1984). In this context effort in coping means actively

maintaining an unencumbered alert, emotionally calm mental state, conducive to greater

cognitive effort and attention.

The association between attention and mental effort was investigated from several

perspectives, framed by whether it emanates from a physiological or psychological perspective.

For example, Oken, et al (2006) described the physiology of arousal, vigilance alertness and

attention as activation states of the cerebral cortex (Oken, Salinsky, & Elsas, 2006). Gomez-

Pinilla extensively examined the central role of nutrition to cognitive functioning, showing

17

energy metabolism and neural plasticity important in learning and that mental effort was

directly affected by diet (Gomez-Pinilla, 2008). Limits to arousal, in turn, were described by

Tanaka, et al, (2014), showing that continuous activation of the prefrontal cortex increased

sleepiness and the frequency of beta brain waves (Tanaka, Ishii, & Watanabe, 2014). The

changes in brain-state indicating that where arousal was high, depleted energy or fatigue at

some point could overwhelm both arousal and motivation.

In addition to the common effect of fatigue acting to reduce mental effort on most tasks (Engle-

Friedman, 2014; Murata, Uetake, & Takasawa, 2005); other common strong influences on

mental effort include depressed moods that moderate motivation to act (Capa, Audiffren, &

Ragot, 2008; Smith, 2012) and the effect of fatigue-inducing anxiety on performance (M. W.

Eysenck et al., 2007). At the biological level, Fairclough & Houston (2004) found direct

correlation with changes of blood glucose levels on mental effort (Fairclough & Houston,

2004) and also significantly, the effect of type of diet on alertness and mental effort (Allen,

Jones, McCarthy, Sheehan-Mansfield, & Sheffield, 2013). The latter finding support in the

work on pilots and diets where protein, carbohydrate and fat weighted diets were compared

(Lindseth. G., Lindseth. P, Jensen, Petros, Helland & Fossum, 2011). Lindseth et al found that

high carbohydrate and fat diets resulted in significantly better and more consistent performance

in flight simulator trials than others, with correspondingly higher scores on cognitive abilities

tests. The findings suggesting that mental as well as physical energy were diet dependent and

could be reflected in test scores; that high protein diets delivered better reaction times compared

to others. The size of the ‘lunch’ was also of importance when the de-energised postprandial

mental dip associated with a state of sleepiness was analysed. The effect being a low energy

state probably due to the activity of the parasympathetic nervous system in response to food in

the digestive tract. Further reinforcing that attention depends on both the availability of mental

effort, an energy source and an absence of cognitive-affective interference.

18

2.7 Attention switching and multitasking

Attention switching, and multitasking are two forms of dealing with apparently simultaneous

events. An investigation of a mathematical model of multi-tasking and task switching decisions

in the STOM with a meta-analytic integration of 31 experiments analysed choices based on

ease and attractiveness of the alternative task (Wickens, Gutzwiller and Santamaria, 2015).

Where multitasking may be said to occur, it would follow that attention as a neural resource is

rapidly reassigned at each change or slice of time. The enemy of multitasking and of being able

to switch rapidly from one thing to another is fixation, or cognitive tunneling, representing a

state of unconscious disregard of external signals, or a conscious reluctance to share attention.

Many activities are done concurrently by sharing attentional resources (Wickens et al., 2015),

with cognitive resources allocated on a priority basis to the more complex and least automatic

task. Wickens et al. pointed out further limitations of the STOM including the additional risks

of neglect, of choice and avoidance and in the prioritisation of attention to less demanding

activities. That there were temporal limitations in multitasking when engaged in a continuously

demanding task were also found in testing for reaction time using a standard test for pilot

assessment (Carretta, 2011; Carretta et al., 2014), with the finding that errors increased as the

time for recall and execution shortened. Emphasising that time on task is a significant

consideration in the application of attention (Kahneman, 1973)(Dismukes, 2006) (Wickens et

al, 2015). Loss of interest due to boredom or typically uneventful execution can leave a task

unfinished or poorly executed. The focus is thus firmly on the pilot’s limitations of how well,

how long and how flexible their attention to cockpit tasks can be. Focusing on the pilot

reinforces the need for a mechanism that can address all the elements of the STOM by enabling

the pilot to rapidly relinquish preoccupation and fixation to self-regulate their attentional state.

Merging Hofmann’s Self-regulation mechanisms (Table 2.1) with Endsley’s (1997) taxonomy

19

of situational awareness and behavioural requirements, suggests the following: eight

(unordered) behavioural markers as key competencies.

(Hofmann, Schmeichel, & Baddeley, 2012)

2.8 Brain State and Mental State A distinction is made for clarity of the difference between mental-state and brain-state when

discussing attention. While not exclusively so, mental-state can be diverse and changeable,

easily influenced by the situation, environment, mood, motivation and agenda of the person,

subject to both endogenous and exogenous inputs. Brain-state is limited here to the

neurobiological expression of mental-state. Note that mental-state and brain-state will clearly

influence each other. Hence any arousal which precedes overt thought such as the startle

response is treated here as associated with brain-state. Arousal, orientation and attention

resulting from a subjective input could then be ascribed to mental-state, while understanding

that brain systems have an underlying role in their expression. When mental-state is the

executive, and the stimulus occurs due to a thought, perception, anticipation or association, it

could inform brain-state in determining the level of arousal, intensity and selectivity of

attention required. (Tang, Rothbart, & Posner, 2012). Primal reflexes such as the startle

response responding to exogenous stimuli and occurring without apparent or overt thought is

1. Can deliver the necessary mental effort 2. Find it easy to remember despite interruption 3. Can switch attention rapidly between demands 4. Can see things others may miss 5. Can anticipate sudden changes 6. Less vulnerable to emotional preoccupation 7. Ability to avoid becoming fixated 8. Remembers procedures, rules and exceptions

Table 2.1 Executive functions and self-regulatory mechanism

20

an example of a reversal where brain-state affects mental-state, likely trading thoughtfulness

for speed of response; albeit at the risk of increased errors as speed of application increases

(Dismukes, 2006).

Tang et al (2012) in reviewing the relationship between mental state and brain state, concluded

that a voluntary and preferred mental state could be achieved by modulating brain states with

integrated MT (IMT). The evidence showing that resultant changes were a good predictor of

subsequent improved cognitive performance. The ability to switch between mental-states was

considered by Tang et al, as “vital” for SR. However, some involuntary mental-switches can

result in undesirable outcomes, where for example, a dysfunctional mental-state is triggered,

by recognition or recall, and the person’s self-regulating defensive mental-switch fails.

PTSD is a mental-state that has a long-term effect on brain-state. Experienced as chronic

fatigue, poor concentration, difficulty in making decisions and subjective feelings of depressed

hopelessness. It frequently includes symptoms such as cued hyperarousal (Heesink et al.,

2017)(APA DSMIV, 1994) while others experience extreme disassociation (Cortese, Leslie, &

Uhde, 2015; Lanius, Bluhm, Lanius, & Pain, 2006). Perpetuation of cognitive impairment is

also seen in typical chronic fatigue, depression and anxiety, emphasising the cross-modal effect

of mental-state and brain-state (Reppermund et al., 2007).

The management of mental-state has long been a preoccupation amongst philosophers.

Humphries (1971) in discussing a Buddhist variant of MT, the Zen technique, related that in

the teaching ‘Satori’ or enlightenment was said to be experienced, rather than something that

is known, implying Satori to be a mental-state rather than knowledge. In Zen the achievement

of Satori is achieved by disciplining the mind to pay attention to the event rather than as is

common the noise in the mental environment.

One mechanism, practiced in the Zen method is redirection of thinking away from the mental

‘noise’; such as the use of contemplation of rationally impossible propositions, such as ‘the

21

sound of one hand clapping’ called ‘Koans’ (Humphries, 1971, pp116). The practical effect of

struggling mentally with an irrational proposition to achieve a less encumbered mental-state,

is the displacement of emotionally laden self-preoccupation and ruminative anxiety. Continued

meditation maintains the preferred mental-state, to control impulse and Freud’s primitive or

hedonic ‘id’ (O'Bryan, 2011). Evidence of the corresponding benefits of MT cited by Tang et

al, (2015), as found in different studies of MT, included improved attention span, measured by

a reduced attentional blink, in others improved conflict monitoring and enhanced attentional

orienting. The process of meditation involving focused contemplation over longer periods of

time was also said by Tang et al, to help in ‘cultivating’ attention with repetition. Thus attention

control is the starting point for emotion regulation, self-awareness and subsequently effortful

doing, mind wandering control and to ultimately achieve ‘effortless being’, which could also

Adapted from Tang, Y. Y., Holzel, B. K., & Posner, M. I. (2015).

2.9 Mental State and Unsafe Behaviour be interpreted as a lack of self-consciousness and preoccupation (fig 2.3) (Tang, Holzel, &

Posner, 2015). Variations of the MT approach with application to stress management,

Mindfulness Based Stress Reduction (MBSR), were reported by Jha, et al, (2007) in a

comparison to differentiate MBSR with MT and a control. Jha et al., found mindfulness training

Figure 2.3 Tang et al, 2015 process of mindfulness meditation

22

as important for attention behaviours through the sub-components of the attention network and

other subsequent cognitive processes of attention, orientation and conflict resolution (Jha,

Krompinger, & Baime, 2007), demonstrating that MT can influence brain state through the

attentional networks.

The psychological literature has extensively referenced how a person’s mental state and coping

style directly influences behaviour. The earlier work on coping styles by Monat and Lazarus

(1977, p150), (Folkman, Lazarus, Dunkel-Schetter, DeLongis, & Gruen, 1986), had shown that

superior performance was more consistent with what they called an externally focused ‘direct

action’ style than an internally focused ‘intrapsychic’ style. A person’s coping style and mental

state, whether it was internally or externally focused, could thus be seen to be a strong indicator

of the ability to respond adequately to situations. A failure in perception and cognitive

processing predicts poorer performance outcomes. The behaviour based assessment of

propensity to human error, as achieved in the development of the ‘Cognitive Failure

Questionnaire’ (CFQ) (Broadbent, 1982), effectively translated observed behaviours into

mental-state (Wallace & Chen, 2005). Observable mistakes and errors in the Broadbent filter

model of attention, associated cognitive impairment with physiological states, such as fatigue

and illness. The model allowed for functional variance in behaviour in contrast to the use of

intentions and attitudes in predicting behavioural outcomes, as in the theory of planned

behaviour (Madden, Ellen & Ajzen, 1992).

Taken together, the three perspectives of coping style and mental state (Lazarus,1976), when

added to cognitive failure (Broadbent et al. 1992) and Endsley’s situation awareness (Endsley,

1995), suggest a natural convergence and sequence with cognitive failure leading to unsafe

behaviours and incidents (fig 2.4).

23

(Broadbent, 1982),

2.10 Cognitive resilience and mental state This study, further detailed in chapter five, centres on students but the need for resiliency in

behaviour applies equally to all personnel engaged in complex tasks and when under increased

mental load or threat. Staal et al., (2008) referred to resilience as the capacity to recover and

overcome adverse situations and the effect of stress on cognitive function (M. A. Staal,

Boulton, Yaroush, & Bourne Jr, 2008). A degree of cognitive resilience is required in those

circumstances to disengage from other preoccupations, attend to the situation and act on the

presenting problem. Mogg et al., (2008) found evidence showing that high anxiety was

associated with a greater bias in shifting attention to threats and slowness in disengaging from

those threats, a state resembling fixation (Mogg, Holmes, Garner, & Bradley, 2008). Once

perceived, a difference or change as in either slowing or increasing the speed of response to a

threat suggests a post-perception evaluation and change in the strategy for dealing with that

threat. The Mogg et al., (2008) study provided support for the tendency to fixation and

cognitive slowing evident in anxious individuals (Eysenck et al, 2004). An observation also

Figure 2.4 Relationship of cognitive failure to incident rates

24

consistent with the theoretical models and outcomes in the Lazarus & Folkman accounts in

which defensive behaviours are characterised by self-preoccupation, anxiety, withdrawal and

inevitable loss of external awareness (Lazarus, 1976. p74), which they called intrapsychic

versus direct action coping. The intrapsychic form involving functional losses in memory,

attention and decision skills consistent with cognitive failure in the Broadbent et al., model

(Wallace & Chen, 2005) and reflected in the Mogg et al., finding. In adverse situations,

recovery to full functioning is dependent on the person’s psychological resilience, which

Lazarus refers to as the capacity for cognitive appraisal in adapting and enabling the return to

a stable mental state (Monet & Lazarus 1977. p145), consistent with a Self-Regulated state.

There are a range of psychopathologies classified as personality disorders (DSMIV) that only

tend to be revealed when triggered by an adverse situation and surface when feeling

undervalued, anxious and depressed (Presniak, Olson, & Macgregor, 2010)(APA DSMIV,

1994). The perspective taken in this study follows the Monet et al model in that cognitive

resilience is the ability to adapt, relinquish or clear mental impediments to full cognitive

functioning. Impediments which would otherwise and variously block functional abilities such

as concentration inhibit mental effort, rapid task switching, prospective memory, the speed of

memory recall and vigilance. Abilities that are essential and interdependent for optimal

problem-solving performance (Rockstroh & Schweizer, 2001; Christopher D Wickens, 2008;

C. M. Wickens, Toplak, & Wiesenthal, 2008).

2.11 Cognitive resilience and performance Performance is an output dependent on resilience and the individual’s capacity to monitor their

mental-state. Easterbrook’s work in 1959, pointed to the effects of stress on perception and

performance referring to the ‘tunnel hypothesis’, a narrowing of the perceptual field with

behaviour restricted and oriented to the object of threat (Dirken, 1983). An aspect that Kohn

25

had previously noted in 1954 as having the potential to increase in severity with ‘emotional

intensity’ of negative self-evaluation (M. A. Staal, 2004). Eysenck et al (2007), pointed

implicitly to SR as a key factor of their ‘Attentional Control Theory’. Their theory proposed

that the ability to pay attention was dependent on the person’s capacity to regulate their mental

state and recover from cognitive-affective interferences, such as anxiety, emotional

preoccupation, distraction or fatigue when under cognitive load (Eysenck, Derakshan, Santos

& Calvo, 2007).

2.12 Cognitive resilience and prospective memory The extent of the disability from these factors in time critical events and processes had

encouraged experimentation with pharmacological agents to increase performance.

Dextroamphetamine (Dexedrine) as an example was used to maintain arousal and as a

countermeasure to fatigue, finding that their use could reverse decrements in performance.

However, the psychological side-effects from the prolonged use of Dexedrine subsequently

shifted the focus to Modafinil, a less injurious drug. The experience had shown that arousal by

the drug did not flow on to benefit all aspects of attention and that ultimately, perception,

orientation and executive functions could all be distorted by the drugs. (Estrada, Kelley, Webb,

Athy, & Crowley, 2012).

Dismukes (2006) in a review of 27 major (USA) airline incidents listed in the NTSB database

highlighted a specific limitation of the human attention management system. Finding that most

incidents, amongst high-experience airline pilots, were due to errors of omission which he

called lapses of prospective memory, due to inadequate information encoding and cueing when

experiencing competing demands and interruptions. Dismukes had approached the

comprehensive assessment of pilots’ behaviour from three different views. Including

ethnographic, incident analysis and laboratory reports; and five types of situations involving

(1) remembering tasks (prospective), (2) unusual events (episodic), (3) typical tasks, (4)

26

interrupted tasks and (5) simultaneous and multitasking (interleaving tasks). The results

supported a prior investigation by Dodhia and Dismukes (2005) that had demonstrated a failure

of attention, missed cues and a lack of explicit encoding of intentions occurring where failure

to perform tasks had progressively deteriorated as time spans shortened. Predictively, where

pilots had longer time to retrieve an intention, the failure rate decreased (Dodhia & Dismukes,

2009). An apparent loss of memory that is not due to a drug or traumatic event and which could

otherwise be prompted and quickly retrieved, suggests a blocking or masking rather than a loss

of the memory. Thus, a loss of prospective memory or forgetting what needs to be done could

be understood to be an example of the limitations involved in the reallocation of priorities as

depicted in Kahneman’s capacity model (fig 2.2) or blocking from overload in Wickens (fig

2.3) model.

Cross-cultural support in that memory recall and loss of attention represent universal

vulnerabilities, was also reported in a more general study of Russian military pilots by Dolgova

et al, (2013), in which they had concluded that the pilot’s resilience and ability to manage

cognitive loads in responding adequately to tasks were important in maintaining performance

under stress (Dolgova, Ivaniuk, & Tukayev, 2013).

2.13 Attention in situation awareness Because perception is logically dependent on attention, a loss of perception of what it is to be

attended to also primarily means loss or reduced attention. Studies utilising Endsley’s (1995)

taxonomy and subsequent evaluation by Jones and Endsley (1996) (Table 2.2), have suggested

that the most frequent cause (at around 76.3%) were level 1 errors of inattention or perception,

including a loss of either mental (i.e., cognitive failure) or visual perception (i.e., attentional

blindness). Subsequent confirmation was obtained from the classification of incidents from a

diverse range of hazardous industries (O’Hare, 2006; Sneddon et al. 2006; Shook et al. 2000).

The ability of the individual to respond safely and promptly to hazardous circumstances, with

27

adequate SA is the ability to perceive, understand and project the likely outcomes in

surrounding events Hence a block in attention will likely block all executive functions,

comparable to Wickens (1992) model of perception being dependent on attention. SA at the

entry point is firstly “the perception of the elements in the environment within a volume of

time and space, secondly the comprehension of their meaning, and thirdly the projection of

their status in the near future” (Endsley, 1995). Investigations across aviation and industrial

domains found support for the utility of the Endsley model (Table 2.2) and in emphasising

The predictive capacity of the Endsley (1995) construct of SA had earlier been suggested by

Endsley and Bolstad (1994) to be due to differences in abilities such as short-term memory,

speed of perception, ability to see the connection between events or objects, attention-switching

and multi-tasking. Stanton, Chambers & Piggott (2001) debating the usefulness of SA reviewed

three similar constructs and found a divergence of opinion as to whether Endsley’s model

Table 2.2 Incident types classified in the SA model by Endsley (1995)

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applied to process or product, but that the concept of SA was based on inherited psychological

principles. Stanton et al., concluded that irrespective of the differences the SA model had

concurred with the primary psychological constructs of attention, perception, understanding,

projection and in some cases memory. Further practical evidence was found by O'Brien &

O’Hare (2007) for SA as an ability, finding it to be a better predictor of performance than either

cognitive skills training or planning behaviour.

The risk of fatigue induced loss of SA and responsiveness due to personal fitness was

extensively investigated from various perspectives, but primarily with respect to emotional

recovery and positive affect mediating mental-state (Gomez-Pinilla, 2008; Bernstein &

McNally, 2017; Feuerhahn, Sonnentag, & Woll, 2012). Personal training was found to augment

the pilot’s ability to withstand the stress of sudden urgent demands and fatigue in flight.

Mentoring and coaching from experienced pilots, expands and models flying skills, which

include crew management and tighter rules, procedures and regulations, in a way that

classrooms cannot do, because as suggested by Wiggins (1997) that it was often difficult to

characterise the cognitive expertise involved. Other more traditional approaches to cognitive

improvement programmes have involved traditional attention skills training (AT) to improve

agility, spatial memory and verbal memory (Adams, 1993).

2.14 Self-regulation Absent in NOTECH Training Addressing the preponderance of pilot error in aviation incidents, the aviation industry has

adopted further advances in training techniques. Achievement by aircrew of satisfactory self-

management and preparation skills are presently embodied in airline threat and error

management (TEM), human factors (HF) and crew resource management (CRM) courses,

typically not fully available to the novice until recruited as an airline pilot. CASA, the civil

aviation regulator has added non-technical skills (NOTECH) competencies to the training

syllabus in recognition of the importance of pilot capacity to maintain effectiveness under

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stress, even though the problem of measuring those in training has been problematic and as

Wiggins (1997) had previously pointed out, difficult to assess. Student progress is subject to

inter-rater reliability (IRR) between instructors and examiners, providing the opportunity for

vulnerabilities to be missed, given the likelihood of a student having several different

instructors in rapid succession. The potential of that inconsistency has led some researchers to

pursue alternative assessment methods. Roth and Mavin (2013) proposed using a ‘fuzzy logic’

approach, moving away from a precise measurement score denoting skill attainment level to

one of categorisation where the variability in performance becomes the focus. It was proposed

by Roth et al, that an understanding of the type, consistency and magnitude of error made would

result in more purposeful and effective intervention, than that obtained from the instructor

simply making a judgement of proximity to a functional criterion score on a checklist. The

Roth et al approach running counter to the prevailing interest in direct measurement techniques

(Mulqueen, Baker, & Dismukes, 2002). However, irrespective of the NOTECH course

components it remains the case that training in SR and personal recovery techniques are not

yet available to assess novices on those skills. Positive SR development through exposure to

actual in-flight risk (as in stress inoculation) has been reassigned from actual airborne flight to

that of ground-based simulator experience and incorporated into the ‘fast track’ multi-crew

license (MPL) for novices, as equivalent experience. Although, some flying activities that

depend on high alertness and responsiveness by the pilot for safety; such as aircraft stall

recovery where flying speed is lost and the risk of crashing is imminent, are now returning to

the syllabus, retitled as ‘upset recovery’ (CASA MOS, 2015). The regulator’s manual of

standards specifies rostered fatigue management, threat and error, human factors and crew

resource management training (CASA, 2015). However, aviation training programmes have

still yet to provide a trainable means to deal with hazardous loss of awareness, with a technique

for the immediate recovery of attention, that is independent of the pilot’s mental-state, so as to

30

function better at the critical moment. Few opportunities for training in self-regulation of

emotions exist for novices in aviation.

Implicit recognition of the impact of degraded attention has seen developments in

ergonomically designed and simplified cockpit layouts and instrumentation to decrease the

pilots’ mental load and the ambiguity of decision information. Improvements in simulator-

based skill development have made it possible to practice skills more often where mistakes can

be corrected (Dahlström, 2008). Fatigue management is addressed through systems (Cicek &

Serres, 2014; Gander et al., 2014) and procedural rules, task planning and shift rostering as

countermeasures (Dawson, Chapman, & Thomas, 2012). The ‘in-flight’ nap over long oceanic

distances was implemented for the purpose of enabling the crew to provide peak attention to

the critical landing phase of the flight. To manage growing mental fatigue due to intensive

episodes of concurrent and multiple tasks and recover from the gradual somnolence induced

by a lack of stimulus or activity en route (Warm, Parasuraman, & Matthews, 2008). A better

understanding of the capability needed by pilots has resulted in more systematic selection

procedures (Gaydos, Curry, & Bushby, 2013).

In civilian pilot selection, a candidate’s capacity to recover from loss of awareness, distraction

and cognitive fatigue, is often identified through normative self-report questionnaires.

Supporting test evidence from computerised hand-eye coordination, tracking and multitasking

exercises complete the assessment; after which cognitive resilience is observed and assessed

by instructors via simulators and flying sorties, as a development objective (Damos, 2014). In

military settings, the measure of capacity for adequate cognitive resilience occurs earlier and

more rigorously assessed in the context of continuous high-stress operations, similarly

identified at the start with abilities tests (Carretta, 2011) to be subsequently verified in a series

of high-intensity practical flying exercises. A lack of cognitive resilience or inability to cope

with the demand resulting in multiple uncorrected errors as described by King would most

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often stream the candidate to a different specialty (King, 2014). The civil aviation regulator in

Australia (CASA) prescribes the training standards and competencies required for the various

licences. While no specific technique or recovery skill is suggested, the training courses for

commercial pilots contained in the ‘CASA Part 61 Manual of Standards’ (CASA, 2015)

provides guidelines regarding common behavioural risks in piloting.

2.15 Self-regulation Absent in Simulator Training The demands made on pilots go beyond the mere control of the aircraft. The means to identify

those less capable has moved to obtaining practical evidence through simulators of varying

kinds. The simulator operating as an objective “show me” type of test by delivering some form

of relevant task complexity or mission profile, makes the test an immediately valid criterion

referenced performance measure (Roscoe, Corl, & LaRoche, 2001). When combined with a

battery of cognitive performance tests, the overall results deliver a valid correlation of

prediction of success in training but not in operations (Carretta, 2011; King et al., 2013).

Simulators such as that of the desktop simulators ‘Wombat’ and its Australian variant the

‘Ausbat’ had also provided coordination and attention testing (Roscoe et al., 2001). Predictive

as they were, they represent point-in-time assessments with candidates well prepared for their

testing session. It is not possible to replicate real life-threatening stress while in a simulator.

The simulator can help with inoculating against the stress of task overload but cannot replicate

the emotional experience of a real emergency. This makes it uncertain that the pilot’s arousal

level has been reached sufficiently to replicate or reveal any tendency to habitual or confused

behaviours when stressed.

2.16 Self-regulation Absent in Profiling Self-regulation, specifically with respect to attention management is not mentioned or

measured in any personality profile. It must be assumed that the attribute is inferred in general

underlying facets such as emotional stability and conscientious behaviour. Popular instruments

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in pilot selection include the NEO-PI-R five factor model, a more sophisticated development

to that of similar instruments such as Cattell's 16PF (Cattell & Mead, 2008), and Eysenck’s

EPI Extraversion, Neuroticism, Psychoticism model (Bartram, 1995; Sato, 2005). A more

recent test, is the PCI by Helmreich (Musson, 2004). Since the instruments were not developed

as criterion-referenced competency assessments of SR for selection purposes, or to elicit

attention and perception ability as defined in the Endsley model of situation awareness

(Endsley, 1995), or correspond with the task-switching need defined in the Wickens model of

attention (Wickens et al, 2015) makes their use difficult for the assessor looking for direct

measures of that attribute. Descriptions of broad constructs like the popular ‘Five Factor’

Model (NEO) typically delivered without the benefit of a task analysis to specify criteria and

performance validated cut-scores. As expected, without criteria and by relying on normative

grading, predictive validities of the NEO were found to be marginal by Tett et al (1991) in the

five factors (r = 0.16 - 0.33). Adding to the long-standing problem that low validities tend to

denote low effect size, making any skilled interpretation difficult when working without a

benchmark to match attributes with tasks. Pursuing the reliability-validity issue of personality

profiles, Tett et al (1991) had been able to improve the validity of the NEO by criterion

referencing the NEO results, thereby obtaining a slightly higher overall mean validity (r = 0.38)

of outcomes with that instrument (Tett, Jackson & Rothstein, 1991).

2.17 Is it Possible to Measure the Right Stuff? Identifying and measuring “The right stuff” (Wolfe, 1979) seems reasonable, pilots need to be

assessed for their strengths and weaknesses to ensure they have the ability required for the role.

Measuring personality with self-report tests of lists of attributes, values attitudes, beliefs and

behaviours to identify the potentially competent ‘error free’ pilot candidate has endured despite

the difficulties of such prediction using trait based psychological profiling techniques. Pilot

33

performance and personality may be linked but clear identification and prediction remain, for

the present, less than conclusive for selection purposes, despite the use of multiple tests for

confirmation of attributes (Rose, Helmreich & McFadden, 1994). Research and development

of selection tests in the American military, specifically testing of pilots beyond that of skills

and abilities was relegated to post-hire evaluation (King et al 2014), for reasons that included

the requirement to adhere to the ADA Act (1990). The act had defined psychological tests as a

medical assessment contravening the principle of no discrimination in employment based on

medical grounds. Nevertheless, investigations seeking to identify the personality profile for the

competent pilot with appropriate leadership skills, have included recently developed

instruments. Helmreich’s Personal Characteristic Inventory (PCI) (Musson et al, 2004)

measures frequently occurring clusters of traits to arrive at either “The Right Stuff, The Wrong

stuff or No Stuff”; a phrase made famous in the novel about the Mercury astronaut selection

(Wolfe, 1979). In Helmreich’s model, findings were essentially a replication of the earlier study

by Rose et al (1994) the right stuff was characterised by higher instrumentality and expressivity

along with lower interpersonal aggressiveness’, while the ‘wrong stuff’ featured low

achievement orientation and higher levels of passive aggressive tendencies. Exploring and

comparing the basis for an identifiable pilot personality Fitzgibbons, Schutte, & Davis (2000)

used the NEO-PI-R five-factor model developed by Costa & McCrae (1995) in a study of 93

commercial pilots to identify the type on five major dimensions and 30 facet scales. Fitzgibbon

et al concluded from their study, that a pilot personality does exist and that pilots could be

defined as one of three types and by a particular set of traits, which they listed as “An

emotionally stable individual, low in anxiety, vulnerability, anger, hostility, impulsiveness and

depression. Typically with a high degree of (belief in their) conscientiousness, striving,

assertiveness and dutifulness. Additionally, they were active, trusting and assertive”;

replicating previous results (Hormann & Maschke, 1996; Picano, 1991) and characteristics that

34

had been noted much earlier by Armstrong (1936) discussing various forms of post-traumatic

stress disorder amongst aging WW1 aviators.

Since the purpose of such personality testing is differentiation in selection, a question arises as

to how self-report personality profiles, where weak linear relationships are found together with

unavoidable statistical error, could be improved (Tett, Jackson, & Rothstein, 1991). The

uncertainty of ‘profiling’ tests is an especially important deficit since their purpose is to gauge

pilot reaction and capability when it matters, under stress; in addition to trainability and their

leadership potential.

2.18 NEO Personality Profile & the PCI As an example, in comparing three types of tests quite different results were found using the

Millon Clinical Multiaxial Inventory (MCMI) to those obtained in the NEO-PI-R (Costa &

McCrea,1995) and the PCI (Helmreich, 2004) studies.

A key consideration in evaluating the different results is that the tests were not all delivering

the same construct with the same normative or criterion evaluation, but based on population

norms and rankings, except for the MCMI which had used the criterion of within-groups norms

from clinical populations.

Campbell et al, (2009) examined 350 USAF undergraduates in pilot training using the MCMI

(Melon, 1983), finding that in the “Right Stuff” category, the sample were highly aggressive,

dominant, exhibitionistic, impulsive and playful, in addition to being narcissistic and histrionic

(Campbell, Moore, Poythress, & Kennedy, 2009). While in the NEO-PI-R study of 93

commercial pilots by Fitzgibbons et al (2000), the “Right Stuff” category described

emotionally stable individuals, low in anxiety, vulnerability, anger, hostility, impulsiveness

and depression, with a high degree of (belief in their) conscientiousness, striving, assertiveness

and dutifulness (Fitzgibbons, Schutte, & Davis, 2000). In the third instrument, (Musson et al,

35

2004) using the PCI by Helmreich in evaluating 259 astronaut candidates most of whom had

been military pilots or were mission specialists such as scientists etc, found that the right stuff

was similarly characterised as in the NEO model by higher instrumentality and expressivity

along with lower interpersonal aggressiveness (Musson, Sandal & Helmreich, 2004).

A promising direction leading to a measure of the ideal candidate may be the research on

identifying the cognitive behavioural attributes and error inducing, internal and external

performance shaping factors that influence the person (O'Hare 2006; CASA 2015; Wiegmann

& Shappell, 2001, Hart & Staveland, 1988). Behavioural attributes and cognitive abilities were

compared in a correlational study, finding cognitive ability to be the best predictor of pilot

training performance. In the same study neuroticism showed a marginally significant

correlation at r = .25, p<.01 for the failure group (Carretta, Teachout, Ree, Barto, King, &

Michaels, 2014). The results replicating an earlier study by Carretta & Ree, (2003) of a

correlation of r = .53, p<.01 in the relation of flying performance to cognitive ability, aviation

job knowledge/experience, and psychomotor ability. p<.01 in the relation of flying

performance to cognitive ability, aviation job knowledge/experience, and psychomotor ability.

2.19 Capability and mental state The capacity to function under extreme duress has been explored with projective assessment

techniques such as the Defensive Measures Test (DMT) (Civelli & Stoll, 1990; Cooper &

Kline, 1986)(Olff, Godaert, & Ursin, 1990) to explore specific coping or SR tendencies, such

instruments are suggested as tests for resilience. Projective measures have proved to be valid,

albeit difficult to administer and require an experienced administrator to extract the necessary

data, particularly when considering the DMT. The DMT provides the opportunity to utilise a

different method than the traditional paper and pencil list of adjectives or behaviours, through

situationally based assessment like the story completion by picture cards of the Thematic

Apperception Test by Murray (1943).

36

The successful identification of neo Freudian ‘defensive measures’ amongst recruits was

demonstrated in the significantly decreased rate of pilot fatalities and aircraft crashes in the

military of several countries, including Australia. Ejections are universally the most critical

decision a pilot can make in single pilot fast jet operations and represent a situation when all

the facets of stress and the need for instant decision making are present for the pilot (Miles,

2015). The history of which shows that even in the most rigorously selected and highly

experienced pilot group, despite specific instruction, there was still a tendency in 55% of such

cases to hesitation and indecision, risking injury or death. The analysis is consistent with the

findings of retarded decision making in failed ejections (Goodman, 1999), as inattention or

distraction, indecision and delay (Nakamura, 2007), factors suggesting cognitive failure at the

moment of greatest demand.

King (2014) reinforcing the importance of mental state, conducted a practical retrospective

study using empirically derived psychiatric criteria with 12,548 student pilots in the USAF

(King, 2014). King found that those who had terminated before completing the course had

symptoms of personality disorders and had been diagnosed with clinically significant

depression or were more neurotically anxious as defined by the DSMIV (1994). In comparison,

a parsimony of effort could be achieved in the highly predictive Cognitive Failures

Questionnaire (CFQ), an instrument based on the Broadbent model to achieve greater certainty

of prediction (Broadbent, Cooper, FitzGerald, Parkes, 1982). Bridger et al were able to

correctly classify 76.8% of the sample as experiencing future strain using the CFQ in a

prospective study with navy personnel (Bridger, Johnsen, & Brasher, 2013). The CFQ

measuring specific behaviours and failures in memory, attention and action that impacted

mental alertness had shown that in a two year follow up and at an odds ratio of 2.05, those with

high CFQ scores were twice as likely to experience and succumb to ‘strain’. Bridger’s modified

workplace form of the questionnaire (Wallace & Chen, 2005) sought answers to specific

37

behaviours and frequency of actions, such as ‘forgets things, switches the wrong thing off ‘etc.

Behavioural measures, such as those have greater face validity for the end-user and tend to

build a more accurate picture of the person and what they did or do through correlation with a

standard when compared with benchmarked performance criteria. With this method,

respondents can be compared against established cut-off scores for a role to provide greater

confidence in achieving a required standard of performance, thereby ultimately reducing error

potential and safety risks.

2.20 The Pilot’s Perception of cognitive demand Being airborne constrains behaviour and decisions, inevitably made more urgent by limited

fuel, weather and landing options. Pilots operate in a continuum, ‘making decisions literally

on the fly’, without the familiar luxury of ‘pausing or pulling over’ to assess a problem on the

road. In the critical take-off and landing phases of flight pilots need to apply and switch

attention rapidly to multiple demands in setting and maintaining a specific flight path, maintain

vigilance and look out for other aircraft which may be on a conflicting trajectory,

communicating with air traffic controllers, preparing the landing parameters, to set aircraft

landing configurations, finally coordinating manual control of the aircraft for touch-down;

aspects made more hazardous by simultaneous take-offs and landings at major airports.

Thus, managing a technologically advanced $350m airliner, subject to a range of possibly

adverse circumstances in flight, requires a clear understanding of the functional and

behavioural criteria and limits for effective performance. Since the issue of stress and the divide

between performance and failure begins with what is in the ‘eye of the beholder’, Hart and

Staveland (1988) approached assessment of need from a subjective point of view in developing

the NASA Task Load Index (TLX). The resultant instrument derived 10 workload factors from

16 different experiments to provide a reliable multidimensional subjective rating scale of six

workload factors. The final dimensions of the instrument included subjective estimates of

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mental demand, physical demand, the pace of work, performance, effort and frustration

experienced in the role and tasks as in the following extractions.

The instrument was designed to be used by the pilot or by an assessor, as a rapid and reliable

low bias indexed score of the salient dimensions of operator perception of the workload.

The NASA Subjective Task Load Index (Binder & Desai, 2011; G. Young, Zavelina, &

Hooper, 2008) has provided a well validated subjective framework for both task analysis and

accident investigation (Belland, Olsen, & Lawry, 2010).

A more objective analysis to inform the performance needs and attributes required of the pilot

has utilised ‘Cognitive task analysis’ (CTA), and its derivative, ‘Applied Task Analysis and

Task Complexity in Flight’ (TCIF) (Zheng, Lu, Wang, Huang, & Fu, 2015). Both CTA and

TCIF measure combinations and relationships between physical tasks and cognitive strategies

such as goal identification, decision making, and the judgements needed to accomplish goals.

These have been augmented as necessary by neuropsychological tests establishing criterion-

based measures analogous of piloting competencies (Carretta, 2011; Carretta et al., 2014).

Examples of the practical application of CTA occurred with the use by Endsley, Farley, Jones,

How stressful do you find the mental effort required to do this job (e.g. thinking, deciding, calculating, remembering, looking, searching, etc.)? Do you find the job/task easy or demanding, simple or complex, exacting or forgiving?

How exhausting do you find the physical activities that are required to do this job/task (e.g. pushing, pulling, turning, controlling, activating, etc.)? Is the job/task easy or demanding, slow or brisk, slack or strenuous, restful or laborious?

How much time pressure is there due to the rate or pace at which the job/tasks or task must be done? Is the pace slow and leisurely or rapid and frantic?

How successful can you be in doing this job/task in achieving the goals of this role as required? Consider if there are any problems in the way the work is done or the way the organisation supports it?

How hard do you have to work (both mentally and physically) to accomplish the required level of performance for this role?

How insecure, discouraged, irritated, stressed and annoyed versus secure, gratified, content, relaxed or complacent do you feel and does the system of work and typical outcomes in the role affect you?

39

Midkiff, & Hansman (1998), Strater and Endsley (2000) in conjunction with experienced

commercial pilots in the development of the Situation Awareness Global Assessment

Technique (SAGAT)(Strater & Endsley, 2000) assessing pilot awareness of their situation,

surroundings and elements operating in the same operational space. The advantage as detailed

by Endsley of SAGAT over the hierarchically oriented CTA with complex relations and

abstractions was the greater focus on simulation and the opportunity to introduce and assess

error inducing parameters into the study. Endsley claimed that the effect of observation in ‘real

time’ assisted in the reduction of statistical measurement error and to reveal hitherto hidden

anomalies through direct observation of task execution. In their model, the simulation would

be paused and the pilot questioned regarding the situation. Strater and Endsley (1995) utilised

the concept of situation awareness as involving perception, comprehension and projection of

the flight elements, as the basis for classification.

2.21 Cognitive Load and Experience Prince and Salas (1998) explored the nature of the effect of experience on cognitive load

comparing the behaviours of general aviation pilots with respect to their levels of experience

(variously with 700, 6000 and 12000 hours of flying time), finding that the gradient of

experience from high to low also reflected the extent to which pilots were respectively either

proactive or apparently passive in their flight preparations. In cognitive-behavioural terms, the

most highly experienced check captains spent more time projecting the potential of the

elements in the coming flight, whereas the least experienced were most concerned with

accessing the minimal information needed for the flight. An aspect which could be interpreted

as low time pilots needing to engage more mental resources to manage the information load in

the flight preparation stage, possibly with an elevated level of concern at not acquiring the

necessary detail. Effective threat and error management defined as anticipation or looking

beyond the immediate circumstances by the experienced pilots was deemed to be a

40

differentiating competency. Another differentiating factor noted by (Eldar & Bar-Haim, 2010;

Todd & Thomas, 2013) in a review of low and high time pilots in three Australian airlines was

the lesser situation awareness of low time pilots when compared with their more senior

colleagues. Reflecting an increased mental load when trying to be competent very consciously.

Experienced pilots, by contrast, operating with less self-preoccupation, in an unconsciously

competent way made greater use of their mental resources. The extent of the difference between

novices and the experienced pilot was also evident in a further study of pilot decision making

in simulator-based weather-related decision exercises. The study showed that experienced

pilots managed their cognitive load so as to be less engrossed in evaluating data, by utilising

heuristics and generalisations in problem-solving more than novices (Wiggins & O’Hare,

1995). A finding replicated in a later study where pilots managed their cognitive load by the

greater use of cues that require fewer cognitive resources and anticipation, by dealing with a

few ‘chunks’ of information rather than grappling with a mass of detail (Wiggins, Azar,

Hawken, Loveday & Newman, 2014). Self-confidence gained by experience allowed the

greater use of cues and heuristics; an example of natural self-regulation of mental resources

under load. The role of ‘ego’ in pilot behaviour has featured as being at the core of numerous

incidents. From indecision to eject and crash a multimillion dollar aircraft for fear of losing

flying status, overriding critical information from others due to own seniority, to engaging in

risky and unnecessary behaviours with fatal potential simply to impress others. More typically,

avoiding responsibility for poor decisions and not reporting deficits or flight disqualifying

ailments. The strength of identity amongst pilots, with most seeing it as a life-long profession

increases sensitivity and reluctance to be measured or grounded due to errors (Ashcraft, 2005;

Fraher & Gabriel, 2014).

2.22 Complacency and awareness

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Aircraft systems are complex but still need a pilot. While the need to remove ‘pilot error’, as

the leading cause of incidents has encouraged the trend to full automation, relegating control

of various technical processes to a self-managing system with an electronic monitoring device

in the cockpit has delivered the potential for yet another type of problem. Prolonged exposure

and reliance on automation have been shown to induce complacency and loss of anticipatory

attention risking poor decision and response to emerging risks (Prinzel III et al., 2001). The

second most frequent of these types of incidents emanating from over-reliance on automation,

being controlled flight into terrain (CFIT) (Richardson & Lang, 2005), typically where critical

weather-related decisions were required in transitioning from a visual to instrument

meteorological conditions, described by Wiggins as an over-reliance on the technology despite

apparent cues and conditions to the contrary (Wiggins, 2007). Pope and Bogart (1992) coined

the phrase “hazardous state of awareness” in their study examining psychophysiological

predispositions associated with the passive monitoring state. Complacency includes rumination

a deactivation of alertness and vigilance produced by low stimulus of feedback, experienced in

monitoring tasks where no response is necessary by the console operator. A common feature

identified as a significant hazard in high-reliability air traffic control, petrochemical process

plants and road traffic grid-control systems where the task involves monitoring without input

(Prinzel III et al., 2001).

2.23 Automation a Reverse Problem

Most training in aviation continues to be about the technology, the engineering and control of

an aircraft in the air and in returning it safely to the ground. Training typically involves the

rehearsal of combinations of specific aircraft management procedures designed for specific

situations. Training on particular types of aircraft (ie, time on type) and rehearsing problem

scenarios has been seen to benefit the development of fluent and fault-free execution of

procedures (Casner et al., 2012); but has also risked ‘automaticity’ an unthinking response with

42

a similar action or sometimes no action at all. Automaticity represents habit formed errors in

the rapid transfer of skills to a different or new and less familiar cockpit environment evident

in incident reports as activation of the wrong control. A familiar parallel sometimes seen when

driving a different make of car, with the inadvertent activation of the wiper blades instead of

the turning signals. Rehearsal and practice in simulators resembles the older techniques of rote

learning, doing it until it is done correctly, so that with constant correct application incorrect

procedures are inhibited or extinguished, and correct procedures are reinforced to be more

likely to occur in a crisis.

Increased automation of the flight deck seeks to alleviate cognitive demand by presenting

information via synthetic displays viewed as flat panel computer screens, referred to as 'glass

cockpits' rather than a mass of analog dials. Unquestionably these have made a significant

impact on pilot workload and error management but still requires that cockpit data be delivered

as a moving collective or summary for anticipatory and pre-emptive action, by projection of

the outcome of the displayed data. Ideally emulating Endsley’s (1995) SA, with the aircraft

system detecting, analysing and projecting outcomes as choices for the pilot. Improved design

for safety and efficiency has changed the functional nature of the pilot’s task but not the

capacity to cope with stress and pressure to maintain attention (Casner, 2009). Meanwhile,

individuals continue to forget procedures, become complacent and rely on the system data

presented ‘after the fact’ for decision and action.

At the low end of the stress spectrum, the elegance of the technology may encourage trust in

the system and add further to another problem, that of ‘reliability-induced complacency’

(Casner & Schooler, 2015). At the upper end of the stress spectrum, the unpredictable and

chaotic nature of crisis, with the potential to surprise, overwhelm and disrupt the pilot, can

exacerbate and expose a vulnerability to cognitive failure and an inability to think, act safely

and accurately (Broadbent, 1982). Despite the added training, numerous structural and

43

procedural changes made in aviation, ‘pilot error’ continues to feature at the core variously

quoted as 70-90% of aviation incidents, highlighting the complexity of the system and

vulnerability of the pilot.

2.24 Necessity to manage the self A pilots’ priorities are to “aviate, navigate, communicate,” to aviate meaning (maintain aircraft

control), navigate (maintain track to destination) and communicate (inform intentions to others

in the airspace). The complexity of flying an aircraft is increased with the added need to manage

multiple systems (involving visual information displays, pressurisation, backup facilities for

undercarriage, engine fire, flaps, fuel transfers, electrical power, etc.). Beyond these functional

tasks, the pilot’s capacity to manage themselves, to make decisions and operate competently in

a timely way, according to numerous procedures, has been investigated by researchers focusing

on pilot behaviour and cognitive processes. Adams (1993) writing on how ‘expert’ pilots think,

(Adams, 1993) had concluded that speed and accuracy coupled with good memory

distinguished the expert pilot. Cognitive competence in Adams findings corresponded with the

notion of a mind ‘unfettered’ by cyclic worry thinking, fixation and preoccupation, providing

the maximum of attentional resources to the task.

In ‘Attentional control theory’ (M. W. Eysenck et al., 2007), suggested the first thing to be

forgotten when stressed was likely to be the recommended procedure, even in the proximity of

checklists (Dismukes & Berman, 2010). Despite this common experience, there has been no

formal behavioural training or technique that a pilot, controller, driver or operator can invoke

to clear the mind and overcome defensive ‘mental blocks’ or some other worrying

preoccupation at the stressful moment. The threshold at which an individual may succumb at

the extreme to defensive ‘freezing or panicking’ varies and in addition to the magnitude of the

mental load has been found to be dependent on moderating factors such as self-efficacy and

self-esteem. Reinterpreting risk or threat (Zeigler-Hill, Chadha, & Osterman, 2008) through

44

experience as manageable or survivable helps to overcome adverse mental states resulting from

defensive retreat and denial styles of coping (Monat & Lazarus, 1977, p152; Folkman et al.,

1986). Traditional means of raising the coping threshold have involved reinforcement through

exposure-based inoculation and desensitisation, development of successful management of

similar events or formal training (Boucher, 2011; Staal et al., 2008). The sudden and

unexpected nature of emergencies makes the development of self-efficacy critically important

in managing later stress (Rochat, Billieux, & Van der Linden, 2012). The ability to switch from

intrapsychic or self-focused thinking style to an externally directed one (Broverman & Lazarus,

1958) is an essential competency. The current fast track training syllabus for the Multi-Crew

Pilot License (CASA, 2015) introduced to expedite novice pilot training discusses self-

management but provides no specific behavioural training that builds self-regulating

competencies for new pilots.

Pilots need to be able to function expertly when suddenly confronted with a life-threatening

potential, such as imminent collision trajectories, engine or structural failure, jammed controls,

invisible wind shear, fuel loss and onboard systems failure (Casner, Geven, & Williams, 2012).

While systems issues have lethal possibilities, so do those that act on the executive in the mental

loop through visual and physical disorientation, fatigue and confusion. For example, inability

to comprehend rapidly changing cockpit instruments in an uncontrolled descent will inevitably

cause flight into terrain (CFIT) under any condition.

Cognitive Resilience (CR), the ability to deliver mental performance in the face of sudden or

prolonged psychological loads as described by Staal et al., (2008), is generally viewed as

‘bounce back’ the positive ability to adapt, improvise and recover from adverse situations and

mental states, rather than freeze, ignore or panic in those situations. CR is implicitly defined as

involving hardiness and a positive coping style with constructive defensive mechanisms or

compensatory measures that enable cognitive performance despite adversity.

45

Eysenck et al., (2005) cited the impact of affective states and Dornic (1980) reported on the

interfering effect of anxiety on performance, finding that the affective state of anxiety required

the supply of significant mental effort to overcome a crisis. Consistent with the view that

anxiety impacts the working memory system to disrupt attention and inhibit processing (

Eysenck, Payne & Derakshan, 2005). The extent of a person’s cognitive resilience thus being

dependent on their defensive repertoire or how well reactions are regulated when demanded.

Cognitive resilience is invoked to avoid cognitive paralysis when threats demand a response

(Leach, 2004). CR could be a defence to basic emotionally generated survival responses and

inhibitions that also include maladaptive behaviours such as aggressive impulsiveness. The

potential for a disaster in aviation emanating from inadequate responses and decision making,

due to poor cognitive recovery, is significant, to the extent that airlines now address the issue

through TEM training (Thomas, 2004) (ICAO, 2005). TEM training is designed to sharpen

anticipation and an implicit recognition that the last line of defence in the airborne socio-

technical system is the crew. TEM training fits with and expands the crew’s existing CRM

interaction skills, with projective exercises, in both the simulator and more conventional

checklist and advisory form. However, TEM and CRM may raise the individual threshold of

susceptibility through being forewarned but neither provides the crew with a self-initiated

activating skill to recover their attention when overwhelmed and under duress. Pilot attributes

such as situation awareness and cognitive resilience have been well researched in functional

and neuropsychological terms attesting to their ability to predict success in pilot training

(Carretta, 2011; King et al., 2013; Sulistyawati, Wickens, & Chui, 2011). However, classroom

and simulated TEM while designed to enhance the anticipatory action and prime readiness for

action, execution relies on the individual and their personal state (Fornette et al., 2012). Pilot

error as at the origin of 70% or more of flying related incidents (ATSB, 2007, p38; ATSB,

2014, p39) continues to be at the source of most fatal incidents. Since few incidents have no

46

contribution from error made by pilots, it is necessary to address self-regulating mechanisms

to recover attention or ‘presence of mind’, to operate resiliently unaffected by preoccupations

or loss of mental energy due to accumulated fatigue (Vine et al., 2015) and the effect of anxiety

or depression, as described from a trait-based perspective in the ‘Attentional Control Theory’

(M. W. Eysenck et al., 2007).

2.25 Summary of Need for self-regulation training

Improved training to cope with the growing demands of safety-critical roles is imperative. In

this chapter, behavioural themes, ‘performance shaping factors’ and cognitive task analysis

have emphasised the need for effective SR skills in one such safety critical occupation, the pilot

domain. Reinforcing SR as the means for goal directed behaviour and achievement is shown

in Table 2.2. Hofmann et al., (2012) detailing core behaviours and functional abilities that

contribute to competent task execution. The list showing attributes that converge on attention

for executive functioning, working memory, inhibition and task switching (W. Hofmann,

Schmeichel, & Baddeley, 2012), together with eight behavioural markers as performance

shaping factors. The development of SR in behaviour commencing in early childhood through

to adulthood has been identified as a trainable attribute for the development of attention (Posner

et al., 2014). The distinction between control and regulation, however, does not have a clear

boundary or suggest whether the two can or need to act in tandem. Control seems to pivot on

inhibiting impulses to reduce ego-depletion and mental effort (Baumeister, Gailliot, DeWall,

& Oaten, 2006). Self-regulating behaviours are more plan-full for the management and

achievement of objectives. SR training can reduce responses to social threat by modifying

attention and subsequent stress reactions (Baert, Casier, & De Raedt, 2012), and recovery in

sports (Behncke, 2004).

47

2.26 Aim of the Study SR acts to control the achievement of objectives under cognitively demanding conditions by

actively monitoring and managing mental state to decrease cognitive overload, effect of fatigue

and negatively charged preoccupation.

1. The aim of the study is to evaluate the take-up of a SR technique as an instantaneous

recovery of attention and mental effort.

2. To test the proposed method as an unsupervised online delivery in a naturalistic

context embodying overload resistance due to cumulative diurnal fatigue.

3. User acceptance of the method will include self-reports of ease of acquisition and

utilisation of the technique outside of the study.

4. Baseline test scales used as performance shaping factors will be assessed to predict

the cognitive-behavioural connection of self-regulation by study completion.

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3. SELF-REGULATION TRAINING

Self-regulation (SR) is a ubiquitous phrase in psychology, the key to performance and pivoting

here on the maintenance of attention in the pursuit of goals and objectives. The development of

SR is central to self-management and success in a variety of occupations, in sporting skills and

relationships. Several forms of SR training are explored and compared with conventional, virtual

and meditative perspectives.

Del Giudice (2015) described SR as the exertion of ‘effortful control,’ an executive function

underlying cognitive resilience (CR) with the purposeful ability to recover from adversity or

overload and to continue to function as required. Theories of SR and self-control (SC) linked to

attention tend to be interpreted as belonging to either resource, capacity or network models.

Because of the similarity of SC and SR, a clearer distinction was required between the two.

3.1 Self-Regulation and Self Control Kopp (1982) in reviewing the antecedents and development of SR, concluded that self-control

was less flexible and adaptive than SR. Kopp using the Oxford dictionary (1964) definition of

control meaning “hold in check”, with ‘regulate’ meaning “subject to restriction, rules and

adaptation” (Kopp, 1982). By contrast, Posner’s network attention theory (ANT) suggests the

difference evolves as a stage in the development of the attentional networks; self-control maturing

in the child to self-regulation in the adult, working in parallel with concurrent socialisation and

nurturing influences (Posner & Rothbart, 2009).

Traditional training approaches to improve SR and SC have tended to be based on the individual’s

emotional regulatory process or their cognitive resources. Staal (2004) suggested a transactional

model where an individual’s tendency to negative appraisal of threatening events, would

determine their mental state and subsequent performance. Here the transactional model involving

a motivational dimension, explains the ability to modify or improve SR with training. A view that

contrasts with the original apparently fixed resource or capacity models of Kahneman (1973) and

Wickens (1992). Posner & Rothbart (2009) however, see the development of SR based on the

49

ANT theory, as developmental occurring with growth in connectivity of the brain attentional

networks that relate to SR, further development of ability in the network model being possible

with specific training exercises.

Self-regulation is a construct that involves various self-management activities, mental states and

associated behaviours (Table 2.2) (Hofmann et al, 2012). Del Giudice (2015) suggested that a

self-regulating act can be proactive, either explicit, overtly deliberate or evolve from unconscious

decisions and valent impulses. Berger et al (2007) saw SR as more to do with monitoring and

self-control, as a reflex, adaptive or defensive behaviours, mediated in turn by the intensity and

temporal needs of the moment (Berger, Kofman, Livneh, & Henik, 2007). Hofmann et al (2012)

draws a distinction between SR and SC, while elsewhere they appear to be used interchangeably.

Hofmann et al saw SR as similar but different to SC which is deemed to be a narrower set of

behaviours, potentially a subset of the broader SR, which were more directly involved with

inhibiting and overriding unwanted impulses and reactions. In the Posner & Rothbart

developmental model, the ability to self–regulate and develop a competent ability to manage the

self, is crucial for social, academic and occupational achievement. SR allows the individual to

adapt to fulfil their intent through their executive faculties and cognitive control of attention and

management of affective states. SR as the executive provides the means by the individual to

withstand difficulties, distractions and overload firstly through planned strategies then through

the timely actions of SC. Suggesting that self-control is a discretionary instrument and surface

manifestation, a momentary expression of a psychologically deeper SR.

Limitations of SR as a limited ego resource was discussed by Baumeister et al., (2006) who saw

depleted ego resources as also diminishing SR (Baumeister, Gailliot, DeWall, & Oaten, 2006; Li,

Nie, Zeng, Huntoon, & Smith, 2013; Vohs, 2005). The question of the source of mental energy

and how it is recovered when depleted, had been investigated by the manipulation of motivation

with added incentives by Muraven & Slessareva (2003), the results of which had shown a direct

effect on ego depletion and subsequent effort. Inzlicht and Schmeichel proposed a mechanistic

50

resource model in which they sought to explain the Baumeister et al., ego depletion model of SR

as due to process rather than capacity. Such that mental effort in SC consumed in a preceding

activity was not immediately recoverable for a later one, referring to ego depletion as depletion

of strength of motivation and attentional focus (Inzlicht & Schmeichel 2012). Baumeister et al.,

had suggested that recovery of depleted ego resources could occur by strengthening SR through

practice, somewhat like muscle building Baumeister et al., (2006). While others such as Capa et

al., (2008) and Staal (2004), had previously concluded that the sustainability of effort had a

motivational dimension preceded by the individual’s expectation of success. The process-based

view appears to tap the omnibus construct of motivation as the available resource suggested by

Hofmann et al., (2012). Baumeister’s reference to ego strengthening with practice however,

provides a possible path for motivation to translate into mental effort through habit formation.

The chunking or assembly of an action sequence into a performance unit or habit, reduces the

level of mental effort required for repetition (Dezfouli & Balleine, 2012; Schmid, Wilson, &

Rankin, 2014; Smith & Graybiel, 2014; Yin & Knowlton, 2006). Comparing process and capacity

perspectives suggests that the ‘capacity’ in the resource model could be more than just a

metaphor. As demonstrated in a practical test by Gailliot, Baumeister et al., (2007) in which blood

glucose was manipulated to mediate self-control, finding that increasing glucose counteracted

ego-depletion; in parallel with the finding that glucose improved cognitive ability and helped to

counteract fatigue (Kennedy & Scholey, 2000; Owen, Scholey, Finnegan, & Sunram-Lea, 2013);

(Fairclough & Houston, 2004).

The view that SR is a self-monitoring mechanism not confined to a single process explains how

it acts differentially in separate functional domains as cognitive SR and emotion SR (Berger et

al. (2007). A position building on the view that SR is a global underlying executive directing

effortful control (Fonagy & Target, 2002; Kopp, 1982). Viewing SR as both a single underlying

process and one that can be selectively directed may seem to be a more logical, simpler and a

familiar explanation where emotion and reason vie for priority in decision and action. In this way,

51

the individual who has little need for cognitive control but more for emotional regulation at a

time, may choose to focus their energy and efforts accordingly. Both cognitive and emotional

perspectives suggest the possibility of a remedy consistent with the Baumeister et al., (2007)

emphasis of SR as being trainable so that mental effort can be improved or recovered.

3.2 Behavioural markers of Self-Regulation Self-regulation as an executive process is also visible in the behaviours of the two broad mental

states of coping posited by Monet & Lazarus (1977). They noted that preoccupation that comes

from concern, worry and fear, was a performance inhibiting characteristic related to an internally

focused mental state and that it reduced external awareness and responsiveness (Monat, 1977).

The internalised form (preoccupied or possibly beset by erroneous or incoherent thinking), tended

to seek avoidance and escape of the discomfort from perceived threat, rather than confronting the

problem. Direct action coping behaviours by contrast, were those they characterised with

immediate action to address a problem or to remove a threat. While the externally oriented mode

(represented by direct action coping) tends to be favoured as having greater potential for success,

it must also be accepted that on occasions avoidance behaviour can also be a more rational or

economic alternative to confrontation.

3.3 Methodological Issues in Evaluating Self-Regulation The object of SR is some performance and the achievement of a goal. However, SR is also present

in the various behaviours that sum to that achievement. Measuring and evaluating SR

operationally is facilitated by standardised behavioural markers that help to bring the various

neural and motivational perspectives used in describing SR towards a uniform taxonomy.

Attributes of operational performance are listed here in a proposed model of interdependent

competencies (Fig 3.1), in which each level provides the foundation and strength for the one

above, such that an increased capability of one will enhance or increase the capability of the next;

similarly, a weakening of one will reduce the capability of the next. The model (Fig 3.1) is

described here in behavioural rather than in a neurological form but consistent with attention

52

network theory involvement of the thalamus, cingulate gyrus and prefrontal cortex in enabling

behaviour, adaptation and subsequent learning in the brain system. The behavioural model is thus

convergent with the cross modal attention network, relating to the alerting, orienting and

executive functions in the developmental and occupational context (Posner & Rothbart,

2007)(Posner, 2012). Additional behavioural level contributions from the Monet et al., (1997)

theory of internal and external coping styles and Endsley’s (1995) performance-oriented construct

of situation awareness that underlie the key behaviours for performance under stress augment the

model. As a reference, at the operational level, the awareness, perception and anticipatory skills

involved in expert decision making by pilots, requires various forms of SR with effortful control,

at each stage or level of dependency, consistent with the demand made on the individuals personal

agenda, stress tolerance and the urgency of need. (Adams, 1993)(O'Hare, 2006).

Performance dependencies Behavioural Markers

Can see things others may miss

Ability to avoid becoming fixated

Can switch rapidly between demands

Remembers procedures and exceptions

Remembers despite interruption

Can anticipate sudden changes

Can deliver the necessary mental effort

Less vulnerable to emotional preoccupation

Figure 3.1 Proposed Hierarchy of Performance dependencies and behaviours

53

3.4 Comparison of Effect Size by Training Methods A common measure such as those established as ‘behavioural markers’ (Klampfer et al., 2001)

could provide a more uniform standard for evaluation and meaning for effect size differences.

The essential difference being the focus on specific observable and measurable acts rather than

inferred personality traits or attitudes, to establish potentially causal relationships and a

common language for utility in application. However, assembling meta-analytic studies of the

efficacy of different training strategies for the self-regulating competencies (fig.3.1), including

the underlying attributes and cognitive resilience overall, revealed few in which the

development group reported pre-post effect sizes for comparison, as defined by Rosnow

(Rosnow, Rosenthal, & Rubin, 2000). Similarly, a lack of consistency in specifying and

identifying behaviours and other attributes introduced ambiguities in their evaluation.

Harmonised occupational markers with a consistent behavioural theme would help to clarify

and standardise the nomenclature allowing for specific performance criteria to be established to

gauge the efficacy of any training.

3.5 Conventional Training Approaches Studies concerned with meditation, mindfulness, zone training and yoga, have until recently

tended to the anecdotal rather than experimental and have rarely listed any metrics or comparative

methodology. Therefore, the selection of studies was limited by categorisation of those from one

or other of the five methods of training as grouped in (Table.3.1) as being either practical

(categories 1,2) or metacognitive methods (categories 3,4). All studies were obtained online via

peer-reviewed journals, from the year 1995 to the present. Conventional training (Table 3.1) has

been well demonstrated to be a beneficial and effective means of transferring skills. Wickens et

al. (2013, p369) commented that when the training was about invariant emergency procedures

that needed to be retrieved rapidly, performance was best for well-formed habits retrieved from

long term memory when under stress. The studies included in the conventional training category

indicated a moderate effect size for task performance outcomes (Table 3.1).

54

However, the specificity of such skill meant that it tended to be vulnerable over the longer term

to a variety of issues. Skill erosion through a lack of currency and changes in the context or

operational, technical and instrumentation differences require constant updating Recent types of

SR training to ensure task compliance have tended to be of a practical form such as threat and

error management for aircrews and executives. The training is focused on preparation,

anticipation of threat and the development of an expectation of the inevitability of error. It is

primarily about crew resource management in the context of the flight deck but delivered in a

traditional classroom. The benefits of well-tried conventional forms of pedagogy are numerous

and include the immediacy and direct reinforcement by expert tutors, group interaction with

participants, responsiveness to student apprehension or befuddlement and the logistic benefits of

having all persons in the same place.

Simulators familiarise, diminish apprehension due to uncertainty and help to lay down action

sequences, simply gaining familiarity with a procedure can serve to raise the stress threshold for

that activity, potentially decreasing the demand made on attention resources and resilience (Keith

& Frese, 2008). Extensions of simulators to ‘Virtual Reality’ (VR) have found application in the

military, aviation, medicine and industry as another form of immersive training. These provide

benefits on the immediacy of access via computerised applications and the ability to set standards

of delivery. Additionally, meta-analytic studies to date have shown VR simulators for training in

Methods of Training Studies

N Sample

N

Effect size Cohens d, Hedges g

Effect Size Sources

1a. Conventional

1b. Simulation

2. Operant learning

397

24

19

15627

2183

2818

d 0.62

d 0.44

d 0.51

(Arthur, Bennett, Edens, & Bell, 2003)

(Keith & Frese, 2008)

(Stajkovic & Luthans, 1997)

3. Mindfulness 1

1

48

174

d 1.44

d 0.58

(Jensen, Vangkilde, Frokjaer, & Hasselbalch, 2012; Lebuda, Zabelina, & Karwowski, 2016) (Carmody & Baer, 2008)

4. CB Counselling 13

27

2334

1496

d 0.96

g 0.73

(Aldao, Nolen-Hoeksema, & Schweizer, 2010; S. G. Hofmann & Smits, 2008; Spek et al., 2007)(Spijkerman, Pots, & Bohlmeijer, 2016)

Table 3.1 Comparison of mean effect size by training methods

55

complete and complex surgical procedures were more efficient than basic simulators (Larsen,

Oestergaard, Ottesen & Soerensen, 2012). A meta-analysis of 14 studies of resilience training in

the military provided further support for efficacy of the method (Pallavicini, Toniazzi, Aceti, &

Mantovani, 2016). Behavioural and cued techniques are essentially reactive, reliable in response

to some prompted need, but not necessarily adaptive, and tend to be vulnerable to habituation. A

self-initiated operant strategy by contrast allows for discretionary responding when cued by

intuitive summations of observations or situational patterns.

3.6 Behavioural Operant and Cued Techniques The context here becomes incidental as the reward for exercising the self-initiated operant is

intrinsic and independent of time and place.

The self-initiated operant can itself withstand disruption or interference, and even increase if

greater effort or tempo of application is needed. The technique is consistent with Thorndike’s law

of effect which states that “Responses that produce a reward will occur again in that context”

(Gendolla et al., 2015, p158). In behaviourist terms, the reward complies with the notion of

contiguity, as when the reward availability is ‘soon, certain and positive,’ it is effective as

reinforcement and perpetuation. Operant conditioning involves a stimulus and reward, the

mechanism could be utilised to deal with preoccupation and deliver relief from anxiety and other

uncomfortable affective states (Skinner, 1953).

Activation of the self-initiated operant can be imperceptible or covert, have immediate effect and

occur without confounding any other task, meaning that uniformity in training is possible due to

independence of context or events. Both similar and general uses of operant techniques have

been reported in the clinical and educational literature to manage adverse mental states.

Cues are generally of two forms, specialised or common. Specialised cues form part of the pattern

matching that informs the expert for better decision making (G. A. Klein, 1997; M. W. Wiggins,

Azar, Hawken, Loveday, & Newman, 2014). They are sought to augment their understanding of

ambiguity, to reveal the truths behind concealed actions and to inspire the next move in dynamic

56

situations. Common cues exist to regulate, safeguard and reliably direct individuals to their

destinations. For example, everything from signs to siren alerts will bring a person to an attentive

state. However, a negative aspect of ‘common’ cues and signals is the tendency for repetition to

diminish their potency, such that habituation decreases their effect over time and distance (Cevik,

2014). Humans have a relatively meagre memory for odours when compared to animals however,

the directness of the neuroanatomical link between the olfactory bulb, the amygdala and

hippocampus, as the neural substrate of memory, provides a rapid direct path to emotional

memory.

Herz and Schooler (2000) presented the evidence that memories evoked by olfactory means

(odours in this reference), “were able to be distinguished by their emotional potency”. They found

that olfactory cues were reported as having a greater ability to tap emotional memory than other

stimuli, such that odours represented a special class of psychophysiological cues. The capacity

for odours to recall emotional memories was further demonstrated by Herz et al where the recall

of emotional autobiographical memory was significantly greater with an olfactory cue than by

any other sensory modality (Herz & Schooler, 2000). Herz et al drew the further distinction

between memory selection and recollection, stating that “it was during recall that the odours

exerted emotion” (p21). They concluded that the emotion brought forward did not retrieve the

specific details of the memory. Apart from the ability of the olfactory cue to access emotional

memory, the Herz et al., observation was that an olfactory cue, could retrieve an emotional

memory more effectively and possibly more rapidly than any other sensory mode, consistent with

separation of the olfactory pathways. The olfactory system may also be able to potentially provide

a rapid path to a select memory if paired at the time of creation, by an operant.

3.7 Metacognitive Self-Regulation Techniques Metacognitive SR techniques monitor and control thinking by maintaining emotional equilibrium

disengaging fear, anger and anxiety to provide a state that is neither aroused nor at rest but open

receptive and responsive. Meditation is a somatic technique that typically involves reflection and

57

exploration of personal thinking processes on the path to emotional equilibrium. Other

metacognitive techniques seek to achieve specific performances, with methods such as

visualisation and affirmation to clarify and focus the achievement of an objective. Cognitive-

behavioural methods (CB) used in therapy represents another form with the added intent of

challenging erroneous thinking and marrying ‘new’ thinking with successful personal action.

Several metacognitive techniques have been developed to enhance specific deficits in memory

recovery and social skills. Techniques vary from those that focus on body states to those that

liberate from self-preoccupation. As an operational method that readies the person for action,

meditation produces behaviour and thinking that is inherently vulnerable and uncertain in crisis.

Cognitive reflection is an inwardly focused state (Monat and Lazarus, 1977. p150) that is not

engaged with external reality and hence unsuitable for operational requirements that demand an

instant remedy. As an example, Keith and Friese (2005) investigated error management training

involving both simulators and metacognitive techniques, reporting that combined SR or

stabilisation of emotion and metacognitive activity successfully mediated performance

differences involving error induced events that required responses to unique situations. Keith et

al had also determined that the approach was better than avoidant and analogous transfer

techniques (Keith & Frese, 2005), lending further support to cognitive recovery and resilience as

an important performance enhancing factor. Trial and error practice has a degree of certainty as

a skills training method, but is reliant on habit formed responses generally not adaptable due to

the invariance of habit, for rapid recovery of attention and to free up cognitive resources when

needed in a variable and dynamic situation.

3.8 Cognitive Behavioural methods Amongst the cognitive-behavioural approaches, a widely-used method of self-management for

performance has involved a self-initiated process involving imagining the faultless achievement

of some needed action and objective, the channelling of thoughts and action. The simplicity of

the process involves an imaginary rehearsal with a successful outcome of the procedure.

58

The method serves to displace anxious preoccupation with success rather than anticipatory failure

in a mental sequence. By recalling a successful flow of the rehearsed process, the method

subsequently helps to prompt and cue-specific actions, such as a cockpit checklist, a tennis

strategy, perfect delivery of a speech or consistent application of a hospital protocol. The benefit

of the method is in the simplicity and utility of the approach and the ability to be invoked when

needed. However, its limitation is specificity of action. It is unlikely that the imagined flow in

playing the piano faultlessly will translate easily to jumping hurdles, conducting an engine failure

procedure in the air or some other unrelated performance-based activity. Nevertheless, the

essential mechanism, the replacement of worry and concern with emotional equilibrium, allows

for purposeful task-relevant behaviour to occur with less cognitive hindrance. The effects of

displacement of self-consciousness, emotional preoccupation and fragmented attention in

performance is a change in mental state, the effect described by Csikszentmihalyi (1990) as the

“Peak Experience” a positive and highly desired outcome of the ‘Flow’, or being in the ‘Zone’.

He described the ‘Flow’ as a mental state where no preoccupation, self-awareness or concern

existed, where all actions and cognitions were natural, effortless and made without error. Being

in the ‘Flow’ was associated with unconscious excellence and performance across all types of

sports (J. A. Young & Pain, 1999). The development by Nideffer (1976) of the attentional and

interpersonal styles theory similarly connected cognitive processes and emotional arousal, which

were specifically correlated with improved performance in sports.

Behncke (2004) summarised other cognitive-somatic methods which were relatively

straightforward and prevalent in sports, such as Schemas or patterned action, to produce a

deliberate reaction and response (Behncke, 2004), somewhat like the boxers automatic ‘jab and

parry’ ritualistic training exercise, that trains to avoid the slow evaluative pause in thought. Other

similar methods include Visuo-Motor behaviour rehearsal, Set theory, CB methods and variations

or combinations of those, all serving to deliver unencumbered action and dispel affective mental

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constraints through positive ‘Self-Talk’ in achieving performance (Hatzigeorgiadis, Zourbanos,

Galanis, & Theodorakis, 2011).

3.9 Self-regulation through Mindfulness A key outcome of SR is the management of affect. Mindfulness based stress reduction

mechanisms assisted by ‘Diaphragm Breathing’ behaviour (Ley, 1999) significantly reduce

negative emotion, reactivity (Brown, Goodman, & Inzlicht, 2013) and improve deployment of

attention (Goldin & Gross, 2010). In the version of mindfulness based on the Attention

Restoration Theory (ART), Kaplan focuses on attention and emotion regulation to manage

depletion of the executive function by engaging with the environment (Berman, Jonides, &

Kaplan, 2008; J. Kaplan, 2001; Kaplan & Berman, 2010; Friese, Messner, & Schaffner, 2012).

The Kaplan theory of ecological mindfulness training is a means for release from preoccupation

or stress achieved by the calming effects of immersion in nature. The method has found support

with evidence of the release in tension produced by more stressful closely built environments

(Berman et al, 2008). Mindfulness in its more usual form has been applied to a variety of clinical

and non-clinical experiments related to social anxiety disorder, generalised anxiety disorder and

error potentiated self-deprecation (Ley, 2003; Delgado et al., 2010; Teper & Inzlicht, 2013).

Underlying the subjective experience, the neurobiological effects of meditation and mindfulness

have indicated alterations of gray and white cranial matter providing evidence that the

mindfulness technique was something more than a mere temporary reduction of tension (Esch,

2014; Esch, Fricchione, & Stefano, 2003).

The Mindfulness method involves de-centering, development of self-compassion, and

progressively the reduction of self-preoccupation, to mediate beneficial change (Holas &

Jankowski, 2013). The general effect of Mindfulness as a psychophysiological method have

found gradual acceptance in organisational training in hospitals and aviation, with further

application in the improvement of student cognitive ability (Chiesa, Calati, & Serretti, 2011) by

mediating academic self-efficacy (Keye & Pidgeon, 2013). The somatic methods include

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relaxation, self-contemplation, abreaction, meditation and the now popular Mindfulness training.

The methods, firstly involve focusing on the self then altering perception and meaning to then

refocus attention - away from the self (Dane, 2010; S. Kaplan, 2001; Prakash, Hussain, & Schirda,

2015). To achieve the desired state, they all share the same mechanism used to stabilise affect.

The achievement of equanimity thus results in more available energy and less inhibition of

personal performance. The method remains popular notwithstanding that they may trigger

cathartic outcomes from the release and surfacing of intense past trauma. The need to escape the

memory of trauma can have the reverse effect in producing increased inhibition and withdrawal

(Finkelstein, Wenegrat, & Yalom, 1982b; Sinhger & Ofshe, 1990) (Gilbert, 1999; Paul, Elam, &

Verhulst, 2007). The possibility of uncontained abreaction in mindfulness training is an issue as

the technique has no established method of dealing with those, unlike CB methods or

psychoanalysis. Despite the absence of tactical management procedures, introspective

Mindfulness involves voluntary self-exploration and is less likely to introduce the same level of

risk when delivered in organisational settings; unlike that experienced in the personal growth

movement seminars of the 1970’s and 80’s in which cathartic re-growth in the absence of close

management of negative effect was seen to have had unpredictable outcomes (Finkelstein,

Wenegrat, & Yalom, 1982a; Glass, Kirsch, & Parris, 1977).

Overall, Mindfulness has been demonstrated as a positive means of clearing the mind, although

it takes some time to reach ‘satori’ or perfect equanimity as the Zen would say (Odajnyk, 1998).

Vulnerability to disruption by crisis is addressed in the Zen by the training to ‘quiet the mind’

and ‘knock the monkey off the shoulder’ (often by the deliberation of Koans, a series of

impossible propositions given to engage the mind), to achieve recovery.

3.10 CB Methods and Counselling Cognitive behavioural methods (CB) are a set of mainstream techniques and procedures now

operating in most therapeutic interactions, variously as cognitive behavioural therapy or cognitive

behavioural training. Hanrahan et al. (2013) conducted a meta and primary data analysis of the

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efficacy of CB on pathological worry with adults, with results showing a high effect size (Cohens

d 1.81) in effectiveness when compared with non-therapy controls (Hanrahan, Field, Jones, &

Davey, 2013). The relevance of CB in SR and attention control was demonstrated in a recent

meta-analysis of 114 studies examining emotion regulation strategies across a mostly normative

population by Aldao et al. (2010). A high main effect was detected in the prevalence of

maladaptive or internalising strategies involving rumination, avoidance, and suppression as SR,

in some conditions (Aldao et al., 2010). The prevalence of self-preoccupying behaviour such as

rumination or ‘worry thinking’ illustrated the characteristic impact that worry has on external

awareness and attention, as detailed in Monat’s intrapsychic coping methods (Monat et al. 1977).

The CB method varies in duration, content and focus between practitioners but typically involves

a reduction and examination of thinking, feeling and clarification of misaligned assumptions,

biases and experience. The objective being to clarify thinking and behaviour. The directness of

the method and verifiability of progress or change in actual behaviour makes it a significant

contributor as an evidence-based method. A key factor of the CB method is the effect on attention

control and SR, related to the refocus of introspection to external awareness and attention. Diehl

et al., in two studies related to goal pursuit used the self- regulation scale (SRS) to show attention

control as the main effect in the outcomes (Diehl, Semegon, & Schwarzer, 2006). While the CB

method validates the need to resolve preoccupying mental issues in delivering the needed

cognitive performance, it remains as a time consuming. ‘hunt and peck’ method to regain mental

performance, unsuitable for the instantaneous recovery of attention when quickly needed.

3.11 Self-Regulation Techniques The four basic methods of SR training as indicated in Table 3.2 show the operant technique as

having the most utility and immediacy of effect (ie, 10 x 10 minute sessions) regarding SR,

including attention, cognitive recovery and generalisability. It is the simplest process requiring

the least expertise to teach and is generalizable, being both independent of context and any other

skills in application. Despite having a self-regulating effect, the method is not introspective and

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invites no need for rationalisation of past events or exploration of personal history. Due to its

operant mechanism, each success in application delivers further positive reinforcement. When a

specific behaviour has an immediate effect, and it is continually rewarded, it is highly resistant to

extinction. Associating an operant technique with an intrinsic reward, such as feeling good or

peaceful, being able to concentrate, manage a task and so on, delivers the necessary behavioural

dictum of ‘soon, certain and positive’ for sustained behaviour.

Table 3.2 Utility of four

self-regulation training

methods Methods of Training

Effect size 95% CI

Cohens d, from

(Table 3.1)

Time to acquire

Sessions

Embodies

recovery

method

Applies

to

context

Requires

expert

tuition

1.Conventional & Simulations Moderate to high Task-practice N/A Specific Yes

2 Behavioural-Operant Moderate to high 10 x 10 min Yes All No

3. Mindfulness Moderate to high 10–20 x 1 hour No All No

4. CB methods & Counselling Moderate to high 8-10 x 1 hour No Specific Yes

The utility of the four methods are compared and summarised in Table 3.2. The

psychophysiological mechanism of attention is explored in chapter four and compared with

alternative and similar mechanisms or procedures that have evolved over time.

Table 3.2 Utility of four self-regulation training methods

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4. PSYCHOPHYSIOLOGY of ATTENTION

4.1 Introduction Achieving objectives, responding to exceptions and recovering capacity, energy and motivation

undeniably result from adequate SR of a person’s psychophysiological state. The previous chapter

advanced the view that triggering mechanisms such as operant techniques were a more rapid

means of achieving attention recovery, than other methods such as Mindfulness training, which

require longer practice in achieving mastery. Operant techniques, by contrast, are instrumental,

based on the stimulus-response model of behaviour and operate at the neural level, are reinforcing

rather than habituation prone and able to be self-initiated rather than reflexive as in classical

conditioning. Operant training has the potential to raise the key performance behaviours (repeated

in Fig 4.1) of personnel in safety critical industries such as aviation, transport and nursing because

of the reduced time to acquire the skill and the immediacy of response when invoked.

Two factors that represent differences between instrumental or operant and classical techniques

of learning are: context independence and sustainability. Continual exposure to a fixed process

Proposed dependencies Related performance behaviours

Can see things others may miss

Ability to avoid becoming fixated

Can switch rapidly between demands

Remembers procedures, rules & exceptions

Remembers despite interruption

Can anticipate sudden changes

Can deliver the necessary mental effort

Less vulnerable to emotional preoccupation

Figure 4.1 Model of Behavioural dependencies and identified attributes

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in the classical conditioning model risks habituation and extinction of the response with time and

distance (Cevik, 2014) and desensitisation (Schmid, Wilson, & Rankin, 2014). The operant

method as a proactive reward seeking mechanism is less vulnerable to such decay or loss of

potency, provided the consequences or reward of such prompted behaviour continue. As an

adjunct to other self-regulating methods for goal achievement, operant techniques are quick to

learn, can be invoked covertly, self-initiated and universal or context-free in application. SR to

overcome hurdles to achievement has been extensively discussed in the literature from several

perspectives. Knoch and Nash (2010) considered the neurophysiological perspective by exploring

differences in SR, proposing a ‘neural-trait’ basis for self-control while implicating the lateral

prefrontal cortex (LPFC) in social decision-making aspects. The phrase ‘self-regulation’ (SR),

when used in reference to social-norms of behaviour is a reminder that the control of a

maladaptive hedonic impulse or its suppression, occurs through the executive functioning

processes of goal-directed and adaptive achievement-oriented behaviour (Hofmann et al., 2012).

SR has been explored with neuroimaging techniques and brain processes involved in mental

effort, finding that it is not just about self-control, that the strength of the intent in delivering the

effort required was important and emphasised SR as a choice (Radulescu, Nagai & Critchley,

2010).

4.2 Energy and Mental Effort Earlier work by (Kahneman, 1973) had proposed a finite optimally managed capacity for mental

effort. Kahneman had observed changes in ocular, auditory and perceptual performance as

demand shifted from one sense to another, concluding that the energy supplying effort was limited

and distributed or reapportioned from one physiological need to meet the demands of another,

demonstrating an automatic displacement or re-allocation of resources. Radulescu et al., (2010)

using functional neuroimaging techniques similarly observed the ability of the brain to marshal

further cognitive support when required from other regions when under load, to increase the

capacity for achievement of problem-solving and behavioural goals. The evidence of the

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reallocation of cognitive resources when needed provides support for SR as the director in a

volitional and interdependent network model (Fig 4.1). Radulescu’s distinction of mental effort

as being either passive (and reactive) or active (and discretionary) helped to separate ‘task driven’

reactive engagement from the active mobilisation of brain resources, further emphasising the

executive in the process. Mental effort was putatively considered by Radelescu et al., (2015), as

pivotal in achieving goal directed behaviour. Intention and realisation demand cognitive effort,

which is dependent on the availability of mental energy; particularly in overcoming mental

fatigue (Cockshell & Mathias, 2012; Engle-Friedman, 2014). Mental effort is logically dependent

on available mental energy and has been graphically observed by manipulating glucose

concentration in the brain (Lindseth et al., 2011; Sourkes, 2006). The process of glucose

degradation being totally dependent on the oxygen being conveyed in the bloodstream

(Fairclough, 2004). Mental effort however also improves with a reduction of stress (Sandi, 2013),

suggesting that mental energy becomes available for attention when released or reallocated from

the wasteful effort of resisting the intrusion of other distractions. Kaplan (1995) proposed an

ecological basis for the restoration of attention, called the Attention Restorative Technique (ART)

which diverted or released captured mental energy and effort. The ART operates by managing

attention through a ‘Mindfulness’ technique, in concert with the calming or tension releasing

influence of rural vistas and gardens. In a study of the ecological technique, improvements were

found in executive attention, that part of the attention network that manages conflict and controls

other networks (Posner, 2012). Cognitive performance had improved when other demands were

decreased, the executive benefiting from re-direction of shared mental resources (Berman,

Jonides & Kaplan, 2008; Kaplan, 2010). In a meta-analysis of 163 studies of the effects of

meditation similar improvements were found with respect to emotional and relationship issues

but only weak effect sizes on attention and cognitive measures with the lesser result ascribed to

various methodological and theoretical issues in the studies (Sedlmeier et al., 2012).

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Overall, SR is concerned with the management of disruptions to attention due to affective or

reflexive reaction, lingering pathologies or dysfunctional behaviours or no longer useful

defensive measures that need to be extinguished operating in a cognitive-affective feedback loop

(Carver, 2011). In a normally functioning brain the apparent availability of mental effort comes

from adaptation of resources through re-allocation (Kahneman, 1973), displacement or release

(Kaplan & Berman, 2010), with ultimate recovery achieved during sleep (R. E. Brown, Basheer,

McKenna, Strecker, & McCarley, 2012). Perceived capacity and effort may also be increased by

removing the cognitive load to improve attention span, arousal and vigilance by increased

cerebral glucose concentration or ingested agents such as the affective drugs amphetamine and

modafinil (Gore , Webb , & Hermes, 2010).

4.3 Mental Energy & Fatigue Mental fatigue is a psychoneurological state of depletion of energy occurring after prolonged

activity. Brain function is dependent on a number of systems and it appears unlikely that central

fatigue is due to just one of those, as a common symptom associated with many subjective

conditions (Hegerl, 2013). Although there are known brain networks that mediate mental fatigue

the exact mechanism of the fatigue symptom is still unclear although it presents as a loss of mental

energy. Neurotransmitters associated with enhanced cortical activation and wakefulness and

those that are present during exercise, are not conclusive in the management of fatigue (Meeusen,

Watson, Hasegawa, Roelands, & Piacentini, 2007). Harrington described the nature of fatigue

according to its source, that is whether it was central or peripheral. For example, peripheral fatigue

results from muscle fatigue and central fatigue refers to central nervous system processes.

Episodic fatigue can be specific and articulated in as many forms as there are human activities,

including the fatigue of stress, a parallel but different affective state. Fatigue most closely

resembles a state of exhaustion commonly identified by how it disrupts or interferes with full

functioning rather than what the cause is.

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Nevertheless, fatigue has many endogenous and exogenous contributors. Symptoms are present

in sleep deprivation, task overload, cognitive overload, circadian troughs (Kyriacou & Hastings,

2010), prolonged inactivity (Roach, Petrilli, Dawson, & Lamond, 2012), excessive food

consumption, exposure to heat (Qian et al., 2015), vibration, noise, oscillatory motion, alcohol,

drugs, dietary imbalances, strobe effect, emotional states - and it exists in most

psychopathologies. It is characteristic in older men and women. It seems that fatigue is a uniform

psychophysiological response by the brain in overload, when it lacks some essential nutrient or

when injured. Fatigued mental states often produce subjective expressions of helplessness,

vulnerability, impatience anxiety, disinclination to continue a thing and emotional numbness

(Harrington, 2012).

Fatigue in aircrew is a significant hazard with regulations and service times designed to avoid

accumulation and development of a chronic state. Cockshell and Mathias (2012) in a study

examining cognitive test performance with chronic fatigue patients found that even when effort

was high, performance was low (Cockshell & Mathias, 2012). The results demonstrated the

intractability of the condition at the extreme state. A low mental energy state occurs within the

daily circadian cycle, irrespective of any prior fatigue-inducing activity. The circadian dip

determines alertness at the characteristic mid-afternoon, early evening and pre-dawn dip, when

cognitive task performance and responsiveness are low (Dongen & Dinges, 2000). Fatigue is

cumulative and sleep is the only known means to recuperate from it.

4.4 Methods of Arousal While attention studies have tended to focus on arousal and sustaining attention, fewer of those

have focused on endogenous means of recovery from fatigue lapsed states, as in initiating a return

to alertness in other ways than sleep. Fatigue features in most operational contexts such as driving

too far, sitting too long in the one position and high cognitive demands from task overload,

inevitably resulting in diminished cognitive performance. That different stimuli can trigger the

physiological effects of arousal suggests that arousal is multidimensional having both specialised

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and generalised components (Langner & Eickhoff, 2013). The pervasiveness of the problem

prompted the search for countermeasures and strategies to initiate revival and recoup mental

energy. These have involved exogenous means such as chemical stimulants, a change in posture

or body movement, vocalising, caffeine drinks and exposing the face to chilly air to break the

descent into sleep. For example, chilly air has been reported to temporarily stimulate an increase

in respiration and arousal, to increase cardiac output, cerebral blood flow, oxygenation and

glucose usage (Reyner & Horne, 1997).

Chemical stimulants afford a rapid and certain effect associated with ease of administration. For

example, Dextro-amphetamine had been widely used as a fatigue countermeasure to sustain

attention by military pilots on combat sorties dating from World War Two and used in almost all

subsequent conflicts despite the potentially serious side effects of long term use. Common side

effects included dizziness and blurred vision, psychosis and hallucinations (Estrada et al., 2012).

Even the seemingly innocuous Ginko has been reported to risk subdural hemorrhages with

prolonged use (Rowin, 1996) failing to fit the criteria for a safe attention recovery pill. The wide

search for wakefulness extenders also included chewing Caffeine impregnated gum. However,

Modafinil to replace Dextroamphetamine has become widely used in the military aviation sector

(Sheng et al., 2013; Turkington, Hedwat, Rider, & Young, 2004; Wesensten et al., 2002) showing

fewer side effects than Dextroamphetamine. Existing pharmacological interventions to improve

cognitive performance either enhance the activity of wakefulness-promoting neurotransmitters

(ie, amphetamines) or inhibit the activity of sleep-promoting neuromodulatory systems such as

Caffeine (Wesensten, 2012, pp37).

Smelling Salts: Smelling salts have also offered an apparent means for instantaneous cognitive

revival. Sensory inputs together with their detection, transmission, cognition and motor effect can

result in automatic as well as discretionary actions.

Keller draws the relationship between attention and consciousness as mediated by the olfactory

system (Keller, 2011). Brain injury patients in a prolonged coma may have a variety of stimulants

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applied to them in the process of recovery, including olfactory stimulants. In the 19th century,

smelling salts were often used to assess if the patient was brain dead based on the speed and

certainty of response via the trigeminal path. McCrory (2006) described the use of Ammonium

Carbonate the most popular of those as a restorative, also called smelling salts (chemical

(NH4)2CO3H20)) with subsequent variations including those of ‘Sal Ammoniac’. Solutions of

mixtures of dilute ammonia, water and ethanol are commonly available as aromatic spirits of

ammonia easily obtained via the internet (Amazon, 2016).

Despite the connection between attention and arousal, there is a difference between chemical

brain-state arousers and the mechanisms that invoke a change in mental-state. The release of NH3

in the nose, of the ‘smelling salt’, triggers the inspiration reflex and a change in breathing results

in an apparent increase in alertness (McCrory, 2006). The reflex triggered by the ‘salts’ has been

used as an on-the-spot diagnostic often rendered to competitors suspected of concussion in

various contact sports, albeit with unconfirmed validity of its efficacy for actual cognitive

revival. Arzi et al., (2010) investigating respiratory patterns due to odours in sleep, found that

while strong odours that activate both olfactory and trigeminal neural receptors can induce

arousal, mild olfactory odours alone appear to not do so (Arzi, Sela, Green, Givaty, Dagan, &

Sobel, 2010). An explanation being that the arousal effect of the smelling salts acts on the

nociceptors to alert the organism by activating the trigeminal pain system, but diverges in their

action from the attention recovery mechanism proposed in this study; where the ‘sniff’ draws a

sudden volume or air at a greater pressure to stimulate the olfactory nerve as an operant to change

mental state. Chemical stimulants by contrast such as ‘smelling salts’ can be experienced without

a link to any specific odour. The action being a neurochemical effect of the NH3 without any

specific action on mental state. Of interest in this study is arousal to effect a change in the level

of attention through the activation of the aspiration reflex and a secondary action involving

retrieval of an associated mental-state. Masaoka et al., (2011) had demonstrated the link and

immediacy of activation and retrieval of autobiographical memories with an associated odour. In

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their study, subjective arousal and consequent association of pleasant memories resulted in deep

slow breathing rates. Masaoka et al., had concluded that since odours are intangible, their

association was one of memory and emotion indicative of higher cognitive functions (Masaoka,

Sugiyama, Katayama, Kashiwagi, & Homma, 2012). In a further confirmation of the association

of the link between an olfactory stimulus and memory, Cortese et al (2015) had tested the

responsiveness of veterans with PTSD. The study finding an attentional bias to specific threat

odours, including fuel, gunpowder, blood and burning hair, resulting in elevated emotional

responses and changes in heart rate; further demonstrating the capacity for direct activation of

specific episodic memories through the olfactory path and the limbic system (Cortese, Leslie, &

Uhde, 2015).

4.5 Nasal olfactory action and the sniff Olfactory action occurs with nasal inspiration or a sniff involving a change in the rate and volume

of air passing through the nose. Normal nasal inspiration has the function of filtering clean moist

air to the lungs. The nasal ‘sniff’, a short sharp inspiration accompanied by only a minor

associated exhalation also has another function, that of passing air over the olfactory neurones in

the dorsal posterior recess of the nasal cavity, which connect with the olfactory bulb in the brain.

Normal sniffing is a semi reflex response that occurs with the detection of new odours by the

receptor neurons in the olfactory mucous (Fig 4.2).

Physically, the receptive area of the nerves in the olfactory epithelium cover some 5 cm2 of the

nasal cavity with 10-20 million bipolar sensing neurons each acting through 10-20 cilia sensitive

to specific odorants (Fig 4.2). The axons of the olfactory sensing neurons pass through the

cribriform plate of the ethmoid bone above the roof of the nose. They then enter the olfactory

bulbs which have a neural path into the limbic system and the five regions of the olfactory cortex,

the anterior olfactory nucleus, olfactory tubercle, piriform cortex, and parts of the amygdala and

entorhinal cortex resulting in rapid access to long-term memory

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Figure 4.2: The Olfactory Pathway Barrett, K., Brooks, H., Boitano, S., Barman, S. (2010). Ganong's Review of Medical Physiology (23 ed). McGraw Hill Medical, p593.

4.6 The Sniff as an Analogy of the Startle The startle resembles the sniff, with a more sudden activated nasal inspiration, after that sharing

the same neural path through the limbic system as the sniff to swiftly stimulate and retrieve

memory.

However, unlike the sniff, the startle involves a sequence of involuntary actions related to fear

conditioning through the two key structures of the amygdala and the hippocampus (Jacobs,

Renken, Aleman, & Cornelissen, 2012). Koch (1999) refers to the startle as a fast twitch of facial

and body muscles evoked by a sudden unanticipated and intense olfactory, tactile, visual or

acoustic stimulus. The physiological responses involve eye-lid-closure, contraction of facial,

neck and overall skeletal muscles, an acceleration of the heart rate, with arrest or pause of ongoing

behaviours. Additionally, reflexive sniffing (Tomori et al, 1998) also occurs in response to sudden

stimuli such as an unanticipated noise modulating the breathing pattern in the preBötzinger

Complex rhythm generator (Feldman, Del Negro, & Gray, 2013).

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4.7 The Role of the Amygdala in Rapidly Cued Attention A sharp short inspiratory sniff can stimulate the epithelium and activate the olfactory bulb

(despite the absence of any odour) to transmit a stimulus via the limbic system and frontal

memory centres of the brain to evoke the associated memory or calm mental-state. An essential

difference between this method and the startle response is the avoidance of the ‘fear’ component

in the startle, presumably because the path bypasses the brainstem startle reflex circuit and the

direct connection with the amygdala (Hitchcock & Davis, 1991). Consistent with the earlier

finding by Lang et al (1990) that the startle response as an aversive reflex, was enhanced under

conditions of fear but was able to be diminished or overridden with a pleasant emotional context.

This variability based on aversive or appetitive inputs separates the startle response from general

arousal conditions (Lang, 1990) and allows for the parallel of the sniff and startle in establishing

arousal, orientation and an attentive mental-state. While much of same neural network is used for

both sniff and startle, involvement of the amygdala in the startle condition suggested that the

amygdala has a central role in modulating stimulus-response associations at the core of Pavlovian

learning (Mirolli, Mannella, & Baldassarre, 2010). Further suggesting that the influence of the

amygdala extends to most affective responses and basic behaviours such as regulating body and

brain states, planning and decision making. Other sensory stimuli such as the acoustic startle

response were tentatively suggested by Koch (1999) as a typical protective reaction, the process

mediated via the circuit path located in the lower brainstem involving the caudal pontine reticular

nucleus which also modulates the acoustic startle response. Additionally, the aspiration reflex,

also known as the ‘gasp’ is commonly associated with other physiological reactions and is also

observed with the acoustic startle response in humans (Yeomans & Frankland, 1996). The

aspiration reflex was suggested by Tomori et al., as occurring with arousal (Tomori, Benacka, &

Donic, 1998) and as a strong inspiratory effort but not one that was necessarily followed by a

corresponding full expiration. It was compared with the sniff and the sigh as being triggered from

the upper nasal airway (fig 4.3) with a short latency, sudden onset and having an equally sudden

termination. However, Tomori et al., draw the distinction that the gasp and the aspiration reflex

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are different in several ways, pointing out that the gasp occurs mainly with severe hypoxia and

asphyxia when ‘eupnoic’ respiration falls and evident as a cyclic repetition of longer-lasting

spasmodic inspirations.

Of specific interest is the speed of activation of the startle response which is near instantaneous

due to the utilisation of multimodal sensors, including the epithelial receptors of the olfactory

bulb with their receptors transmitting directly into the limbic system of the brain (fig 4.2).

Olfactory stimuli are more rapidly activated than visual stimuli (Keller, 2011) because of their

direct relay to the limbic system and areas which are associated with memory and emotion rather

than first going via the Thalamus (Sullivan, Wilson, Ravel, & Mouly, 2015). The overall path

subsequently linking the aspiration (sniff) with emotion and motivation (amygdala), learning and

memory (hippocampal formation), and sexual behaviour (hypothalamus) (Blumenthal, 2015).

Mainland et al., had proposed that the sniff and the gasp are an integral part of the olfactory

system. Their review suggested that the ‘sniff’ was an essential part of the ‘Olfactory Percept’ ,

featuring in odorant detection and sampling, that it drives activity in the olfactory cortex and is

modulated by the olfactomotor system (Mainland & Sobel, 2006). The difference between the

nasal sniff and normal breathing is a physiological one, so that nasal ingestion of air carries both

air and any potential airborne contaminants to the olfactory receptors, without also transporting

particles to the lungs.

Laing (1983) described the rate and volume of air in the sniff as averaging an intake velocity of

27 L/min with a volume of 500 cm3. During the sniff, air enters through the anterior nares (nasal

opening) passing through to the upper airway and to the pharynx at the top of the throat at which

time any particulates can be coughed out or swallowed. Mainland cited Zhao (2005) as suggesting

that the internal structure of the nose channelled a lesser amount of air, between 5-10% that passed

into the nose, to reach the olfactory epithelium (fig 4.3).

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The change in the rate and volume of air inspired increases with exertion and logically decreases

in both REM and NREM sleep (Scott, 2006) at a low arousal state, and shows a modulating

olfactory bulb response during normal sniffing. Supporting the relation between sniffing and the

hippocampal theta rhythm (Balu, 2004), that during concentrated attention of mental tasks the

theta rhythm is generated elsewhere in the frontal midline cortex.

4.8 The Breathing mechanism In normal breathing, the lungs are the interface between body and environment and operate as

both voluntarily and autonomic transfer air into and out of the body. Despite variations in oxygen

(O2) and carbon dioxide (CO2), metabolic control of breathing maintains the arterial pressures

(PaCO2 and PaO2) and acid-base (pH) levels of the blood at constant levels. A neurochemical

process originating in the respiratory foci in the brain-stem activate a rhythmic action by the lungs

to draw in air. Action by the lungs subsequently increases O2 in the blood and triggers the lungs

to release excessive CO2 from blood into the atmosphere. Peripheral chemoreceptors in the aorta

and carotid artery help regulate this CO2 / O2 / pH balance in the blood and brain. The breathing

process can automatically respond to changes in psychophysiological demands such as additional

exertion as well as emotional demands like anxiety (Ley, 1999).

Figure 3 Path of Nasal Air (From Kandel ER, Schwartz JH, Jessell TM [editors]: Principles of Neural Science, 4th ed. McGraw-Hill, 2000.)

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Dual control of breathing is physically possible via the volitional expansion of the chest cavity

and the downward and upward movement of the diaphragm situated below the rib cage. By

thrusting the stomach muscles in and out to manipulate the movement of the diaphragm the lung

space is expanded or compressed. Using the assisted abdominal action on the diaphragm enables

a fuller aeration of the lung space, which does not occur in normal breathing. Lung volume

changes also occur with both voluntary and reactive changes in the rate of breathing to meet

bodily demands and activities. The CO2 / O2 balance in the body is critical to functioning as

variations in saturation, blood acid-base level (pH), rate of inspiration (over-breathing and under-

breathing) and cellular metabolic requirements determine the dilation or constriction of blood

vessels, delivery of glucose to brain cells and modulation of heart rhythms. Additionally,

hypocapnia or reduced CO2 levels (from over-breathing), may change blood pH levels,

particularly at times of stress. If these conditions are persistent and not normalised in renal

buffering of pH by excreting the fixed acids and recycling the bicarbonate, reduced physical

endurance and fatigue may occur, with extended alkalosis risking platelet aggregation and life

threatening blood clotting. As a near automatic and unconscious process, breathing is regulated

for metabolic and homeostatic purposes in the brainstem having a singularly important role in

regulating the health of both body and mind (Homma & Masaoka, 2008; R. Ley, 1999).

4.9 Measured Breathing and Mental State The oldest known reports of systematic manipulation of breathing appear to have evolved from

the yoga tradition with the intention of heightening perception and philosophical enlightenment,

with other practical benefits observed for stress management (Riley & Park, 2015). Prana is the

Sanskrit word for "life force" or vital principle, a central concept in Yoga. In Yoga breathing is

focused on the maximum absorption of ‘prana’ through the nose with channels (analogous to the

epithelial-olfactory system) situated in the upper part of the nostrils for circulation to the brain

(Gilbert, 1999). Beyond that, Yoga and similar philosophical approaches to obtaining

enlightenment and self-regulation have few references identifiable in modern biology, usually

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favouring metaphysical discourse rather than experimentation, although some superficial

similarities are evident (Barrett, 2010).

A measured and self-inspired repetition or cadence in breathing, to control mood and cognition

has long held a central role in SR, forming the basis of present and traditional self-management

training techniques in Christian ritual, Mindfulness, Yoga (Gilbert, 1999) and deeply embedded

in Buddhist rituals. Mental changes in perception and mood were attributed as a positive

achievement and a reflection of a higher consciousness. More practically, breathing techniques

were used as preparatory training in enhancing speed, attention and response for various eastern

martial arts training systems. Bernardi et al., (2001) had suggested that the introduction into

western cultures of formal rhythmic breathing techniques, as a self-regulating mechanism,

originated with Knights returning from 11th-12th century Crusades. The Knights had probably

been exposed to monks and devotees, in Arabia, India and Tibet, for whom measured breathing

techniques generated the mental states representative of an enlightened awareness. The Rosary,

a ritual associated with a rhythmic verbal cadence, had also been coincidentally traced back to

the 12th Century by Thurston (1856-1939) an English priest and scholar. The ‘Rosary’, was aided

by rhythmic recitation using a string of beads, with each bead fingered for the verses of the ‘Ave

Maria’, while responding to a rhythmic counterpoint by a priest. The assisted recitation technique

had become a common religious ritual in western Christianity, Buddhist rituals, the Hindu ‘Japa

mala’, Asian ‘Juzu’, and the Islamic ‘Tasbeeh’ rituals. Bernardi et al. had investigated the effect

of rhythmic breathing on cardiovascular rhythms and baroreflex sensitivity. By comparing the

vocal effects of the Ave Maria with the classic Hindu Mantra, finding that both had a measure

that regulated breathing with an approximate 10 second, six breath/min cycle of respiration,

compared with that of the normal adult of about 12 breaths/min). The respiratory range for the

average adult being 12-20 breaths/min (Barrett et al. 2010, p593). Bernardi had reported that the

rhythmicity of the slowed breathing study to have significantly positive effects on regulating the

cardiovascular system to enhance heart rate variability and baroreflex sensitivity in their study.

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Measured breathing with repetitive verbalisations, has the effect of altering perception, reducing

negative emotion and fear. Efficacy of the psychophysiological process, used for relaxation had

also been employed to extend physical endurance in sports (Litchfield, 2003). The model

supported Ley’s (2003) dual view that changes in breathing follow changes in emotion and

cognition and conversely that breathing can lead to changes in emotion and cognition (Ley, 2003).

4.10 Operant Learning It is suggested that a possibility exists in utilising the olfactory sniff as a cue for the rapid retrieval

of memory, to generate a practical self-regulating technique assisting the recovery of attention.

The established mechanism of pairing a stimulus with a response is known as ‘Operant or

instrumental learning’ (Blaisdell, 2008; Fantino & Stolarz-Fantino, 2012). The behaviours are

enabled or strengthened consequent to a reward or weakened if the consequence is aversive,

corresponding with the first theory of instrumental learning – termed Thorndike’s ‘The law of

effect’ and the subsequent work of Skinner (1953) and Watson (1924). Operant procedures are

deemed to be superior to classical conditioning as a means of learning to engage in a preferred

behaviour limited to a simple reaction. In operant learning, a specific behaviour is reinforced in

a schedule associated with a specific (conditioned) stimulus. As the training progresses the

behaviour becomes part of a feedback loop. The effect depends on the strength of the consequence

(Jozefowiez & Staddon, 2008) and the natural relatedness of the reinforcer, such that the

contiguity and outcome are closely linked (Gendolla, Tops, Koole, 2015). The ‘Skinner-Watson’

behavioural dictum of reinforcement to obtain repetition, ‘soon certain and positive’ describes

the reliability of the outcome and the parameters for sustainable performance.

The stimulus, response and outcome model (s-r-o) where the response is motivated by the

outcome (Blaisdell, 2008) assists in the achievement of goals through self-directed and self-

regulated behaviour. The way associative learning occurs beyond the normal acquisition,

formation, consolidation, reinforcement and extinction of learning stages is not clear yet, or how

a behaviour can change mental-state. The Hebbian view that associated learning arising from

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experience modified the synaptic transmission and behaviour of other neurons, when consistently

fired the presynaptic neuron would likely excite or attenuate the postsynaptic neuron (Hebb,

1949).

In summary, in the rART model a calm and clear mental state is achieved by the initial diaphragm

breathing practice at (A) (fig 4.4) (Arch & Craske, 2006) to establish the operant or link with the

cue (the sniff). The sniff (B) acts as the cue that transmits the stimulus or signal for recall of the

associated positive or pleasant mental state, previously established with diaphragm breathing.

The rART procedure as a self-regulating skill links several psychophysiological events to recover

attention when needed. The means to impart the skill and establish a measure of mental state to

gauge status and change is detailed in the next chapter.

The rART exercise was unlikely to meet the logistics and quality control criteria for large scale

training while using the traditional classroom format, with minimally trained instructors. The

rART exercise was thus designed to be delivered over the internet in video form with a specific

computer-controlled delivery sequence to participants amid everyday distractions and cumulative

fatigue, as detailed in chapter 5.

(A) Association In Preparation (B) In Execution

Figure 4.4 Proposed operant recall of mental state to improve attention

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5.0 METHODOLOGY

5.1 Introduction The preceding research had identified the criteria and outcomes for a rapid psychophysiological

SR control mechanism to recover attention and improve mental effort. A testable countermeasure

to address diminished cognitive performance due to stress and fatigue was hypothesised, for the

recovery of waned attention and mental effort. The proposed mechanism would allow the

individual to self-regulate their mental state to escape the disruption and discomfort of anxiety,

preoccupation or overload - to regain the mental clarity achieved and previously memorised, by

a short series of repetitive diaphragm breathing and cueing exercises.

5.2 Research Design An empirical study was formulated that could explore a self-regulating countermeasure as a

recovery skill, also that it could be conducted in-vivo to take advantage of naturally occurring

‘noise’ in daily life. The natural development of fatigue and inevitable distractions adds to the

external validity of the process and avoids laboratory bias (Levitt & List, 2007) through the

‘Hawthorne effect’ (Adair, 1984). The research design would thus involve a sequence of daily

unsupervised online exercises to be completed at night just prior to sleep, with each second

session loaded with two extra, mildly taxing, questions typical of standardized IQ tests and

characteristic short term memory question forms that had been assessed as sensitive to fatigue

(Ackerman & Kanfer, 2009).

By taking advantage of the natural level of fatigue and decrease in alertness due to the late night

circadian trough (Kyriacou & Hastings, 2010), recovery of mental effort and task focus could be

tested with the proposed rapid attention recovery technique. The object of recalling a calm mental

state is to reduce preoccupation and reluctance to engage in arduous mental activity when tired.

The study would utilise the challenge questions as the last items in sequence delivered in the daily

sessions and as a means of confirming completion of the session, further verify compliance with

the unsupervised training. The study incorporates both qualitative and quantitative components

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consistent with a mixed methods approach (Johnson & Onwuegbuzie, 2004). A baseline of mental

alertness and fatigue management constructed and tested with a similar and independent group

of participants would provide comparative measures to classify pre-training SR status. Questions

requesting information about actual self-management of behaviours preceding the study would

provide comparatively greater predictive validity than attitudinally oriented questions.

5.3 A Technique to Achieve Attention Recovery A method for recovery of attention is proposed based on Thorndike’s 1898 instrumental learning

theory and the law of effect involving an operant control (Jozefowiez & Staddon,

2008)(Blaisdell, 2008). The direct psychophysiological nature of the operant mechanism could

deliver a more efficient and easily repeatable procedure than other meta-cognitive methods;

independent of time, location and context. The proposed application of the SR technique for

hazardous occupations may have greater utility than mindfulness training and yoga. The

procedure involves a sequence of natural psychophysiological behaviours that activate specific

brain mechanisms to disencumber mental state and regain mental alertness when fatigued.

Comparatively productive outcomes could be achieved devoid of the complexity found in

classical cognitive-behavioural methods (CB), by circumventing the need for introspection and

the typically longer training period of meditatively oriented procedures needed to achieve

mastery.

A sequence of short exercises was designed in which the nasal ‘sniff’ was established as the

activating stimulus or operant (Fantino & Stolarz-Fantino, 2012), and paired in sequence at the

end of the expiration phase of the diaphragm breath (fig.5.1) to achieve the switch in mental

state; a procedure that in part mimic’s the common and natural means of reducing affective

discomfort when stressed by taking a deep breath or making a sigh. In the procedure, the

initiating nasal ‘sniff’ acts as a stimulus to instantly retrieve the associated memory from the

frontal cortex via the olfactory path and the limbic system to invoke the mental state that had

been previously practiced and located in memory during the training exercise. Once firmly

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established in memory the use of the recovery technique or mechanism would thus be available

‘on-demand’ to clear the mind through the activating sniff alone, without requiring concurrent

diaphragm breathing or intense focus on the self, as in mindfulness preparation. The rapid action

of a covert self-regulating device, independent of context, would make the technique usable in

any context.

5.4 Issues in Delivering Online Training Online delivery of testing and training has become popular as ‘distance education’, offering

logistic advantages over classroom attendance and traditional paper and pencil forms. Once

unique, online courses now emanate from most universities and number millions of students per

annum who appear to be very comfortable with the technology (Means, Toyama, Murphy, Bakia,

& Jones, 2009). The teaching presence and community offered by the classroom may be in

distinct contrast to that of online learning, but does not appear to have been discouraging. Specific

effectiveness of online or screen-based learning offers in terms of the rate of skill, satisfaction

and knowledge acquisition was assessed in a three-year longitudinal assessment of medical

students at an American university (Schimming, 2008). Results indicated increased satisfaction

due to increased control via self-paced units which also resulted in greater engagement. Questions

of engagement, certainty of achievement and the efficacy of online delivery have received

positive support. However, it is not yet known if engagement with a computerised course

specifically equates to the assistance that social engagement offers by group-classroom training.

In the report of a study of a web-based college course focused on teacher presence and sense of

community (Shea, 2006), ‘directed facilitation’ contributed more to the factors of engagement

and sense of ‘connectedness and learning’, with instructional design and organisation running

second. At the structural level, Means et al. (2009) concluded from their meta-analysis of

evidence-based practices in online learning, that significant differences were found when the level

of interaction in the delivery format was considered, further moderated (in some cases) by the

perceived attractiveness of the on-screen presentation. The finding is important for the online

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delivery of the short course in the proposed rapid attention recovery technique which had evolved

out of typical practical face to face interaction and where instruction had required scrutiny of

participant behaviour and practice. The need for remedial tuition for participants’ who were

learning an unfamiliar behaviour was inevitable. The active and supportive presence of the

facilitator thus seen as necessary to ensure that practise occurred in the development of the skill.

Student engagement and subjective perceptions had been raised as an issue following completion

in an online course on aviation physiology by aviation students’ (Artino, 2009). Artino had found

that students’ intent on becoming aviators employed greater SR strategies, pointing to the

perceived regard they held for their career choice. Despite the greater self-regulating capacity of

the aviation cohort, Artino found an unexpected dissatisfaction evident in their reports that were

related to the design of the online presentation, which may have simply emphasised the need for

some novelty in presentation of the subject-matter, greater context or a smoother sequence of

learning points. It has long been known that practical demonstrations and learning to ‘do’ a thing

offer greater opportunity for relatedness and engagement than simply learning ‘about’ a thing.

For example, a course in physiology, short of participating in a dissection, must essentially be an

abstract ‘look and remember’ exercise. When there is no opportunity to participate and become

immersed, by manipulating or experimenting the subject the content must rely on interest and

novelty to ensure continued attention and engagement.

The efficacy of an online delivery of a course in Mindfulness-based intervention, generally

defined as an awareness of body sensations and mental state focused on the moment, was

explored in a meta-analysis by Spijkerman et al., finding that stress was well mediated by the

interactive or guided method of Mindfulness-based intervention in contrast to the unguided

method, as demonstrated by a slightly larger effect size (g = 0.51) in the study (Spijkerman et al.,

2016). While support for the social context in guided interventions was provided in the Artino

and Spijkerman studies, Kearns (2011) in a simulator study evaluating a computerised ‘single-

pilot’ resource management course adapted from crew resource management principles (Salas,

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Wilson, Burke, & Wightman, 2006; Seamster, Boehm-Davis, Holt, & Schultz, 1998), found that

online training as an instructional delivery technique had proved effective in improving

situational awareness, even though the crew context was absent, as it would normally be in single-

pilot general aviation general aviation operations (Kearns, 2011). The finding supported the view

that the key difference in online training related to the perceived quality of the content and the

degree of interaction available in a course. Kearns study would suggest that the interaction in the

single pilot resource management course was one of involvement with the screen material and

active manipulation of the simulator rather than the social interaction with others, had they been

available, for example, by interaction with air traffic control personnel. With the perspective of

involvement and engagement in mind for the rART study, the proposed attention recovery

technique instruction to be conducted unsupervised and administered remotely was designed with

video and text, and some voice-over on images, the main component being the demonstration and

guided exercise in practicing the technique.

5.5 Unsupervised Baseline Testing The attention recovery study was constructed to be delivered online and unsupervised. The

preliminary baseline testing involved anonymous self-reports of how well participants managed

their sleep and recovered from fatigue, further evidenced by the extent they were experiencing

cognitive failures and level of workload strain. Additional tasks related to on-screen problem

solving with items that delivered additional mental load exercising executive functioning, short-

term memory and choice reaction speed in processing. It was presumed that the ‘cloak of

anonymity’ would encourage authentic responding on the self-report scales.

Unsupervised online testing can be problematical with respect to the authenticity of responses

where personal gain is dependent on performance. Cheating, in contrast to response inflation in

this study, was not seen as relevant as response distortion or inflation may be. Distortion and

inflation tending to occur where items are perceived as likely to violate social norms, risk a stigma

(Trappmann, Krumpal, Kirchner, & Jann, 2013), or are perceived to diminish positive regard for

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the self. However, some inaccurate responding can also be authentic. Respondents may simply

not know what they think or feel about a thing or indeed how much so - and thus default to the

most socially desirable response. Hence accuracy and authenticity of responses are potentially

significant confounding issues in unsupervised self-report based testing that does not specify clear

and observable behaviours and response sets.

Approaches used to check for authenticity, have included statistical response probability

procedures, repeat with mirror forms of tests and objective post-test verification. Another method

for questionnaires has involved writing questions that are concrete and objective with greater face

validity. The Item Sum Total method (IST) for sensitive questions introduced by Trappmann

(2013), diverts perceived threat to the respondent by switching the object of the question to focus

on something external to the respondent and less likely to evoke defensive responding. Early

experience with computerised testing (Richman, 1999) had also shown that controls such as

preventing backtracking and skipping on questionnaires resulted in less distortion than on the

alternative modes of pencil tests and face to face interviews. Richman et al. (1999) in a meta-

analysis comparing pencil, computer and interviews on social desirability responding, reported

finding a near zero effect size across 61 studies showing a lesser distortion on the computerised

delivery than in interviews and pencil forms.

A Baseline test was constructed to be delivered for anonymous execution, preparatory to the

attention recovery exercises. The delivery system was programmed to control entry and departure

at each stage of the course. Participants would complete a question set and be politely ejected

with a popup note to return the same time the next day. The mental alertness and fatigue scales

were delivered as a single screen-full of 20 Likert-scaled questions (Appendix B), while the

performance items were delivered as single question screens. Participants could not go back once

the submit button had been pressed, nor could they skip a question, some performance questions

allowed for repeated attempts with the average score taken as the final item score. The challenge

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questions delivered daily after each training session were single screen displays that only allowed

forward movement.

5.6 The Research Hypothesis The study investigated whether the proposed rapid attention recovery technique, as a means of

self-regulation added or improved the ability to maintain performance on unwelcome cognitive

tasks at the end of the day when fatigued. The challenge for participants was to complete the

training sequence just prior to sleep over the ten days of the course.

Hypothesis 1. Self-regulation scores on the independent perception variables of mental

alertness and workload strain would predict the dependent variable of fatigue

management scores.

Hypothesis 2. Participants reporting higher scores on the independent variables of mental

alertness and fatigue management would report transfer and utilisation of the attention

recovery technique to their daily activities.

Hypothesis 3. Participants, who reported higher scores on mental alertness and fatigue

management, were more likely to adhere to instructed session times and complete the

course within the allocated time.

Hypothesis 4. Participants who had identified themselves as pilot candidates would be more

likely to complete the attention recovery training and score higher as predicted on the

baseline measures denoting positive self-regulating than others.

5.7 Overall Structure of the Study A short online course including a baseline test and video demonstration was constructed to

administer the attention recovery training with the objective of establishing the viability and

efficacy of the technique as an addition to a person’s self-regulating repertoire of skills, when

delivered unsupervised to anonymous participants.

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The study design specified late night activity before sleep to practice the method and to take

advantage of any natural reluctance to engage in an arduous mental activity, due to end-of-day

fatigue and the characteristic post-dinner circadian trough (Kyriacou & Hastings, 2010) that

signals a decline in alertness and impending sleep at that time. The process was also expected to

encounter levels of emotional resistance where the ‘imposed study activity’ competed with the

distracting influence of a more attractive or novel experience, such as watching the ball game in

progress on TV or conversations with a friend. The need to introduce complicated or artificial

laboratory stressors to replicate those stressors was removed by positioning the attention recovery

training exercise to take advantage of the inevitable accumulated stressors participants experience

each day. Participants had the opportunity to exercise the self-regulating attention recovery

technique as they developed the skill. By utilising the technique, reluctance, and emotional

tension would be released. The effect of the technique would be reinforced by the established

‘behavioural’ technique that delivered a positive perception and an immediate outcome in the

context of the behaviourist dictum to ensure reinforcement of a procedure, as depending on the

exercise or consequence being ‘soon-certain-and positive’.

5.8 The Study Development Group The baseline development group comprising students in an aviation course from a different

university were requested by their instructor to complete the development survey (Appendix A)

to provide a specific benchmark sample for the development of test items to be subsequently

delivered to the experimental group in the study. The development group were asked to complete

the baseline test in the one sitting and at night after day classes, to approximate the conditions the

volunteer experimental group would experience at a later stage. The context of the development

group was as a semi-supervised group insofar as the request to do the test was formal and results

were reviewed by the course lecturer in feedback sessions with participants. A feature that was

examined in the retest of the baseline measures at an average of 30-40 days later.

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5.9 Criteria and Development of the Test Instruments Participants achieving the goals of the study, as evidenced by the baseline test and sequence of

challenge items, over the programmed ten days of the training, would provide data supporting

improvements in self-regulation. The baseline test was developed as a measure of pre-existing

ability to self-regulate and potentially to predict completion of the 10 session course.

The baseline test was used to detect any subsequent change in cognitive performance by

participants over the sequence of the five sets of challenge test items, that were delivered

concurrently with the training sessions. The baseline test included behavioural self-report and

cognitive test items constructed for online delivery to participants. Test items were derived from

established commercially available instruments displaying performance figures and

demographics further modified with a development group of like students from the aviation

school of another university, who had not been informed of the purpose and were not part of the

rART attention recovery training exercises.

5.10 Baseline test development Construction of the baseline test for the experimental group involved delivery of the test items

online to an independent development group (n=61) of aviation students (Appendix A, table A1).

The test items were assembled to correspond as much as possible with the behavioural markers

established in chapter 2, subsequently analysed for their utility and reliability based on student

performance and modified for subsequent inclusion into the study baseline test together with the

diurnal challenge questions accompanying the 10-minute training video. Self-report test items

below a reliability of r = 0.8 were discarded.

Functional test items incorporating the dimensions of executive functioning, short-term memory

and choice reaction speed, demonstrating a mean item difficulty in the average range (50-75)

(Table 5.2), were retained to contribute to mental load in the process, rather than as a specific test

of proficiency or intelligence.

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Confirmation of the usability of the items was achieved with retesting over a period of 30-40

days. Items deemed to be too specific to aviation and unsuitable for the general population of

students were discarded. Construction of the questionnaire items was confined to items that were

clearly behaviourally based. More traditional questionnaire measures of values, attitudes and

beliefs were rejected as potentially unverifiable in this study. Test items were sourced from

Goldberg’s, (2006) international personality item pool (IPIP), utilising a reduced version of

Broadbent’s (1982) cognitive failure scale, as modified and reduced by (Wallace & Chen, 2005).

The fatigue management scale (Appendix B), a behavioural scale previously developed for an

evaluation of mobile equipment operators had shown the required accuracy and reliability (10

items; α = .85) was utilised for the benchmark test. Results from the initial development group

sample of student pilots (n = 64) (M = 76.23, SD = 11.7) were compared with those from an

industrial sample (n = 1129) of rail, road and public transport drivers (M = 76, SD = 14.18). A

feature of the scale was the similarity of style with the mental alertness scale, a modified cognitive

failure scale (Broadbent et al, 1982), involving behaviourally phrased questions indicating

increased fatigue and potentially poorer self-regulation (SR).

The eight behavioural markers previously associated with the SR model (fig 4.1) and the

Hofmann et al., (2012) SR mechanisms dependent on executive functions (Table 2.1) were

matched with the baseline test items as measures of SR (Table 5.1).

The cognitive performance items delivered every second day as challenge questions to the

experimental group were constructed to provide added cognitive load, to the naturally fatigued

late night mental state of the participant, potentially evoking emotional resistance and cognitive

reluctance as commonly experienced under stress. Overcoming affective resistance was thus

posited to be a measure of SR and recovery of attention, attributes designed to be enhanced by

the rART training. Similarly, the array of questions comprised of verbal reasoning and spatial

memory items that were gauged as in the moderate to easy level of difficulty (Table 5.2), required

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no particular knowledge or learned ability, but needed to be read carefully, requiring attention

and mental effort to solve, drawing on behaviours as attributes defined in Table 5.1.

The items had the singular purpose of adding cognitive load as affective interference and were

not delivered to gauge intelligence. The established taxonomy of the three SA levels (Endsley,

1995) provided relevant behavioural criteria and categories to frame the study objectives. The

subsequent baseline test instrument items (Table 5.1) were those that had also been supported by

prior research before use and had documented evidence for their construct validity, reliability and

sensitivity. The original coping and resilience scale obtained from the IPIP library (Goldberg,

2006) used in the development group trial, was replaced for the study group by the modified

NASA Task Load Index (TLX) (Binder & Desai, 2011; Hart & Staveland, 1988) as an alternative

measure able to combine both affect and perceived load. The TLX parallels the Trappmann

(2013) ‘Item Sum Total’ (IST) approach in eliciting greater authenticity and accuracy of

responses by way of externalising the apparent target of the self-report questionnaire away from

the self to the role. For example, “How difficult is this role to do” versus “I find this role difficult

to do”. The use of the NASA TLX instrument also provided context to the strain felt by

participants that was absent in the more traditional coping scale (i.e., time pressure frustration

Table 5.1 Eight Behavioural markers associated with baseline test measures

Can deliver the

necessary mental

Find it easy to

remem

ber despite

Rem

embers

procedures, rules

Can sw

itch

attention rapidly

Can see things

others may m

iss

Ability to avoid

becoming fixated

Can anticipate

sudden changes

Less vulnerable to

emotional

Memory √  √  √  √           √ 

Choice Reaction Speed √     √  √  √  √  √  √ 

Executive Functioning √  √  √  √  √  √     √ 

Mental alertness √  √  √  √  √  √  √  √ 

Manages Fatigue √  √  √  √     √  √  √ 

Work Strain/Coping √  √  √  √     √     √ 

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and difficulty). Items in the development survey that were not directly comparable with the

primary study outcomes to be measured were also dropped to achieve a more parsimonious test.

The final form of the baseline test was reassessed according to the performance shaping factors

identified as behavioural markers in chapter two and the investigation of behaviours by

experienced aviators (Adams, 1993; O'Brien & O'Hare, 2007). The final delivery version was

modified with a reduced number of items and scales (Table 5.1). The baseline test emphasises

the cycle of perception, cognition and behaviour in the self-regulation of stress, such that the

better behavioural management of fatigue would results from perception of the stress of the

increased mental effort required for the cognitive tasks.

5.11 Development of the baseline test The six scales in the baseline test previously suggested as measures of the behavioural markers

(Table 5.1) and as a set of competencies (Hofmann et al, 2012), were proposed as self-regulation

(SR) skills by matching with defined supportive behaviours in the baseline test (table 5.2).

Consequently, the eight behavioural markers were linked to short term memory, choice reaction

speed and executive functioning (table 5.1). The final 30 items in the survey delivered to the

experimental group were designed as a baseline assessment of individual SR skills using self-

reports of functioning and fatigue management, rather than any expression of values or precedent

knowledge. The benchmark test was subjected to trials by the aviation students at another

university (Table 5.3) demonstrating acceptable reliabilities, inter-item correlation, and

functional-item discrimination.

A web-based test delivery system was coded and a website constructed with the URL

‘satraining.com.au’ established for the study. The system delivered both the testing and the staged

daily training components in the one instrument to provide a seamless experience for participants,

track progress in real time and accommodate a daily SMS reminder system to alert participants

to continue to complete the training. Participants were controlled in both the sequence and in how

much they could specifically do in each daily session. Instructions were reiterated at each entry

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or login and questions regarding understanding and ease of execution were also provided. As

most participants were anticipated to come from the study university, anonymity was a

prerequisite to prevent leakage of performance results from the tests that could result in

inadvertent influence on student reputation. The study was designed with mechanisms to ensure

delivery of the content according to a structure that afforded anonymity and provided more

experimenter control in the execution of the sequence of exercises in the delivery of the content

5.12 The Attention Recovery Demonstration Video A five-minute demonstration video was scripted and filmed. A young female actor was hired to

demonstrate the diaphragm breathing technique and cued sequence, accompanied with voice-over

instructions and a static image (Appendix C). Actors were already familiar with the diaphragm

breathing technique as a method for improving voice production in singing and acting.

Instructions were provided and displayed to participants prior to each video session

demonstrating the sequence (fig 5.1). The video was designed to be controlled by the participant

and able to be played as often as required in each session. Each step of the process was timed and

audibly counted down to provide a timing reference for later practice.

Participants were recruited using a passive broadcast method, posters (Appendix E), endorsing

the required skills for aviators and were attached to faculty and library notice boards inviting

aviation and all other students to access the website to participate in the baseline test and the

attention recovery training.

5.13 The Aviation Student Groups Students from the aviation school represented the initial source for the experimental group.

Demographics of the development group included academic years, age and flying credentials

listed in the results section (Table 6 .1).

Experimental sample demographics together with completion rates are shown in the results

section (Table 6.4).

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To continue required participants to acknowledge and download the ethics page on the website if

required. Participants for the attention recovery training were enrolled as volunteers from all

levels of the aviation school and from other faculties of the university. Notices and flyers

informed the website URL (http://satraining.com.au) to access the study webpage (Appendix E).

The required description of the study was displayed as was the procedure in point form.

Enrolment in the study was dependent on progressive acknowledgement of the ethics criteria and

indicated procedures. The required participation information form and privacy assurance

document were also linked as PDF files for inspection and download on the first webpage.

Participants accessed the setup page nominating their location as the study university or some

another location. Confirmation enabled access to the next page displaying a cryptic login ID.

On conclusion of the baseline questionnaire, participants were instructed that they would be

reminded by SMS to return the next day/night to begin the first session of the attention recovery

Table 5.2 Baseline test scale definitions

Manages Fatigue3 Self-management of sleep requirements to ensure adequate cognitive resources

Mental Alertness1 Measures the extent of every day slips in perception, memory and coordination

Choice Reaction speed 4 Ability to make rapid and effective decisions to changing circumstances

Executive Functioning4 Use of logic and deductive thinking to manage developing issues.

Working Memory4 Ability to retain and recall information in the short term

Subjective (strain)2 Identifies perception of stress on six potential influencers described in the TLX

1Mental alertness derived from the Broadbent et al (1982) Cognitive Failures scale.

2TLX (NASA Task Load Index) (Hart & Staveland, 1988) 3, 4 Derived from the SSA Mobile Equipment Operator test V2.1a (Rosenweg, 2015)

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training (fig 5.1) requiring a 10-minute application each day and the completion of two further

challenge questions every second session. A confirmation SMS was sent to participants with the

login ID as the message.On login to the baseline test participants were advised to record their

respondent ID and that no retrieval of the login code was possible if lost. Instruction in the rapid

attention recovery technique proceeded with a slow and rhythmic display of the diaphragm

breathing method by the model. At each exhalation of breath, participants were instructed to do

an exaggerated nasal sniff (fig 5.1), pause for a few seconds then begin again.

5.14 The Rapid Attention Recovery Instruction Set

Progressing automatically to the next page, participants provided their mobile phone numbers

and the time for their preferred late-night SMS reminder. The exercise could be done as often as

needed, with a minimum of five expirations and sniffs advised and modelled in the video

demonstration and as depicted in the static image, to allow for the association of the ensuing

calmed mental state to form an association with the nasal sniff for later retrieval. No other

instruction was given except that the procedure did not need to be repeated in full once the whole

94

training sequence had been completed, with the advice that that the ‘sniff’ would retrieve the

calmed mental state automatically.

5.15 Data Collection Participant responses were collected via the online system and retained in the system database for

analysis. The question / item number was recorded for each time of entry and exit and exported

to MS Excel files and input to SPSS V23 for analysis. In this study both full and partial

completion records were sought to identify the point at which participants departed from the

study.

Hypothesis One was examined using a multiple linear regression with the self-regulation

subscales of mental alertness and fatigue management as the predictor variables and

perception of workload strain as the criterion variable.

Hypothesis Two was assessed using multiple linear regressions, with mental alertness and

fatigue management as the predictor variables. For the first regression, perceived rART

efficacy was the criterion variable, and was intended to represent the likelihood that

participants would transfer the attention recovery technique to other daily activities. For

the second regression, participant perception of the exercise was the criterion variable

and was intended to represent how participants felt about the exercise, which would also

imply carry-over to other daily activities.

Hypothesis Three. Participants, who reported higher self-regulation scores on mental alertness

and fatigue management, were more likely to complete the course as instructed and

within the specified time allocated. Non-compliant progress through the training

sequence by participants would predict lower retention rates and early termination

before completion of the training sessions.

Hypothesis Four. Student pilots would be more likely to complete the attention recovery

training and score higher on the self-regulating scales than general non-aviation

students.

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5.16 Statistical procedures. To examine Hypotheses 1, 2, 3 and 4, a series of multiple linear regressions were performed to

ascertain if the independent (predictor) variables significantly predict each dependent (criterion)

variable. Multiple linear regressions were used to determine a predictive relationship amongst a

set of dichotomous or interval predictor variables and an interval level criterion variable.

The standard “enter” method of multiple regressions was used, in which predictor variables are

entered simultaneously into the model. To determine whether the predictor variables collectively

predicted the criterion variable, the F test was used. The R2, the coefficient of determination, was

the statistical output used to ascertain how much variance in each criterion variable was accounted

for by the total collection of predictor variables. Where any overall regression model was

significant, the individual predictors were examined using t tests. For significant predictors, every

one unit increase in the predictor would correspond to an increase or decrease of the criterion

variable relative to the unstandardized beta coefficient.

Prior to each analysis, the assumptions of the multiple linear regression were assessed. These

assumptions included linearity, homoscedasticity, and absence of multicollinearity. Linearity is

the assumption that there is a straight-line relationship between the predictor variables and the

criterion variable and assessed through the examination of scatterplots. Homoscedasticity is the

assumption that the distances between observed and predicted values are normally distributed

and examined through scatterplots. The absence of multicollinearity as a check for the assumption

that the predictor variables were not too highly correlated, was assessed using Variance Inflation

Factors (VIF). Any VIF scores higher than 10 were deemed to be are indicative of the presence

of multicollinearity (Stevens, 2009). Comparative Analyses were assessed using one independent

sample t test and one multivariate analysis of variance (MANOVA).

The independent sample t test is appropriate for use in determining if any statistically significant

differences between two independent groups exist on a single continuous dependent variable

(Field, 2009). MANOVAs are the appropriate analysis to use when the effect of one categorical

96

independent variable is assessed on more than one dependent variable but is only applicable when

the dependent variables are related in some way (Field, 2009).

Based on this rationale, the study completion rate is a single score that should not be correlated

with either of the subscales of SR, meaning that it is appropriate for use in the t- test. Similarly,

because the scales of mental alertness and fatigue management are both subscales of SR, they can

be used together as a series of dependent variables in a MANOVA. The independent variable for

both analyses was the type of participant (i.e., pilot candidate vs. non-pilot candidate).

Prior to analysis, the assumptions of these comparative analyses were assessed. Both analyses

were conducted based on the assumption that data followed normality and homogeneity of

variance; however, the MANOVA also assumes that homogeneity of covariance has been met,

as the multivariate equivalent of homogeneity of variance. Normality in terms of these

comparative analyses is the assumption that the data are normally distributed was examined using

a Shapiro-Wilk test for each dependent variable. Homogeneity of variance and its multivariate

equivalent, homogeneity of covariance, are the assumptions that the groups under examination

have equal error variances. Homogeneity of variance was assessed using Levene’s test, while

homogeneity of covariance was assessed using Box’s M test.

97

6. RESULTS

6.1 Introduction

The following chapter details

The present study investigated a technique of Self-Regulation (SR) that resulted in a rapid

recovery of attention (rART). Based on a self-initiated, context-free cued-recall or operant for

retrieval. The study was designed to occur in a naturalistic context within the range of normal

distractions and interruptions experienced by participants at the end of the day, when the effects

of fatigue and temptation were likely. The online and unsupervised delivery of the testing and

training requires SR for continued achievement in the face of competing demands, distractions

and the inevitable end-of-day fatigue. Cognitive processing and performance varies

systematically across circadian time, tasks tend to become more arduous around 7 pm in the

evening and very early in the morning around 4-4:30 am than they are at 9 am in the morning

(Dongen & Dinges, 2000; Kyriacou & Hastings, 2010). Progress and achievement with the rapid

attention recovery training task and the challenge questions were reviewed at each session to

identify the success by way of reports of the extending their use of the rapid ART technique to

other activities outside of the study. Overall, perceived efficacy of the technique was determined

by the extent of participant self-reports of utilisation outside the study.

The development group demographics The baseline test development and retest results The demographics of the experimental group Administration of the online study Issues in the attraction, retention and performance by participants Time and duration in the study by group, age and gender Study completion rates by session, group, age and gender Experimental group performance on the study Results of the four hypotheses Efficacy of the attention recovery technique Gender differences in the study

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6.2 Hypothesis

Hypothesis 1. Self-regulation scores on the independent baseline self-perception

variables of mental alertness and workload strain predict fatigue management.

Hypothesis 2. Participants reporting higher scores on the independent variables of

mental alertness and fatigue management would report transfer and utilisation of the

attention recovery technique to their daily activities.

Hypothesis 3. Participants, who reported higher scores on mental alertness and

fatigue management, were more likely to adhere to instructed session times and

complete the course within the allocated time.

Hypothesis 4. Participants who had identified themselves as pilot candidates would

be more likely to complete the attention recovery training and score higher as

predicted by the baseline measures denoting positive self-regulation, than others.

6.3 Demographics of the baseline test development group The baseline test development sample (n = 61) results provided comparative evidence for the

group of scales indicative of positive self-regulation with that of a subsequent similar group in

the study experimental sample. The baseline test development sample of 61 students from the

aviation faculty of another university were in one of the three years of their course or in a post-

graduate aviation program (Table 6.1), predominantly male (69%), with a modal age of 22 years

with most in the second year of their course - with an overall 18% complement of female trainees.

Five trainees of the 61 representing 8.2% of the sample had a first language other than English.

The test of comprehension as gauged by the Kincaid model (Appendix B, table B2) preceded the

delivery of the baseline test. Respondents who failed to achieve the required 100%

comprehension rate were excluded from the study.

99

Flying Credentials 

1st Year  2nd Year  3rd Year  Post Graduate  Totals 

  M F Mn

(Age) M F

Mn (Age)

M F Mn

(Age) M F

Mn (Age)

N

Nil  32 8 22 1 22 1 1 36 43

Ab initio  6 1 21 1 33 8

GFPT  2 22 2

PPL  1 22 1

CPL  1 (37) 1 (21) 4 (29) 6

ATPL  1 27 1

Totals  2 51 2 6 61

1See footnote for credentials

Pilot credentials of most respondents in the development sample reflected their early stage of

flying training, leading to the initial general flying progress test status (GFPT). The majority of

the sample (71%, n=43) in the second year of their course had not commenced flying training at

the time of testing. Ten (16.4%) participants had either basic or professional pilot qualifications

with the required medical clearance denoting a standard of physical fitness (CASA_Medical,

2016). The study baseline test development group of 61 student pilots completed the first test and

a second test after a period of 21 – 30 days. The development sample showed an overall

improvement of scores on second administration due to familiarity with the test. The scale means

In summary, the effect size of the difference between the two test administrations, utilising

Cohen’s d, showed a moderately high difference between first and second administration on two

of the five scales related to mental alertness (d = .70) and self- management of fatigue (d = .85),

1 1 License / minimum experience categories Ab-initio (Commenced flying training – instructor supervised) GFPT (General flying progress test – Solo + 30 hours experience – flight in training area) - Class 2 medical clearance PPL (Private Pilot License – Graduated 50+ hours experience – non-paying passenger carrying) - Class 2 medical clearance. CPL (Commercial Pilots License – 150+ hours experience – paying passenger carrying) - Class 1 medical clearance ATPL (Airline Transport Pilots Licence – 1500+ hours experience) - Class 1 medical clearance11

Table 6.1 Aviation Experience Development Group Benchmark Test

100

of the initial development group test (Table 6.2) were subsequently used for comparison with the

experimental group baseline test results (table 6.3). but a lower mean effect size of (d = .31) on

the cognitive abilities scales of executive functioning, choice reaction speed and short term

memory. Improved scores were expected on retesting suggesting greater familiarity with test

items (table 6.3); a marginal retest effect was also evident in the difference in standard deviations

between the two results.

Development Group Baseline Scales

n of

Dev’t test items

Group

Mean n= 61

SD Alpha Stand’

Intra class Corr’n

Mental Alertness 14 80.95 10.97 0.873 .870

Manages Fatigue 10 75.25 11.62 0.808 .797

Functional Performance Items Mean Item Diff’y

Executive Functioning element 6 61.56 18.21 .74

Choice Reaction Time element 5 69.83 18.61 .76

Working Memory element 5 61.370 12.81 .77

The functional or performance items, were introduced to marginally increase the arduousness of

the test rather than their difficulty. These involved elements of executive functioning, choice

reaction time and working memory. A high degree of stability was observed with no practical or

significant differences and low effect sizes (Cohens d) in test-retest results.

6.4 Overview of Experimental Group Study Participation The study was delivered online and unsupervised to volunteers attracted by the flyer detailing a

study of a means of possible improvement in Self-Regulation comparative to those of an Air

Force pilot (Appendix E). The study proceeded with the online system executing the

questionnaire and display of the videos in a programmed sequence.

Table 6.2 Initial development group baseline statistics

101

Completion of the training sequence in the specified time was variable (Table 6.4) with sustained

application achieved by fewer than 21% of the original participants who had commenced the

training. Consistent with the requirements for anonymity in the study, no reasons were sought or

provided for early departures.

Scale Title  Effect size

d

Retest 

Mean n=61 

Std Error 

Retest Mean 

 St Dev Retest 

Initial Test 

Mean 

N=61 

Std Error 

Initial Mean  

 St Dev Initial 

Sig Diff 

Means 

Table 6.3 Re-test development group baseline test results

Mental Alertness 0.70 87.95 1.22 9.52 80.95 1.35 10.57 .000

Manages Fatigue 0.85 84.48 1.37 10.67 75.25 1.43 11.2 .000

Perception of workload n/a n/a n/a n/a n/a n/a n/a n/a

Executive Functioning 0.35 64.23 2.3 17.96 58.02 2.33 18.21 .060

Choice Reaction Time 0.43 72.87 1.95 15.21 65.67 2.35 18.33 .020

Working Memory 0.30 63.02 1.21 9.48 59.67 1.65 12.85 .104

102

Table. 6.4. Demographics by study completion and departure rates

Session Study

Item No Completed

Number of respondents

leaving study

% Male Mean Age Male

Female Mean Age

Female

Baseline Test

1 27 12.22% 27 29.78 0 - 11 3 1.36% 3 22.0 0 - 14 1 0.45% 1 33.0 0 - 21 1 0.45% 1 22.0 0 - 23 4 1.81% 4 26.25 0 - 24 13 5.88% 13 28.62 0 - 25 1 0.45% 1 38.0 0 - 26 3 1.36% 3 35.33 0 - 27 3 1.36% 3 28.67 0 - 28 2 0.90% 2 35.0 0 - 29 1 0.45% 1 22.0 0 - 30 43 19.46% 18 24.11 25 29.431 3 1.36% 1 22.0 2 25.0

Baseline test completed – Training commenced

32 3 1.36% 2 21.0 1 42.0 Session 1 33 18 8.14% 7 25.0 11 26.8

35 2 0.90% 0 0.0 2 32.0 Session 2 36 11 4.98% 6 31.5 5 23.6

38 1 0.45% 0 0.0 1 33.0 Session 3 39 9 4.07% 5 33.8 4 26.3

40 1 0.45% 1 42.0 0 0 Session 4 42 4 1.81% 3 39.0 1 22.0

43 1 0.45% 0 0.0 1 28.0 44 2 0.90% 1 33.0 1 28.0

Session 5 45 5 2.26% 1 33.0 4 28.8 46 1 0.45% 0 0.0 1 20.0

Session 6 48 3 1.36% 2 25.0 1 28.0 Session 7 51 5 2.26% 1 33.0 4 28.5 Session 8 54 2 0.90% 1 42.0 1 38.0 Session 9 57 2 0.90% 0 0.0 2 31.0

Session 10 63 46 25 21

Incomplete 175 108 67

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6.5 Experimental Group - Enrolment, Retention & Performance A total number of 385 participants comprising student volunteers from a university aviation

training school and general non-aviation students accessed the study website and established an

anonymous login. Of the 221 entries that had commenced the baseline test, 175 (79.3%)

proceeded to completion of the baseline test and some portion of the training sequence, with

46 participants completing the full study test and training sequence, representing a completion

rate of (20.72%) (table 6.4).

Participants terminating their participation before completion (n=175) included n=131 students

with no flying experience and n=44 with some aviation experience (table 6.6). Individuals who

had commenced and terminated their participation prior to completion (n=121), represented 69%

of total terminations, indicating a ratio of 3:1 of ab-initio and general students to experienced

pilots departing the study. The number of young and mature age participants terminating early

were near equal at 92 and 93 persons respectively. The rate of departure was assessed and

compared with results on the mental alertness and fatigue management scales of positive self-

Table 6.5 Aviation experience by age and gender completing the study

Years Aviation

Experience

Age Range 18-25

Age Range 26-30

Age Range 31-35

Age Range 36-40

Age Range 40-45

Age Range 46+

Totals

M F n M F n M F n M F n M F n M F n N

None 4 4 8 2 2 4 2 1 3 4 3 7 1 3 4 3 2 5 31

1-2 1 1 2 1 1 1 1 1 1 5

3-10 1 1 1

11-15 1 1 3 1 4 5

16+ 1 1 3 - 3 4

Totals 5 5 10 2 2 4 2 2 4 5 4 9 2 4 6 9 4 13 46

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regulation. Pilots scored significantly higher than non-pilots on mental alertness t (219) = 2.617,

p< .009 and management of fatigue t (219) = 2.810, p<.005.

6.6 Experimental Group Compliance with Instructions Compliance behaviour and a capacity to remember and adhere to instructions is deemed to be a

facet of the conscientiousness dimension and of positive SR. Instructions provided to participants

in this study included the request to login nightly and attend to the training just before sleep at

night. Fig 6.1 shows slight variation to instructions on entry time with a non-significant difference

between participants who completed the training to those who did not. Partial completers tended

to engage in the study earlier in the evening, prior to 2000hrs, compared with the completion

group who tended to engage late evening, between 21:00 and 24:00 hrs (Table 6.7). Data related

to compliance with instructions to access the study just before sleep is indicated in the time of

entry and compared with their membership of the complete or incomplete group (fig 6.2) and

table 6.8. The greatest number of early terminations occurred during the baseline testing stage

with 105 (47.5%) individuals departing the study prior to commencement of the rapid attention

recovery sequence of training. Represented by 27 (51%) females with a mean age of 27.25 years

and 78 males with a mean age of 28.2 years. Another 70 male and female participants (32%)

progressively departed during the training before completion. The remainder of the 46

Table 6.6 Demographics of early terminations experimental group

Years Aviation

Experience

Age Range 18-25

Age Range 26-30

Age Range 31-35

Age Range 36-40

Age Range 40-45

Age Range 46+

Totals

M F n M F n M F n M F n M F n M F n N

None 26 15 41 14 13 27 15 10 25 5 - 5 11 6 17 10 6 16 131

1-2 6 6 3 3 1 1 2 2 2 13

3-10 5 5 5 2 7 1 1 1 1 14

11-15 1 1 1 1 1 1 1 1 1 1 5

16+ 1 1 1 1 1 2 3 1 1 5 1 6 12

Totals 38 16 54 23 15 38 18 11 29 7 2 9 13 7 20 18 7 25 175

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participants 25 males and 21 females completed the training. Expected days to complete the

course was 13 days or 312 hours’ total including weekends. Table 6.7 shows the mean days to

complete the course by the Days-Full (completion) group and the Days-Partial (non-completion)

group.

Compliance with the study instructions for entry to the training on successive days-nights were

not adhered to by either course completers (Days-Full) or non-completers (Days-Partial) (Table

6.7). Participants had been sent an SMS daily reminding them to enter the course at a time they

had specified during their setup in the study. That this was not adhered to suggesting the free

times nominated, were in conflict with other personal demands, the SMS being ignored.

Table 6.8 shows the mean hours between entry at each question number (stage). The actual

duration of 240 hours was extended with additional hours due to the non-entry on the three

weekend nights (Friday – Sunday) when participants would likely be otherwise engaged. By

contrast, both groups complied with the preferred late evening entry time except for females in

Table 6.7 Time and days on course by age and completion status

Age Range 18-35 26-30 31-35 36-40 40-45 46+

Gender M F M F M F M F M F M F

Days - Full 31.5 23.8 83.1 87.3 19 27.8 49.1 15.8 44.8 18.9 24.3 42.7

Days - Partial 27.3 31.8 34.3 45 33.3 27.1 - 58.7 25.9 42.1 68.3 54.3

Accessed Time of day/night 00:00 - 24:00hrs

Time Full 23:00 22:30 21:00 22:00 10:30 18:00 21:30 22:00 20:30 21:30 18:30 20:30

Time Partial 00:00 23:30 21:00 20:30 22:00 13:30 - 18:30 00:00 21:00 18:00 22:00

106

the 31-35 age group and males in the 46+ age group. Entry times for both groups are shown

graphically in fig 6.1.

(showing percent of the total sample accessing the online system by time of day)

Figure 6.1 Study login times by course completion status

Table 6.8 Hours elapsed between log-ins and training sessions - all participants 

Q'n No 

Completed Group  Mean time (hours) between sessions (ideal = 24 hrs) 

n completed all sessions 

Incomplete Group mean time (hours) between sessions 

n not completed all sessions 

30  59  46  242  73 

31  12  46  35  70 

33  104  46  281  49 

36  59  46  214  36 

39  55  46  361  26 

42  102  46  145  21 

45  55  46  151  13 

48  58  46  160  9 

51  87  46  274  4 

54  89  46  445  2 

57  76  46   62  45  46   

107

Figure 6.2 Comparison of elapsed log-in times between sessions.

weekday. Study sessions (table 6.8, fig 6.2) concluded with the question at end of each session

regarding ease of use of the technique. A notable difference was found between completers and

non-completers on their compliance in sequentially executing each session at 24 hour intervals

as requested. While neither group were precisely compliant with the 24-hour sequence between

sessions, fig 6.2 illustrates the completion group as significantly more compliant than the non-

completion group, with a lesser variation in elapsed time between sessions. Non-completers with

inconsistent and highly variable attendances or log-ins had all left the study by the third last

session (question 54) (Table 6.8, fig 6.2).

0

50

100

150

200

250

300

350

400

450

500

30 31 33 36 39 42 45 48 51 54 57 62

Completed Group (hours) Incomplete Group (hours)

108

6.7 Perception of Workload Strain Stressful influences related to workload that were not directly generated by the study were

identified by participants with the modified Task Load Index (TLX) instrument. The modified

TLX was delivered online as a component of the baseline test and completed before

commencement of the rART training exercises. Perceived strain, denoting greater demand on

coping resources were expected to be inversely related to mental alertness and fatigue

management, were not evident in the data. However, a significant difference was seen on

perceived strain by gender (fig 6.5).

Frequencies and percentages were calculated for each of the strain sources that participants

indicated as present in their student role (Table 6.9).

The largest overall source of strain reported was mental demand, with more than twice the number

of participant’s n = 72 (31%) identifying this source of strain in the total sample. The least strain

type reported by participants was physical demand n = 7 (3%). The ‘Effort’ facet was the second

least reported source of perceived workload strain (PWS), with n = 11 participants (5%). While

frustration, n = 30 (13%), pace of work n = 33 (14%), and performance n = 25 (15%) had similar

frequencies. A total of n = 41 (18%) participants did not provide a response to the TLX question.

A moderate level (M=57.5, SD=18.79) of PWS was reported by 72% of the final sample (n=46)

(Table 6.10) who had completed the full sequence of study tests and training. Participants

(n=144). terminating the study at various stages (Table 6.8), prior to full completion of the 63

items in the study exercises, indicated no significant difference in their level of PWS (M= 38.3,

SD=17.72) compared to those who had completed the study sequence t (186) = 1.26, p<.05.

To further distinguish participants not completing the study from those who had completed, the

two groups were compared as a ratio of completed to total group (Table 6.10).

The ratio of those completing to those not completing was 1:5.2, representing approximately 20%

of the total sample as depicted in the coping with perceived workload (PWS) (Table 6.8)

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and (Fig 6.3). Mental demand was reported as the strain facet making the highest demand on

coping skills, as represented by 41% of the sample who had completed the course.

Analysis of the means for perceived workload strain (PWS) (Table 6.11) with a test value = 50,

showed results significantly lower by 7.8 (95% CI, -13.7 to -2.0) than a hypothesised normal

population score of 50 (43) = -2.692, p = .01. A comparison by Gender between high and low

coping scores on PWS revealed two distinct distributions in the six facets of the scale with the

Table 6.10 Ratio of Perceived workload strain by completions group

Workload Strain n = 44 Frequency Percent Cumulative

Percent

Ratio of completed

to not completed

Frustration 6 13.6 13.6 1:5

Mental Demand 18 40.9 54.5 1:4

Pace of Work 10 22.7 77.3 1:3.3

Performance 9 20.5 97.7 1:3.9

Physical Demand 1 2.3 100 1:7

Effort 0 0 0 -

Total 44 100

Figure 6.3 Perceived source of workload strain – completion group

110

theme of how hard it is to achieve and cope with the workload. Males reported higher coping

(n = 25, M = 50.24, SD = 18.06) than Females. Levene’s test for homogeneity showed the

assumption for homogeneity was met with the equality of variances for Mental Alertness (p=.22),

Manages Fatigue (p=.14), reported Efficacy of the rART, Perception of workload strain (p=.11).

No difference was found in the equality of means by Gender on the Manages Fatigue scale t (42)

=.96, p = n.s. There was a statistically significant difference in PWS scores between males and

females t (42) = 3.23, p < .002. The mean difference for Males was 16.97 (CI95%, 6.4 to 27.6)

higher than female PWS scores with a higher effect size for Cohens d = 0.97. Results for the

cognitive performance items of working memory, choice reaction speed, executive functioning

and the daily challenge test items 1- 5, showed considerable variability between genders and

successive executions, while there was no significant difference between the genders in reports

of the efficacy of the rapid attention recovery technique

t (42) = .636, p > .528, and corresponding ease of use of the technique (fig 6.5).

Table 6.11 Perceived Workload Strain one-sample test

test value = 50 t df Sig.

(2-tailed) Mean

Difference

95% Confidence Interval of the DifferenceLower Upper

Coping with PWS -2.692 43 .010 -7.7955 -13.635 -1.956

111

6.8 Experimental group baseline test performance by Gender Comparison of gender differences on all results for both males and females are shown in table

6.12 and fig 6.6. PWS is indicated as the measure having the largest effect size (d = .97) amongst

the non-functional abilities test items. The data in table 6.12 shows the relative performance on

the TLX PWS scale as 44% of males having reached baseline compared with 9.52% of females.

Results for the cognitive performance items of working memory, choice reaction speed, executive

functioning and the daily challenge test items 1- 5, showed the most variability.

Figure 6.4 Gender by coping with perceived workload strain

112

Note: Fourth Challenge group items deleted due to outliers.

Scale Title Effect Size d A‐B 

 %=> criterion A Male 

 %=> criterion B Female  

Mean A  

n=25 Male   

 St Dev A        

Male 

Mean B  

n=21 Female 

  

 St Dev B  

Female 

Mental Alertness  0.15  84.00% 71.43% 60.72 12.00  58.52  17.36

Manages Fatigue  0.34  60.00% 47.62% 52.16 14.39  46.29  21.39

Coping Work Load Strain   0.97  44.00% 9.52% 49.76 18.06  33.81  15.17

Choice Reaction Speed  ‐0.64  48.00% 80.95% 49.72 25.78  63.62  16.81

Executive Functioning  0.02  76.00% 66.67% 68.64 27.23  68.00  29.08

Working Memory  ‐0.51  64.00% 80.95% 59.24 17.68  68.10  18.07

Reported rART Efficacy  0.08  80.00% 76.19% 60.24 24.58  58.52  20.12

First Challenge Question  0.05  76.00% 76.19% 77.88 23.10  76.90  20.24

 Second Challenge uestion  ‐0.02  96.00% 80.95% 83.72 22.64  84.10  23.34

Third Challenge Question  0.89  84.00% 47.62% 82.52 20.58  61.62  27.49

 Fifth Challenge Question  0.32  96.00% 85.71% 77.56 15.86  72.14  19.45

Table 6.12 Experimental group baseline test means by Gender  

Figure 6.5 Experimental Group total test results by gender

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6.9 Sample Bias and Generalisability of results To achieve generalisation of the results, the recruiting advertising attracted students with a desire

to perform better academically or seek to experience a novel self-improvement process. As such

the total sample could be deemed to be a biased sample. The ‘Hawthorne effect’ was not deemed

to be present in the study. Participation was anonymous and not about any particular or special

attention or even about an awareness of their individual part in the study or relevant to the

presence of any experimenter. Nevertheless, it would be true to say the sample of students (both

aviation and other faculty) were self-selected by the invitation to learn a technique to improve,

thus representing other than a disinterested random group. In that invitation, they are viewing the

experiment in terms of their personal needs, rather than the experimenters, in which case the

resultant bias is difficult to identify; but one likely to be replaced by their sense of self-efficacy.

6.10 Results for Hypothesis 1. Fatigue affects Behaviour

The independent variables of mental alertness and workload strain would predict the dependent

self-regulation variable scores for fatigue management.

A multiple linear regression analysis was conducted for hypothesis one to assess whether a

relationship existed between Mental Alertness (behaviours denoting prevalence of cognitive

failure or alertness) and Perceived Workload Strain (PWS) (extent and type of strain experienced)

with Fatigue Management (how well fatigue was being managed). The 'Enter' variable selection

method was chosen to include all selected variables into the model. The assumption of normality

was assessed by plotting the quantiles of the model residuals against the quantiles of a Chi-square

distribution, shown as a Q-Q scatterplot (DeCarlo, 1997).

The data was inspected for the assumption of normality and whether the quantiles of the residuals

deviated from the theoretical quantiles, such that strong deviations from the theoretical quantiles

would have indicated that the parameter estimates were unreliable. Assumption of

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homoscedasticity was also assessed by plotting the model residuals against the predicted model

values (Osborne & Waters, 2002) and inspected to confirm that the points appeared as randomly

distributed with a mean of zero and no apparent curvature.

A Q-Q scatterplot of the model residuals is shown in Fig 6.6 and a scatterplot of predicted values

and model residuals is depicted in Fig 6.7. Because both plots followed the parameters necessary

to assume normality and homoscedasticity, both assumptions were met. Variance Inflation

Factors (VIFs) were calculated to detect multicollinearity between predictors where the VIFs

were in the unacceptable range of five to ten (Menard, 2010). VIFs for this model were both

shown to be 1.12, such that multicollinearity was not a concern. Table 6.13 presents the Variance

inflation factor for each predictor in the model.

Table 6.13 VIF for Mental Alertness and Fatigue Management

Variable VIF

Workload strain 1.12

Fatigue Management 1.12

Table 6.14 Regression predicting Fatigue Management on Mental Alertness and Perceived Workload Strain

Variable B SE β t p

(Intercept) 3.23 3.41 .00 -0.95 .344

Mental Alertness 0.73 0.06 .67 13.07 < .001

Perceived workload strain 0.21 0.05 .20 3.84 < .001

Note: F (2,185) = 122.16, p<.001, R2 = .57

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The results of the linear regression model, F (2,185) = 122.16, p < .001, R2 = .57, were significant

indicating that Mental Alertness and Perceived Workload Strain explained approximately 57%

of the variation in Management of Fatigue. Since the overall model was significant, the individual

predictors were examined further. Both mental alertness and coping with perceived workload

strain were found to be significant predictors of fatigue management at the p < .001 level. Results

indicated that a single unit increase in mental alertness corresponded with a 0.73-unit increase in

management-of-fatigue scores, while a single unit increase in perceived workload strain

corresponded with a 0.21unit increase in Management of Fatigue. Table 6.14 summarizes the

results of the regression model.

Figure 6.6 Q-Q scatterplot for normality for Mental Alertness and Perceived Workload Strain predicting Fatigue Management.

Figure 6.7 Residuals scatterplot for homoscedasticity of Mental Alertness and Perceived Workload Strain predicting Fatigue Management.

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6.11 Results for Hypothesis 2 - Self-regulation predicts rART usage

Participants reporting higher scores on the independent variables of self-regulation, mental

alertness and fatigue management, would report transfer and utilisation of the attention recovery

technique to their daily activities. To inform hypothesis two, a series of two multiple linear

regressions were conducted. The first multiple linear regression analysis was conducted to assess

whether a relationship existed between Mental Alertness and Management of Fatigue and

perceived efficacy of the rART. The variable of Perceived rART Efficacy was intended to

represent the extent to which participants had gained some competence due to the training and

had transferred the attention recovery technique to assist with other daily activities.

Hypothesis two – regression one.

To inform hypothesis two, a series of two multiple linear regressions were conducted. The first

multiple linear regression analysis was conducted to assess whether a relationship existed

between Mental Alertness and Management of Fatigue and perceived efficacy of the rART. The

variable of Perceived rART Efficacy was intended to represent the extent to which participants

had gained some competence due to the training and had transferred the attention recovery

technique to assist with other daily activities. The 'Enter' variable selection method was chosen

for the model, forcing all selected variables into the analysis simultaneously. The assumption of

normality was assessed by plotting the quantiles of the model residuals using a Q-Q scatterplot

(DeCarlo, 1997). The assumption of homoscedasticity was assessed by plotting the model

residuals against the predicted model values (Osborne & Waters, 2002).

Fig 6.8 presents a Q-Q scatterplot of the model residuals. Fig 6.9 presents a scatterplot of

predicted values and model residuals. Visual examination of these plots indicated that the

assumptions of normality and homoscedasticity were met. Variance Inflation Factors (VIFs) were

calculated to detect the presence of multicollinearity between predictors. Because neither VIF

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exceeded 5, there was no cause for concern of multicollinearity. Table 6.15 presents the Variance

Inflation Factor for each predictor in the model.

The results of the linear regression model were not significant, F (2,41) = 0.83, p = .445, R2 =.04,

indicating Mental Alertness and Fatigue Management did not explain a significant proportion of

variation in Perceived rART Efficacy. Since the overall model was not significant, there was no

evidence of the individual predictors of the technique showing any effect over the study training

development time and were not examined further.

Table 6.16 summarizes the results of the regression model for hypothesis two and regression one.

Table 6.15 VIF for Mental Alertness and Management of Fatigue

Variable VIF

Mental Alertness 2.85

Management of fatigue 2.85

Figure 6.8 Q-Q scatterplot for normality for Mental Alertness and Fatigue Management predicting Perceived rART Efficacy

Figure 6.9. Residuals scatterplot for homoscedasticity for Mental Alertness and Management of Fatigue predicting Perceived rART Efficacy

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Table 6.16 Regression predicts rART Efficacy on Mental Alertness and Fatigue

F (2,41) = 0.83, p = .445, R2 =.04

Hypothesis two – regression two.

The second multiple linear regression analysis for hypothesis two was conducted to assess

whether a relationship existed between Mental Alertness and Fatigue Management and Perceived

Ease of rART Method. The use of Perceived Ease of rART Method as the criterion variable was

intended to represent how participants felt about the exercise, which may also imply a carry-over

to other daily activities. The 'Enter' variable selection method was chosen for the linear regression

model. The assumption of normality was assessed by visual examination of a Q-Q scatterplot

(DeCarlo, 1997). The assumption of homoscedasticity was assessed by plotting the model

residuals against the predicted model values (Osborne & Walters, 2002). Variance Inflation

Factors (VIFs) were calculated to detect the presence of multicollinearity between predictors. All

predictors in the regression model have variance inflation factors (VIF) less than 10, indicating

an absence of multicollinearity.

Variable B SE β t p

(Intercept) 44.84 13.81 0.00 3.25 .002

Mental Alertness 0.46 0.37 0.32 1.24 .222

Fatigue Management -0.24 0.30 -0.21 -0.80 .430

Table 6.17 VIF for Mental Alertness and Fatigue

Variable VIF

Mental Alertness 2.85

Fatigue Management 2.85

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Table 6.17 presents the Variance Inflation Factor for each predictor in the model. Fig 6.10

presents a Q-Q scatterplot of the model residuals. Fig 6.11 presents a scatterplot of predicted

values and model residuals. Visual examination of both plots indicated that normality and

homoscedasticity could be assumed. The results of the linear regression model were not

significant, F (2,41) = 0.89, p = .416, R2 = 0.04, indicating Mental Alertness and Fatigue

Management did not explain a significant proportion of variation in Perceived Ease of rART

Method. Since the overall model was not significant, the individual predictors were not examined

further. Table 6.18 summarizes the results of the regression model.

Note. F (2,41) = 0.89, p = .416, R2 = 0.04

Variable B SE β t p

(Intercept) 30.70 5.38 0.00 5.70 < .001

Mental Alertness 0.16 0.15 0.28 1.09 .281

Fatigue Management -0.05 0.12 -0.11 -0.42 .674

Table 6.18 Regression predicting Perceived rART Efficacy on Mental Alertness and Fatigue Management

Figure 6.10 Q-Q scatterplot for normality for Mental Alertness and Fatigue Management predicting Perceived Ease of rART Method

Figure 6.11 Residuals scatterplot for homoscedasticity for Mental Alertness and Fatigue Management predicting Perceived Ease of rART Method

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6.12 Results for Hypothesis 3. Self-regulation predicts course completion

Participants, who reported higher scores on mental alertness and fatigue management, were more

likely to adhere to instructed session times and complete the course within the allocated time.

To examine hypothesis three, a multiple linear regression analysis was conducted.

This analysis was designed to assess whether a relationship existed between Mental Alertness

and Fatigue Management and Course completion. The 'Enter' variable selection method was

chosen for the linear regression model. The standard test for normality was assessed using a

Q-Q scatterplot (DeCarlo, 1997). Homoscedasticity was assessed by plotting the model residuals

against the predicted model values (Osborne & Walters, 2002). Fig 6.12 presents a Q-Q

scatterplot of the model residuals. Fig 6.13 presents a scatterplot of predicted values and model

residuals. Visual examination of this plot indicated that there may be a deviation from normality

based on the combination of the variables Mental Alertness, Fatigue Management and Course

completion. However, homoscedasticity could be assumed, based on the visual examination of

fig 6.12. Variance Inflation Factors (VIFs) were calculated to detect the presence of

multicollinearity between predictors. Again, the use of these two predictors did not result in VIFs

that exceeded 5, and multicollinearity was not established in this analysis. Table 6.19 presents

the VIF for each predictor in the model.

While late evening login time for task execution showed a slight non-significant difference in

time of entry by the two groups of completers and non-completers as depicted in (Ch 5, fig 6.2),

the time between sessions varied considerably with no respondents successfully completing

Table 6.19 VIF for Mental Alertness and Fatigue Management

Variable VIF

Mental Alertness 2.31

Fatigue Management 2.31

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within 12 days of start despite the SMS reminders. However, the variance between completers

and non- completers was significant.

Figure 6.12 Q-Q scatterplot for normality for Mental Alertness and Fatigue Management predicting Rate of Completion

Figure 6.13 Residuals scatterplot for homoscedasticity for Mental Alertness and Fatigue Management predicting Rate of Completion

Consequently, the time between sessions variable could not be used in the analysis, as it did not

vary either side of the required time. It is worth noting that the entire sample followed this trend.

The results of the linear regression model were not significant, F (2,217) = 2.70, p = .070, R2 =

0.02, indicating Mental Alertness and Fatigue Management did not explain a significant

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Table 6.20 Regression predicting Completion by Mental Alertness and Fatigue Mgmt

Variable B SE β t p

(Intercept) 53.90 5.51 0.00 9.79 < .001

Mental Alertness -0.11 0.14 -.08 -0.79 .433

Fatigue Management 0.26 0.13 0.21 2.03 .043

Note. F (2,217) = 2.70, p = .070, R2 = 0.02

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proportion of variation in Rate of Completion. Since the overall model was not significant, the

individual predictors were not examined further.

6.13 Results for Hypothesis 4 - More aviation students complete the course

Participants who were identified as aviation students would be more likely to complete the

attention recovery training and score higher on the self-regulating scales than others.

Hypothesis four was assessed using one independent sample t- test and one multivariate analysis

of variance (MANOVA). The independent sample t-test (table 21) was conducted first and was

designed to examine whether the mean of Rate of Completion was significantly different between

the aviation candidates and non-aviation candidates, as grouped and based on their flying

experience. One group consisted of participants with zero years of flying experience, and one

group consisted of participants with one or more years of flying experience. Prior to the analysis,

the assumptions of normality and homogeneity of variance were assessed.

A Shapiro-Wilk test was conducted to determine whether Rate of Completion could have been

produced by a normal distribution. The results of the test were significant, W = 0.90, p < .001,

suggesting that Rate of Completion was unlikely to have been produced by a normal distribution,

thus normality cannot be assumed. However, consistent with the Central Limit Theorem the

mean of any random variable will be approximately normally distributed as the sample size

increases, therefore, with a sufficiently large sample size (n > 30), deviations from normality

would have negligible effect on the results. Levine's test was used to assess whether the

homogeneity of variance assumption was met, such that the variance of the dependent variable

was approximately equal in each group, represented by each combination of factor levels in the

independent variables. Levine's test F (1, 227) = 1.34, p = .248, indicated that the assumption of

homogeneity of variance was met.

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The result of the independent samples t-test (table 6.21) was not significant, t (227) = -0.76, p =

.447, suggesting that the mean of Rate of Completion was not significantly different between the

pilot and non-pilot groups.

A MANOVA was conducted to determine differences in Mental Alertness and Fatigue

Management. Because the scales of mental alertness and fatigue management are both subscales

of the self-regulation construct, they could be used together as the correlated series of dependent

variables in this MANOVA. In the analysis, the assumptions of the MANOVA were assessed and

normality was assessed using a Shapiro-Wilk test for each of the two dependent variables, while

homogeneity of variance and covariance matrices were assessed with Levine’s test and Box’s M

test, respectively.

The results of the Shapiro-Wilk test for Mental Alertness were not significant (W = 0.99, p =

.101), but were significant for Fatigue Management (W = 0.99, p = .022), given that the sample

size was large enough to reduce some of the effects of non-normality on the results.

Results of Levine’s test indicated that neither the Mental Alertness (p = .869) nor the Fatigue

Management variable (p = .091) had any significant violation of homogeneity of variance.

Similarly, Box’s M test did not indicate any significant degree of violation to the assumption of

homogeneity of covariance matrices (p = .370).

Table 6.21 Independent Samples t-Test for pilot experience by completion

Variable

Nil years pilot experience

Some year’s pilot experience

M SD M SD t p d

Rate of Completion 56.36 25.56 59.36 28.20 -0.76 .447 0.11

Note. Degrees of Freedom for the t-statistic = 227. d represents Cohen's d.

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Results of the MANOVA showed a significant difference between student pilots and non-pilot

students in terms of the two dependent variables (i.e., Mental Alertness and Fatigue Management)

at the p < .05 level F (2, 217) = 3.20, p = .043.

This finding

indicated that the difference could exist on one or both subscales. Additional testing was

conducted to identify where the pilot versus non- pilot differences lay. To accomplish this, two

ANOVAs were conducted, one for each dependent variable. (Mental Alertness and Fatigue

Management)

Analysis of variance for mental alertness. The first analysis of variance (ANOVA) was conducted to determine whether there were

significant differences in Mental Alertness based on placement in a pilot versus non-pilot group.

Prior to the analysis, the ANOVA assumptions were examined.

The Shapiro-Wilk test conducted for the MANOVA had previously indicated that any deviation

from normality in this variable was explainable by random chance, thus normality can be

assumed. Levine's test for equality of variance was used to assess whether the homogeneity of

Table 6.22 Analysis of Variance for Fatigue Management by Pilot Experience

Variable df SS MS F p η2

Pilot Experience 1 1722.03 1722.03 5.35 .022 0.02

Residuals 219 70548.89 322.14

Table: 6.22b Descriptive statistics for Mental Alertness by Pilot Experience

Pilot Experience M SD n

Nil years pilot experience 55.93 17.79 161

One or more years’ pilot experience 62.21 18.38 60

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variance

assumption was met. The result of Levine’s test was not significant when examining this variable

alone, F (1, 219) = 0.09, p = .761, indicating that the assumption of homogeneity of variance was

met for the first Anova. The second ANOVA was conducted to determine whether

there were significant differences in Fatigue Management based on placement in pilot versus non-

pilot group. Prior to the analysis, the ANOVA assumptions were examined. The overall model

was significant at the 95% confidence level, F (1, 219) = 5.35, p = .022 (Table 6.22b), indicating

that there were significant differences in Mental Alertness based on group placement in either the

pilot or non-pilot group. Examination of group means showed that participants in the group with

one or more years of flying experience had on average significantly higher levels of Mental

Alertness. The means and standard deviations are presented in Table 6.24. Although the overall

model was significant, post-hoc testing was not conducted. Since all independent variables in the

model had only two factor levels, pairwise testing would not contribute to the analysis.

Analysis of variance for fatigue management.

The Shapiro-Wilk test conducted for the MANOVA previously indicated that the distribution for

this variable was unlikely to have been explainable by random chance (W = 0.99, p = .022).

Figure 6.14 Means of Rate of Completion by Pilot Experience.

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However, as per the CLT the sample size was large enough to reduce some of the effects of non-

normality on the results. The result of Levine's test was therefore not significant when examining

this variable alone, F (1, 218) = 2.13, p = .146, indicating that the assumption of homogeneity of

variance was met for the ANOVA.

The overall model was significant at the 95% confidence level, F (1, 218) = 6.08, p = .014 (Table

6.23), indicating that there were significant differences in Fatigue Management by student pilots.

Examination of the group means indicated that, on average, participants in the group with one or

more years of pilot experience had significantly higher levels of Fatigue Management skills than

those in the group with no aviation experience.

Means and standard deviations are presented in Table 6.24. Although the overall model was

significant, posthoc testing was not conducted. Since all independent variables in the model had

only two factor levels, pairwise testing would not contribute additional information to the

analysis.

Table 6.23 Analysis of variance for Fatigue Management by Pilot Experience

Variable df SS MS F p η2

Pilot Experience 1 2260.80 2260.80 6.08 .014 0.03

Residuals 218 81012.24 371.62

Table 6.24    Descriptive statistics Fatigue Management by Pilot Experience 

Pilot experience  M  SD  n 

Zero years Pilot experience  44.59  18.23  160 

Some years Pilot experience  51.79  21.84  60 

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6.14 Contribution of Gender to the study.

Gender differences were not specifically hypothesised and included in the study. However, the

data suggests a significant contribution by that variable and are worth noting. Self-Regulation

differences as measured by the independent variables of mental alertness, fatigue management

and perceived workload gender differences were evident in the data, which was assessed using a

multiple linear regression with the three predictor variables, but also included the control variable

of gender. By controlling for gender, gender-based differences were parsed out, and all

participants could be treated as equal in terms of gender. This allowed the analysis to identify

whether gender-based differences explained more of the variance in study completion rates than

the independent variables of mental alertness, fatigue management, and perceived workload

strain. The 'Enter' variable selection method was chosen for the linear regression model.

Normality was assessed through visual examination of a Q-Q scatterplot. Homoscedasticity was

similarly assessed by plotting the model residuals against the predicted model values. Fig 6.16

presents the Q-Q scatterplot of the model residuals. Fig 6.17 presents the scatterplot of predicted

values and model residuals. A slight deviation from the hypothetical quantiles in the Q-Q plot,

indicated that the assumption of normality may not have been met for this analysis and the results

should be interpreted cautiously. Variance Inflation Factors (VIFs) to detect the presence of

multicollinearity between predictors were calculated. All predictors in the regression model had

variance inflation factors (VIF) less than 5, indicating no cause for concern (Table 6.26). The

results of the linear regression model were not significant, F (4,171) = 2.26, p = .064, R2 = 0.05

(table 6.27), indicating the variables of Gender, Mental Alertness, Perceived Workload Strain,

and Fatigue Management did not explain a proportion of the Rate of Completion. Because the

model was not significant, individual predictors were not assessed.

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Note. F (4,171) = 2.26, p = .064, R2 = 0.05

6.15 Gender bias in task completion

Table 6.26 VIF for Gender, Mental Alertness, Workload Strain and Fatigue Management

Variable VIF

Gender 1.02

Mental Alertness 2.25

Fatigue Management 2.27

Perceived Workload Strain 1.18

Table 6.27 Analysis of variance for Gender, Mental Alertness, Fatigue & workload strain

Variable B SE β t p

(Intercept) 55.30 6.39 0.00 8.65 < .001

Gender 3.73 3.48 0.08 1.07 .286

Mental Alertness -0.12 0.14 -0.09 -0.85 .397

Fatigue Management 0.16 0.14 0.13 1.20 .231

Perceived Workload Strain 0.20 0.11 0.15 1.85 .066

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Extending the exploration of the Gender variable with the same statistical treatments as for the

hypothesis, a multiple linear regression analysis was conducted to assess whether a relationship

existed between Gender, Mental Alertness, Fatigue Management, and Perceived Workload Strain

and Overall Cognitive Abilities as measured by short term memory, choice reaction speed and

executive functioning. By controlling for gender, gender-based differences were parsed out, and

all participants could be treated as equal in terms of gender. This allowed the analysis to identify

whether gender-based differences explained more of the variance in completion rates than the

independent variables of mental alertness, fatigue management, and perceived workload strain.

The 'Enter' variable selection method was chosen for the linear regression model.

The assumption of normality was assessed by visual examination of a Q-Q scatterplot. The

assumption of homoscedasticity was assessed by plotting the model residuals against the

predicted model values. Fig 6.18 presents a Q-Q scatterplot of the model residuals. Fig 6.19

presents a scatter plot of predicted values and model residuals. Based on examination of the two

plots, the assumptions of normality and homoscedasticity were met. Variance Inflation Factors

(VIFs) were calculated to detect the presence of multicollinearity between predictors. All

Figure 6.16 Q-Q scatterplot for normality for Gender, Mental Alertness, Perceived Workload Strain, and Fatigue Management predicting Rate of Completion

Figure 6.17 Residuals scatterplot for homoscedasticity for Gender, Mental Alertness, Perceived Workload Strain, and Fatigue Management predicting Rate of Completion

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predictors in the regression model have satisfactory variance inflation factors (VIF) less than 5.

Table 6.28 presents the VIF for each predictor in the model.

Table 6.28 VIF for Gender, Mental Alertness, Fatigue Management, and Perceived Workload Strain

Variable VIF

Gender 1.03

Mental Alertness 2.07

Fatigue Management 2.21

Perceived Workload Strain 1.18

Figure 6.18. Q-Q scatterplot for normality for Gender, Mental Alertness, Fatigue Management, and Perceived Workload Strain predicting Overall Cognitive Performance

Figure 6.19. Residuals scatterplot for homoscedasticity for Gender, Mental Alertness, Fatigue Management, and Perceived Workload Strain predicting Overall Cognitive Performance

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Note. F (4,170) = 1.31, p = .269, R2 = 0.03

The results of the linear regression model were not significant, F (4,170) = 1.31, p = .269, R2 =

0.03, indicating Gender, Mental Alertness, Fatigue Management, and Perceived Workload Strain

did not explain a significant proportion of variation in Overall Cognitive Abilities scores. Since

the overall model was not significant, the individual predictors were not examined further. Table

6.29 summarizes the results of the regression model.

(Intercept) 62.16 5.56 0.00 11.18 < .001

Gender: Male 4.37 3.03 0.11 1.44 .152

Mental Alertness -0.19 0.12 -0.17 -1.59 .114

Fatigue Management 0.16 0.12 0.15 1.37 .174

Perceived Workload Strain -0.04 0.09 -0.04 -0.43 .669

Table 6.29 Regression predicting Cognitive Performance on Gender, Mental Alertness, Fatigue Management and Perceived Workload Strain.

Figure: 6.19 Total Group Report of RART Efficacy

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6.16 Efficacy of the rapid ART Technique by gender

While reports by participants of the efficacy of the rART training were independent of study

predictors, total group result showed an overall positive trend in utilisation with corresponding

reports of ease of use of the technique (Fig 6.20). Efficacy of the technique, reported as having

been extended into other aspects of participant daily activities was seen to be near equal in number

for females and males, with a total group mean of 59.4 and with females reporting a slightly

higher level of efficacy and utilisation than males.

Figure: 6.20 Efficacy of rART by Gender

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7. DISCUSSION

Introduction Study Objectives The study had two objectives and four testable hypotheses to explore the rapid attention recovery

technique as a Self-Regulation skill and as a means for recovery of attention. . The first objective

of the study involved the investigation of the efficacy of a rapid attention recovery technique as

a SR skill when subject to fatigue, preoccupation and distraction. The second, investigated the

efficacy of delivering that training technique unsupervised and online.

It was emphasised in this study that human error continues to be the major contributor to incidents

across all industries and the reason for the non-achievement of zero-harm in organisations. While

human error and fatigue specifically, have received considerable emphasis and are now well

accepted as a category of fault, little or none of the insights developed in the social and neural

sciences regarding self-regulation have emerged to influence the way organisations prepare

people for their employment. A growing trend has emerged in the education industry which has

recently seen the selective implementation of MT (mindfulness training) as preparatory training,

offered as an option in some secondary schools and to medical school graduates. SR (self-

regulation) also has a specific and crucial application in the healthcare industry due to the threat,

vulnerability and temporal constraints that exists in those environments. SR training to maintain

situational awareness, attention to the task and responsiveness to threat, requires understanding

of the mechanism involved in the training. Meditation for example, long identified as a

contemplative rather than as an ‘action-based’ solution has tended to be ignored in western

cultures. The rART (rapid attention recovery technique) study in utilising a different mechanism

works to achieve a similar and more rapid effect. A significant consideration in the study also

related to what it was that was to be improved, what universal markers could be advanced,

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measured and assessed as indicators for achievement, for example what behaviours do you see

that shows the rArt is working and effective? Since for many the training medium is important

for engagement and retention, this study evaluated the online medium as the vehicle for such

training.

In the preceding chapters the behavioural markers for performance were identified as a standard

for the achievement of action and a target for subsequent testing. A valid neurologically based

underlying mechanism for the expression of those was proposed in terms of an actionable process

of SR following the Hofmann, Endsley and elements of the three attention models discussed in

chapter two.

Training modalities and psychophysiological correlates were examined and compared for their

utility and efficacy resulting in one universal and rapid means to activate attention. Achieved

through a neurophysiological device acting to clear mental space to allow for a clearer focus and

increased responsivness. Comparison was made with MT (mindfulness training) and CB

(cognitive -behavioural) methods as having similar objectives through the process of meditation

and diaphragmatic breathing in one and improving coping through symantic clarification in the

other. Other more conventional training approaches by comparison had less certainty of effect

and a much slower rate of achievement. They were also subject to the same interruptions that they

were seeking to overcome, ie., forgetting to use a checklist, and could only be implemented after

the fact of cognitive failure, when there was evidence of failure, rather than as preventative action.

The Study Background

The second objective of this study was in delivering the training unsupervised and in a naturalistic

setting, competing with the distractions and fatigue that are an inevitable part of every students

life. The study was the second investigation of the rART mechanism (Rosenweg, 2006), the first

having been a small pilot-study with 32 police candidates and chemical plant operators, in the

disciplined peace of a classroom setting with face-to-face instruction and personal follow-up. The

difference in engagement between the two applications was subsequently evident in the high

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attrition rate and variable attendance in participation in the online study. The unsupervised online

context was expected to provide the means to test the the model with the least input by an

instructor.

Training that allows the individual to learn to recover their presence of mind under stress or to do

so without further reminders, has favoured action environments in which development was

assumed to go hand in hand with application and practice, rather than contemplation. How to

improve SR, has become more formal and scientific, through the exploration of brain-states and

the neural networks involved. As with the Tang, Rothbart & Posner, (2012) studies utilising

Posner’s attention network theory to better understand the Mindfulness technique in

development. While conventional SR training methods may be used to regulate behaviour and

help to ensure achievement for the individual, in the rART study the recovery technique is

proposed as an instantaneous method, triggered by an operant to unload the persons stressed or

distracted mental-state and direct their full attention to the problem. A mechanism suitable for

troubled times.

Comparison of the technique has been made with other methods and training modalities,

including Cognitive Behavioural methods and Mindfulness training with which it shares

diaphragmatic breathing in the establishment phase. The mechanism necessarily diverts from

meditation in using the attention network theory and instrumental conditioning. Other well

developed cognitive behavioural, meditative or ecologically oriented measures, as described in

Kaplan’s ART theory (Kaplan & Berman, 2010) have demonstrated the validity and viability of

adding the management of mental-state to more fully define self-management. Tang, Posner and

Rothbart have similarly applied Posner’s attention network theory to mental-state training and a

better understanding of the neurological or network basis contributing and enabling the

development of SR. Further identifying the action in the neural paths will help to sharpen and

guide future strategies and interventions.

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Controls and Structure in the Course

The proposed training exercise was anticipated to be offered to those who had an interest in

improving their self-regulation. That interest suggests an immediate bias in the sample. The

objective was to teach a SR skill to students attracted to the possibility of improving their attention

and resilience, but with no experience to understand the concept. Consequently, participants were

informed at start regarding the manner in which the study was to be conducted. Additionally the

course was structured to ensure that any variation due to the way it was done would not confound

the results. Systematic controls included a daily reminder via an SMS message to each participant,

access to the course was constrained to occur after a a certain time in the evening, just prior to

sleep and the computerised process ensured that the delivery was uniform and identical for all

participants. The one-way direction of the program prevented digression and distraction as all

participants could only go forward. The video instruction was timed to run for a certain time but

that could be replayed as often as required. A progressive question check inviting self-report of

engagement in the process ran after each session. Weekends were avoided as likely not available

due to social needs. While entry time of day to the sessions were very similar for most

participants, consistently late evening as suggested in the setup, the daily return to the study was

not as consistent with some participants doing a part then returning after a week or more to resume

the training. There was no exit interview for those terminating the course prior to completion,

hence it is not known why students departed. It could be surmised that some forgot to return,

captured by other tasks, or were faced with more seductive attractions at the late night time for

the exercise, such as sleep. Course experience of the design may not have been sufficiently

engaging, attractive or entertaining leaving the participant open to boredom and to downgrade

the study as a priority. Time between sessions were significantly greater than planned. A number

of students returned to the study after an absence of a week or two to resume the course before

eventually ceasing their participation altogether. It is not known if their return was based on a

waning sense of obligation to continue or whether they had experienced sufficiently positive

137

results after a few short sessions to encourage continuation, for a time. Benchmark test results

were obatained from 175 participants who terminated early and final full study results from an

additional 46 participants.

What difference was sought

A key difference sought in implementation, compared to mindfulness, CB, resilience training and

other metacognitive methods in general, was the rapidity of acquiring the self-regulating skill at

a basic level. The target condition being instant access to greater mental clarity and

responsiveness while in or together with, an energised mental state not crowded with unfinished

business. An ability that may be considered as more appropriate for ‘high personal demand’

scenarios and occupations and where sustained attention was required. Although an optimum

training schedule was not defined in this study and no measure or grade of achievement was

obtained, it appeared that the short 10 minutes per day for 10 days’ practice of the rART was

sufficient for some, with participants reporting a 65% degree of efficacy and presumably

satisfaction with the results based on report of use of the technique elsewhere.

At the theoretical level, SR helps to shape the individual’s choice of coping style, which in turn

influences the individual’s resilience or recovery and related capacity for mental effort. In this

respect, the development of SR in childhood has the potential capacity to inoculate the person in

later life to serious stressors that might otherwise result in problem behaviours. The pivotal issue

being the acquisition of mental control, rather than experience the trauma of the unprepared. SR

invoked by a simple operant, suggests the operation of an explicit choice. In this respect, SR

should be distinguished from self-control. While self-control has a similar and limited capacity

to reduce disruptive behaviours, its focus is significantly more directed to repression of impulse

rather than in freeing up cognitive capacity and may be counter-productive. SR is an executive

process directing behaviour into the direct-action form of coping and mental state as characterised

by Monet & Lazarus (1977). As an executive function, SR techniques with the ability to remove

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preoccupations and worry thinking will reinforce cognitive resilience, the ability to recover from

adversity or overload and to continue to function as required.

Study Outcomes The study attracted some 400 students in total, of whom 228 initiated a log on and progressed to

the baseline survey stage. Of those, 175 completed the baseline test and partially completed the

training, prior to relinquishing their involvement. This left 46 students in the 18-55 age range

with a gender and flying experience distribution of 9 pilots amongst 25 males and 6 pilots amongst

21 females, with n=31 (two thirds) of general non-pilot students in the sample, completing the

course. A nominal criterion was used in the study as the cutting score for the mental alertness and

fatigue management scales serving as the baseline criteria for comparative purposes. When the

experimental group were compared with the development group and a further industrial sample

of drivers (total n = 1129), the experimental group mean test performance was found to be

between 50-70% of other groups relative to the criterion previously established empirically for

safe behaviour in industrial settings. The significant difference between the samples confirming

less well developed self-regulation skills in the experimental group.

Control Group A control group (n=61) was established with a like group of trainee pilots, at another university.

Baseline tests were completed as required prior to their engaging in a traditional Resilience

training course conducted at the university. The resilience course involved emotion management

and development of self-awareness, coupled with semantic reframing of critical situations to build

a strategy against personal vulnerabilities. Management of the control group was subsequently

unsuccessful, their contribution to the study was negated as a comparison to the rART technique

due to lack of data with respect to the Resilience course outcomes.

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Outcomes with respect to the first hypothesis were partially met. It was expected that participant

mental alertness and their perception of workload strain would result in greater self-regulation of

their fatigue levels and sleep behaviour. That is, that all three measures would show a logical

correlation. If coping with personal strain was low then a corresponding lower fatigue

management score would be achieved and a lower alertness result. The implication suggesting

that poor self-regulation of fatigue resulted in functional decrements on alertness. This aspect was

evident in the female sample but only slightly mirrored in the male sample (fig 6.4). In both

groups scores on mental alertness and management of fatigue were well below normative

graduate and adult samples indicating less maturity in coping overall. In a future study of the

same dimensions an extended measure of coping styles would help to explain what kind of coping

the participants had used to manage their stress. For example, was it addressed directly or was it

escaped as in the Cosway, Endler, Sadler & Deary, (2000) model of coping in which the coping

options included task related, emotional, avoidance, distractive or social behaviours. Except for

the option of ‘task related’ the remainder of the options are clearly avoidance measures consistent

with Monat & Lazarus (1977) theory of direct action and intrapsychic coping. Pointing to the

tendency for those with intrapsychic tendencies to tolerate their condition and discomfort and

remain unresolved more than others, thereby accumulating stress.

The second hypothesis also counted on proactive behaviour amongst the students and was

partially supported. The hypothesis being that those reporting greater mental alertness and the

management of their fatigue would also tend to be alert to utilising the rART technique elsewhere

in their academic experience. They would report efficacy of the rART by transfer and utilisation

of the attention recovery technique to their daily activities. In the event a larger number (M =

65%) of those completing the training reported greater efficacy of the rART in doing so, however

that outcome measure was not associated as predicted by their mental alertness and fatigue

management scores.

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The third hypothesis was not supported. It detailed that participants, who reported higher mental

alertness and fatigue management, were less likely to be disrupted and hence more likely to

adhere to instructed session times and complete the course within the allocated time, was not

found. A significant difference existed between course completers and non-completers (fig 6.2),

on both study entry and completion. However, there were no significant score differences on the

self-regulating scale of mental alertness and fatigue management to predict that outcome.

Similarly, there were no specific attributes in the demographics of the two groups that would

differentiate them. The data showed just as many males and females completed the course,

removing a possible gender factor. The finding that females scored lower with respect to the

perceived strain of their academic workload, than males, had no significant effect on any of the

measures. The strain may have been a factor in participating in the study and account for the

slightly greater appreciation of the efficacy of the rART method reported by females.

The fourth hypothesis was not found, it was expected that those having started flying training,

would show greater interest in developing the rART SR skill and complete the course. That

difference was not seen in the data, as some pilots also terminated their participation early in the

study. The hypothesis was not found to be true and participants who were identified as aviation

students were no more likely complete the attention recovery training than others. While the pilots

did score significantly higher on the self-regulating scales than others, that was not translated to

completing the course.

Participants provided a near uniform agreement of the ease of the method. The baseline test was

administered as a means to assess current SR skills, before the training sessions. Participants were

instructed to practice the rapid ART (SR) technique for ten minutes just prior to sleep at night. It

was considered that the two major distractions of end-of-day-fatigue and possible reluctance due

to the temptation of alternative activities (ie, late night movie on TV) would provide sufficient

natural fatigue and hedonic resistance to tempt avoidance of what would seem an arduous activity,

testing the participants resolve to continue and complete the training sequence.

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Participant behaviour

The study design and delivery was an unsupervised and online course to augment self-regulation

skills with a technique to rapidly recover attention. While encouraging, results were obtained on

several aspects leading towards a take-up of the skill by participants, a number of other study

management issues and the research design prevented a fuller exploration of the technique. The

study attracted an initially large number of interested volunteers amongst the student population

at the start of the academic year. That most participants were in their second year of a course

suggested that their perception of strain and feelings of the need to improve their mental output

and effort was likely to have been driven by their academic experience to date. However, because

a large number also departed the study after the benchmark testing, suggested that the online

training while convenient, may not have been well enough explained. Alternatively, the brevity

of the process may have disguised the potential in the exercises. By attracting a large number to

the study revealed a desire by students for a means to cope and perform better in their courses or

in general. Student conduct within the study had shown that their SR skills in the achievement of

the study protocoles were quite marginal and evidence of a conscientious approach was lacking

given the generally spasmodic, intermittent participation and final termination before completion

by some 78% of the enrolled group. When these aspects were considered in comparison with the

observations of the Artino et al (2010) study and the early classroom based pilot study

(Rosenweg, 2006), the importance of a social context became apparent, it seemed likely that the

absence of a person (teacher) in the process contributed to the disengagement. Another aspect of

the study distancing rather than engaging participants concerned the ethics requirement

demanding anonymity for students, which also meant that there was no way to personalise any

one participants activity or progress with feedback or acknowledgment. A future study in this

direction could remedy that aspect by personalisng the relationship, for example, with a

welcoming letter and progressive acknowledgement of progress.

142

Analysis of the final retention rate indicated the largest number of early terminations or departures

occurring between the completion of the baseline test component and the first training session.

The retention and completion rate for pilots was 25.4% and 23.7% for non-pilots. Being a pilot

did not predict completion. No exit questions were provided to participants in the anonymous

study and no pattern was seen in the data. Relinquishing the training at that incomplete stage,

suggests that having been attracted to the study offering training in a SR technique, then having

inspected the first training session - the technique had no personal appeal to those seeking a

different method of SR. Later terminations may possibly have been due to boredom with an oft-

repeated procedure. A third possibility may have been the lack of positive results emanating from

a poorly executed attempt at the training procedure, as it had become known from the earlier

pilot-study with police academy candidates and chemplant operators that some people had

difficulty in achieving diaphragm breathing. Another possible reason for withdrawal is

discouragement when no immediate correction or feedback was available to help achieve a level

of mastery. Artino et al had found a further important aspect in a study with an aviation training

group, that involved their perception of the value of the training (Artino & Berman, 2010),

disengagement occurring where the training process did not seem to add any benefit to what

participants were doing. In conventional training devaluation of content can occur because of a

confused message, poor material and sometimes due to a defensive reaction to an inability to

acquire or understand the subject matter.

Perceived ‘mental demand’ of daily tasks (derived from the NASA TLX) was reported with

female students tending to report greater strain than males, however, this difference did not appear

to have had any impact on reports of the efficacy and transfer of the rART skill to activities

outside of the study with female and male participants reporting equal efficacy and utilisation.

The baseline measures of mental alertness and management of fatigue at commencement did not

predict subsequent retention rates for those departing the study before completion, even though

the SR relationship between the two behavioural measures in the baseline test at start were highly

143

correlated. Results for both genders showed a lesser ability to manage fatigue resulting in greater

perceived workload strain. Conversely, increased mental alertness and decreased perception of

workload strain together benefited from the better management of fatigue. The two SR scales of

mental alertness and management of fatigue did not however predict the perceived efficacy of the

rART technique by those completing the sequence of training, but by inference the completion

of the training could infer at the least a greater interest in acquiring additional SR skills.

Whilst efficacy of the rART technique was reported by the sample that had completed the

training, it was not predicted by the baseline measures of those who had left the study before

completion, or in the progressive study retention rates. A lack of confirmation of that data could

signify that SR has many masters, not just alertness and fatigue. If the individual feels immune

or has become numb with fatigue (as with prolonged high level exposure) then normal indicators

that signal a needed change may become unreliable or defensively suppressed until sleep was

available. A similar state to continuing to drive late night while experiencing short duration head-

snapping micro-sleeps. A significant difference was shown, in which pilots showed greater ability

than others, to maintain their mental alertness and manage fatigue.

Study methodology and issues The attractiveness of the study and the possibility of improving cognitive ability was attractive to

more than 400 students opening the possibility that a re-engineered approach to the study could

improve retention for the training. While not a universal finding, the research by others pointing

to the social benefits of a collaborative and an interactive format such as that is found in classroom

teaching, suggests a need for a closer and more intensive contact with participants to achieve the

needed engagement. This was not possible in this study as participants were anonymous and the

training remotely administered. In parallel, the study did not have a programmed feedback system

such that success or failure with the training sessions and activities could be assessed and

addressed with further inputs. Another aspect that relates to perceived ‘value’ involves the brevity

of the training sessions, with no intellectual content to support the cause and effect of the

144

technique as a self-regulating mechanism, the credibility of the process may not have been clear

to participants. The poor retention rate and disregard of the training schedule, except for the time

of day in which it needed to be done, for example, extending for some participants to two to four

weeks for the planned 12-day schedule. Structural aspects of the study involving the presentation

of the baseline tests at the commencement of training may have been better explained or displaced

to another stage, perhaps offered progressively with the training sessions. That would have

obviated the need for the challenge questions and promoted the training as something other than

a school exam. In this study, the quality of the presentation was mostly acceptable and there were

no faulty programming issues experienced to discourage participants.

At the research design level, the study achieved a partial support of several of the hypothesis. The

use of the established scales to measure the extent of SR with behavioural evidence of self-

maintenance could potentially have limited the range of the SR construct despite their statistical

accuracy and reliability. To assess actual change or efficacy of the technique two different forms

of validation could be used. The first a comparative study with a similar method to establish

superiority against other techniques and secondly an experimental before and after design to

deliver accurate metrics regarding acquisition and mastery.

The absence of a control group in the classical sense made generalisability impossible to

determine. Using mindfulness training as a control was not deemed to be viable as that technique has no

comparative recovery mechanism, other than the breathing, that can be used to change mental state in a

lengthy process over some time. Secondly, proving experimentally that the switch in mental state with a

nasal sniff produces a significant change in mental state was not able to be conducted by remote online

administration. A means of distinguishing the calmness of mental state in a before and after assessment of

cortisole as a stress indicator may have been reliable even though the main effect was deemed to be the

action of the Amygdala. Anecdotal evidence of extended use of the rART method was used to identify

successful acquisition of the rART technique.

145

Future Directions The study pointed to a more critical experimentally based investigation of the neurophysiology

of the rART technique. The action of taking a deep breath to feel better and the sniff to mentally

direct attention is a common experience amongst sports people and counsellors with their clients.

The difference between the olfactory path of the sniff and the multi-sensory activation of the

startle appears to be the threat context of the startle and the directing action of the amygdala.

Location of the calm mental state memory accessed by the operant sniff, in the brain and whether

that has a consistent hemispherical location could also have implications for mental health,

although this was not able to be explored within the constraints of the current study. A

confirmation that the threat is replaced by the calmness obtained by the initially established

diaphragmatic breathing could have implications for a simple means to stabilise mental-state

under stress. Of necessity a more conclusive outcome would need to be obtained such that testing

with objective tests of attention, reaction time and cortisol secretion could provide validation that the method

improves calmness, attention and self-regulation.

146

8.0.0 REFERENCES

Ackerman, P. L., & Kanfer, R. (2009). Test length and cognitive fatigue: an empirical examination of effects on performance and test-taker reactions. J Exp Psychol Appl, 15(2), 163-181. doi:10.1037/a0015719

Adair, J. G. (1984). The Hawthorne Effect a Reconsideration of the Methodological Artefact. Journal of Applied Psychology, 69(2), 334-345.

Adams, R. J. (1993). How Expert Pilots Think: Cognitive Processes in Expert Decision Making. National Technical Information Service Springfield Virginia 22161.

Aldao, A., Nolen-Hoeksema, S., & Schweizer, S. (2010). Emotion-regulation strategies across psychopathology: A meta-analytic review. Clin Psychol Rev, 30(2), 217-237. doi: 10.1016/j.cpr.2009.11.004

Allen, M. S., Jones, M., McCarthy, P. J., Sheehan-Mansfield, S., & Sheffield, D. (2013). Emotions correlate with perceived mental effort and concentration disruption in adult sport performers. Eur J Sport Sci, 13(6), 697-706. doi:10.1080/17461391.2013.771381

Amazon. (2016). Aromatic ammonium products for sale on the Internet. Amazon.com Accessed Jan 10, 2017

Antoniadis, S., Passauer-Baierl, S., Baschnegger, H., & Weigl, M. (2014). Identification and interference of intraoperative distractions and interruptions in operating rooms. J Surg Res, 188(1), 21-29. doi: 10.1016/j.jss.2013.12.002

Arch, J. J., & Craske, M. G. (2006). Mechanisms of mindfulness: emotion regulation following a focused breathing induction. Behav Res Ther, 44(12), 1849-1858. doi: 10.1016/j.brat.2005.12.007

Armstrong, H. G. (1936). A special form of functional psychoneuroses appearing in airplane pilots. Journal of the American Medical Association, 106(16), 1347-1354.

Arthur, W., Jr., Bennett, W., Jr., Edens, P. S., & Bell, S. T. (2003). Effectiveness of training in organizations: A meta-analysis of design and evaluation features. Journal of Applied Psychology, 88(2), 234-245. doi:10.1037/0021-9010.88.2.234

Artino, A. R. (2009). Online learning: Are subjective perceptions of instructional context related to academic success? The Internet and Higher Education, 12(3-4), 117-125. doi: 10.1016/j.iheduc.2009.07.003

Ashcraft, K. L. (2005). Resistance Through Consent? Occupational Identity, Organizational Form, and the Maintenance of Masculinity Among Commercial Airline Pilots. Management Communication Quarterly, 19(1), 67-90. doi:10.1177/0893318905276560

ATSB. (2004). Survey of common flying errors. ATSB Transport Safety Report Aviation Research and Analysis Report.

ATSB. (2007). Australian Safety Review. ATSB Transport Safety Report Aviation Research and Analysis Report.

1 4 7

A T S B. ( 2 0 1 4). A vi ati o n O c c urr e n c e St atisti cs 2 0 0 4- 2 0 1 3. A ustr ali a n Tr a ns p ort S af et y B ur e a u( Fi n al).

B a ert, S., C asi er, A., & D e R a e dt, R. ( 2 0 1 2). T h e eff e cts of att e nti o n al tr ai ni n g o n p h ysi ol o gi c al str ess r e c o v er y aft er i n d u c e d s o ci al t hr e at. A n xi et y, Str ess, & C o pi n g, V ol. 2 5( N o. 4), 3 6 5 3 7 9. d oi: 1 0. 1 0 8 0/ 1 0 6 1 5 8 0 6. 2 0 1 1. 6 0 5 1 2 2

B al u R, L. P., Str o w bri d g e B W. . ( 2 0 0 4). P h as i c sti m uli e v o k e pr e cis el y ti m e d s pi k es i n i nt er mitt e ntl y dis c h ar gi n g mitr al c ells. N e ur o p h ysi ol o g y , 9 2, 7 4 3- 7 5 3.

B ar k er, L. M., & N uss b a u m, M. A. ( 2 0 1 1). T h e e ff e cts of f ati g u e o n p erf or m a n c e i n si m ul at e d n ursi n g w or k. Er g o n o mi cs , 5 4( 9), 8 1 5- 8 2 9. d oi: 1 0. 1 0 8 0/ 0 0 1 4 0 1 3 9. 2 0 1 1. 5 9 7 8 7 8

B arr ett, K., Br o o ks, H., B oit a n o, S., B ar m a n, S. ( 2 0 1 0). G a n o n g's R e vi e w of M e di c al P h ysi ol o g y ( 2 3 e d). M c Gr a w Hill M e di c al, p 5 9 3.

B artr a m, D. ( 1 9 9 5). T h e pr e di cti v e v ali dit y of t h e E PI a n d 1 6 P F f or milit ar y fl yi n g tr ai ni n g. J o ur n al of O c c u p ati o n al a n d Or g a niz ati o n al Ps y c h ol o g y , 6 8( 3), 2 1 9.

B a u m eist er, R. F., G ailli ot, M., D e W all, C. N., & O at e n, M. ( 2 0 0 6) . S elf-r e g ul ati o n a n d p ers o n alit y: h o w i nt er v e nti o ns i n cr e as e r e g ul at or y s u c c ess, a n d h o w d e pl eti o n m o d er at es t h e eff e cts of tr aits o n b e h a vi or. J o ur n al of P ers o n alit y , 7 4( 6), 1 7 7 3- 1 8 0 1. d oi: 1 0. 1 1 1 1/j. 1 4 6 7- 6 4 9 4. 2 0 0 6. 0 0 4 2 8. x

B e ar m a n, C., P al et z, S. B. F., & Or as a n u, J. ( 2 0 0 9). Sit u ati o n al Pr ess ur e s o n A vi ati o n D e cisi o n M a ki n g: G o al S e d u cti o n a n d Sit u ati o n A v ersi o n. A vi ati o n, S p a c e, a n d E n vir o n m e nt al M e di ci n e , 8 0( 6), 5 5 6- 5 6 0. d oi: 1 0. 3 3 5 7/ as e m. 2 3 6 3. 2 0 0 9

B e h n c k e, L. ( 2 0 0 4). M e nt al S kills Tr ai ni n g f o r S p orts: A Bri ef R e vie w. At hl eti c I nsi g ht. T h e O nli n e J o ur n al of S p orts Ps y c h ol o g y , M ar c h, V 6. ( 1).

B ell a n d, K. M., Ols e n, C., & L a wr y, R. ( 2 0 1 0). C arri er Air Wi n g Mis h a p R e d u cti o n Usi n g a H u m a n F a ct ors Cl assifi c ati o n S yst e m a n d Ris k M a n a g e m e nt. A vi ati o n, S p a c e, a n d E n vir o n m e nt al M e di ci n e , 8 1( 1 1), 1 0 2 8- 1 0 3 2. d oi: 1 0. 3 3 5 7/ as e m. 2 5 3 9. 2 0 1 0

B er g er, A., K of m a n, O., Li v n e h, U., & H e ni k, A. ( 2 0 0 7). M ulti dis ci pli n ar y p ers p e cti v es o n att e nti o n a n d t h e d e v el o p m e nt of s elf-r e g ul ati o n. Pr o gr ess i n N e ur o bi ol o g y , 8 2( 5), 2 5 6-2 8 6. d oi: 1 0. 1 0 1 6/j. p n e ur o bi o. 2 0 0 7. 0 6. 0 0 4

B er m a n, M. G., J o ni d es, J., & K a pl a n, S. ( 2 0 0 8) . T h e c o g niti v e b e n efits of i nt er a cti n g wit h n at ur e. Ps y c h ol S ci, 1 9( 1 2) , 1 2 0 7- 1 2 1 2. d oi: 1 0. 1 1 1 1/j. 1 4 6 7- 9 2 8 0. 2 0 0 8. 0 2 2 2 5. x

B er n ar di, L., Sl ei g ht, P., B a n di n elli, G., C e n c etti, S., F att ori ni, L., W d o w c z y c S z ul c, J., L a gi, A. ( 2 0 0 1). Eff e ct of r os ar y pr a y er a n d y o g a m a ntr as o n a ut o n o mi c c ar di o v as c ul ar r h yt h ms- c o m p ar ati v e st u d y. Britis h M e di c al J o ur n al , 3 2 3 ( 2 2 – 2 9 D e c e m b er).

B er nst ei n, E. E., & M c N all y, R. J. ( 2 0 1 7). A c ut e A er o bi c E x er cis e H ast e ns E m oti o n al R e c o v er y Fr o m a S u bs e q u e nt Str ess or. H e alt h Ps y c h ol o g y , 1- 9, d oi: 1 0. 1 0 3 7/ h e a 0 0 0 0 4 8 2

Bi n d er, J. R., & D es ai, R. H. ( 2 0 1 1). T h e n e ur o bi ol o g y of s e m a nti c m e m or y. Tr e n ds C o g n S ci , 1 5( 1 1), 5 2 7- 5 3 6. d oi: 1 0. 1 0 1 6/j.ti cs. 2 0 1 1. 1 0. 0 0 1

Bl ais d ell, A. P. ( 2 0 0 8). C o g niti v e Di m e nsi o n of O p er a nt L e ar ni n g a n d M e m or y. A C o m pr e h e nsi v e R ef er e n c e.

Bl u m e nt h al, T. D. ( 2 0 1 5). St artl e R efl e x a n d H e alt h. 3 3 1- 3 3 7. d oi: 1 0. 1 0 1 6/ b 9 7 8- 0- 0 8- 0 9 7 0 8 6-8. 1 4 1 5 1- 0

B o u c h er, H. C. ( 2 0 1 1). S elf- k n o wl e d g e d ef e ns es t o s elf-t hr e ats. J o ur n al of R es e ar c h i n P ers o n alit y , 4 5( 2), 1 6 5- 1 7 4. d oi: 1 0. 1 0 1 6/j.jr p. 2 0 1 0. 1 2. 0 0 6

148

Bridger, R. S., Johnsen, S. A., & Brasher, K. (2013). Psychometric properties of the Cognitive Failures Questionnaire. Ergonomics, 56(10), 1515-1524. doi:10.1080/00140139.2013.821172

Broadbent, D. E. C., P. F.; FitzGerald, P.; Parkes, K. R.. (1982). The Cognitive Failures Questionnaire (CFQ) and its correlates. British Journal of Clinical Psychology, 21, 1-16.

Broverman, D. M., & Lazarus, R. S. (1958). Individual Differences in task performance under conditions of negative interference. Journal of Personality and Social Psychology, 26(1), 94. doi:10.1111/1467-6494.ep8931019

Brown, K. W., Goodman, R. J., & Inzlicht, M. (2013). Dispositional mindfulness and the attenuation of neural responses to emotional stimuli. Soc Cogn Affect Neurosci, 8(1), 93-99. doi:10.1093/scan/nss004

Brown, R. E., Basheer, R., McKenna, J. T., Strecker, R. E., & McCarley, R. W. (2012). Control of sleep and wakefulness. Physiol Rev, 92(3), 1087-1187. doi:10.1152/physrev.00032.2011

Campbell, J. S., Castaneda, M., & Pulos, S. (2009). Meta-Analysis of Personality Assessments as Predictors of Military Aviation Training Success. The International Journal of Aviation Psychology, 20(1), 92-109. doi:10.1080/10508410903415872

Campbell, J. S., Moore, J. L., Poythress, N. G., & Kennedy, C. H. (2009). Personality Traits in Clinically Referred Aviators: Two Clusters Related to Occupational Suitability. Aviation, Space, and Environmental Medicine, 80(12), 1049-1054. doi:10.3357/asem.2491.2009

Capa, R. L., Audiffren, M., & Ragot, S. (2008). The interactive effect of achievement motivation and task difficulty on mental effort. Int J Psychophysiol, 70(2), 144-150. doi: 10.1016/j.ijpsycho.2008.06.007

Carmody, J., & Baer, R. A. (2008). Relationships between mindfulness practice and levels of mindfulness, medical and psychological symptoms and well-being in a mindfulness-based stress reduction program. J Behav Med, 31(1), 23-33. doi:10.1007/s10865-007-9130-7

Carretta, T. R. (2011). Pilot Candidate Selection Method. Aviation Psychology and Applied Human Factors, 1(1), 3-8. doi:10.1027/2192-0923/a00002

Carretta, T. R., Teachout, M. S., Ree, M. J., Barto, E. L., King, R. E., & Michaels, C. F. (2014). Consistency of the Relations of Cognitive Ability and Personality Traits to Pilot Training Performance. The International Journal Of Aviation Psychology, 24(4), 247-264. doi:10.1080/10508414.2014.949200

Carver, C. S., & Scheier, M. F. (2011). Self-regulation of action and affect. In K. Vohs & R. F. Baumeister (Eds.), Handbook of self-regulation: Research, theory, and applications (2nd ed., pp. 3–21). New York: Guilford.

CASA. (2015). Schedule 2 – Flight crew licensing competency standards. Civil Aviation Safety Authority.

CASA_Medical. (2016). Classes of medical certificates. Civil Aviation Safety Authority: Accesssed via (http://services.casa.gov.au/avmed/pilots/info/classes.asp) February 5, 2016.

Casner, S. M. (2009). Perceived vs. measured effects of advanced cockpit systems on pilot workload and error: are pilots' beliefs misaligned with reality? Appl Ergon, 40(3), 448-456. doi: 10.1016/j.apergo.2008.10.002

149

Casner, S. M., & Schooler, J. W. (2015). Vigilance impossible: Diligence, distraction, and daydreaming all lead to failures in a practical monitoring task. Conscious Cogn, 35, 33-41. doi: 10.1016/j.concog.2015.04.019

Casner, S. M., Geven, R. W., & Williams, K. T. (2012). The Effectiveness of Airline Pilot Training for Abnormal Events. Human Factors: The Journal of the Human Factors and Ergonomics Society, 55(3), 477-485. doi:10.1177/0018720812466893

Cattell, H. E. P., & Mead, A. D. (2008). The Sixteen Personality Factor Questionnaire (16PF). Accessed via (www.central.rcs.k12.tn.us/Teachers/cowartg/documents/16PFCattell.pdf) 150205, 135-159.

Cevik, M. O. (2014). Habituation, sensitization, and Pavlovian conditioning. Front Integr Neurosci, 8, 13. doi:10.3389/fnint.2014.00013

Chappelle, W., Thompson, W., Goodman, T., Bryan, C. J., & Reardon, L. (2015). The Utility of Testing Noncognitive Aptitudes as Additional Predictors of Graduation from US Air Force Air Traffic Controller Training. Aviation Psychology and Applied Human Factors, 5(2), 93-103. doi:10.1027/2192-0923/a000082

Cheny, K. (2005). The Id, Ego, and Superego: The Structural Model of Personality. Cited in O'Bryan, K. M. (2011). Freud's Ego in the Cockpit. Journal of Aviation/Aerospace Education & Research, 21(1), 9.

Chiesa, A., Calati, R., & Serretti, A. (2011). Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings. Clin Psychol Rev, 31(3), 449-464. doi: 10.1016/j.cpr.2010.11.003

Cicek, I., & Serres, J. L. (2014). Safe-to-Fly Test and Evaluation of Fatigue Research Study Test Devices. Aviation, Space, and Environmental Medicine, 85(4), 473-479. doi:10.3357/asem.3729.2014

Civelli, U. M., & Stoll, F. (1990). An Evaluative Study of the Defense Mechanism Test. United States Army Research Institute for the Behavioral and Social Sciences.

Cockshell, S. J., & Mathias, J. L. (2012). Test effort in persons with Chronic Fatigue Syndrome when assessed using the Validity Indicator Profile. J Clin Exp Neuropsychol, 34(7), 679-687. doi:10.1080/13803395.2012.668176

Cohen, G. L., & Sherman, D. K. (2014). The psychology of change: self-affirmation and social psychological intervention. Annu Rev Psychol, 65, 333-371. doi:10.1146/annurev-psych-010213-115137

Conway, G. E., Szalma, J. L., & Hancock, P. A. (2007). A quantitative meta-analytic examination of whole-body vibration effects on human performance. Ergonomics, 50(2), 228-245. doi:10.1080/00140130600980888

Cooper, C., & Kline, P. (1986). An evaluation of the DMT. British Journal of Psychology, 77, 19-31.

Cortese, B. M., Leslie, K., & Uhde, T. W. (2015). Differential odor sensitivity in PTSD: Implications for treatment and future research. J Affect Disord, 179, 23-30. doi: 10.1016/j.jad.2015.03.026

Costa, J. P. T., & McCrae, R. (1995). <Domains and Facets: Hierarchical Personality Assessment Using the Revised NEO Personality Inventory NEO-. Journal Of Personality Assessment, Lawrence Erlbaum Associates, Inc., 64(1), 21-50.

150

Cosway, R., Endler, N. S., Sadler, J., & Deary, I. J. (2000). The Coping Inventory for Stressful Situations: Factorial Structure and Associations with Personality Traits and Psychological Health Journal of Applied Biobehavioral Research, 5(2), 121-143.

Dahlström, N. (2008). Pilot training in our time ? use of flight training devices and simulators. Aviation, 12(1), 22-27. doi:10.3846/1648-7788.2008.12.22-27

Damos, D. L. (2014). Editor's Preface to the Special Issue on Pilot Selection. The InternationalJournal of Aviation Psychology, 24(1), 1-5. doi:10.1080/10508414.2014.860839

Dane, E. (2010). Paying Attention to Mindfulness and Its Effects on Task Performance in the Workplace. Journal of Management, 37(4), 997-1018. doi:10.1177/0149206310367948

Dawson, D., Chapman, J., & Thomas, M. J. (2012). Fatigue-proofing: a new approach to reducing fatigue-related risk using the principles of error management. Sleep Med Rev, 16(2), 167-175. doi: 10.1016/j.smrv.2011.05.004

DeCarlo, L. T. (1997). On the meaning and use of kurtosis. Psychological methods, 2(3), 292., 2(3), 292-307.

Defense-Jobs. (2015). Air Force Pilot (RAAF). defencejobs.gov.au/airforce/jobs/Pilot.

Dekker, S. (2011). The criminalization of human error in aviation and healthcare: A review. Safety Science, 49(2), 121-127. doi: 10.1016/j.ssci.2010.09.010

Delgado, L. C., Guerra, P., Perakakis, P., Vera, M. N., Reyes del Paso, G., & Vila, J. (2010). Treating chronic worry: Psychological and physiological effects of a training programme based on mindfulness. Behav Res Ther, 48(9), 873-882. doi: 10.1016/j.brat.2010.05.012

Dezfouli, A., & Balleine, B. W. (2012). Habits, action sequences and reinforcement learning. Eur J Neurosci, 35(7), 1036-1051. doi:10.1111/j.1460-9568.2012. 08050.x

Diehl, M., Semegon, A. b., & Schwarzer, R. (2006). Assessing Attention Control in Goal Pursuit a Component of Dispositional Self-regulation. Journal of Personality Assessment, 86(3), 306-317.

Dirken, G. R. (1983). Cognitive Tunneling: Use of Visual Information Under Stress. Perceptual and Motor Skills, 56, 191-198.

Dismukes, K. (2000). Rethinking Crew Error: Overview of A Panel Session. NASA Ames Research Center Moffett Field, CA.

Dismukes, K. (2006). Concurrent Task Management and Prospective Memory: Pilot Error as A Model For The Vulnerability Of Experts. Proceedings of The Human Factors and Ergonomics Society 50th Annual Meeting—2006.

Dismukes, R. K., & Berman, B. (2010). Checklists and Monitoring in the Cockpit: Why Crucial Defenses Sometimes Fail. NASA/TM—2010-216396, National Aeronautics and Space Administration Ames Research Center Moffett Field, California 94037.

Dodhia, R. M., & Dismukes, R. K. (2009). Interruptions create prospective memory tasks. Applied Cognitive Psychology, 23(1), 73-89. doi:10.1002/acp.1441

Dolgova, O., Ivaniuk, M., & Tukayev, S. (2013). Psychological resources air force pilots use for self-regulation. Aviation, 17(1), 30-32. doi:10.3846/16487788.2013.777221

Dongen, H. P. A., & Dinges, D. F. (2000). Circadian Rhythms in Fatigue, Alertness and Performance. In: M.H. Kryger, T. Roth & W.C.Dement (Eds.), Principles and Practice of Sleep Medicine. W. B. Saunders, Philadelphia, Pennsylvania., 20., 391-399.

151

DSMIV. (1994). Diagnostic and Statistical Manual Of Mental Disorders 4th Edition. American Psychiatric Association Washington, DC.

Edmonds, S. L., Brown, M. R., White, C., & Kirk, J. (2013). Impact of Electronic Monitoring and Hawthorne effect on HH compliance Rates. Poster presented at: APIC 2013 Annual Conference and International Meeting June 8 – 10, 2013 • Ft. Lauderdale, FL.

Eldar, S., & Bar-Haim, Y. (2010). Neural plasticity in response to attention training in anxiety. Psychol Med, 40(4), 667-677. doi:10.1017/S0033291709990766

Endsley, M. R. (1995). Toward a Theory of Situation Awareness in Dynamic Systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1), 32-64. doi:10.1518/001872095779049543

Endsley, M. R., & Garland, D. J. (2000). Pilot Situation Awareness Training in General Aviation. Proceedings of the Trioenniaal Congress of the International Ergonomics Association and the 44th Annual Meeting of the Human Factors and Ergonomics Society.

Engle-Friedman, M. (2014). The effects of sleep loss on capacity and effort. Sleep Science, 7(4), 213-224. doi: 10.1016/j.slsci.2014.11.001

Esch, T. (2014). The neurobiology of meditation and mindfulness. Meditation–Neuroscientific Approaches and Philosophical Implications, 153-173.

Esch, T., Fricchione, G. L., & Stefano, G. B. (2003). The therapeutic use of the relaxation response in stress-related diseases. Medical Science Monitor, 9(2), RA23-RA34.

Estrada, A., Kelley, A. M., Webb, C. M., Athy, J. R., & Crowley, J. S. (2012). Modafinil as a Replacement for Dextroamphetamine for Sustaining Alertness in Military Helicopter Pilots. Aviation, Space, and Environmental Medicine, 83(6), 556-567. doi:10.3357/asem.3129.2012

Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: attentional control theory. Emotion, 7(2), 336-353. doi:10.1037/1528-3542.7.2.336

Eysenck, M. W., Payne, S., & Derakshan, N. (2005). Trait anxiety, visuo-spatial processing, and working memory. Cognition and Emotion. 19, 1214–1228.

Fairclough, S. H., & Houston, K. (2004). A metabolic measure of mental effort. Biol Psychol, 66(2), 177-190. doi: 10.1016/j.biopsycho.2003.10.001

Fan, J., McCandliss, B. D., Fossella, J., Flombaum, J. I., & Posner, M. I. (2005). The activation of attentional networks. NeuroImage, 26(2), 471-479. doi: 10.1016/j.neuroimage.2005.02.004

Fantino, E., & Stolarz-Fantino, S. (2012). Operant Conditioning. Elsevier Inc. 749-756. doi:10.1016/b978-0-12-375000-6.00262-7

Feldman, J. L., Del Negro, C. A., & Gray, P. A. (2013). Understanding the rhythm of breathing: so near, yet so far. Annu Rev Physiol, 75, 423-452. doi:10.1146/annurev-physiol-040510-130049

Feuerhahn, N., Sonnentag, S., & Woll, A. (2012). Exercise after work, psychological mediators, and affect: A day-level study. European Journal of Work and Organizational Psychology, 23(1), 62-79. doi:10.1080/1359432x.2012.709965

152

Finkelstein, P., Wenegrat, B., & Yalom, I. (1982b). Large Group Awareness Training annurev.ps.33.020182. Ann. Rev. Psychol, 33, 515-539.

Fitzgibbons, A., Schutte, P., & Davis, D. (2000). Pilot Personality Profile Using the NEO-PI-R. Summary of Research Report NASA Langley Research Center Training Grant NGT-1-52193.

Folkman, S., Lazarus, R. S., Dunkel-Schetter, C., DeLongis, A., & Gruen, R. J. (1986). Dynamics of a stressful encounter: Cognitive appraisal, coping, and encounter outcomes. . Journal of Personality and Social Psychology, 50(5), 992-1003.

Fonagy, P., & Target, M. (2002). Early Intervention and the Development of Self-Regulation. Psychoanalytic Inquiry, 22(3), 307-335. doi:10.1080/07351692209348990

Ford, J., Henderson, R., & O'Hare, D. (2013). Barriers to Intra-Aircraft Communication and Safety: The Perspective of the Flight Attendants. The International Journal of Aviation Psychology, 23(4), 368-387. doi:10.1080/10508414.2013.834167

Fornette, M.-P., Bardel, M.-H., Lefrançois, C., Fradin, J., Massioui, F. E., & Amalberti, R. (2012). Cognitive-Adaptation Training for Improving Performance and Stress Management of Air Force Pilots. The International Journal of Aviation Psychology, 22(3), 203-223. doi:10.1080/10508414.2012.689208

Fraher, A. L., & Gabriel, Y. (2014). Dreaming of flying when grounded: Occupational identity and occupational fantasies of furloughed airline pilots. Journal of Management Studies, 51(6), 926-951.

Friese, M., Messner, C., & Schaffner, Y. (2012). Mindfulness meditation counteracts self-control depletion. Conscious Cogn, 21(2), 1016-1022. doi: 10.1016/j.concog.2012.01.008

Frissen, I., & Guastavino, C. (2014). Do whole-body vibrations affect spatial hearing? Ergonomics, 57(7), 1090-1101. doi:10.1080/00140139.2014.910611

Gallistel, C. R., & Matzel, L. D. (2013). The neuroscience of learning: beyond the Hebbian synapse. Annu Rev Psychol, 64, 169-200. doi:10.1146/annurev-psych-113011-143807

Gander, P. H., Mangie, J., van den Berg, M. J., Smith, A. A. T., Mulrine, H. M., & Signal, T. L. (2014). Crew Fatigue Safety Performance Indicators for Fatigue Risk Management Systems. Aviation, Space, and Environmental Medicine, 85(2), 139-147. doi:10.3357/asem.3748.2014

Gay, P. (1999). Sigmund Freud: Psychoanalyst. Cited in O'Bryan, K. M. (2011). Freud's Ego in the Cockpit. Journal of Aviation/Aerospace Education & Research, 21(1), 9.

Gaydos, S. J., Curry, I. P., & Bushby, A. J. R. (2013). Fatigue Assessment: Subjective Peer-to-Peer Fatigue Scoring. Aviation, Space, and Environmental Medicine, 84(10), 1105-1108. doi:10.3357/asem.3728.2013

Gendolla, G. H. E., Tops, M., Koole, S. L. (Eds). (2015). Handbook of biobehavioural approaches to self-regulation. Springer Science+Business Media New York.

Gilbert, C. (1999). Yoga and breathing. Journal of Bodywork and Movement Therapies, January.

Glass, L. L., Kirsch, M. A., & Parris, F. R. (1977). Psychiatric Disturbances Associated with Erhard Seminars Training A report of cases. American Journal of Psychiatry, 134(3), 245-247.

153

Goldin, P. R., & Gross, J. J. (2010). Effects of mindfulness-based stress reduction (MBSR) on emotion regulation in social anxiety disorder. Emotion, 10(1), 83-91. doi:10.1037/a0018441

Gomez-Pinilla, F. (2008). The influences of diet and exercise on mental health through hormesis. Ageing Res Rev, 7(1), 49-62. doi: 10.1016/j.arr.2007.04.003

Gomez-Pinilla, F. (2008). The influences of diet and exercise on mental health through hormesis. Ageing Res Rev, 7(1), 49-62. doi: 10.1016/j.arr.2007.04.003

Goodman, C. (1999). Factors Affecting the Decision to Eject. Flying Safety 55(3), 11-15.

Gore, R. K., Webb, T. S., & Hermes, E. D. A. (2010). Fatigue and Stimulant Use in Military Fighter Aircrew During Combat Operations. Aviation, Space, and Environmental Medicine, 81(8), 719-727. doi:10.3357/ASEM.2755.2010

Hanrahan, F., Field, A. P., Jones, F. W., & Davey, G. C. (2013). A meta-analysis of cognitive therapy for worry in generalized anxiety disorder. Clin Psychol Rev, 33(1), 120-132. doi: 10.1016/j.cpr.2012.10.008

Harrington, M. E. (2012). Neurobiological studies of fatigue. Prog Neurobiol, 99(2), 93-105. doi: 10.1016/j.pneurobio.2012.07.004

Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Advances in psychology, 52, 139-183.

Hatzigeorgiadis, A., Zourbanos, N., Galanis, E., & Theodorakis, Y. (2011). Self-Talk and Sports Performance: A Meta-Analysis. Perspect Psychol Sci, 6(4), 348-356. doi:10.1177/1745691611413136

Heesink, L., Kleber, R., Hafner, M., van Bedaf, L., Eekhout, I., & Geuze, E. (2017). Anger and aggression problems in veterans are associated with an increased acoustic startle reflex. Biol Psychol, 123, 119-125. doi: 10.1016/j.biopsycho.2016.12.004

HHA. (2015). Hand Hygiene Australia - National Data QTR3 2015.pdf. Hand Hygiene Australia: Infectious Diseases Dept. Austin Health Heidelberg Australia.

Hitchcock, J., & Davis, M. (1991). Efferent pathway of the amygdala involved in conditioned fear as measured with the fear-potentiated startle paradigm. Behavioral neuroscience, 105(6), 826-842.

Hofmann, S. G., & Smits, J. A. J. (2008). Cognitive-behavioral therapy for adult anxiety disorders: A meta-analysis of randomized placebocontrolled trials. J Clin Psychiatry., 69((4)), 621–632.

Hofmann, W., Schmeichel, B. J., & Baddeley, A. D. (2012). Executive functions and self-regulation. Trends Cogn Sci, 16(3), 174-180. doi: 10.1016/j.tics.2012.01.006

Holas, P., & Jankowski, T. (2013). A cognitive perspective on mindfulness. Int J Psychol, 48(3), 232-243. doi:10.1080/00207594.2012.658056

Homma, I., & Masaoka, Y. (2008). Breathing rhythms and emotions. Exp Physiol, 93(9), 1011-1021. doi:10.1113/expphysiol.2008.042424

Hormann, H. J., & Maschke, P. (1996). On the relation between personality and job performance of airline pilots. The International Journal of Aviation Psychology, 6(2), 171-178.

Huis, A., Schoonhoven, L., Grol, R., Donders, R., Hulscher, M., & van Achterberg, T. (2013). Impact of a team and leaders-directed strategy to improve nurses' adherence to hand

154

hygiene guidelines: a cluster randomised trial. Int J Nurs Stud, 50(4), 464-474. doi: 10.1016/j.ijnurstu.2012.08.004

Humphries, C. (1971) Zen Buddhism. George Allen & Unwin Ltd. London.

ICAO. (2005). Threat and Error Management (TEM) in Air Traffic Control. ICAO Preliminary Unedited Version – 6 October 2005.

ICAO. (2015). ICAO-Safety-Report. International Civil Aviation Organization (ICAO) 999 Robert-Bourassa Boulevard Montreal, Quebec, Canada.

Inzlicht, M., & Schmeichel, B. J. (2012). What Is Ego Depletion? Toward a Mechanistic Revision of the Resource Model of Self-Control. Perspect Psychol Sci, 7(5), 450-463. doi:10.1177/1745691612454134

Jacobs, R. H., Renken, R., Aleman, A., & Cornelissen, F. W. (2012). The amygdala, top-down effects, and selective attention to features. Neurosci Biobehav Rev, 36(9), 2069-2084. doi: 10.1016/j.neubiorev.2012.05.011

Jensen, C. G., Vangkilde, S., Frokjaer, V., & Hasselbalch, S. G. (2012). Supplemental Material for Mindfulness Training Affects Attention—Or Is It Attentional Effort? Journal of Experimental Psychology: General. doi: 10.1037/a0024931.supp

Jha, A. P., Krompinger, J., & Baime, M. J. (2007). Mindfulness training modifies subsystems of attention. Cognitive, Affective, & Behavioral Neuroscience, 7(2), 109-119.

Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed Methods Research: A Research Paradigm Whose Time Has Come. Educational Researcher, 33(7), 14-26.

Jones, D. G., & Endsley, M. (1996). Sources of situation awareness errors in aviation. Aviation, Space & Environmentalk Medicine, 67(6), 507-512.

Jozefowiez, J., & Staddon, J. E. R. (2008). Operant Behavior. 75-97.

Kahneman, D. (1973). Attention and Effort. Prentice-Hall, Inc., Englewood Cliffs, New Jersey.

Kaplan, S. (2001). Meditation, restoration, and the management of mental fatigue. Environment and Behavior, 33, 480+.

Kaplan, S., & Berman, M. G. (2010). Directed Attention as a Common Resource for Executive Functioning and Self-Regulation. Perspect Psychol Sci, 5(1), 43-57. doi:10.1177/1745691609356784

Kearns, S. (2011). Online Single-Pilot Resource Management: Assessing the Feasibility of Computer-Based Safety Training. The International Journal of Aviation Psychology, 21(2), 175-190. doi:10.1080/10508414.2011.556499

Keith, N., & Frese, M. (2005). Self-regulation in error management training: emotion control and metacognition as mediators of performance effects. J Appl Psychol, 90(4), 677-691. doi:10.1037/0021-9010.90.4.677

Keith, N., & Frese, M. (2008). Effectiveness of error management training: a meta-analysis. J Appl Psychol, 93(1), 59-69. doi:10.1037/0021-9010.93.1.59

Keller, A. (2011). Attention and olfactory consciousness. Front Psychol, 2, 380. doi:10.3389/fpsyg.2011.00380

Keller, J., Leiden, K., & Small, R. (2003). Cognitive Task Analysis of Commercial Jet Aircraft Pilots during Instrument Approaches for Baseline and Synthetic Vision Displays. Cognitive task analysis of commercial jet aircraft pilots during instrument approaches for baseline and synthetic vision displays. In D.C. Foyle, A. Goodman & B.L. Hooey (Eds.), Proceedings of the 2003 Conference on Human Performance Modelling of Approach and

155

Landing with Augmented Displays. (NASA/CP-2003-212267), 15-69. Moffett Field, CA: NASA., pp 16-69.

Kennedy, D. O., & Scholey, A. B. (2000). Glucose administration, heart rate and cognitive performance effects of increasing mental effort. Psychopharmacology (2000) 149:63–71, 149, 63-71.

Keye, M. D., & Pidgeon, A. M. (2013). Investigation of the Relationship between Resilience, Mindfulness, and Academic Self-Efficacy. Open Journal of Social Sciences, 01(06), 1-4. doi:10.4236/jss.2013.16001

King, R. E. (2014). Personality (and Psychopathology) Assessment in the Selection of Pilots. The International Journal of Aviation Psychology, 24(1), 61-73. doi:10.1080/10508414.2014.860844

King, R. E., Carretta, T. R., Retzlaff, P., Barto, E., James Ree, M., & Teachout, M. S. (2013). Standard Cognitive Psychological Tests Predict Military Pilot Training Outcomes. Aviation Psychology and Applied Human Factors, 3(1), 28-38. doi:10.1027/2192-0923/a000040

Klampfer, B., Flin, R., Helmreich, R., Häusler, B., Sexton, G., Fletcher, . . . Amacher, A. (2001). Enhancing Performance in High Risk Environments. Behavioural Markers Workshop Gottlieb Daimler and Karl Benz Foundation, Swissair Training Centre Zurich Swissair Training Centre Zurich 5 - 6 July 2001

Klein, G. (2008). Naturalistic Decision Making. Human Factors: The Journal of the Human Factors and Ergonomics Society, 50(3), 456-460. doi:10.1518/001872008x288385

Klein, G. A. (1997). Developing Expertise in Decision Making. Thinking and Reasoning, 3((4)), 337–352.

Koglbauer, I., Kallus, K. W., Braunstingl, R., & Boucsein, W. (2011). Recovery Training in Simulator Improves Performance and Psychophysiological State of Pilots During Simulated and Real Visual Flight Rules Flight. The International Journal of Aviation Psychology, 21(4), 307-324. doi:10.1080/10508414.2011.606741

Kopp, C. B. (1982). Antecedents of Self-Regulation: A Developmental Perspective. Developmental Psychology, 18(2), 199-214.

Kyriacou, C. P., & Hastings, M. H. (2010). Circadian clocks: genes, sleep, and cognition. Trends Cogn Sci, 14(6), 259-267. doi: 10.1016/j.tics.2010.03.007

Lang, P. J. B., Margaret M.; Cuthbert, Bruce N. (1990). Emotion, Attention, and the Startle Reflex. Psychological Review, 97(3), 377-395.

Langner, R., & Eickhoff, S. B. (2013). Supplemental Material for Sustaining Attention to Simple Tasks: A Meta-Analytic Review of the Neural Mechanisms of Vigilant Attention. Psychological Bulletin, Psychological Bulletin 139(4),870–900. doi: 10.1037/a0030694.supp

Lanius, R. A., Bluhm, R., Lanius, U., & Pain, C. (2006). A review of neuroimaging studies in PTSD: heterogeneity of response to symptom provocation. J Psychiatr Res, 40(8), 709-729. doi: 10.1016/j.jpsychires.2005.07.007

Leach, J. (2004). Why People Freeze in an Emergency: Temporal and Cognitive Constraints on Survival Responses. Aviation, Space, and Environmental Medicine, 75(6), 539-542.

Leahy, J. (2015). Airbus Global_Market_Forecast_2015-2034.

156

Lebuda, I., Zabelina, D. L., & Karwowski, M. (2016). Mind full of ideas: A meta-analysis of the mindfulness–creativity link. Personality and Individual Differences, 93, 22-26. doi: 10.1016/j.paid.2015.09.040

Levin, S., Sauer, L., Kelen, G., Kirsch, D., Pham, J., Desai, S., & France, D. (2011). Situational Awareness and Workload in The Emergency Department. IIE Transactions on Healthcare Systems Engineering, 2:2, 172-180

Levitt, S. D., & List, J. A. (2007). What do laboratory experiments measuring social preferences reveal about the real world? . The journal of economic perspectives, 21(2), 153-174.

Ley, R. (1999). The Modification of Breathing Behavior: Pavlovian and Operant Control in Emotion and Cognition. Behavior Modification, 23(3), 441-479. doi:10.1177/0145445599233006

Ley, R. (2003). Respiratory Psychophysiology and the Modification of Breathing Behavior. Behavior Modification, 27(5), 603-606. doi:10.1177/0145445503256313

Li, J. B., Nie, Y. G., Zeng, M. X., Huntoon, M., & Smith, J. L. (2013). Too exhausted to remember: ego depletion undermines subsequent event-based prospective memory. Int J Psychol, 48(6), 1303-1312. doi:10.1080/00207594.2012.762778

Linden, D. V. D., Keijsers, G. P. J., Eling, P., & Schaijk, R. V. (2005). Work stress and attentional difficulties: An initial study on burnout and cognitive failures. Work & Stress, 19(1), 23-36. doi:10.1080/02678370500065275

Lindseth, G. N., Lindseth, P. D., Jensen, W. C., Petros, T. V., Helland, B. D., & Fossum, D. L. (2011). Dietary Effects on Cognition and Pilots' Flight Performance. The International Journal of Aviation Psychology, 21(3), 269-282. doi:10.1080/10508414.2011.582454

Litchfield, P. (2003). A Brief Overviewof The Chemistry of Respiration And The Breathing Heartwave. California Biofeedback, 19(Spring 1).

Lloyd, J., Buscell, P., & Lindberg, C. (2008). Positive Deviance Diminishes MRSA. Prevention Strategist(Spring), 10-15.

MacLeod, C., Mathews, A., & Tata, P. (1986). Attentional bias in emotional disorders. Journal of Abnormal Psychology, 95(1), 15-20. doi:10.1037/0021-843x.95.1.15

Mainland, J., & Sobel, N. (2006). The sniff is part of the olfactory percept. Chem Senses, 31(2), 181-196. doi:10.1093/chemse/bjj012

Marcora, S. M., Staiano, W., & Manning, V. (2009). Mental fatigue impairs physical performance in humans. J Appl Physiol (1985), 106(3), 857-864. doi:10.1152/japplphysiol.91324.2008

Masaoka, Y., Sugiyama, H., Katayama, A., Kashiwagi, M., & Homma, I. (2012). Slow breathing and emotions associated with odor-induced autobiographical memories. Chem Senses, 37(4), 379-388. doi:10.1093/chemse/bjr120

McCrory, P. (2006). Smelling salts. Br J Sports Med, 40(8), 659-660. doi:10.1136/bjsm.2006.029710

McLeod, S. (2007). Id and Ego cited in O'Bryan, K. M. (2011). Freud's Ego in the Cockpit. Journal of Aviation/Aerospace Education & Research, 21(1), 9.

Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of Evidence-Based Practices in Online Learning A Meta-Analysis and Review of Online Learning Studies. U.S. Department of Education Office of Planning, Evaluation, and Policy Development Policy and Program Studies Service.

157

Meeusen, R., Watson, P., Hasegawa, H., Roelands, B., & Piacentini, M. F. (2007). Brain neurotransmitters in fatigue and overtraining. Appl Physiol Nutr Metab, 32(5), 857-864. doi:10.1139/H07-080

Menard, S. (2010). Logistic regression: From introductory to advanced concepts and applications Thousand Oaks, CA: SAGE Publications Ltd. Retrieved from http://methods.sagepub.com/book/logistic-regression-from-introductory-to-advanced-concepts-and-applications doi:DOI: http://dx.doi.org.ezproxy.lib.swin.edu.au/10.4135/9781483348964

Miles, J. E. (2015). Factors Associated with Delayed Ejection in Mishaps Between 1993 and 2013. Aerospace Medicine and Human Performance, 86(9), 774-781. doi:10.3357/amhp.4057.2015

Mirolli, M., Mannella, F., & Baldassarre, G. (2010). The roles of the amygdala in the affective regulation of body, brain, and behaviour. Connection Science, 22(3), 215-245. doi:10.1080/09540091003682553

Mogg, K., Holmes, A., Garner, M., & Bradley, B. P. (2008). Effects of threat cues on attentional shifting, disengagement and response slowing in anxious individuals. Behav Res Ther, 46(5), 656-667. doi: 10.1016/j.brat.2008.02.011

Monat, A. L., Lazarus, R. S. (1977). Stress and Coping: An Anthology. Columbia University Press, New York, New York, U.S.A.

Mulqueen, C., Baker, D. P., & Dismukes, R. K. (2002). Pilot Instructor Rater Training: The utility of the Multifacet Item Response Theory Model. The International Journal of Aviation Psychology, 12((3)), 287–303.

Murata, A., Uetake, A., & Takasawa, Y. (2005). Evaluation of mental fatigue using feature parameter extracted from event-related potential. International Journal of Industrial Ergonomics, 35(8), 761-770. doi: 10.1016/j.ergon.2004.12.003

Musson, D. M. S., Sandal, G. M., Helmreich, R. L. (2004). Personality Characteristics and Trait Clusters in Final Stage Astronaut Selection. Aviation, Space, and Environmental Medicine, 75(4), Section 1.

Nakamura, A. (2007). Ejection experience 1956-2004 in Japan: an epidemiological study Aviat Space Environ Med, 78(1), 54-58.

Nideffer, R. M. (1976). Test of Attentional and Interpersonal Style. Journal of Personality and Social Psychology, 34(3), 394-404.

O’Hare, D. (2006). Cognitive Functions and Performance Shaping Factors in Aviation Accidents and Incidents. The International Journal of Aviation Psychology, 16((2)), 145–156.

O'Brien, K. S., & O'Hare, D. (2007). Situational awareness ability and cognitive skills training in a complex real-world task. Ergonomics, 50(7), 1064-1091. doi:10.1080/00140130701276640

O'Bryan. (2011). Freud's Ego in the Cockpit. Journal of Aviation/Aerospace Education & Research, 21 (1 (9)), 8-16.

Odajnyk, V. W. (1998). Zen meditation as a way of individuation and healing. Psychological Perspectives, 37(1), 122-131. doi:10.1080/00332929808405936

Oken, B. S., Salinsky, M. C., & Elsas, S. M. (2006). Vigilance, alertness, or sustained attention: physiological basis and measurement. Clin Neurophysiol, 117(9), 1885-1901. doi: 10.1016/j.clinph.2006.01.017

158

Olff, M., Godaert, G., & Ursin, H. (1990). Quantification of human defense mechanisms (Eds). Recent Research in Psychology. Springer-Verlag New York.

Orasanu, J., & Martin, L. (1998). “Errors in Aviation Decision Making: A Factor in Accidents and Incidents.” Proceedings of the Second Workshop on Human Error, Safety and System Development, April 1 – 2 1998, Seattle, WA.

Owen, L., Scholey, A., Finnegan, Y., & Sunram-Lea, S. I. (2013). Response variability to glucose facilitation of cognitive enhancement. Br J Nutr, 110(10), 1873-1884. doi:10.1017/S0007114513001141

Pagano, R. R., & GAULT, M. A. F. P. (1964). Amygdala Activity A Central Measure Of Arousal. Electroencephalography and clinical. Neurophysiology, 17(3), 255-260.

Pagano, R. R. (1966). The Effects Of Central Stimulation And Nasal Air Flow On Induced Activity Of Olfactory Structures. Electroencephalography and Clinical Neurophysiology, 21, 261-277.

Pan S-C, T. K.-L., Hung I-C, Lin Y-J, Sheng W-H , et al. (2013). Compliance of Health Care Workers with Hand Hygiene Practices Independent Advantages of Overt and Covert Observers. doi: 10.1371/journal.pone. 0053746.t001

Paul, G., Elam, B., & Verhulst, S. J. (2007). A longitudinal study of students' perceptions of using deep breathing meditation to reduce testing stresses. Teach Learn Med, 19(3), 287-292. doi:10.1080/10401330701366754

Pecena, Y., Keye, D., Conzelmann, K., Grasshoff, D., Maschke, P., Heintz, A., & Eißfeldt, H. (2013). Predictive Validity of a Selection Procedure for Air Traffic Controller Trainees. Aviation Psychology and Applied Human Factors, 3(1), 19-27. doi:10.1027/2192-0923/a000039

Petersen, S. E., & Posner, M. I. (2012). The attention system of the human brain: 20 years after. Annu Rev Neurosci, 35, 73-89. doi:10.1146/annurev-neuro-062111-150525

Picano, J. J. (1991). Personality types among experienced military pilots. Aerospace Medical Association, June.

Posner, M. I. (2012). Expanding horizons in ergonomics research. NeuroImage, 59(1), 149-153. doi: 10.1016/j.neuroimage.2011.07.060

Posner, M. I., & Boies, S. J. (1971). Components of attention. Psychological Review, 78(5), 391-408. doi:10.1037/h0031333

Posner, M. I., & Rothbart, M. K. (2007). Research on attention networks as a model for the integration of psychological science. Annu Rev Psychol, 58, 1-23. doi: 10.1146/annurev.psych.58.110405.085516

Posner, M. I., & Rothbart, M. K. (2009). Toward a physical basis of attention and self regulation. Phys Life Rev, 6(2), 103-120. doi: 10.1016/j.plrev.2009.02.001

Posner, M. I., Rothbart, M. K., Sheese, B. E., & Voelker, P. (2014). Developing Attention: Behavioral and Brain Mechanisms. Adv Neurosci (Hindawi), 2014, 405094. doi:10.1155/2014/405094

Posner, M.I. (1978) Chronometric Explorations of Mind, Erlbaum. Cited in Tang, Y. Y., Rothbart, M. K., & Posner, M. I. (2012). Neural correlates of establishing, maintaining, and switching brain states. Trends Cogn Sci, 16(6), 330-337. doi: 10.1016/j.tics.2012.05.001

159

Prakash, R. S., Hussain, M. A., & Schirda, B. (2015). The role of emotion regulation and cognitive control in the association between mindfulness disposition and stress. Psychol Aging, 30(1), 160-171. doi:10.1037/a0038544

Presniak, M. D., Olson, T. R., & Macgregor, M. W. (2010). The role of defense mechanisms in borderline and antisocial personalities. J Pers Assess, 92(2), 137-145. doi:10.1080/00223890903510373

Prinzel III, L. J., DeVries, H., Freeman, F. G., & Mikulka, P. (2001). Examination of Automation-Induced Complacency and Individual Difference Variates. NASA Langley ResearchCentre Hampton Virginia.

Qian, S., Li, M., Li, G., Liu, K., Li, B., Jiang, Q., . . . Sun, G. (2015). Environmental heat stress enhances mental fatigue during sustained attention task performing: Evidence from an ASL perfusion study. Behav Brain Res, 280, 6-15. doi: 10.1016/j.bbr.2014.11.036

Raz, A., & Buhle, J. (2006). Typologies of attentional networks. Nat Rev Neurosci, 7(5), 367-379. doi:10.1038/nrn1903

Reason, J. (2000). Human error: models and management. BMJ, 320.

Reppermund, S., Zihl, J., Lucae, S., Horstmann, S., Kloiber, S., Holsboer, F., & Ising, M. (2007). Persistent cognitive impairment in depression: the role of psychopathology and altered hypothalamic-pituitary-adrenocortical (HPA) system regulation. Biol Psychiatry, 62(5), 400-406. doi: 10.1016/j.biopsych.2006.09.027

Richardson, M., & Lang, R. (2005). CFIT Are you at risk. Civil Aviation Safety Authority. Accessed via https://www.casa.gov.au/sites/g/files/net351/f/_assets/main/.../8-15.pdf 160215.

Richman, W. L. K., Sara; Weisband, Suzanne; Drasgow, Fritz. (1999). A meta-analytic study of social desirability distortion in computer-administered questionnaires, traditional questionnaires, and interviews. Journal of Applied Psychology, 84(5), 754-775. doi: http://dx.doi.org/10.1037/0021-9010.84.5.754

Riley, K. E., & Park, C. L. (2015). How does yoga reduce stress? A systematic review of mechanisms of change and guide to future inquiry. Health Psychol Rev, 9(3), 379-396. doi:10.1080/17437199.2014.981778

Roach, G. D., Petrilli, R. M., Dawson, D., & Lamond, N. (2012). Impact of layover length on sleep, subjective fatigue levels, and sustained attention of long-haul airline pilots. Chronobiol Int, 29(5), 580-586. doi:10.3109/07420528.2012.675222

Rochat, L., Billieux, J., & Van der Linden, M. (2012). Difficulties in disengaging attentional resources from self-generated thoughts moderate the link between dysphoria and maladaptive self-referential thinking. Cogn Emot, 26(4), 748-757. doi:10.1080/02699931.2011.613917

Rockstroh, S., & Schweizer, K. (2001). The contributions of memory and attention processes to cognitive abilities. The Journal of General Psychology, 128(1), 30-42.

Roscoe, S. N., Corl, L., & LaRoche, J. (2001). Predicting human performance (wombat) (5.0 ed). Helio Press, Canada.

Rosenweg P, N. (2006). Recovery of SA with Operant Techniques - PilotStudy. Proceedings of the Swinburne Multi Modal Safety Management and Human Factors Conference. Melbourne Australia.

Rosnow, R. L., Rosenthal, R., & Rubin, D. B. (2000). Contrasts and correlations in effect size estimation. Psychol Sci, 11(6), 446-453.

160

Ross, K. G., Klein, G. A., Thunholm, P., Schmitt, J. F., & Baxter, H. C. (2004). The Recognition-Primed Decision Model. Military Review.

Rowin, J., & Lewis, S. L. (1996). Spontaneous bilateral subdural hematomas associated with chronic Ginkgo biloba ingestion. Neurology, 46(6), 1775-1776.

Safe_Work. (2015). Work-related Traumatic Injury Fatalities Australia 2014. Safe Work Australia accessed via swa.gov.au Dec 12, 2015.

Salas, E., Wilson, K. A., Burke, C. S., & Wightman, D. C. (2006). Does Crew Resource Management Training Work? An Update, an Extension, and Some Critical Needs. Human Factors: The Journal of the Human Factors and Ergonomics Society, 48(2), 392-412. doi:10.1518/001872006777724444

Sandi, C. (2013). Stress and cognition. Wiley Interdiscip Rev Cogn Sci, 4(3), 245-261. doi:10.1002/wcs.1222

Sato, T. (2005). The Eysenck Personality Questionnaire Brief Version: Factor Structure and Reliability. The Journal of Psychology, 2005, 139(6), 545–552, 139((6)), 545–552.

Schmid, S., Wilson, D. A., & Rankin, C. H. (2014). Habituation mechanisms and their importance for cognitive function. Front Integr Neurosci, 8, 97. doi:10.3389/fnint.2014.00097

Scott, J. W. (2006). Sniffing and Spatiotemporal Coding in Olfaction. Chemical Senses., 31(2), 119-130.

Seamster, T. L., Boehm-Davis, D. A., Holt, R. W., & Schultz, K. (1998). Developing Advanced Crew Resource Management (ACRM) Training: A Training Manual.

Sedlmeier, P., Eberth, J., Schwarz, M., Zimmermann, D., Haarig, F., Jaeger, S., & Kunze, S. (2012). The psychological effects of meditation: A meta-analysis. . Psychological Bulletin, 138(6), 1139. doi: 10.1037/a0028168.supp

Sheng, P., Hou, L., Wang, X., Wang, X., Huang, C., Yu, M., . . . Dong, Y. (2013). Efficacy of modafinil on fatigue and excessive daytime sleepiness associated with neurological disorders: a systematic review and meta-analysis. PLoS One, 8(12), e81802. doi: 10.1371/journal.pone.0081802

Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T. L., Cohen, J. D., & Botvinick, M. M. (2017). Toward a Rational and Mechanistic Account of Mental Effort. Annu Rev Neurosci. doi:10.1146/annurev-neuro-072116-031526

Shook, R. W. C., Bandiero, M., Coello, J. P., Garland, D. J., & Endsley, M. R. (2000). Situation awareness problems in general aviation. Proceedings of 14th and the 44th Triennial Congress of the International Ergonomics Association Annual Meeting of the Human Factors and Ergonomics Society — 2000.

Sinhger, M. T., & Ofshe, R. (1990). Thought Reform Programs and the Production of Psychiatric Casualties. Psychiatric Annals, 20(4).

Sitzmann, T. (2012). A theoretical model and analysis of the effect of self-regulation on attrition from voluntary online training. Learning and Individual Differences, 22(1), 46-54. doi: 10.1016/j.lindif.2011.11.004

Skinner, B. F. (1953). Science and Human Behavior. B.F. Skinner Foundation website: http://www.bfskinner.org/books4sale.asp.

Smith, B. (2012). Depression and motivation. Phenomenology and the Cognitive Sciences, 12(4), 615-635. doi:10.1007/s11097-012-9264-0

161

Smith, K. S., & Graybiel, A. M. (2014). Investigating habits: strategies, technologies and models. Front Behav Neurosci, 8, 39. doi:10.3389/fnbeh.2014.00039

Sneddon, A., Mearns, K., & Flin, R. (2006). Situation awareness and safety in offshore drill crews. Cognition, Technology & Work, 8(4), 255-267.doi:10.1007/s10111-006-0040-1

Sourkes, T. L. (2006). On the energy cost of mental effort. J Hist Neurosci, 15(1), 31-47. doi:10.1080/096470490944860

Spek, V., Cuijpers, P., Nyklicek, I., Riper, H., Keyzer, J., & Pop, V. (2007). Internet-based cognitive behaviour therapy for symptoms of depression and anxiety: a meta-analysis. Psychol Med, 37(3), 319-328. doi:10.1017/S0033291706008944

Spijkerman, M. P., Pots, W. T., & Bohlmeijer, E. T. (2016). Effectiveness of online mindfulness-based interventions in improving mental health: A review and meta-analysis of randomised controlled trials. Clin Psychol Rev, 45, 102-114. doi: 10.1016/j.cpr.2016.03.009

Staal, M. A. (2004). Stress, Cognition, and Human Performance: A Literature Review and Conceptual Framework. Ames Research Center, Moffett Field, California.

Staal, M. A., Boulton, A. E., Yaroush, R. A., & Bourne Jr, L. E. (2008). Cognitive Processing and Resilience to stress. Biobehavioral resilience to stress, 259-299.

Stajkovic, A. D., & Luthans, F. (1997). A meta-analysis of the effects of organizational behavior modification on task performance, 1975–95. The Academy Of Management Journal, 40(5), 1122-1149.

Stevens, J. P. (2009). Applied multivariate statistics for the social sciences (5th ed.). Taylor & Francis. Swinburne library Ebooks Central

Strater, L., & Endsley, M. (2000). SAGAT: A Situation Awareness Measurement Tool For Commercial Airline Pilots. Proceedings of the Human Performance, Situation Awareness and Automation: User Centered Design for the New Millennium Conference — October 2000.

Sturm, W., & Willmes, K. (2001). On the functional neuroanatomy of intrinsic and phasic alertness. NeuroImage, 14(1 Pt 2), S76-84. doi:10.1006/nimg.2001.0839

Sulistyawati, K., Wickens, C. D., & Chui, Y. P. (2011). Prediction in Situation Awareness: Confidence Bias and Underlying Cognitive Abilities. The International Journal of Aviation Psychology, 21(2), 153-174. doi:10.1080/10508414.2011.556492

Sullivan, R. M., Wilson, D. A., Ravel, N., & Mouly, A. M. (2015). Olfactory memory networks: from emotional learning to social behaviors. Front Behav Neurosci, 9, 36. doi:10.3389/fnbeh.2015.00036

Tanaka, M., Ishii, A., & Watanabe, Y. (2014). Neural effects of mental fatigue caused by continuous attention load: a magnetoencephalography study. Brain Res, 1561, 60-66. doi: 10.1016/j.brainres.2014.03.009

Tang, Y. Y., & Posner, M. I. (2009). Attention training and attention state training. Trends Cogn Sci, 13(5), 222-227. doi: 10.1016/j.tics.2009.01.009

Tang, Y. Y., Holzel, B. K., & Posner, M. I. (2015). The neuroscience of mindfulness meditation. Nat Rev Neurosci, 16(4), 213-225. doi:10.1038/nrn3916

162

Tang, Y. Y., Rothbart, M. K., & Posner, M. I. (2012). Neural correlates of establishing, maintaining, and switching brain states. Trends Cogn Sci, 16(6), 330-337. doi: 10.1016/j.tics.2012.05.001

Tang, Y.-Y., & Posner, M. I. (2009). Response to Jaeggi et al.: Exploring training methods. Trends Cogn Sci, 13(5), 192-193. doi: 10.1016/j.tics.2009.02.001

Teper, R., & Inzlicht, M. (2013). Meditation, mindfulness and executive control: the importance of emotional acceptance and brain-based performance monitoring. Soc Cogn Affect Neurosci, 8(1), 85-92. doi:10.1093/scan/nss045

Tett, R. P., Jackson, D. N., & Rothstein, M. (1991). Personality Measures as Predictors of Job Performance: A Meta-Analytic Review. Personnel Psychology, 44(4), 703-742. doi:10.1111/j.1744-6570. 1991.tb00696.x

Thomas, M. J. W. (2004). Predictors of Threat and Error Management: Identification of Core Nontechnical Skills and Implications for Training Systems Design International Journal of Aviation Psychology, Volume 14, Issue 2 April 2004 , pages 207, 14( 2 ), 207-231.

Thornton, S. P. (2001, April 16). Sigmund Freud (1856-1939) cited in O'Bryan, K. M. (2011). Freud's Ego in the Cockpit. Journal of Aviation/Aerospace Education & Research, 21(1), 9.

Tinseth, R. (2015). Current Matrket Ooutlook Boeing 2015 Presentation. Boeing.COM.

Tomori, Z., Benacka, R., & Donic, V. (1998). Mechanisms and clinicophysiological implications of the sniff- and gasp-like aspiration reflex.pdf. Respiration Physiology, 114, 83-98.

Trappmann, M., Krumpal, I., Kirchner, A., & Jann, B. (2013). Item Sum: A New Technique for Asking Quantitative Sensitive Questions. Journal of Survey Statistics and Methodology, 2(1), 58-77. doi:10.1093/jssam/smt019

Turkington, D., Hedwat, D., Rider, I., & Young, A. H. (2004). Recovery from chronic fatigue syndrome with modafinil. Hum Psychopharmacol, 19(1), 63-64. doi:10.1002/hup.554

Van de Mortel, T. F., Apostolopoulou, E., & Petrikkos, G. (2010). A comparison of the hand hygiene knowledge, beliefs, and practices of Greek nursing and medical students. Am J Infect Control, 38(1), 75-77. doi: 10.1016/j.ajic.2009.05.006

Van Winsen, R., Henriqson, E., Schuler, B., & Dekker, S. W. A. (2014). Situation awareness: some conditions of possibility. Theoretical Issues in Ergonomics Science, 16(1), 53-68. doi:10.1080/1463922x.2014.880529

Vine, S. J., Uiga, L., Lavric, A., Moore, L. J., Tsaneva-Atanasova, K., & Wilson, M. R. (2015). Individual reactions to stress predict performance during a critical aviation incident. Anxiety Stress Coping, 28(4), 467-477. doi:10.1080/10615806.2014.986722

Vohs, K. D., Baumeister, R. F., Twenge, J. M., Schmeichel, B. J., Tice, D. M., & Crocker, J. (2005). Decision fatigue exhausts self-regulatory resources—But so does accommodating to unchosen alternatives. (Unpublished Manuscript).

Wallace, J. C., & Chen, G. (2005). Development and validation of a work-specific measure of cognitive failure: Implications for occupational safety. Journal of Occupational and Organizational Psychology, 78(4), 615-632. doi:10.1348/096317905x37442

Warm, J. S., Parasuraman, R., & Matthews, G. (2008). Vigilance Requires Hard Mental Work and Is Stressful. (cover story). Human Factors, 50(3), 433-441. doi:10.1518/001872008X312152

Watson, J. B., Behaviorism (New York, 1924), p. 233.

163

Wesensten, N. J., Belenky, G., Kautz, M. A., Thorne, D. R., Reichardt, R. M., & Balkin, T. J. (2002). Maintaining alertness and performance during sleep deprivation: modafinil versus caffeine. Psychopharmacology (Berl), 159(3), 238-247. doi:10.1007/s002130100916

Wickens, C. D. (2008). Multiple resources and mental workload. Human Factors: The Journal of the Human Factors and Ergonomics Society, 50(3), 449-455.

Wickens, C. D. (2014). Effort in human factors performance and decision making. Hum Factors, 56(8), 1329-1336. doi:10.1177/0018720814558419

Wickens, C. D., Gutzwiller, R. S., & Santamaria, A. (2015). Discrete task switching in overload: A meta-analyses and a model. International Journal of Human-Computer Studies, 79, 79-84. doi: 10.1016/j.ijhcs.2015.01.002

Wickens, C. M., Toplak, M. E., & Wiesenthal, D. L. (2008). Cognitive failures as predictors of driving errors, lapses, and violations. Accid Anal Prev, 40(3), 1223-1233. doi: 10.1016/j.aap.2008.01.006

Wickens, C.D., (1992) Engineering Psychology and Human Performance, Harper Collins, NY.

Wiegmann, D. A., & Shappell, S. A. (2001). A Human Error Analysis of Commercial Aviation Accidents using the human factors analysis and classification system (HFACS). U.S. Department of Transportation Federal Aviation Administration.

Wiggins, M. (2007). Perceived GPS Use and Weather-Related Decision Strategies Among Pilots. The International Journal of Aviation Psychology, 17(3), 249-264. doi:10.1080/10508410701341593

Wiggins, M. W., Azar, D., Hawken, J., Loveday, T., & Newman, D. (2014). Cue-utilisation typologies and pilots’ pre-flight and in-flight weather decision-making. Safety Science, 65, 118-124. doi: 10.1016/j.ssci.2014.01.006

Yeomans, J. S., & Frankland, P. W. (1996). The acoustic startle reflex neurons and connections. Brain Research Reviews, 21, 301-314.

Yin, H. H., & Knowlton, B. J. (2006). The role of the basal ganglia in habit formation. Nat Rev Neurosci, 7(6), 464-476. doi:10.1038/nrn1919

Young, G., Zavelina, L., & Hooper, V. (2008). Assessment of workload using NASA Task Load Index in perianesthesia nursing. J Perianesth Nurs, 23(2), 102-110. doi: 10.1016/j.jopan.2008.01.008

Young, J. A., & Pain, D. M. (1999). The Zone: Evidence of a Universal Phenomenon for Athletes Across Sports Athletic Insight: The Online Journal of Sport Psychology, 1(3), 21-30.

Yu, R. (2016). Stress potentiates decision biases: A stress induced deliberation-to-intuition (SIDI) model. Neurobiol Stress, 3, 83-95. doi: 10.1016/j.ynstr.2015.12.006

Zeigler-Hill, V., Chadha, S., & Osterman, L. (2008). Psychological defense and self-esteem instability: Is defense style associated with unstable self-esteem? Journal of Research in Personality, 42(2), 348-364. doi: 10.1016/j.jrp.2007.06.002

Zheng, Y., Lu, Y., Wang, Z., Huang, D., & Fu, S. (2015). Developing a Measurement for Task Complexity in Flight. Aerospace Medicine and Human Performance, 86(8), 698-704. doi:10.3357/amhp.4189.2015

164

APPENDICES

Appendix A: Test Development Statistics

Group Statistics TIME N Mean Std. Deviation Std. Error Mean

Mental Alertness 2 59 88.1963 9.5163 1.2389

1 65 81.6694 10.8942 1.3512

Manages Fatigue 2 59 84.6485 10.5655 1.3755

1 65 76.0755 11.4920 1.4254

Executive Functioning 2 59 64.2039 18.1069 2.3573

1 65 58.8305 17.8715 2.2166

Choice Reaction Time 2 59 72.9360 15.6396 2.0361

1 65 67.0000 18.7524 2.3259

Working Memory 2 59 62.9530 9.7160 1.2649

1 65 59.8370 12.8992 1.6000

Table A1: Statistics Baseline test-retest administration

Table A2. Comparison of development group baseline retest scores time 2 with time 1

t‐test for Equality of Means ‐  Equal Variances not assumed 

  t df Sig.

2-tailed Mean

Difference Std. Error Difference

95% Confidence Interval of the

Difference Effect size 

Cohens d Lower Upper

Mental Alertness 3.560 121.829 .001 -6.52689 1.83326 -10.156 -2.89771 0.65

Manages Fatigue 4.328 121.978 .000 -8.57294 1.98088 -12.494 -4.65159 0.78

Executive Functioning 1.661 120.522 .099 -5.37344 3.23584 -11.779 1.03303 0.30

Choice Reaction Time 1.920 121.161 .057 -5.9356 3.0912 -12.055 .1842 0.35

Working Memory 1.528 118.098 .129 -3.1156 2.0396 -7.1545 .9233 0.28

165

Development Group Baseline Scales

n of

Dev’t test items

Group

Mean n= 61

SD Alpha Stand’

Intra class Corr’n

Mental Alertness 14 80.95 10.97 0.873 .870

Manages Fatigue 10 75.25 11.62 0.808 .797

Functional Performance Items Mean Item Diff’y

Executive Functioning element 6 61.56 18.21 .74

Choice Reaction Time element 5 69.83 18.61 .76

Working Memory element 5 61.370 12.81 .77

Scale definitions:

Manages Fatigue: Self-management of sleep requirements ensure availability of cognitive resources

Mental Alertness: Measures the extent of every day slips in perception, memory and coordination

Choice Reaction Speed: Ability to make rapid and effective decisions to changing circumstances

Executive Functioning: Use of logic and deductive thinking to manage developing issues.

Working Memory: Ability to retain and recall information in the short term

Subjective (strain): Identifies perceived strain in coping with the task via six graded elements in the NASA TLX.

Table A4. rART Baseline Test Statistics Experimental Group n= 221

n/N of

items

Cases

Alpha

(Std)

Intra-class

Correl’n

Inter-item

Correlat’n

R2 F

(.001)

KMO

rART Baseline Survey Student Pilots1

33/632

221 .833 .807 .094(ns) .675 58.5 .823

1Dependent variable: Mental alertness. Predictors: Choice Reaction Speed, Executive Functioning, Manages Fatigue, Working Memory. 2 30 items in the rART exercise & procedure are not included in the baseline test statistics.

Table A3 Summary – Development Group Baseline test scale reliabilities and item statistics

166

Table. A5. Study completion rates, age & gender by session for 221 enrolled participants

Session Study Item

No Completed

Number of respondents

leaving study % Male

Mean Age Male

Female Mean Age

Female

Baseline

1 27 12.22% 27 29.78 0 - 11 3 1.36% 3 22.0 0 - 14 1 0.45% 1 33.0 0 - 21 1 0.45% 1 22.0 0 - 23 4 1.81% 4 26.25 0 - 24 13 5.88% 13 28.62 0 - 25 1 0.45% 1 38.0 0 - 26 3 1.36% 3 35.33 0 - 27 3 1.36% 3 28.67 0 - 28 2 0.90% 2 35.0 0 - 29 1 0.45% 1 22.0 0 - 30 43 19.46% 18 24.11 25 29.431 3 1.36% 1 22.0 2 25.0

Baseline test completed – Training commenced 32 3 1.36% 2 21.0 1 42

Session 1 33 18 8.14% 7 25.0 11 26.8 35 2 0.90% 0 0.0 2 32.0

Session 2 36 11 4.98% 6 31.5 5 23.6 38 1 0.45% 0 0.0 1 33.0

Session 3 39 9 4.07% 5 33.8 4 26.3 40 1 0.45% 1 42.0 0 0

Session 4 42 4 1.81% 3 39.0 1 22.0 43 1 0.45% 0 0.0 1 28.0 44 2 0.90% 1 33.0 1 28.0

Session 5 45 5 2.26% 1 33.0 4 28.8 46 1 0.45% 0 0.0 1 20.0

Session 6 48 3 1.36% 2 25.0 1 28.0Session 7 51 5 2.26% 1 33.0 4 28.5Session 8 54 2 0.90% 1 42.0 1 38.0Session 9 57 2 0.90% 0 0.0 2 31.0

Session 10 63

Sample total 175 108 67

Completed Training 46 25 21

167

Appendix B. Detail of the Online Questionnaire

Mental Alertness ‐ The Modified Cognitive Failures Scale Question Text   

  Please respond with 0 = never to 5 = Always 

1  Forgetting where I have left something I use in my job, like keys, a file or equipment.  

2  Becoming distracted by co‐workers into doing something else (unintentionally).  

3  Not hear instructions or important sounds when doing something else.  

4  Drifting off into a daydream when I ought to be listening.  

5  Going back to check if I have turned off work equipment.  

6  I need to check names of people at work over and over because I haven't remembered them.  

7  I forget the procedures for some tasks.  

8  Looking and not seeing the thing I want, even though it's there, like notices or equipment etc.  

9  Unintentionally hitting the wrong switch on things.  

10  Remembering what was needed to complete the job or a task.  

Manages Fatigue Scale 

11  I am falling asleep without meaning to do so.  

12  My thoughts wander and become disconnected.  

13  I wish I had more energy to do things.  

14  It is hard to sleep when thoughts keep me awake.  

15  I don't rest well and wake up still tired.  

16  I need more sleep now than I did before.  

17  I miss things that are normally obvious.  

18  Lack of sleep affects my coordination and judgement.  

19  I don't rest well enough to recover my energy for the next day.  

20  I struggle to concentrate at night  

21  * NASA TLX perceived workload strain   

22 Can you make a story here?  INSTRUCTIONS: Complete the exercise by dragging the pictures into the boxes below with using your mouse to make a logical story. (Exec Functioning)   

23 You have a hat with 35 black tags and 35 red tags in it. Without looking into the hat, how many do you have to take out of the hat, one at a time, to be sure that you have two of the same colour? (Exec Functioning) 

24  *Free text entry = Number Pyramid – Add the missing number. (Exec Functioning) 

25  *Simple Maze – memory question (Short term Memory) 

Table B1. Online questionnaire and presentation order

168

26 Can you remember the order these images were shown in? INSTRUCTIONS: Use your mouse to drag the images into the boxes IN THE ORDER they were shown here.  (Short term Memory) 

27  * Short Term Memory question find pairs of images in a grid hidden after viewing. 

28 Can you tell how many small grey squares there are in each group of changing squares? 

Type in the number into the text box. (Choice reaction speed) 

29  * Count back with 5 sec time  (Choice reaction speed) 

30  *Target and shoot down aircraft symbol (Choice reaction speed)  

31 

 

INFORMATION  for the rapid ART Exercise. Welcome to the training part of this project. You have now  completed  the benchmark portion of  the project.  This next part  involves  you  in practicing the rapid attention recovery technique. You will be sent an SMS reminder message each day at your nominated time. Please login with your id to do this exercise for 5 minutes at the end of each day, directly before sleep. In addition to the exercise, every second day you will be asked to complete two challenge questions. The total time of your participation is 10 days. You should now answer the question below and proceed to view the 5 minute  introductory video The rapid attention recovery technique instruction will follow. It is very important that the whole sequence of exercise is completed Thank you for your participation.   

 

32  VIDEO intro by male presenter 

33  Video Schematic and female presenter demonstrates technique.  

34 Exercise 2:‐ Video Schematic and female presenter demonstrates technique. Then also please indicate how easy you are finding this technique to learn.  

35 My house is east of my office and west of the station, If I want to catch a train straight from work, in which direction shall I travel?  

36  *Simple Maze – short term memory 

37 

INFORMATION for the rapid ART Exercise. Welcome back to the training part of this project. This is the third of ten sessions of the project. This next part involves you in practicing the rapid attention recovery technique again. You should now answer the question below and proceed to view the 5 minute introductory video The rapid attention recovery technique instruction will follow. A reminder ‐ It is very important that the whole sequence of exercise is completed Thank you for your participation.   

38  VIDEO intro by male presenter 

39 Video Schematic and female presenter demonstrates technique. Then also please indicate how easy you are finding this technique to learn.  

40 Exercise 4: ‐ Video Schematic and female presenter demonstrates technique. Then also please indicate how easy you are finding this technique to learn.  

41  *Simple Maze – short term memory 

169

42  The opposite of 'right' is the opposite of. . . .  

 

43 

 

INFORMATION for the rapid ART Exercise. Welcome back to the training part of this project. This is the fifth of 10 sessions of the project. This next part involves you in practicing the rapid attention recovery technique again. You should now answer the question below and proceed to view the 5 minute introductory video The rapid attention recovery technique instruction will follow. A reminder ‐ It is very important that the whole sequence of exercise is completed Thank you for your participation.   

 

44  VIDEO intro by male presenter 

45 Video Schematic and female presenter demonstrates technique. Then also please indicate how easy you are finding this technique to learn.  

46 Exercise 6:‐ Video Schematic and female presenter demonstrates technique. Then also please indicate how easy you are finding this technique to learn. Then also please indicate how easy you find this technique to learn.  

47  *Simple Maze – short term memory 

48  Please read the text carefully and identify the correct answer   

49 

INFORMATION for the rapid ART Exercise. Welcome back to the training part of this project. This is the seventh of 10 sessions of the project. This next part involves you in practicing the rapid attention  recovery  technique  again. You  should now answer  the question below  and proceed  to  view  the  5 minute  introductory  video  The  rapid  attention  recovery  technique instruction will follow. A reminder ‐ It is very important that the whole sequence of exercise is completed Thank you for your participation.   

50  VIDEO intro by male presenter 

51 Video Schematic and female presenter demonstrates technique. Then also please indicate how easy you are finding this technique to learn.  

52 Exercise 8:‐ Sound  is required  in  this video.  {#VIDEO=CATE_RARTn2.mp4#} Then also please indicate how easy you find this technique to learn.  

53  *Simple Maze  

54 

After making a trip to the market and a trip back you are given another job. Your taxi can only take 5 passengers at a time. You need to transport 16 people from A to B How many trips or journeys do you have to make?  

170

55 

 

INFORMATION for the rapid ART Exercise. Welcome back to the training part of this project. This is the ninth of 10 sessions of the project. This next part involves you in practicing the rapid attention recovery technique again. You should now answer the question below and proceed to view the 5 minute introductory video The rapid attention recovery technique instruction will follow. A reminder ‐ It is very important that the whole sequence of exercise is completed Thank you for your participation.   

56  VIDEO intro by male presenter 

57 Video Schematic and female presenter demonstrates technique. Then also please indicate how easy you are finding this technique to learn.  

58 Exercise  10:‐  Video  Schematic  and  female  presenter  demonstrates  technique. ‐ Then please indicate how easy you are finding this technique to learn.  

59  PLUGIN = Short Term Memory  

60 Aircraft manufacturers sold  to 35 Airlines, 30 bought Boeings and 15 bought Airbuses, how many airlines bought both?  

61  I now constantly use the rapid attention recovery technique in my work.  

62  The rapid ART technique now helps me to recover attention when interrupted.  

63  Ease of use of the rapid ART technique – using a modified TLX question format  

171

Table B.2 Preliminary literacy Test (conducted at start of the baseline survey)

"When we were very young we lived on a farm and every year it sometimes rained non-stop for about a month. We had to put buckets in the rooms to cope with the leaks. At the time as kids we thought it was funny, particularly when we knocked the buckets over and spilled water everywhere. Our neighbour who had a parallel experience was not as amused as we were. These days, living in a high rise, we don't have that problem, although the basement sometimes floods in a heavy rain"

1. Spilling water was funny

Yes

No

2. I live on the farm

Yes

No

3. Basements always flood

Yes

No

4. It always rained on the farm

Yes

No

5. Our neighbour was also amused

Yes

No

Table B2. Study Survey Reading Grade

Readability Formula Study Test

Score 7th grade level Fairly easy to read.

Range 0 - 100 Flesch-Kincaid Reading Ease 74.1

Table B3. Flesch-Kincaid Formulae Grade Levels

A grade level (based on the USA education system) is equivalent to the number of years of education a person has had. A score of around 10-12 is roughly the reading level on completion of high school. Text to be read by the general public should aim for a grade level of around 8.An average grade six student's written assignment (age of 12) has a readability index of 60–70 (and a reading grade level of six to seven). - Accessed via (https://readability-score.com

Score School Level Notes

90.0–100.0 5th grade Very easy to read. Easily understood by an average 11-year-old student.

80.0–90.0 6th grade Easy to read. Conversational English for consumers.

70.0–80.0 7th grade Fairly easy to read.

60.0–70.0 8th & 9th grade Plain English. Easily understood by 13- to 15-year-old students.

50.0–60.0 10th to 12th grade Fairly difficult to read.

30.0–50.0 college Difficult to read.

0.0–30.0 college graduate Very difficult to read. Best understood by university graduates.

172

The modified form of the NASA Task Load Index utilised in the study.

Higher ratings denote greater strain placed on coping (self-regulation) skills.

How stressful do you find the mental effort required in what you do (e.g. thinking, deciding, calculating, remembering, looking, searching, etc)? Do you find the tasks easy or demanding, simple or complex, exacting or forgiving?

Mental Demand

Low High

How exhausting do you find the physical activities that are required in what you do (e.g. pushing, pulling, turning, controlling, activating, etc)? Are the tasks easy or demanding, slow or brisk, slack or strenuous, restful or laborious?

Physical Demand

Low High

ow much time pressure is there due to the rate or pace in what you do? Is the pace slow and leisurely or rapid and frantic?

Pace of Work

Low High

How successful can you be in what you do in achieving the goals in what you do? Consider if there are any inhibiting problems in the way the work is done or the way the organisation supports it?

Performance

High Low

How hard do you have to work in what you do (both mentally and physically) to accomplish the required level of performance for this role?

Effort

Low High

How insecure, discouraged, irritated, stressed and annoyed versus secure, gratified, content, relaxed or complacent do you feel and does the system of work and typical outcomes in what you do affect you?

Frustration

Low High

Table B3 Perception of stress and coping with the TLX

173

Table B.4 Fatigue Management & Alertness Baseline Questions

174

.

Left Column: Reaction speed & Accuracy.

1. Count grey squares constant changing position

2. Shoot (Green button trigger) when a/c targeted

3. Select number on Timed count down x 2

Middle column Short term memory.

1. Order the briefly displayed symbols.

2. Locate pairs of images after memorising

3. Trace button path in maze after memorising

Right column: Executive functioning

1. Drag into logical order of story

2. Identify and enter missing number.

3. Simple problem-solving question

Table B.5 Functional performance test questions

175

Appendix C: Rapid ART Instruction

Figure C1. Online demonstration of the rART procedure Figure C0.1. Video: Actor voice over demonstrating the rART technique

176

Appendix D. Ethics Clearance From: RES Ethics <resethics@swin.edu.au> Date: Tuesday, 22 September 2015 17:21

SHR Project 2015/164 – Evaluation of a self-regulating skill for rapid attention recovery (rART).

(Attention at the moment of demand: an operant technique for recovery of situation awareness).

I refer to the ethical review of the above project revised protocol by a Subcommittee (SHESC2) of Swinburne’s

Human Research Ethics Committee (SUHREC). The responses to the review, as emailed today with attachments

on behalf of the chief investigator/supervisor and student investigator, were put to the Subcommittee delegate

for consideration and feedback sent to you. Your response to the feedback also emailed today with attached

revised consent information, accords with the feedback.

I am pleased to advise that, as submitted to SHESC2 and today, the project may proceed in line with standard

on-going ethics clearance conditions here outlined.

All human research activity undertaken under Swinburne auspices must conform to Swinburne and external

regulatory standards, including the current National Statement on Ethical Conduct in Human Research and with

respect to secure data use, retention and disposal.

‐ The named Swinburne Chief Investigator/Supervisor remains responsible for any personnel appointed to or

associated with the project being made aware of ethics clearance conditions, including research and consent

procedures or instruments approved. Any change in chief investigator/supervisor requires timely

notification and SUHREC endorsement.

‐ The above project has been approved as submitted for ethical review by or on behalf of SUHREC.

Amendments to approved procedures or instruments ordinarily require prior ethical appraisal/clearance.

SUHREC must be notified immediately or as soon as possible thereafter of (a) any serious or unexpected

adverse effects on participants any redress measures; (b) proposed changes in protocols; and (c) unforeseen

events which might affect continued ethical acceptability of the project.

At a minimum, an annual report on the progress of the project is required as well as at the conclusion (or

abandonment) of the project. Information on project monitoring and variations/additions, self-audits and

progress reports can be found on the Research Intranet pages.

A duly authorised external or internal audit of the project may be undertaken at any time.

Please contact the Research Ethics Office if you have any queries about on-going ethics clearance. The SHR

project number should be quoted in communication. Researchers should retain a copy of this email as part of

project recordkeeping. Best wishes for the project, in particular to Mr Rosenweg.

Yours sincerely, Keith Wilkins for Astrid Nordmann Secretary, SHESC2

Keith Wilkins Secretary, SUHREC & Research Ethics Officer Swinburne Research (H68) Swinburne University

of Technology P O Box 218 HAWTHORN VIC 3122 Tel +61 3 9214 5218 9214 5267

177

Appendix E. Call for Volunteers