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Attention and distraction in pilots: A study of the relationship between flight experience, aircraft control, and distraction management among pilots

Dissertation
Author: Steven T. Sparks
Abstract:
Scope and Method of Study. The purpose of this study was to evaluate how pilots comprised of different total flight hours, different instrument flight hours, and different pilot qualification levels performed in maintaining aircraft control along with properly identifying traffic advisories, while flying in an airport traffic pattern scenario. The study focused on the performance of 35 pilots assigned to one of three Pilot Level Groups and one of three Instrument Time Groups. Each participant was evaluated based on their ability to successfully accomplish a primary task, while interrupted by distractions during an airport traffic pattern scenario. Performance was scored based on the pilots ability to maintain aircraft control and properly identify traffic advisories announced by air traffic control (ATC). Findings and Conclusions. As set forth by the results, all pilot groups considered in this study experienced showed lapses in performance in maintaining aircraft control and properly identifying traffic advisories, while attending to a primary task. Experience in terms of total flight hours, instrument flight hours, and overall pilot qualifications should not be the only variables used in predicting pilot performance. Periodically and regardless of experience, pilots performed at levels much below their perceived capability. Pilots of all levels of experience are periodically prone to fall short of expectations for various reasons influencing their abilities inside the cockpit.

v TABLE OF CONTENTS

Chapter Page

I. INTRODUCTION.............................................................................................1

Problem Statement....................................................................................2 Purpose Statement.....................................................................................3 Research Hypotheses................................................................................3 Contribution Statement.............................................................................4 Organization of the Study.........................................................................5 Operational Definitions.............................................................................6

II. REVIEW OF THE LITERATURE.....................................................................9

Mental Cognition/Cognitive Psychology……………………………..... 9 Auditory Preemption Theory: Who Wins in the Interruption Contest ..............................................................................................11 Effects of Modality (Auditory versus Visual)........................................13 Attention and Performance, While Driving A Vehicle...........................14 Pilot Experience and Skill.......................................................................15 Defining Distractions/Interruptions: Who Are the Pilots at Risk?.........21 Broad Categories and Characteristics of Distractions............................23 Cognitive Ability in Handling Tasks and Distractive Situations............24 Saliency of the Cues Signaling the Need to Divert Attention................26 Concurrent Task Management (CTM)....................................................28 Chapter Summary...................................................................................31

III. METHODOLOGY..........................................................................................33

Hypotheses..............................................................................................33 Research Design......................................................................................34 Scenario Briefings...................................................................................35 Free-Play Exercise..................................................................................36 Traffic Pattern Scenario..........................................................................36 Overall Traffic Pattern............................................................................37 Downwind Leg: Starting Point...............................................................39 Base Leg..................................................................................................40 30 Degree Vector-to-Final......................................................................41

vi Chapter Page

Final Approach........................................................................................42 Scenario Conclusion...............................................................................43 Sampling Method....................................................................................43 Description of Pilot Groups, Base-Level, Mid-Level, and Advanced-Level Pilots...................................................................44 Low, Medium, and High Instrument Time Pilots...................................46 Consent Process......................................................................................47 Evaluation Methodology.........................................................................48 Scoring of Aircraft Control.....................................................................49 Scoring of Traffic Recognition...............................................................51 Storyboard-Timeline...............................................................................53 Validity Statement..................................................................................53 Test Setting.............................................................................................53 Equipment...............................................................................................54 Data Analysis..........................................................................................55 Measures of Central Tendency...............................................................55 Analysis of Variance (ANOVA).............................................................56 Assumptions............................................................................................56 Limitations..............................................................................................57

IV. FINDINGS ..................................................................................................58

Scenario Test Responses.........................................................................58 Measures of Central Tendency: Pilot Level Groups and Instrument Time Groups..................................................................................59 Pilot Level Group Results.......................................................................60 Instrument Time Pilot Group Results.....................................................67 Before Overt Flight Parameter Changes Occurred in the Traffic Pattern............................................................................................76 After Overt Flight Parameter Changes Occurred in the Traffic Pattern............................................................................................76 Analysis of Variance (ANOVA): Pilot Level Groups............................77 Analysis of Variance (ANOVA): Instrument Level Groups..................79

V. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS...................86

Testing of the Hypothesis.......................................................................87 Hypothesis One..............................................................................87 Hypothesis Two.............................................................................88 Conclusions.............................................................................................88 Future Use of the Traffic Pattern Scenario.............................................89 Homogeneity Between Pilot Groups......................................................90 Variance Between Pilot Groups..............................................................90

vii

Chapter Page

Areas of Future Research........................................................................91 Concluding Remarks...............................................................................93

REFERENCES............................................................................................................95

APPENDIXES...........................................................................................................106

APPENDIX A -- CONSENT FORM........................................................107

APPENDIX B -- FLIGHT SIMULATION BRIEFING CHECKLIST.................................................................110

APPENDIX C -- INDIVIDUAL PARTICIPANT TESTING DATES AND TIMES.................................................................113

APPENDIX D -- PARTICIPANT FLIGHT DEMOGRAPHIC INFORMATION...........................................................115

APPENDIX E -- STORYBOARD AND ATC TIME-LINE OF EVENTS.................................................................117

APPENDIX F -- PLAN VIEW OF THE TEST ENVIRONMENT.........122

APPENDIX G -- TRAFFIC ADVISORY COLOR AND SHAPE DIMENSIONS..............................................................124

APPENDIX H -- AIRCRAFT CONTROL PERFORMANCE CHART.........................................................................127

APPENDIX I -- IRB APPROVAL FORM.............................................129

viii LIST OF TABLES

Table Page

1. Description of Pilot Groups.....................................................................44.

2. Participants in Each Level Pilot Group....................................................... 45

3. Description of Instrument Time Pilots: Low Medium and High.................46

4. Participants in Each Instrument Time Pilot Group .....................................47

5. Times Where Aircraft Control Measures Were Recorded ………..............48

6. Scoring of Heading Control…………………………….. …………..........50

7. Scoring of Altitude Control………………..………….……………...........50

8. Scoring of Airspeed Control…………………………….…………............50

9. Scoring of Traffic Recognition Performance ………………………..........52

10. Cross Tabulation Chart (Pilot Level Groups x Instrument Time Groups)...60

11. Aircraft Control Results by Pilot Level, Before Overt Flight Parameter Changes Occurred in the Traffic Pattern......................................................61

12. Traffic Recognition Results by Pilot Level, Before Overt Flight Parameter Changes Occurred in the Traffic Pattern.....................................62

13. Aircraft Control Results by Pilot Level, After Overt Flight Parameter Changes Occurred in the Traffic Pattern.....................................64

14. Traffic Recognition Results by Pilot Level, After Overt Flight Parameter Changes Occurred in the Traffic Pattern.....................................66

15. Aircraft Control Results by Instrument Time, Before Overt Flight Parameter Changes Occurred in the Traffic Pattern...................................68

ix Table Page

16. Traffic Recognition Results by Instrument Time, Before Overt Flight Parameter Changes Occurred in the Traffic Pattern.....................................70

17. Aircraft Control Results by Instrument Time, After Overt Flight Parameter Changes Occurred in the Traffic Pattern.....................................72

18. Aircraft Control Results by Instrument Time, After Overt Flight Parameter Changes Occurred in the Traffic Pattern.....................................74

19. Pilot Level Groups Descriptives (Base, Mid, and Advanced)......................78

20. Test of Homogeneity of Variances (Pilot Level Groups).............................78

21. ANOVA-Pilot Level Groups .......................................................................79

22. Instrument Time Groups Descriptives (Low, Medium, and High)...............81

23. Test of Homogeneity of Variances (Instrument Time Groups)....................82

24. ANOVA-Instrument Time Groups...............................................................83

25. Robust Tests of Equality of Means...............................................................84

26. Post-Hoc Analysis for Instrument Time Groups: Multiple Comparisons....85

x LIST OF FIGURES

Figure Page

1. Traffic Pattern Setup: Plan View .................................................................38

2. Traffic Pattern Setup: Descent Profile View................................................38

3. Downwind Leg Sequence of Events.............................................................39

4. Base Leg Sequence of Events ......................................................................41

5. 30 Degree Vector-to-Final Leg Sequence of Events ...................................42

6. Mean of Aircraft Control Results by Pilot Level, Before Overt Flight Parameter Changes Occurred in the Traffic Pattern.....................................61

7. Mean of Traffic Recognition Results by Pilot Level, Before Overt Flight Parameter Changes Occurred in the Traffic Pattern.....................................63

8. Mean of Aircraft Control Results by Pilot Level, After Overt Flight Parameter Changes Occurred in the Traffic Pattern.....................................65

9. Mean of Traffic Recognition Results by Pilot Level, After Overt Flight Parameter Changes Occurred in the Traffic Pattern.....................................67

10. Mean of Aircraft Control Results by Instrument Time, Before Overt Flight Parameter Changes Occurred in the Traffic Pattern...........................69

11. Mean of Traffic Recognition Results by Instrument Time, Before Over Flight Parameter Changes Occurred in the Traffic Pattern...........................71

12. Mean of Aircraft Control Results by Instrument Time, After Flight Parameter Changes Occurred in the Traffic Pattern.....................................73

13. Mean of Traffic Recognition Results by Instrument Time, After Flight Parameter Changes Occurred in the Traffic Pattern.....................................75

1 CHAPTER I

INTRODUCTION

Distraction management in aircraft cockpits has been widely studied in the field of aviation (Bureau, 2005; Dismukes, 1999; Gillie, 1989; Helleberg, 2001; Loukopoulos, 2001). Passive systems, such as state-of-the-art avionics and other automation features in the cockpit, present so many competing interests and monitoring requirements that a pilot and/or flight crew may find it difficult to multi-task while flying an aircraft. Sometimes multiple tasks come one at a time, while others demand a pilot’s attention simultaneously (Damos, 2001; Dismukes, 1999; Funk 1991; Helleberg, 2001; Hoover, 2005; Wickens, 2001). Other studies conducted on aviation distraction management have focused on events ranging from a pilot’s cognitive ability (O’Hare, 2006; Robertson, 2005; Tsang 1996); distracting situations (Ho, 2004; Loukopoulos, 2001; Nikolic, 2004; Purcell, 2001; Sarter, 2003; Symer, 1999); different types of distractions (visual or audible) (Damos, 1998; Funk, 1999; Symer, 1999; Wickens, 2001); modality of distraction; and duration of distraction. Industry stakeholders such as the Federal Aviation Administration (FAA), general and commercial flight operators and passengers are very much interested in knowing whether or not pilots are being properly trained to handle distractions in the cockpit. Managing both routine (within normal operating parameters) and non-routine (emergency

2 procedures) events in the cockpit is a required skill for pilots to be effective in conducting their job responsibilities (Chou, 1996; Colvin, 2005; Funk, 1991). Whether a flight is conducted in a single or multi-crew environment, distraction management is a key element influencing the safety of a flight. Even with the number of studies already conducted in the field of distraction management, it is still challenging to comprehend and document how effective pilots are at avoiding distractions in the cockpit (Bar-Eli, 1998; Helleberg, 2003; Maas, 2006). Pilots, because of their training and because of the amount of experience after their training, represent various levels of experience. Each pilot possesses his or her own unique set of capabilities and motivational factors (Hunter, Kochan, & Robertson, 2005). This study was designed to evaluate the relationship between pilot experience, currency, and a pilot’s ability to inhibit unwanted distractions, while attending to a primary task.

Problem Statement

In aviation, flight experience has been recognized as a critical component in helping determine a pilot’s skill level (Bell, 1995; Bellenkes, 1999; Denihan, 2005; Guilkey, 1997). However, defining a pilot’s level of safety and/or skill level must take into consideration a variety of factors such as training and total instrument flight hours. If total flight experience was the only variable used in determining pilot skill then it would be almost impossible to explain why accidents still occur involving high-time pilots (Helmreich, 2001; Lubner, 1992; Wickens, 2001)

3 Purpose Statement

The purpose of this study was to evaluate how pilots comprised of different total flight hours, different instrument flight hours, and different pilot qualification levels performed in maintaining aircraft control along with properly identifying traffic advisories, while flying in an airport traffic pattern scenario. A high level of attention is needed by pilots and ATC during traffic pattern operations to insure separation minimums are not compromised due to improper aircraft control (Barshi, 1997; Endsley 2001). Results uncovered from this study will help the reader understand how more than one factor influences pilot performance, while flying in an airport traffic pattern.

Research Hypotheses

The hypotheses for this study are as follows: H 01 : In terms of operating an aircraft in the traffic pattern during which traffic advisories are announced, there is no difference in traffic recognition or aircraft control between basic, mid, and advanced level pilots. H 02 : In terms of operating an aircraft in the traffic pattern during which traffic advisories are announced, there is no difference in traffic recognition or aircraft control between pilots with low, medium, and high instrument time.

4 Contribution Statement

From this research, industry stakeholders will have better understanding in how certain distractions influence pilot performance in the cockpit. Results from this study will also help training institutions develop effective training initiatives for helping mitigate the negative effects distractions have on pilot performance. Furthermore, this study will help industry evaluate the effectiveness of how pilots with various levels of experience are trained regarding their management of distractions in the cockpit. Additionally, this research will highlight how pilots with different levels of flight experience and pilot qualifications handle distractions in regards to how effectively they attend to a primary task. Practical Test Standards (PTS), defined by the Federal Aviation Administration (FAA), explicitly require evaluators to introduce distractions during check rides for determining the capabilities of pilots in handling such instances. Pilots must demonstrate the ability to multi-task effectively and efficiently in the process of managing their situational awareness for insuring the safety of a flight (Endsley, 2001; Garland, 2004; Prince, 2007). Pilots must demonstrate specific skill sets for managing disruptions efficiently. Because of the dynamic involved in flying, pilots must be educated and trained on how best to focus their attention on variables most critical to the safety of any given flight (Helleberg, 2003; Latorella, 1997; Sears, 2000). Results from this study will reveal some of the critical aspects distractions can have on pilot performance and how such distractions influence pilot performance. The purpose for comparing pilot groups categorized based on total flight hours, total instrument hours, and overall pilot qualifications was to evaluate whether pilots of

5 one classification handled distractions better than pilots of a different classification, while attempting to achieve a primary task. Sampling the performance of these pilot groups establishes a baseline for generalizing characteristic and performance measures across the related pilot population. This evaluation will help industry stakeholders evaluate the effectiveness of the various training programs and techniques to which pilots are exposed for improving their skills in identifying, interrupting, and efficiently handling distractions. Additionally, results from this study generated data for comparing pilot-to- pilot performance and how their levels of experience influenced performance outcomes.

Organization of the Study

Although there have been many initiatives focused on investigating pilot performance (Cowings, 2001; Fanjoy, 2004; Helleberg, 2003; Kole, 2006; McCoy, 1995), this study looks particularly at how pilot experience and traffic advisories influence aircraft control and traffic recognition, while pilots fly an airport traffic pattern scenario. In Chapter II, the reader will find an overview of the various topics related to this study and how previous discoveries from other research initiatives have contributed to the body of knowledge as it relates to pilot performance/distraction management/aviation cognitive psychology/and pilot experience. Chapter III discusses the methodology used for designing the airport traffic pattern scenario. In addition, the analytical methods used for breaking the study down into measurable sections will be discussed and explained. Chapter IV reports the study’s overall findings and Chapter V closes with a summary of the results, recommendations, and final suggestions regarding future research initiatives.

6 Operational Definitions

The following definitions are provided to explain and/or clarify the meaning of certain words as they relate to the context and design of this study. Pilot experience: the various situations, total flight hours, total instrument flight hours, and certificates/ratings achieved by a pilot. Routine traffic advisories: information given to pilots about other aircraft in the vicinity that might affect flight safety. Aircraft control parameters are based on FAA acceptable deviations from heading, airspeed, and altitude. Pilot distraction: any event that interrupts the normal control of aircraft and has a negative effect, such as making a pilot lose control in one or more of the aircraft control parameters (Dismukes, 1999). Upset of aircraft control occurs when one or more of the aircraft control parameters exceeds FAA acceptable deviations from heading, airspeed, and altitude. Recovery time: the time taken to bring all three parameters into the acceptable range (heading, airspeed, and altitude). Reasonable amount of time to reestablish aircraft control is the time it takes a pilot to change and reestablish one or all three flight parameters using standard control inputs. Traffic Recognition: receipt of confirmation from a pilot that he or she positively and correctly sees the traffic advisory announced by air traffic control Flight parameters: the heading, altitude, and airspeed indications associated with an aircraft.

7 Before overt flight parameter changes occurred in the traffic pattern: the beginning segment of the traffic pattern scenario that did not involve flight parameter change instructions given by air traffic control. After overt flight parameter changes occurred in the traffic pattern: the latter segment of the traffic pattern scenario that involved flight parameter change instructions given by air traffic control. Total instrument time: the documented time flown strictly by reference to an aircraft’s flight instruments. FAA Practical Test Standards (PTS): specific flight condition requirements that pilots are responsible for demonstrating, while being evaluated in the course of obtaining certain flight privileges. Reestablished aircraft control: the point where all three flight parameters are within tolerance levels. Base-Level Pilots: Private Pilots with less than 100 hours of total flight experience. Mid-Level Pilots: Private/Instrument Rated pilots with between 100 and 250 hours of total flight experience. Advanced-Level Pilots: Commercial/Instrument Rated pilots with between 250 and 400 hours of total flight experience. Visual Flight Conditions (VFR): weather conditions appropriate for non- instrument flight. Instrument Flight Conditions (IFR): weather conditions prescribed below those authorized for VFR.

8 Low Instrument Time Pilots: pilots with between 1-20 hours of total instrument time logged. Medium Instrument Time Pilots: pilots with between 31-50 hours of total instrument time logged. High Instrument Time Pilots: pilots with between 51-90 hours of total instrument time logged. Shared level of currency: a condition in which a pilot meets FAA requirements to serve as Pilot-In-Command under flight conditions respective of that pilot’s qualification level.

9 CHAPTER II

REVIEW OF THE LITERATURE

Pilots are tasked with dividing their attention during the process of managing various assignments, while flying an aircraft. This study looked at how traffic advisories from ATC affect pilots’ ability in maintaining aircraft control, while flying an airport traffic pattern scenario. Research indicates auditory distractions can cause problems for pilots in completing certain tasks effectively. Flight parameters assigned by ATC require pilots to maintain specific headings, altitudes, and/or airspeeds to insure separation minimums. Separation minimums established by the FAA serve to protect persons and property, and to support an efficient flow of air traffic. Pilots are required to comply with ATC instructions only to the point where aircraft safety and pilot capability are not compromised.

Mental Cognition/Cognitive Psychology

The methods by which humans process information is an area widely studied in the field of aviation (Athanassiou, 2003; Beaubien, 2003; Guilkey, 1997). Various cognitive models as they relate to aviation have strongly influenced many factors in regards to aviation and pilot performance (O’Hare, 2006; Robertson, 2005). Cognitive processing influences the design of aircraft systems, cockpit layouts, automation

10 channeling, general flight procedures, and other facets influencing pilot efficiency and safety. Scientific research conducted in various aviation domains as well as in other contexts outside the field of aviation has led to numerous cognitive models being designed from which to assess/predict pilot performance and behavior. Attention tunneling and pilot dwell time are major factors influencing performance in the cockpit (Alexander, 2005; Beer, 1996; Helleberg, 2003; Wickens, 2000). At any given time, pilots are faced with a variety of choices on what and where to focus their attention. In addition to where pilots focus their attention, the amount of time they attend to each specific domain will influence overall performance (Fenner, 1999; Helleberg, 2003; Kasarskis, 2001). Domains attended to by pilots for prolonged periods of time will usually cause neglect to other domains of importance. For example, new instrument students are prone towards fixating on one or two instruments and neglecting areas of importance (Beer, 1996; Bell, 1995; Bellenkes, 1999; Kasarskis, 2001; Wong, 1999). Depending on an aircraft’s flight status, the amount of dwell time (fixation) a pilot spends on a specific domain can have positive and/or negative consequences to overall performance (Wong, 1999). Many situations in the cockpit require a pilot’s full attention to bring about positively safe and efficient results. Other situations may require pilots to share their attention resources equally or in unison between different factors for bringing positive resolve to each (Dismukes, 2001; Funk, 1991; Iani, 2004).

11 Auditory Preemption Theory: Who Wins in the

Interruption Contest?

The strong influence auditory factors have on pilot performance has spearheaded much research in related areas of study. Through research, Human Factor Scientists have uncovered many interesting findings related to attention and how different types of distractions influence cognitive processing in pilots. Researchers and Human Factor experts try to lay claim to which type of distraction causes the greatest effect in occupying human attention (Dismukes, 1999; Gillie, 1989; Helleberg, 2001; Latorella, 1997; Loukopoulos 2001). The effects modality distractive occurrences possess can have influence on the overall disruptiveness each occurrence carries (Helleberg, 2001; Symer 1999). Of the four major distraction categories identified by Dismukes (1999), auditory, visual, head-down time, and handling abnormalities, researchers rank auditory distractions as being the most distractive to pilot performance. Auditory Preemption Theory states auditory announcements are prone to capture pilot attention more effectively and for longer periods of time compared to other distracters (Wickens, 2005). Because of such influence, automation activated auditory announcements in the cockpit are becoming the norm (Damos, 2005). Auditory channeling methodologies are highly utilized in the design of new aircraft cockpit environments (Barshi, 1997; Clamann, 2004; Driscoll, 2002). These methodologies are used to insure pilots are supplied with time critical bits of information for increasing awareness and safety. Auditory announcements made in the cockpit are highly capable and strongly probable of capturing pilot attention regardless of the activity being attended to at the present time (Colcombe, 2006; Helleberg, 2003). Theory

12 indicates that pilots are more likely to attend to auditory distractions more frequently, because of the amount of attention required to fully capture, process, and respond to auditory cues (Ho, 2004). Auditory cues increase the probability in pilots to forget or misinterpret instructions when compared to receiving information in text form (Helleberg, 2003; Ho, 2004; Sarter, 2003). Because of the general limits of working memory, most pilots are conditioned to providing auditory information cues with the highest degree of attention (Dismukes, 1999). As studies have indicated, auditory interruption requires considerable working memory (Sohn, 1999). In comparison to visual cues, communicating via an auditory method can tax working memory up to 4 or 5 times as much in certain cases (Wickens, 2005). To help counteract the consequences of auditory disruptions, researchers and designers have experimented with data link technology as a means for communicating with pilots in the cockpit (Helleberg, 2003; Olson, 2000). Historically speaking, many aircraft accidents and/or incidents have occurred because of pilot misinterpretation of auditory information presented to them via ATC, company dispatch, and/or other crew members (Barshi, 1997; Driscoll, 2002; Prinzo, 2002). Data link technology provides a means for presenting instructional information to pilots in text form to help mitigate or reduce the chances of pilots misinterpreting the information. Data link technology also serves as a backup reference system which pilots can revisit when time permits to confirm instructional accuracy.

13

Effects of Modality (Auditory versus Visual)

Studying the effects of modality (auditory versus visual) on pilot attention has revealed numerous results on pilot performance (Helleberg, 2001; Symer, 1999). The modality of an event has major influence on pilot task prioritization in the cockpit. Many cockpit alert systems are integrating the use of auditory announcements to influence pilot attention versus using visual methods for obtaining pilot attention to influence their prioritization level (Sarter, 2003; Wickens, 2002). As previously mentioned, auditory methodologies have a higher probability in capturing pilot attention compared to visual methods. It has been hypothesized that many past Controlled Flight Into Terrain (CFIT) accidents could have been prevented had the pilot(s) been alerted in different fashion compared to having to rely solely on visual interpretation of their flight instruments for determining their relationship to external obstacles (Bliss, 2003; Chou, 1996). Presently, more high risk flight alert systems such as Enhanced Ground Proximity Warning Systems are now using digitized voice commands to capture pilots’ attention, causing them to take more rapid and timely action in resolving their aircraft’s critical flight situation. The influence voice commands have on pilot reaction time and prioritization levels is strongly taken into consideration as new cockpit technologies for alerting pilots are being developed (Helleberg, 2003; Ho, 2004; Purcell, 2001;Ulla, 2006; Wickens, 2001).

14 Attention and Performance While Driving A Vehicle

Extensive research has been conducted involving human attention and its influence on vehicle driving performance (Regan, 2002). Similar to flying an aircraft, driving a vehicle often involves performing such duties in distractive environments. Many car and/or truck accidents occur each year because of driver distractibility. Safety and industry experts are striving to mitigate these occurrences; however, yearly results continue to indicate that driver distractibility is one of the fasting growing causes of accidents on or around highways (Regan, 2002). High amounts of attention are often required of persons driving vehicles. A driver’s ability to properly time-share and focus between tasks is often reflective of his or her past driving history (Regan, 2002). A major factor influencing driver distractibility has been the advent and integration of in-vehicle technology (IVT) (Horrey, 2005; Underwood, 2007). In-vehicle technologies such as global positioning systems (GPS), advanced alert systems, advanced radio/entertainment systems, and other enhanced navigational systems can tax a driver’s ability to maintain vehicle control. These technologies have both positive and negative attributes and only through additional research and experience will their true overall value be determined. Another major source of distraction to those driving vehicles is the use of cell phones. The use of cell phone technology while driving has increased substantially over the past several years (Milewski, 2006; Rakauskas, 2004). Research has helped reveal many of the negative aspects talking on a cell phone can have on driving performance. Driver attention and degree of situational awareness can be substantially degraded by

15 talking on cell phones (Rakauskas, 2004). Such influences can cause detrimental effects to even the most rudimentary of driving tasks. The degree of risk between novice and experienced drivers has also been explored to evaluate the effect experience has on driving performance (Underwood, 2007). The diversity defining the driving population makes it extremely difficult to generalize certain research findings; however, statistics gathered from insurance agencies and industry sources have made predicting the number of accidents/incidents scientifically accurate (Horrey, 2005). Researchers use demographic information to aid in predicting driver performance. Factors such as age, general driving environment, type of vehicle predominately driven, and years of experience all influence performance expectations. The ability to forecast high risk areas more accurately has led to the design of certain safeguards instilled to better protect drivers and other persons and/or property (Horrey, 2005; Milewski, 2006; Rakauskas, 2004; Tham, 1995).

Pilot Experience and Skill

The variables helping define pilot experience are many. Characteristics such as hours accumulated, certificates/ratings held, aircraft flown, environment predominately flown in, and years of operational experience are just a sample of the variables often considered. However, the one variable that frequently receives the most attention in determining pilot experience is total flight time logged (Bell, 1995; Cain, 2001; Guilkey, 1997). Federal Aviation Regulations require all registered pilots to physically document hours of flight experience conducted for the purpose of meeting currency requirements. However, it is somewhat customary for most pilots to meticulously document all flight

16 hours/minutes accumulated for progressive purposes. From total accumulated flight hours, pilots are often judged in regards to their level of proficiency and/or safety (Bell, 1995; Guilkey, 1997). It is seemingly fair to say that total flight hours logged is a good snap-shot from which to assess the general experience level of pilots. However, just knowing the total accumulated hours flown does not serve as an excuse for making unfounded assumptions. Industry regulators and aviation researchers are forever trying to determine what methodologies serve best in predicting pilot performance in relationship to experience as defined by total flight time (Kole, 2006). Also, regulators and industry work together in collaboration to design and implement various initiatives for pilots to obtain flight experience as efficiently and safely as possible. Research indicates total flight hours should not serve as the only measure used in differentiating novice from expert pilots (Bell, 1995; Fanjoy, 2004; Tsang, 1996). Total hours accumulated can provide a general sense of what a pilot has experienced, but total hours logged is not always a fair overall predictor of pilot proficiency and/or safety. Results revealed in a fairly recently published book indicated that pilots can be at more risk as they accumulate additional hours of flight experience (Craig, 2001). Craig also reveals various hourly ranges where pilots become more susceptive to committing error and thus increasing overall risk. Problem solving skills within pilots of various experience levels is an area of great interest and research (Adams, 2002; Craig, 1998; Dawes, 2006). The processes by which pilots make aeronautical decisions can profoundly influence performance levels in pilots. Numerous research studies have been conducted comparing and contrasting the

17 various problem solving techniques used between expert and novice pilots (Bell, 1995; Denihan, 2005; Guilkey, 1997; Kasarskis, 2001). The level of problem solving skills within pilots can be either positively or negatively correlated to their levels of experience (Guilkey, 1997). Pilots of various experience levels have been observed in how they solve complex and rudimentary tasks, while assuming the primary responsibilities of flying an aircraft (McKinney, 1991). Pilots are prone towards displaying a variety of methodologies when trying to solve normal and/or abnormal flight situations (Bell, 1995). A Progressive Problem Solving (PPS) methodology for solving both normal and abnormal in-flight events is the preferred method of action versus the knee-jerk reactive methods sometimes used by certain pilots (Guilkey, 1997). Progressive Problem Solving style attempts to figure out the root cause of an occurrence by using deductive reasoning to figure out the most optimal way of bringing proper resolution to the situation. Many aircraft accidents are influenced by a chain of events, rather than being caused by one single factor (Helmreich, 2000). Many system designs have been developed to provide redundancy to help mitigate the effect of singular errors from causing catastrophic failures and/or accidents (Lubner, 1992). Such system designs provide pilots with greater flexibility in time for handling abnormal events in the cockpit. By giving pilots more time to analyze abnormal events, many pilots feel less pressured to make hasty decisions versus possibly taking more time to derive the most effective solution to a given problem. Past research indicates that pilots are more likely to make better decisions when they are not pressured by time or other tasks when trying to handle abnormal events in-flight (Adams, 2002; Bliss, 2003; Craig, 1998). Certain flight instructors will teach their

18 students not to overreact to any abnormal situation until they are consciously aware of the variables influencing the occurrence. In such cases, pilots must utilize all available resources to exhaust all possible solutions for resolving the event. A driving force behind Progressive Problem Solving methodology is the appropriate use of Crew Resource Management skills and the ability to remain as calm as possible for making logical decisions (Craig, 1998; Helmreich, 1999). The idea that a pilot’s level of safety is directly proportional to his or her level of experience is highly debated in various aviation realms. As research indicates, aviation safety is much more difficult to quantify and/or predict than by just taking into account the number of hours a pilot has flown during his or her career (Bell, 1995). Accident results reveal that total hours logged does not preclude a pilot from being involved in accidents and/or incidents. However, it is fair to state that experience reflective of hours flown can enhance a pilot’s awareness of certain risk factors (Chou, 1996; Craig, 1998; Helmreich, 2000). Also, experience serves to help improve a pilot’s general skill sets compared to pilots without such experience. The notion that overall safety is strictly attributed to a pilot’s experience level is unfounded (Soares, 2002). However, this very topic has been of much debate over past years and was recently addressed in regards to the mandatory retirement age for airline pilots. Until just recently, airline pilots were required by federal law to retire their flying privileges as airline pilots at the age of 60. However, in 2007, authorities extended the mandatory retirement age to 65. This age extension was granted for various reasons. First, studies indicated piloting skills were not always adversely affected by such pilots reaching an arbitrary age (Bell, 1995; Lubner, 1992; Schnabel, 1999). Secondly, the

Full document contains 143 pages
Abstract: Scope and Method of Study. The purpose of this study was to evaluate how pilots comprised of different total flight hours, different instrument flight hours, and different pilot qualification levels performed in maintaining aircraft control along with properly identifying traffic advisories, while flying in an airport traffic pattern scenario. The study focused on the performance of 35 pilots assigned to one of three Pilot Level Groups and one of three Instrument Time Groups. Each participant was evaluated based on their ability to successfully accomplish a primary task, while interrupted by distractions during an airport traffic pattern scenario. Performance was scored based on the pilots ability to maintain aircraft control and properly identify traffic advisories announced by air traffic control (ATC). Findings and Conclusions. As set forth by the results, all pilot groups considered in this study experienced showed lapses in performance in maintaining aircraft control and properly identifying traffic advisories, while attending to a primary task. Experience in terms of total flight hours, instrument flight hours, and overall pilot qualifications should not be the only variables used in predicting pilot performance. Periodically and regardless of experience, pilots performed at levels much below their perceived capability. Pilots of all levels of experience are periodically prone to fall short of expectations for various reasons influencing their abilities inside the cockpit.