# Fatigue and FPI Inspection

# 

Colin G. Drury, Brian D. Green, & Jui-Feng Lin

Research Institute for Safety and Security in Transportation

University at Buffalo: SUNY

429 Bell Hall, Buffalo, NY 14260

# 

# 

# Year 3/4 FAA Report

December 2006

Prepared for:

Federal Aviation Administration

William J. Hughes Technical Center

Atlantic City International Airport, NJ 08405

Contract #03-G-012

## Executive Summary 

## This report gives the results of a series of studies extending over a three year period with the aim of understanding the effects of inspector fatigue on the performance of complex non-destructive inspection tasks such as Fluorescent Penetrant Inspection (FPI). The work was promoted by a series of inspection failures which resulted in accidents (Sioux City, Pensacola) where Non-Destructive Inspection (NDI) techniques were used by inspectors working relatively normal schedules. Earlier years reported the results of a literature review of fatigue and inspection, a survey of hours of work of NDI personnel, development and validation of a computer-based simulation of FPI, and a factorial experiment using the simulator to evaluate the effects of factors important in the literature. This experiment used industrially- experienced members of the general public of generally middle age, performing the experiment at the Research Institute for Safety and Security in Transportation (RISST) laboratory at University at Buffalo (UB). It produced results somewhat as expected but did not find the "vigilance decrement" in probability of detection over time-on-task typical of many laboratory studies of long-term detection performance. Neither was performance measures correlated with sleep measures or ratings by participants of sleepiness, workload or fatigue. Short breaks in the task (3 minutes every 20 minutes) were found to be effective only under limited conditions. For these reasons, the final phase of the project partially replicated the experiment using aviation inspectors at their normal place of work to increase the face validity of any findings.

This experiment repeated Experiment 1 with the exception of factors that
could not be controlled in a maintenance hangar setting. These were
Lights, because we had to take the rooms offered to us by airline
partners, and Shifts where scheduling limitations meant that inspectors
could not be tested at both 0300 and 0900. The Lights factor was omitted
and the Shifts factor became a between-participants factor instead of
within-participants. Also we had to relax the 0300 condition as some
inspectors on night shifts were only available at 0000 instead of 0300.
Four sites were used in the experiment, two at airlines and two at
third-party maintenance contractors. In all 70 participants performed
inspection, and, although some had little specific experience with FPI,
all were familiar with the visual inspection task which is the part of
FPI tested by the simulation. This experiment was thus a 2^3^
between-participants factorial experiment for Shifts, Period and Breaks
as in Experiment 1 and Time on Task again a within-participants factor
at either 3 or 6 levels depending upon the 1 or 2 Hours factor. Using
the same measurements as Experiment 1, most inspectors found the program
to be a valid simulation of FPI except for one site where lighting had
recently been changed so that the predominant colour of the blades was
no longer what the inspectors had been used to.

For Experiment 2, there were significant main effects of Time on Task
(PoD and speed) Breaks (PoFA) and Period (speed), and interactions
between Shift and Breaks for speed and PoFA. The interaction of Shift
and Breaks was very similar to Experiment 1, with breaks helping in the
day but not at night, primarily for the Hour Period. As in Experiment 1,
there were again very few (1 out of 16) significant correlations between
performance and sleep measures. The main interest lay in the different
Time on Task effects in the two experiments. There was a decrease in PoD
as a function on Time on Task for Experiment 2 but not for Experiment 1,
whereas for PoFA there was a decrease over time in both experiments.
This would give a classic vigilance decrement result for Experiment 2,
but not for Experiment 1. Note also that performance in Experiment 2 was
better overall than in Experiment 1, with higher PoD and about the same
PoFA rates. In terms of speed, while both experiments start at about the
same rate of 12 blades per 20 min (36 per hour) the participants in
Experiment 2 became much faster, up to 60 blades per hour for the 2 hour
Period and 75 per hour for the 1 hour Period.

Conclusions and implications of these studies for NDI practice are:

1.  The FPI simulation experiment reported here was validated using
    industry inspectors, and produced results that confirmed the
    validation, e.g. PoD curves of the expected form in Experiment 1,
    and expected effects of day vs. night in measures of sleep and
    fatigue. These two experiments are probably a more reliable guide to
    fatigue in inspection than the research literature on vigilance
    tasks. Experiment 2 using experienced aviation inspectors produced
    the expected better performance than Experiment 1 that used
    industrially-experienced members of the general public. These
    findings together show that the experiments were valid simulations
    of NDI performance. Where there was a discrepancy between the
    results of the two experiments, we should consider Experiment 2 as
    most valid.

2.  There was almost no correlation between sleep measures and
    performance measures in either experiment: this task appears more
    resistant to fatigue than typical laboratory vigilance tasks.
    Inspectors appear to devote enough mental resources to the task to
    maintain performance even under difficult conditions, e.g. starting
    at 0300 or working for two hours without pause.

3.  There was a steady decline in PoD performance in Experiment 2 but
    not in Experiment 1. Inspectors should understand this and limit
    periods of continuous inspection, although no specific time limit
    can be recommended from these studies. For NDI tasks such as FPI
    where considerable non-inspection work is needed (e.g. part
    preparation in the FPI process), work should be organized so that
    each inspector processes their own parts so as to give shorter
    periods of continuous inspection.

4.  Short breaks from continuous inspection every 20 minutes were
    effective in improving performance and reported fatigue, workload
    and sleepiness in both experiments in daytime, but not at night, and
    not for the one-hour inspection period.

5.  Higher levels of illumination improve performance slightly in the
    day, but darker conditions are better at night.

## Introduction

**Results from Previous Years:** Inspection of airframes and engines of
commercial airliners is vital to public safety as all fatigue programs
for aircraft are based on the ability of inspection systems to detect
defects with a known reliability before they become dangerous (Goranson
and Rogers, 1983). Inspection reliability is the result of tasks
performed by an often complex human machine system so that all aspects
of human/ system interaction need to be understood if accurate estimates
of reliability are to be obtained. Recently, we have completed a series
of studies leading to Good Practices Guides for the principle
technologies of aviation Non-Destructive Inspection (NDI), starting from
the technique of Fluorescent Penetrant Inspection (FPI), see Drury
(2003). A number of highly-visible accidents as a result of failures of
the inspection system, such as engine failures at Pensacola and Sioux
City have led to a number of initiatives to examine aspects of inspector
performance. Because in both of these incidents the inspectors were
working under normal conditions, the National Transportation Board asked
the Federal Aviation Administration in the USA to investigate issues of
fatigue in an FPI task. The current project is the result of that
request.

This three year project began by examining the literature of fatigue,
inspection and vigilance and the findings showed very little similarity
between the complex inspection tasks performed in the hangar and the
classic laboratory vigilance studies (Drury, Saran and Schultz, 2003).
Temporal effects likely to impact inspection were classified as Week
Scale (shift work, cumulative fatigue), Day scale (circadian rhythms),
Hour scale (vigilance) and Minute scale (sequential effects in
repetitive tasks), concluding that the last of these was of only minor
importance. As noted by Horowitz, Cade, Wolfe, and Cziesler (2003),
vigilance decrements may only be found in tasks having no search
element, whereas most inspection tasks do contain search.

A survey of 40 NDI inspectors at two airlines showed a rather older
population (mean age 47 years) performing NDI tasks over about 2-hour
intervals for a 40-hour week, with about 10 min breaks between
inspection intervals. This set the stage for developing an FPI
simulation task using high-quality digital photographs of six views of
each of 63 engine blades. Cracks were inserted using Photoshop as were
excess penetrant patches typical of the task of FPI. Inspectors could
view each blade from each viewpoint, swab off excess penetrant patches
with a swab tool using the mouse, and discover whether a patch in fact
concealed a crack. Cracks could sometimes be seen on multiple faces, as
in normal FPI. Finally, participants would write up a short description
of each crack found using a dialog box similar to the Non-Routine Repair
report used on the hangar floor. This simulation was tested with NDI
inspectors from one airline who agreed that conclusions from using the
simulation would be valid. This simulation was used in the two
experiments detailed in this report to provide findings and
recommendations to the FAA concerning fatigue and hours of work for NDI
inspectors. The original aim was to use the initial 2^5^ factorial
experiment to determine which interactions were significant, and then
explore these in more detail in subsequent parametric experiments.
However after Experiment 1 there was little need for experiments with
more levels of the significant factors but a great need to confirm the
findings on a population of aviation inspectors.

**Experiment 1**: The first experiment was designed to test known
effects from the vigilance and fatigue literature, here Shift (0300 vs.
0900), Period (1 vs. 2 hour task), Defect Rate (7% vs. 15%) and Time on
Task (each 20 min interval). Additional factors represented potential
interventions: Breaks (None vs. 3 minute break after each 20 min
inspection) and Lights (20 vs. 400 Lux). The design chosen for this
experiment was a between-participants design with 5
industrially-experienced participants in each of the 16 (=2^4^) cells of
the design. With the addition of a single within-participants factor of
Shift, this gave a 2^5^ design so that many two-way interactions could
be measured. We measured performance (probabilities of detection and
false alarm, time per blade), sleep in the prior 24 hours using an
ActiWatch, and rated fatigue and sleepiness from validated scales.

Three major findings were negative, casting doubts upon the
correspondence between FPI inspection and the vigilance literature.
First, there were almost no correlations between any performance
measures and any sleep measures. Second, there was only a very small
effect of time-on-task on performance, mainly the probability of false
alarm falling over the one or two hours of the task. Probability of
detection remained approximately constant. Finally, the probability of a
defect occurring had no effect on performance measures. Vigilance tasks
are sensitive to all of these factors, but our experiment was not. This
was not due to low power of the design: with 80 participants spending 4
to 6 hours each there was considerable statistical power, and indeed
other effects were found showing that the design was sensitive.

Positive effects included the factors of Shift, Beaks and Period. For
the two-hour participants, breaks at 20 minute intervals helped
performance (primarily PoFA) and speed when used during the day at 0900,
but had the opposite effect at night (0300). This interaction was
eliminated or perhaps slightly reversed for the one-hour participants.
Breaks are a good intervention, but only under certain conditions:
during the day and with longer continuous inspection sessions. There was
also higher throughput when the lighting conditions matched the outside
lighting. The pattern of sleep with shift, where there was a longer
previous sleep for 0900 than for 0300, showed that participants did not
change their sleep start time much in anticipation of a 0300 task
appointment: they just awoke earlier. This loss of sleep before the 0300
task was reflected in many measures of sleepiness and fatigue, all of
which were worse for the 0300 task.

The effects of crack size on PoD were extracted from the data and
standard probit curves fitted to produce a PoD curve, in the same way as
for other NDI reliability studies. An excellent fit was obtained, with a
second parameter of contrast also being a good fit to the data. This
showed again the validity of the experimental simulation.

**Experiment 2:** The current study tested aviation inspectors from four
different FAA certified inspection cites across the country to increase
the face validity of the previous results in an actual maintenance
setting. Due to the availability of inspectors some aspects of the
previous experimental design were modified. The earlier study utilized a
partially within participants design having each participant run the
simulation at 0300 and 0900. This was impossible with aviation
inspectors because their work shifts would have to be altered so that
they could be available at both times. This would have then altered
their sleep patterns and likely produced additional fatigue, thereby
biasing the study. Therefore this study utilized a fully between
subjects design. Participants ran the simulation only once during their
normal work shift.

Because a between participants design was used, it was necessary to run
more participants than in Experiment 1 to reach adequate statistical
power. Since such a large number of aviation inspectors would be
impractical to test it was decided to reduce the number of factors in
this phase of the study. All inspectors were presented with the same
defect rate presented in the same order. The lighting condition was also
eliminated for the current study (all participants were tested under
normal lighting conditions).

In addition to the changes mentioned above, a few changes to the
simulation program were made. The method by which inspectors reported a
crack was changed from a text box (with a written description of the
location of the crack provided by the inspector) to a mouse-based tool
(inspectors simply clicked on the crack to confirm its location). This
change provided a more precise account of what the inspector believed to
be a crack as well as more closely replicating the way an inspector
would mark a crack on a real blade (with a grease pencil). Reporting in
this manner also presented the inspector with the next blade
automatically, which allows for more precise calculation of search
times.

## Methodology

### Participants: A total of seventy professional aviation inspectors, not limited to NDT certified inspectors, were tested using the FPI simulation program. These inspectors were from four domestic inspection stations throughout the US. All inspectors were male, mean age 43.3 years, mean years of experience 8.9 years.

**Table 1. Characteristics of inspectors used in Experiment 2**

### Simulation: Inspection performance data was collected using a Fluorescent Penetrant Simulation program (v2.0: an upgraded version of the simulation used in a previous phase of this study). The description of the simulation software in the Introduction can be supplemented with reference to the previous reports.

### Procedures: Data collection began with visual acuity and color vision testing and the Group Embedded Figures Test (GEFT) to test for Field Dependence. Five inspectors failed the color vision test but were allowed to continue in the study since their color vision deficiency did not adversely affect their job performance. Inspectors then completed a demographic form and the Adapted Folkhard fatigue measure. 

Participants then attended an informational session which provided
information regarding FPI mainly for those inspectors were not currently
certified in NDT who were somewhat unfamiliar with the process. This
training session was read from a script to assure that all inspectors
had the same knowledge regarding the simulation program. It explained
how to manipulate the blade, swab penetrant from the blade, zoom in,
report defects and switch from blade to blade. At the end of the
training, inspectors viewed six practice blades (three blades were
defective) under the supervision of the experimenter who allowed
participants to search the blades and ask questions. Feedback was
provided to assure that cracks on all three defective blades were seen
and correctly reported.

After completion of the training program, inspectors completed the
Stanford Sleepiness Scale (SSS) and the Swedish Occupational Fatigue
Inventory (SOFI). These tests were also given to provide information
regarding the inspectors' baseline fatigue before the simulation.

Inspectors then completed the simulation program working during their
normal work shift. This created a day condition (9am) and a night
condition (either 12am or 3am depending on the work schedule at the
inspection site). Participants either ran the simulation for 1 hour or 2
hours (time on task). Some inspectors received a 3 minute break for
every 20 minutes run time while others ran the entire duration without a
break (Break condition). The assignment of inspectors to one of the
eight conditions was performed by assigning each inspector to the next
condition in the list of 8, to ensure that approximately equal numbers
of inspectors were tested under each condition at each site. Site 2 did
not run a night shift so that all inspectors there were tested under the
Day condition. This meant that more inspectors had to be tested under
Night conditions at sites 3 and 4 to preserve approximate equality of
numbers.

After completing the simulation program, inspectors completed the SSS &
SOFI again creating a post test measure. They then completed the
NASA-TLX workload measure.

### Measures: Performance was measured by the simulation program which collected data on the actual responses to each blade, plus the performance times for each blade. In this way, responses could be classified as Hit (correct detection) or Miss (failed detection) for each blade with a crack, or False Alarm (incorrect detection) or Correct Acceptance for blades without a crack. There was only a single crack per blade, although some cracks were visible on more than one of the six faces of the blade. Times for each response were measured, as was the total blades inspected in each 20 minute interval. Because times for False Alarm and Hit are highly correlated, as are times for Miss and Correct Acceptance, only the times for a hit (Reject Time) and Correct Acceptance (Accept Time) were analyzed. 

### A series of fatigue measures was used to assess inspector's fatigue levels. In the previous study it was found that using Actigraph Sleep Monitors to measure sleep was highly correlated with the subjective measures described above. These Actigraph Monitors were not used in this phase of the study to avoid unnecessary intrusion into the already busy aviation industry. Instead, the Stanford Sleepiness Scale (SSS) was used to determine the subjective feelings of sleepiness experienced at the time of administration. This is simply a seven-point scale, each point describes a different degree of sleepiness across a continuum. Participants select which degree is appropriate. The Swedish Occupational Fatigue Inventory (SOFI) was also used. This scale has several symptoms of fatigue that reduce to a five-factor scale including: 1) Physical Exertion, 2) Physical Discomfort, 3) Sleepiness, 4) Lack of Energy and 5) Lack of Motivation. The adapted Folkhard Survey also contains questions regarding long-term fatigue and a subjective account of the amount of sleep experienced for each night for five nights prior to experimentation. Although not a measure of fatigue, NASA-TLX was used to estimate workload. 

### Data Handling: Experiment 2 was a fully between-participants design so that only a single trial (1 or 2 hour) was conducted for each inspector. Experiment 2 was thus a 2^3^ between-participants factorial experiment for Shifts, Period and Breaks as in Experiment 1 and Time on Task again a within-participants factor at either 3 or 6 levels depending upon the 1 or 2 Hours factor. As in Experiment 1, two Analyses of Variance (ANOVAs) were performed. The first used three time conditions: the one-hour condition, the first hour of the two-hour condition and the second hour of the two-hour condition. The second used only the two hour condition. In this way the discrepancy between six 20-min periods in the two-hour condition and only three in the one-hour condition was resolved. A separate ANOVA was performed for each performance measure using Age of inspector as a covariate. The large set of scale measures (TLX, SSS, etc.) was first analysed using an inter-correlation matrix to determine how these multiple measures related to each other. At that point, ANOVAs were performed on selected subjective measures. 

## Results

## Performance Comparison to Experiment 1: The first result of interest is to compare the overall performance measures across the two experiments. Experiment 1 was conducted in a laboratory with industrially-experienced members of the general public, after a 3-hour training course in FPI and the simulation program. In contrast, Experiment 2 used aviation maintenance inspectors, mostly but not all NDI certified, working at their home maintenance base. To perform a direct test of any between-experiment differences, two-independent-sample t-tests were performed for each measure. Table 2 shows the differences between the overall results, and the t-test results.

## 

+-----------------------+-------------+------------+-------+----------+
| ##                    | ## E        | ## Ex      | ## t- | ## Pro   |
|                       | xperiment 1 | periment 2 | value | bability |
+-----------------------+-------------+------------+-------+----------+
| ## Pro                | ## 0.742    | ## 0.906   | ##    | ##       |
| bability of Detection |             |            |  6.56 | p\<0.001 |
+-----------------------+-------------+------------+-------+----------+
| ## Proba              | ## 0.348    | ## 0.029   | ##    | ## p     |
| bility of False Alarm |             |            |  0.56 |  = 0.578 |
+-----------------------+-------------+------------+-------+----------+
| ## Blades / 20 min    | ## 11.04    | ## 18.2    | ##    | ##       |
|                       |             |            |  5.47 | p\<0.001 |
+-----------------------+-------------+------------+-------+----------+
| ## Accept Time, s     | ## 134.0    | ## 100.0   | ##    | ## p     |
|                       |             |            |  1.70 |  = 0.091 |
+-----------------------+-------------+------------+-------+----------+
| ## Reject Time, s     | ## 94.3     | ## 56.0    | ##    | ##       |
|                       |             |            |  4.40 | p\<0.001 |
+-----------------------+-------------+------------+-------+----------+
| #                     | ## 52.3     | ## 49.1    | ##    | ## p     |
| # Workload, TLX Day 1 |             |            |  1.11 |  = 0.271 |
+-----------------------+-------------+------------+-------+----------+
| ## Workload TLX Day 2 | ## 49.0     | ## \*      | ##    | ## p     |
|                       |             |            |  0.02 |  = 0.981 |
+-----------------------+-------------+------------+-------+----------+

## Table 2. Differences between Experiments 1 and 2. 

##  Shaded rows show statistically significant differences.

## The participants in Experiment 2 were better at detection, with the miss rate reduced from about 25% to about 9% over those in Experiment 1, with no change in the low False Alarm Rate (\~ 3%). In addition, the aviation maintenance inspectors processed more blades per minute, interestingly by finding cracks more quickly (Reject Time) but not by stopping inspection of each blade earlier (Accept Time). Their subjective ratings of Workload were no different between the experiments at about a 50 on the NASA-TLX scale. The overall conclusion is that, as expected, aviation inspectors are faster and more accurate than the general population. Note that they spend about twice the Reject Time on each good blade, whereas the general public spends only about 1.4 x the Reject Time, implying that inspectors are more conscientious then the general public. Again, we would be surprised if inspectors were not better overall than their inexperienced counterparts. The rest of the analysis explores whether or not inspectors show different patterns of performance and fatigue, not just absolute values.

## Inter-correlations between sleep, fatigue and performance: In Experiment 1, there were almost no correlations between performance and sleep variables, so this result needs exploring in Experiment 2. There were four performance measures (Blades/20 min was redundant with two time measures) and four measures of sleep (Hours of last sleep, SSS scores before and after the task, and the difference After -- Before). Of the 16 inter-correlations, none were significant at p \< 0.05. For subjective fatigue inter-correlations there were the same four performance measures and five fatigue components, each of which was measured by before, after and difference, giving 15 fatigue measures. Of the 4 x 15 = 60 inter-correlations only 5 were significant at p \< 0.05:

> Accept Time with Physical Discomfort After Task
>
> Accept Time with Physical Discomfort After - Before
>
> Accept Time with Lack of Energy After Task
>
> Accept Time with Lack of Energy After - Before
>
> Probability of Detection with Physical Discomfort After -- Before

All were in the positive direction, i.e. slower times but higher
detections with increased reported fatigue. None of these correlation
exceeded 0.20. Finding of up to 5 out of 60 inter-correlations at p \<
0.05 would be expected by chance about 9% of occasions. Thus this result
is not unexpected but neither is it statistically significant. A
Chi-square test confirms this. Thus while there were a few significant
inter-correlations between sleep, fatigue and performance measures (5
out of 16 + 60 = 76), such a result could easily be explained as a
chance occurrence.

**Performance ANOVAs**: The significant ANOVA results at the p \< 0.05
level are summarized in Tables 2 (for all data) and 3 (2-Hour data
only). As expected with many dependent variables, four factors and a
covariate, there were many significant results. Age was a significant
covariate only for TLX in the overall study, but for PoD and Blades/20
min in the 2-Hour data. Older inspectors tended to have lower PoD but
work faster, and scatter-graphs of the data revealed only very weak
correlations. For both data sets reported workload (TLX) was inversely
related to Age, as shown in Figure 1 for the overall data. The
relationship is not strong, although significant, with older inspectors
reporting lower workloads.

+-----------------+---------+-------+------+--------+--------+--------+
|                 | **Bl    | **    | **Po | **     | **     | *      |
|                 | ades/** | PoD** | FA** | Reject | Accept | *TLX** |
|                 |         |       |      | Time** | Time** |        |
|                 | **20m** |       |      |        |        |        |
+-----------------+---------+-------+------+--------+--------+--------+
| **Age**         |         |       |      |        |        | \      |
|                 |         |       |      |        |        | <0.001 |
+-----------------+---------+-------+------+--------+--------+--------+
| **Time on       | 0.009   | \<    |      |        |        |        |
| Task**          |         | 0.001 |      |        |        |        |
+-----------------+---------+-------+------+--------+--------+--------+
| **Shift**       |         |       |      |        |        | \      |
|                 |         |       |      |        |        | <0.001 |
+-----------------+---------+-------+------+--------+--------+--------+
| **Period**      | 0.001   |       |      |        | 0.05   |        |
+-----------------+---------+-------+------+--------+--------+--------+
| **Breaks**      |         |       | 0    |        |        |        |
|                 |         |       | .026 |        |        |        |
+-----------------+---------+-------+------+--------+--------+--------+
| **Shift X       | \<0.001 |       |      |        | 0.004  | 0.001  |
| Period**        |         |       |      |        |        |        |
+-----------------+---------+-------+------+--------+--------+--------+
| **Shift X       |         |       |      |        | 0.003  | \      |
| Breaks**        |         |       |      |        |        | <0.001 |
+-----------------+---------+-------+------+--------+--------+--------+
| **Period X      |         |       |      |        |        |        |
| Breaks**        |         |       |      |        |        |        |
+-----------------+---------+-------+------+--------+--------+--------+
| **Shift X       | 0.001   |       |      |        | 0.003  |        |
| Period X        |         |       |      |        |        |        |
| Breaks**        |         |       |      |        |        |        |
+-----------------+---------+-------+------+--------+--------+--------+

**Table 3. ANOVA Summary for 1-Hour plus 2-Hour data**

+-------------+---------+-------+--------+-------+--------+--------+
|             | **Bl    | **    | **     | **R   | **     | *      |
|             | ades/** | PoD** | PoFA** | eject | Accept | *TLX** |
|             |         |       |        | T     | Time** |        |
|             | **20m** |       |        | ime** |        |        |
+-------------+---------+-------+--------+-------+--------+--------+
| **Age**     | 0.014   | 0.007 |        |       |        | \      |
|             |         |       |        |       |        | <0.001 |
+-------------+---------+-------+--------+-------+--------+--------+
| **Time on   |         | 0.001 |        |       |        |        |
| Task**      |         |       |        |       |        |        |
+-------------+---------+-------+--------+-------+--------+--------+
| **Shift**   | 0.030   |       | 0.027  | 0.027 | 0.012  | 0.018  |
+-------------+---------+-------+--------+-------+--------+--------+
| **Breaks**  |         |       | 0.007  |       |        |        |
+-------------+---------+-------+--------+-------+--------+--------+
| **Shift X   | \<0.001 |       | 0.047  | 0.039 | \      | \      |
| Breaks**    |         |       |        |       | <0.001 | <0.001 |
+-------------+---------+-------+--------+-------+--------+--------+

**Table 4. ANOVA Summary for 2-Hour data only**

![](media/image1.wmf)

**Figure 1. Reduction of reported Workload with age.**

## The main effects and interactions between the factors will be considered for both analyses (Tables 2 and 3) together. The major finding was that in Experiment 2 there was a main effect of Time-on-Task on probability of detection, in contrast to Experiment 1 where only an interaction between Time-on-Task and Shift was found. As Figure 2 shows, in Experiment 2 there was a gradual decrease in PoD as time progressed, both for the 1-Hour and 2-Hour participants. In contrast, Experiment 1 showed no consistent effect for either group of participants, and was lower overall as noted earlier. For Experiment 2, the Time-on-Task effect reflects the classic Vigilance Decrement result found in many studies of long-term detection studies (e.g. Parasuraman and Davies, 1977). Note that as with most Vigilance Decrement results, performance becomes slowly worse over time, rather than showing constant performance until some critical time after which performance drops away.

## There was also a Time-on-Task effect for Blades/20 min for the combined conditions, which was almost significant for the 2-Hour condition only (p = 0.054). As shown in Figure 3 this was a speeding up over time, more pronounced than in Experiment 1. Again, inspector participants in Experiment 2 showed superior performance. Note that the 1-Hour participants in Experiment 2 performed faster than the 2-Hour participants even in the first hour. It appears that the 2-Hour participants were pacing themselves more slowly even at the start.

For the 2-Hour conditions only there were a number of significant Shift
effects: PoFA, Accept Time, Reject Time. The data are given in Table 5
where it can be seen that all three performance measures were better at
night than in the day: Participants worked faster and made less False
Alarms while keeping their PoD constant. We shall return to the PoFA
effect later.

![](media/image2.wmf)

**Figure 2. Time-on-Task effects on PoD for Experiments 1 and 2**
![](media/image3.wmf)

**Figure 3. Time-on-Task effects for Blades / 20 min for Experiments 1
and 2**

  -------------------------- --------------------- ----------------------
                             **Day (0900)**        **Night (0001 or
                                                   0300)**

  **Probability of False     0.045                 0.003
  Alarm**                                          

  **Reject Time**            65.0                  45.0

  **Accept Time**            113.2                 85.6
  -------------------------- --------------------- ----------------------

**Table 5. Shift effects on performance for 2-Hour participants in
Experiment 2.**

There were no interactions involving the most important measure, PoD.
Shift, Break and Period effects will now be considered for the remaining
performance variables. First, the PoFA result for Shift shown in Table 5
was in fact mainly caused by an interaction with Breaks for the 2-Hour
participants. As seen in Figure 4, three combinations of Shift and
Breaks produced low and consistent False Alarm rates, but for the Day
condition with No Breaks the False Alarm rate was much higher -- almost
30 times higher than the average of the other conditions. A
participant-by-participant analysis of PoFA showed that of the 35
inspectors tested under the 2-Hour conditions, 25 (71%) had no False
Alarms, 7 (20%) had less than 5% False Alarm rate and 3 (9%) had False
Alarm rates greater than 15%. All three in this high PoFA category were
in the Day/No Breaks condition, as were 4 of the 7 with PoFA less than
5%. The ten with non-zero PoFA values were not concentrated at any
single site so the conclusion is that the Day/No Breaks condition does
indeed results in a consistently high PoFA.

A major finding of Experiment 1 was that Breaks had different effects at
night from during the day, mainly for 2-Hour participants. This effect
was found again in Experiment 2 for Blades / 20 min, presented here as
Figure 5. For the 2-Hour participants, the throughput was greatest with
Breaks in the Day but without Breaks at Night. The 1-Hour participants
did not show this interaction effect, just achieving greater throughput
in the Day, and slightly better with Breaks. A very similar effect was
found for Accept Time, as was to be expected given that Blades / 20 min
was significant. There was also a Breaks X Shift interaction for TLX
(Figure 6) with lower workload reports for Breaks in the Day and No
Breaks at Night. This reflects the speed performance data with lower
workload reported when participants were able to work more rapidly. Note
that the task was always identical, so that changes in workload were
participants' responses to changed experimental conditions beyond the
task itself.

Finally, for the whole experiment, there was a significant Shift X
Period interaction for TLX. When plotted it showed lower workload for
the 1-Hour period in the Day but for the 2-Hour period at night. This is
an unlikely finding but it was in fact quite small and is not plotted
here.

**Sleep and Fatigue ANOVAs:** There were many significant effects of the
factors and interactions on the 18 measures of sleepiness (SSS) or
fatigue (SOFI) so only the main results will be presented here. The
ANOVA results are summarized in Table 6. Figure 7 shows the significant
main effects of Shift. Comparable to the results of Experiment 1, Figure
7 demonstrates that for most measures, sleepiness and fatigue were worse
on Night than Day shift -- hardly an unexpected result. The exceptions
were Lack of

![](media/image4.wmf)![](media/image5.wmf)![](media/image6.wmf)

**Figure 5. Three-Way interaction between Breaks, Shift and Period for
Blades/20 min The three graphs represent respectively 2-Hour
participants during the first hour, 2-Hour participants during the
second hour and 1-Hour participants.**

> ![](media/image7.wmf)

**Figure 6. Breaks X Shift interaction for Workload TLX**

![](media/image8.wmf)

**Figure 7. Significant Sleep and Fatigue results by Shift**

Motivation (Before) and Physical Exertion (Before), both of which were
better at night than in the day. Concentrating on the *changes* in
reported fatigue and sleepiness (the Deltas) there was a main effect of
Breaks for SOFI Physical Exertion, and significant interaction effects
for Shift X Breaks in SSS, and SOFI Physical Discomfort and Lack of
Energy, Period X Breaks for SOFI Lack of Motivation and Sleepiness.
Figure 8 shows

  -------------------------------- ----------------- ---------------- ----------------
  **SSS**                          **SSS(before)**   **SSS(after)**   **SSS(delta)**

  **Shift**                        \<0.001           \<0.001          0.002

  **Period**                                                          

  **Breaks**                       0.001                              

  **Shift X Period**                                                  

  **Shift X Breaks**                                 0.002            0.003

  **Period X Breaks**                                                 

  **Shift X Period X Breaks**                                         
  -------------------------------- ----------------- ---------------- ----------------

  ------------------- ------------ -------------- ---------- -------------- ----------------
  **SOFI Before**     **Physical   **Physical     **Lack of  **Lack of      **Sleepiness**
                      exertion**   discomfort**   energy**   motivation**   

  **Shift**           0.008                                  0.013          

  **Period**                                      \<0.001    0.047          \<0.001

  **Breaks**          \<0.001      \<0.001                   0.001          

  **Shift X Period**  0.041        0.001                                    

  **Shift X Breaks**                              \<0.001                   \<0.001

  **Period X Breaks** 0.039        \<0.001                                  

  **Shift X Period X  0.007        \<0.001        0.001      0.002          
  Breaks**                                                                  
  ------------------- ------------ -------------- ---------- -------------- ----------------

  ------------------- ------------ -------------- ---------- -------------- ----------------
  **SOFI After**      **Physical   **Physical     **Lack of  **Lack of      **Sleepiness**
                      exertion**   discomfort**   energy**   motivation**   

  **Shift**                        0.036                     0.006          0.031

  **Period**                                      0.003                     

  **Breaks**                                                                

  **Shift X Period**               0.014                                    

  **Shift X Breaks**               \<0.001        \<0.001                   0.041

  **Period X Breaks**              0.016                     0.035          

  **Shift X Period X               0.012          0.018      0.012          0.007
  Breaks**                                                                  
  ------------------- ------------ -------------- ---------- -------------- ----------------

  ------------------- ------------ -------------- ---------- -------------- ----------------
  **SOFI Delta**      **Physical   **Physical     **Lack of  **Lack of      **Sleepiness**
                      exertion**   discomfort**   energy**   motivation**   

  **Shift**                                                  \<0.001        0.004

  **Period**                                                                

  **Breaks**          \<0.001                                0.019          0.023

  **Shift X Period**                                                        

  **Shift X Breaks**               \<0.001        0.025                     

  **Period X Breaks**                                        0.039          0.027

  **Shift X Period X                                                        
  Breaks**                                                                  
  ------------------- ------------ -------------- ---------- -------------- ----------------

**Table 6. Summary ANOVA results from Sleepiness (SSS) and Fatigue
(SOFI)**

**Responses**

the significant Shift X Breaks interactions, all of which display the
same pattern of lower changes in reported sleepiness and fatigue for
Breaks during the day and No Breaks at night. This is the same pattern
as the productivity data, Blades / 20 min, shown in Figure 5 for the
2-Hour data only. With the reported fatigue and sleepiness there was no
interaction with Period so that the results in Figure 8 are for both
Period lengths. The Shift X Period Interaction in Figure 9 shows that
for both measures, the changes were greatest for the Breaks/1-Hour
condition, with quite similar changes in the other three conditions.
Note that as in Figure 8, all changes were in the positive direction,
implying that perceptions of fatigue and sleepiness grew more severe
over the course of the task.![](media/image9.wmf){width="3.0in"
height="2.0937007874015747in"}![](media/image10.wmf){width="3.0in"
height="2.0937007874015747in"}![](media/image11.wmf){width="3.0in"
height="2.0929133858267717in"}**Figure 8. Significant Shift X Breaks
interactions for fatigue and sleepiness changes**

**over the task.** ![](media/image12.wmf){width="3.0in"
height="2.0937007874015747in"}![](media/image13.wmf){width="3.0in"
height="2.0937007874015747in"}

**Figure 9. Significant Period X Breaks interactions for fatigue and
sleepiness changes**

**over the task.**

## Discussion and Conclusions

There are both scientific and practical conclusions to the whole
Inspection Fatigue project and to this experiment in particular. Of
interest to the scientific community in Human Factors Engineering are
the facts that first there were few significant correlations between
performance measures and sleepiness or fatigue measures, and second that
the results of the more face-valid simulation in Experiment 2 did not
produce identical results to the less realistic simulation in
Experiment 1. Of practical interest are the findings of a decrease in
PoD, a "vigilance decrement" in Experiment 2 and the differential effect
of introducing breaks in the task throughout the two studies. Because
this is the final report on the study, relevant parts of the Discussion
from the previous report will be repeated here.

These experiments, and the preceding literature analysis, were aimed at
providing quantitative evidence about the role of fatigue in repetitive
non-destructive inspection of aircraft components, particularly FPI and
MPI. From prior research we found several bodies of knowledge that could
potentially be applied to repetitive NDI, particularly research on human
vigilance and studies of daily body rhythms and shift work. If these
findings apply to aviation NDI tasks, then solid recommendations could
be made based on studies outside aviation or inspection, for example
driving studies or military watch-keeping. The current experiments were
a direct test of a number of factors found significant from prior
research in a high-fidelity simulation of one NDI task, FPI of engine
blades for cracks.

Great care was taken to ensure a valid simulation. The simulation was
based on a detailed task analysis of FPI (Drury and Watson, 1998) using
photographs of each of the six faces of 60 different blades. Cracks and
residual penetrant were added to the blades based on actual cracks seen
at different maintenance sites. The actual simulation program was used
by several FPI inspectors at one maintenance site, who agreed that any
results from experiment using that software would be applicable to their
tasks. The inspectors tested in Experiment 2 also largely agreed that
the simulation was valid. We also ensured that our participant pool in
each experiment was not the student pool typical of much of the research
literature. In Experiment 1 we used industrially-experienced members of
the public in the Western New York region, whose age ranged from 24 to
79 with a mean of 46 years, and who spent between 4 and 6 hours on the
task (including training). In Experiment 2 we used experienced aviation
maintenance inspectors, although not always NDI inspectors, with the
rather more restricted age range of 27 -- 62 years typical of
inspectors. It was not of great concern that some inspectors were not
currently NDI certified as the only function of FPI being tested was the
Reading function, which uses largely the visual skills common to all
aviation inspectors. Because we had both industrially-experienced non-
inspectors and aviation inspectors in about the same numbers (80 vs. 70)
we could directly compare both the absolute level and pattern of
performance across these groups.

The experiments were designed to test known effects from the vigilance
and fatigue literature, here Shift, Period, Defect Rate and Time on Task
for Experiment 1. Additional factors in that experiment represented
potential interventions: Breaks and Lights. The design chosen for
Experiment 1 was a between-participants design with 5 participants in
each of the 16 (=2^4^) cells of the design. With the addition of a
single within-participants factor of Shift, this gave a 2^5^ design so
that many two-way interactions could be measured. The design ensured
that there were no unwanted carry-over effects between conditions
(except perhaps Shift which was given in a random order) and so was
safer, if less powerful, than a within-participants design. In
Experiment 2 we dropped the Defect Rate factor (not significant anywhere
in Experiment 1) and the Lights factor (not practical to control at each
work site). We also were not able to test inspectors on more than the
one shift they were working at the time of our visit, and so Shift
became a between-participants factor. This gave a 2^3^
between-participants design (Shift, Break, Period) with three or six
levels of Time-on-Task representing each 20 minutes of work. Such large
designs were increased in complexity by measuring a number of different
dependent variables, centering on performance, and rating scales of
sleep, workload and fatigue. As expected the overall performance
comparison between the two experiments showed aviation inspectors to be
more capable of detecting cracks and faster-working than there
non-inspector counterparts, and more conscientious in terms of not
stopping looking for a crack prematurely (Table 2).

Our results were again both positive and negative. As in Experiment 1,
we found no more correlations between sleep / fatigue measures and
performance measures that would be expected by chance. This was a
remarkable finding in that logically one would expect that the more
fatigues / sleepy inspectors were, the worse would be their performance.
However, this is not always the case for complex tasks where people
regulate the effort they devote to the task. Nobody can maintain their
peak effort over hours, so that the resources devoted to the task may
not be constant, e.g. Wickens (1984). For short periods, such as the one
or two hours of our experiments, or for task that have a high
consequence for error, such as aviation inspection, people can and do
devote more resources to maintaining performance at a high level. This
may reflect a higher subjective workload, although in our experiments
that was not always the case, as measured by NASA's TLX workload
measure. Also we can note that the Period effects on speed measure,
Figure 3, implied that inspectors were deliberately pacing themselves,
working more slowly even at the start on the 1-Hour task than the 2-Hour
task. We have also seen in earlier experiments on performing inspection
in the severely restricted spaces typical of aircraft structures, that
people were able to maintain their performance despite severe space
restrictions (Mozrall et al, 2000). In those experiments, participants
were significantly less comfortable and more stressed in restricted
spaces, but their inspection performance did not suffer. We suspect the
same is true here, where inspectors reported more sleepiness and fatigue
in, for example, the night condition (Figure 7) but there were no Shift
effects on performance measures in the overall experiment. In the 2-Hour
only conditions there were Shift effects on performance but all three
significant measures (Table 5) were better at night than in the day.
Inspectors worked faster at night and made fewer false alarms, but their
most operationally-important performance measure, PoD did not change.

In contrast to Experiment 1, we did find a significant decrement of PoD
across the one or two hours of the experiment. In the current
experiment, inspectors all started with a 100% PoD but this level fell
to about 90% after one hour and 80% after two hours. The decrement was
not affected by Shift or Breaks. This is almost a classic vigilance
decrement. Experiment 1 had shown no decrement in PoD which remained a
relatively constant and lower level (\~80%) over the two hours, combined
with a gradual decrease in PoFA. Both show different evidences of a
vigilance decrement, but the form found in Experiment 2 is the most
typical and operationally significant. Note that there were no other
significant effects on PoD, so that Time-on-Task is the only factors we
need to consider. After Experiment 1, we had concluded that the
vigilance decrement found in almost all laboratory studies of detecting
weak signals did not apply to complex inspection tasks. Indeed we had
rationalized this with a table of differences between the typical
laboratory vigilance task and our more complex inspection task,
reproduced here as Table 7. We now need to rethink this conclusion in
the light of data from Experiment 2. From this table we had earlier
concluded that while inspection tasks have some characteristics in
common with classical vigilance tasks (important outcomes, long
duration, low defect rate) they have many points of difference.
Noteworthy differences are the pacing, the participant experience and
the task variety. The experiment we have completed has re-emphasized
these differences. The task was complex, with a large procedural
component and took about 2 minutes per blade, a time chosen by the
inspector. For comparison, vigilance tasks typically have a stimulus
presented for a few seconds and requires little or no eye movements for
search. Our participants were industrially experienced, generally middle
aged people. Vigilance participants are typically students who are given
no or minimal training on the task.

Experiment 2 was certainly more face-valid than Experiment 1, using
actual aviation inspectors tested at their place of work rather than
industrially-experienced members of the public tested in our RISST
laboratories. It also used a more fully between-participants design,
which was less powerful but safer from unwanted carry-over effects such
as might have occurred when the same participants were tested under both
day and night conditions. For these reasons, we believe that the results
of Experiment 2 better represent actual practice in the industry where
the two studies give differing results. Our conclusion then is that a
vigilance decrement, i.e. a reduction in PoD with Time-on-Task, does
occur in this simulated FPI task. There was no effect of Breaks on PoD,
so that introducing breaks into the work does not alleviate the
decrement. This is likely to have practical consequences for NDI
practice but does not lend itself to a simple prescription of maximum
working time. The decrement measured here was almost linear, and did not
change between the 1-Hour and 2-Hour conditions. Thus restricting
continuous periods of inspection to, say, one hour would not improve
performance, but merely limit the amount of decrement occurring. If we
had used only the results of Experiment 1 (Figure 2) we would have
concluded that the 1-Hour period was definitely better. However, our
practical recommendation is to suggest that inspectors refrain from long
periods of continuous inspection but not to set time limits on the work
period. Note again that although short breaks affected several aspects
of performance and fatigue, they did not affect PoD so that breaks are
not a "cure" for decrement in this task.

  ------------------ ----------------------------------------------------
  **VIGILANCE TASK   **INSPECTION TASK ATTRIBUTE**
  ATTRIBUTE**        

  Important Signals  Cracks or other defects that can have direct safety
                     consequences.

  Rare Signals       Defects can range from quite common, e.g. corrosive
                     areas on older aircraft, to extremely rare (e.g.
                     cracks in jet engine titanium hubs). However, under
                     most circumstances far less than 1 out of 10
                     inspected components will contain a reportable
                     defect.

  Low Signal         Most defects are perceptually difficult to detect,
  Strength           often occurring within a background of non-defects,
                     e.g. cracks among dirt marks and scratches.

  Long Time on Task  Time on task can vary from a few minutes to about 2
                     hours without a break. Scheduled breaks are
                     typically four 15-min breaks per shift, but many
                     tasks are self-paced so that inspectors can break
                     early or continue beyond scheduled time to complete
                     an area or component.

  High Memory Load   Prototypical defects are usually stored in the
                     inspector's memory, rather than being presented as
                     part of the task. Sometimes typical defects are
                     illustrated on task cards, but task cards are often
                     poorly integrated into the inspection task.

  Low Observer       Inspectors are highly skilled and practiced, after
  Practice           3-10 years as an AMT before becoming an inspector.
                     However, for some rare defects, even experienced
                     inspectors may literally never have seen one in
                     their working lifetime.

  Sustained          Inspectors may have some tasks where just one defect
  Attention on One   type is the target, but these are often interspersed
  Task               with other tasks (e.g. different components) where
                     different defects, often less rare defects, are the
                     target.

  Externally-paced   Typically self-paced where the inspector can devote
  task               a variable time to each item, and choose a stopping
                     criterion based on expectations.

  Time Uncertainty   Defect occurrence is rarely predictable although
                     inspectors often return to the same area of the same
                     aircraft or engine and attempt to predict when
                     defects are likely.

  Spatial            While the actual occurrence of defects at specific
  Uncertainty        places on specific components may be unpredictable,
                     the inspector can have much useful information to
                     guide the inspection process. Training, service
                     bulletins and shared experiences can help point
                     inspectors to specific locations where defects are
                     more likely.

  Low Feedback       Aircraft inspectors do not get good feedback, mainly
                     because there is no easy way to find what is truly a
                     signal, especially a missed signal. Feedback on
                     missed defects only comes when one is found at a
                     subsequent inspection, or when an operational
                     incident occurs. Even feedback on false alarms is
                     sporadic. Feedback of both Misses and False Alarms
                     is at best severely delayed and therefore of little
                     use to the inspector.

  Unrealistic        For more common defects, expectations from training
  Expectations       can translate relatively faithfully into practice.
                     However, for very rare defects, expectation may
                     still be unrealistically high after considerable
                     practice.

  Isolated           The hangar and even the shop inspection environment
  Inspection         are typically noisy, social and distracting. Both
  Environment        noise and social interaction and even some forms of
                     distraction have been found to *improve* vigilance
                     performance in laboratory tasks.
  ------------------ ----------------------------------------------------

**Table 7. Comparison between vigilance tasks and aircraft inspection
tasks**

The effects of short breaks were complex and not obvious, but they were
consistent across several measures and across the two experiments. The
3-minute break following each 20 minutes of inspection had positive
effects during daytime work (0900) but not negative effects at night
(0300 or 0001). This was true across the whole experiment for the
Workload measure (Figure 6), three fatigue / sleepiness measures (Figure
8) and for the speed measures (Figure 5) in the 2-Hour condition only.
For the 1-Hour condition speed, breaks were slightly effective both day
and night (Figure 5) Breaks represent a useful intervention resulting in
higher performance speed (with no loss of accuracy) and better reported
workload, fatigue and sleepiness, but only in the 2-Hour condition and
only during the day. Practically, the recommendation is unchanged since
Experiment 1, but we still have not found a solid scientific reason for
this finding. It makes "common" sense, in that inspectors are more eager
to get on with the inspection work and finish it at night so that breaks
make their actual and perceived situation worse. But "common sense" is a
notoriously unreliable guide to measured human performance, so we will
continue to treat this as a practical intervention we can recommend, but
without any theoretical backing.

Summarizing these findings we can state a number of practical
conclusions, here modified from those given after Experiment 1:

1.  The FPI simulation experiment reported here was validated using
    industry inspectors, and produced results that confirmed the
    validation, e.g. PoD curves of the expected form in Experiment 1,
    and expected effects of day vs. night in measures of sleep and
    fatigue. These two experiments are probably a more reliable guide to
    fatigue in inspection than the research literature on vigilance
    tasks. Experiment 2 using experienced aviation inspectors produced
    the expected better performance than Experiment 1 that used
    industrially-experienced members of the general public. These
    findings together show that the experiments were valid simulations
    of NDI performance. Where there was a discrepancy between the
    results of the two experiments, we should consider Experiment 2 as
    most valid.

2.  There was almost no correlation between sleep measures and
    performance measures in either experiment: this task appears more
    resistant to fatigue than typical laboratory vigilance tasks.
    Inspectors appear to devote enough mental resources to the task to
    maintain performance even under difficult conditions, e.g. starting
    at 0300 or working for two hours without pause.

3.  There was a steady decline in PoD performance in Experiment 2 but
    not in Experiment 1. Inspectors should understand this and limit
    periods of continuous inspection, although no specific time limit
    can be recommended from these studies. For NDI tasks such as FPI
    where considerable non-inspection work is needed (e.g. part
    preparation in the FPI process), work should be organized so that
    each inspector processes their own parts so as to give shorter
    periods of continuous inspection.

4.  Short breaks from continuous inspection every 20 minutes were
    effective in improving performance and reported fatigue, workload
    and sleepiness in both experiments in daytime, but not at night, and
    not for the one-hour inspection period.

5.  Higher levels of illumination improve performance slightly in the
    day, but darker conditions are better at night.

## Overall, fatigue in NDI inspection does not appear to be a large problem if handled using the recommendations given above. Also, we should continue to apply with care results from laboratory experiments to practical aviation inspection situations. Results from Experiment 2 confirmed many of those from Experiment 1, but the most important measure of inspection performance, PoD gave different results between the two experiments.

## References

Drury, C. G. and Watson, J. (2002). *Good Practices in Visual
Inspection,* Final Report for the FAA/Office of Aviation Medicine.

Goranson U. F. and Rogers J.T. (1983). Elements of damage tolerance
verification, *12th Symposium ofInternational Commercial Aeronautical
Fatigue*, Toulouse, France.

Horowitz, T. S., Cade, B. ., Wolfe, J. M. and Cziesler, C. A. (2003).
Searching night and day: a dissociation of effects of circadian phase
and time awake on visual selective attention and vigilance.
*Psychological Science,* **14(6),** 549-557.

Mozrall, J. R., Drury, C. G., Sharit, J. and Cerny, F. (2000). The
effects of whole-body restriction on inspection performance. Ergonomics,
43(11), 1805-1823

Parasuraman, R. and Davies, D.R. (1977). A taxonomic Analysis of
Vigilance Performance*.* In R. R. Mackie (ed), *Vigilance, Theory,
Operational Performance, and Physiological Correlates*, Plenum Press,
New York.

[W]{.smallcaps}ickens[, C. D.]{.smallcaps} (1984). Processing resources
in attention. In R. Parasuraman & D.R. Davies (Eds.), *Varieties of
attention*. (pp. 63-102). New York, NY: Academic Press.