DOT/FAA/AM 96/9 Blinks, Saccades, and

Fixation Pauses During

^

r VigilanceTask Performance:

Office ofAviation Medicine °

Washington, D.c. 20591 II. Gender and Time of Day

19960422 025

John A. Stern

Donna J. Boyer

Department of Psychology

Washington University

St. Louis, Missouri 63130-4899

David J. Schroeder

R. Mark Touchstone

Civil Aeromedical Institute

Federal Aviation Administration

Oklahoma City, Oklahoma 73125

N. Stoliarov

State Scientific Institute for Civil Aviation

Volocolamskoe Hwy, 26

Moscow 123 182

Russia

March 1996

Final Report

This document is available to the public

through the National Technical Information

Service, Springfield, Virginia 22161.

O

U.S. Department

of Transportation

Federal Aviation

Administration

3TTfi ° PALEST INSPECTED I
NOTICE

This document is disseminated under the sponsorship of

the U.S. Department ofTransportation in the interest of

information exchange. The United States Government

assumes no liability for the contents or use thereof.
Technical Report Documentation Page

1. Report No.

DOT/FAA/AM-96/9

2. Government Accession No. 3. Recipient's Catalog No.

4. Title and Subtitle

Blinks, Saccades, and Fixation Pauses During Vigilance Task Performance:

II. Gender and Time of Day

5. Report Date

March 1996

6. Performing Organization Code

7. Author(s)

J.A. Stern, D. Boyer, DJ. Schroeder, R.M. Touchstone, and N. Stoliarov

8. Performing Organization Report No.

9. Performing Organization Name and Address

Department of Psychology Human Resources Research Division

Washington University FAA Civil Aeromedical Institute

St. Louis, MO 63130-4899 Oklahoma City, OK 73125

10. Work Unit No. (TRAIS)

State Scientific Research Institute for Civil Aviation

Volocolamskoe Hwy, 26

Moscow 123 182 Russia

11. Contract or Grant No.

DTFA-02-91-C-91056

12. Sponsoring Agency name and Address

Federal Aviation Administration

Office ofAviation Medicine

800 Independence Ave., S.W.

Washington, DC 20591

13. Type of Report and Period Covered

14. Sponsoring Agency Code

15. Supplemental Notes

This collaborative research project was developed through the US-USSR Aviation Medicine and Human Factors

Working Group. The working group was initiated under the US-USSR Agreement on Cooperation in Transportation

Science and Technology.

16. Abstract

As operators are required to spend more time monitoring computer controlled devices in future systems, it is critical to define the

task and situational factors (i.e., fatigue) that may impact vigilance and performance. Aspects ofthe gaze system can be monitored

relatively unobtrusively, although we used conventional electro-oculographic techniques in this study. Can gaze control measures be

used to reflect, and hopefully predict, periods ofimpaired attention and performance? Gaze control measures (blinks, saccades, and

fixations) were recorded while subjects performed on an air ttaffic control simulation task. Twenty-five subjects performed the task

for 3 days at 2 successive hours per day. Blinks and saccades were sampled for 5 consecutive minutes after 10, 30, 50, 70, 90, and

110 minutes oftask performance. Significant Time-On-Task (TOT) effects were obtained for all ofthe 13 variables abstracted. A

number of main effects for DAY and a number ofinteractions involving DAY were significant. TOT effects were obtained for blink

rate, blink closing duration, 50% window, blink amplitude, long closure duration blinks, eye closure frequency, blink flurry

frequency, number of blinks part offlurries, saccade rate, saccade amplitude, fixation duration, long duration fixations, and

performance decrements.

The changes in blink frequency and other blink attributes are interpreted within a framework suggesting a breakdown of

inhibitory control as a function ofTOT. We believe that this TOT effect is not a tonic one, i.e., a steady decline in the ability to

inhibit, but a phasic process, in that periods of poorer inhibitory control increase in frequency and duration as a function ofTOT.

This conceptual model is akin to one proposed by Bills (1931) dealing with performance "blocks."

Performance declined as a function ofTOT but improved over days oftask performance. This improvement is mirrored by

changes in blink parameters, suggesting that the task had become easier to perform.

17. Keywords

Fatigue Blinks

Eye Movements Vigilance

Time-on-Task

18. Distribution Statement

Document is available to the public through

the National Technical Information Service,

Springfield, Virginia 22161

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

47

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
BLINKS, SACCADES, AND FIXATION PAUSES DURING VIGILANCE

TASK PERFORMANCE: II. GENDER AND TIME OF DAY

INTRODUCTION

It has long been our conviction that the gaze

control system should reflect time-on-task (TOT) or

"fatigue" effects. The literature on which that conviction is based is, at best, spotty. Many occulometric

measures have been tested for their ability to detect

"fatigue" effects. Most have been found wanting, with

respect to reliably demonstrating such effects. For

example, the literature on Critical Flicker Fusion

Frequency (CFF) as a measure of fatigue finds more

studies claiming the absence of an effect than those

which report a decrease in CFF as a function of

"fatigue." Similarly, studies ofsteady state, high frequency, brain-evoked response following (i.e., the

brain, especially the visual cortex, is "driven" at the

frequency or harmonic frequency ofthe flashed light)

in response to sinusoidally (or other waveform) modulated light have occasionally found a reduction in the

peak frequency at which such "driving" occurs, but

again, the literature is weighted in favor ofnot finding

significant TOT effects. (TOT and "fatigue" effects

will be used synonymously.)

It is our contention that TOT effects are best

measured while performing the task on which the

subject has been "fatigued." Barlett (1943), whom we

will quote more fully later, indicated that any change

in the nature of the task a subject is required to

perform will produce a return to normal or better than

normal responding. The fact that recovery from

"fatigue" is rapid, though such recovery may be shortlived, has been known for many years, but not appreciated by many investigators who have studied fatigue

effects by evaluating, for example, changes in CFF as

a function of interposed "work."

Bills and McTeer (1932) report that recovery from

fatigue induced by specific task performance is a

function of similarity between the condition under

which fatigue was induced and the task in which the

effect offatigue is evaluated. The more dissimilar the

tasks, the greater the recovery.

Thus, a 2- or 5-minute CFF task administered

seconds or minutes after the end of an exhausting 8-

hour work day may not show any changes in CFF.

Many researchers dealing with aspects of visual activity have reported that a relatively slight change in an

experimental situation produced marked changes in

the variable under investigation. Ponder and Kennedy

(1927) have demonstrated this, for example, for eye

blink frequency. Thus, we believe that the optimal

strategyfor demonstrating TOTeffects is to record biological variables while the subjectisperforming the task.

A second constraint imposed by us in much of our

research on aspects of vigilance is that we wish to

utilize measures which, ifthey proved useful in simulation environments, could be readily transferred to

field settings. We contend that techniques which

remain dependent on the application ofelectrodes are

not acceptable in field settings, both because oftechnical skills required for the application of electrodes,

and non-acceptance of attachment of electrodes on

the part of operators.

Thus, rather than recording brain activity (because

we are interested in mental "fatigue" and the brain is

presumably the place where such fatigue would be best

manifested), which for the foreseeable future will

require the application of electrodes to the scalp, we

selected gaze control variables and the eye blink.

Many components ofsuch variables can be recorded

without the application of electrodes. Another concern was with "ecological validity." It seemed to us

that the air traffic controller's task is visually demanding. Measures that reflect what the eyes are doing

would seem to be relevant in generating useful information in subjects performing such tasks for extended

periods of time.

Prior research from our laboratory has demonstrated that both frequency and other components of

blinking are sensitive to task demands, as well as TOT

manipulations (Orchard & Stern, 1991; Fogarty &
Blinks, Saccades, andFixation Pauses

Stern, 1989; Goldstein, Bauer & Stern, 1992; Stern,

Beideman & Chen, 1976; Stern & Skelly, 1984). We

were, thus, reasonably optimistic about demonstrating such effects in the present context. With respect to

saccadic eye movements, fixation pauses, and other

eye movements, the results from the literature were

less reassuring. However, there is evidence of both

drug and "fatigue" induced changes in saccade duration and velocity, and in long duration fixation pauses

in situations requiring frequent gaze shifts. We thus

believed that these measures would also be sensitive to

TOT demands.

In the current study, our initial concern was not

with identifying gaze control inefficiencies specifically associated with missed signals, but with changes

in such variables as a function of TOT. We suspect

that some, but not all missed signals, are associated

with gaze control inefficiencies. We are also convinced that, if an event to which a rapid response is

required occurs concurrent with a period of gaze

control inefficiency, then the likelihood is great that

the response to that signal will be inappropriate, or

that no response will be made. We suggest that most,

if not all gaze control inefficiencies, when they occur

concurrent with the need to detect and process infrequently occurring information will lead to inappropriate responding. The converse is not true:

Inappropriate responses can occur in the absence of

gaze abnormalities.

What do we mean by "gaze control inefficiencies,"

a concept we have introduced a number of times

above? We are now on shaky grounds, and to mix

metaphors, will climb out on a limb and identify some

possible measures.

Closure duration ofblink

Obviously, ifan important event occurs during a

limited time period and that period is occupied by

a blink, the event will be missed when the lid is

closed for long, as compared to a short period.

b. EYE CLOSURES

Eye closures are identified iflid reopening, following a closure, does not occur within one-half

second. It is obvious that no visual information

can be acquired if the eyes are closed.

c. EYE MOVEMENTS

Saccades

Saccade velocity decreases as a function ofa number of variables, including TOT. Since information intake for a period preceding, during, and

following a saccade is limited (saccade suppression), such intake is likely to be restricted for a

longer period of time when saccade duration is

extended.

d. COMBINATIONS OF THE ABOVE

MEASURES

For example, blinks and saccades.

Blinks and saccades generally occur in tight temporal relationship to each other. Ifblinks begin to

appear with greater frequency during fixation

pauses, it might indicate inefficiency. During a

blink there is a period of non-seeing, and during

a saccade there is also a period of minimal information intake. Ifthe two occur concurrently there

is a reduction in the time for which information is

not available.

a. EYE BLINK

Eye blinkfrequency

Eye blink frequency is related to the visual

demandingness of a task. The average blink rate

during reading is significantly lower than during

rest. Thus, an increase in blink rate during reading

and other task performance might index such

inefficiency.

These then, are some examples of gaze control

inefficiencies. The model we entertained to account

for the increase in missed signals as a function ofTOT

is, in many respects similar to that proposed many

years ago by Bills (1931), and accounts for delayed or

missed responses using the concept of "blocks." Bills

demonstrated such "blocks" in subject paced tasks.

Others (Teichner, 1968) have extended the concept

to more complex and not necessarily subject paced
Blinks, Saccades, andFixation Pauses

tasks. Stave (1977) described blocks in helicopter

pilots flying a simulator for several hours. Others

(Oswald, 1962 and Williams, et al., 1959) have described similar blocks and invested them with the

label "microsleep" or "daydreaming." More recently

Kecklund and Akerstedt (1993) have suggested that

during vigilance task performance one sees short bursts

ofEEG alpha or theta activity suggestive ofsleepiness.

These "bursts" are best seen during the last 2 hours of

task performance, and have been reported in studies

on long haul truck drivers, train engineers, process

operators, as well as in laboratory investigations.

We contend that periods of microsleep and, perhaps, precursors to such periods can be identified

from a study of gaze control variables. In prior research (Stern, Goldstein and Walrath, 1984 and Lobb

and Stern, 1986), we have demonstrated oculometric

variables associated with the operator missing signals.

Morris (1984) demonstrated a significant relationship between performance measures associated with

flying a GAT-1 simulator for an extended period of

time, and aspects of blinking. This work was done in

sleep deprived subjects, and one might invoke the

concept of microsleep to account for at least some of

the results. Thus, gaze control measures may well be

effective in demonstrating TOT effects, and such

effects may be related to performance deficits.

Whether speaking of blocks, microsleep, or daydreaming we suspect that, during such periods, attention is diverted away from the task at hand. Such

periods of inattention should be reflected in gaze

control variables in tasks that are visually demanding.

Visually demanding tasks require that major portions

ofthe attentional "resources" available to the operator

are focused on the task. One thus has to inhibit

attending to other aspects of the external or internal

environment. We believe that the inability to maintain inhibitory control over attending to other sources

of information leads to lapses in attention, and that

such lapses in attention to the task are reflected in

"gaze control inefficiencies," some of which were

described above.

This study is a follow-on to an earlier study using

an identical experimental protocol (Stern, Boyer,

Schroeder, Touchstone, and Stoliarov, 1994). In the

earlier report, the issue of Gender and Time ofDay at

which the subjects participated was confounded. To

resolve questions that were raised regarding the effects

of those 2 variables and interactions with certain of

the gaze measures, additional data collection was

initiated. Thus, the present study is based on data

gathered from 12 new subjects and 13 subjects who

were included in the previous report. This provided

for a more complete and conclusive determination of

the role of Gender and Time of Day in changes that

occurred in the gaze measures during performance of

the vigilance task.

METHODS

Subjects

Twenty-five paid subjects (12 male, 13 female)

performed the air traffic control (ATC) task (described below) on 3 separate occasions, 2 hours on

each occasion. Halfofthe subjects performed the task

starting at 9 a.m.; halfstarted at 1 p.m. Subject had no

prior experience with the task or had been involved in

ATC training.

Apparatus and task description

Equipment available to the subject included a 19

inch graphic display terminal, a keyboard attached to

the lower right edge and in-line with the CRT, and a

joystick. A VAX11/730 computer controlled the display and was used to abstract response information.The

task required subjects to continually monitor the

CRT display. The display consisted of 2 nonintersecting vectors oriented from the lower right to

the upper left side of the CRT. A small rectangle on

the flight path defined location of an aircraft (A/C)

with 8 A/C displayed on each vector. In an adjacent

alphanumeric data block, displayed were A/C identification, altitude, and groundspeed. A/C position and

change in alphanumeric information were updated

every 6s. The update was done by quadrant, rather

than by vector; thus, the displayed information seemed

to be continually changing. Subjects were asked to

identify and respond to 3 infrequently occurring

events:

a) A nontracked unidentified A/C appeared on the

CRT as a steady green triangle;
Blinks, Saccades, andFixation Pauses

b) An A/C lost altitude information with the numbers reflecting altitude replaced by threeX's (XXX);

and

c) Two A/C on the same flight path were at the same

altitude.

The operator identified the latter event by pressing

the appropriate button on the control panel and then

returned gaze to the display to determine if the A/C

were flying toward or away from each other. If they

were flying away from each other, a control button

had to be pressed and no further response was required. If, however, they were flying toward each

other, the operator was required to press a "conflict"

button and to use a joystick to place a cursor over one

ofthe A/C and request reassignment ofaltitude. Ifthe

operator did not detect these latter events within 28s

from onset both a visual and auditory "conflict alert"

occurred. The visual alert consisted of the 2 A/C

targets at the same altitude flashing, the auditory alert

presented concurrent with the visual one was a 600hz,

65dB tone pulsed at 2 per s. Forty-four such events

were presented over the 2-h period. The minimum

interevent time was approximately 1.5 m, the maximum about 4m . Background "noise" consisted of a

recording of "normal" activities in an ATC facility.

EXPERIMENTAL PROCEDURES

Subjects were instructed about the task. Each subject participated in three 2-h sessions, approximately

1 week apart. They were given short practice sessions

to familiarize them with the task, the nature of the

alarms, etc. Rating scales dealing with feelings of

attentiveness, tiredness, strain, boredom, and irritation were administered both before and after the 2-

hour session. At the end ofthe 2-h session, additional

rating scales relating to perceived task difficulty and

amount of effort required to perform the task were

completed. The results ofthe rating scale analyses are

not included in this report.

Subjects were prepared for the recording of both

horizontal and vertical electrooculograpy by attaching AgAgCl electrodes to the outer canthi ofthe 2 eyes

for the recording of horizontal, and above and below

the right eye for the recording of vertical eye movements and blinks. Inter-electrode impedance was generally below 10000 ohms. Where that was difficult to

achieve, we made sure that the impedance between the

electrode pair from which activity was recorded was

approximately the same, when measured against an

indifferent electrode. Signals were amplified, with

special purpose high common mode rejection amplifiers. Amplifier output was linear from DC to lOOhz.

The output of these amplifiers was fed into a Kyowa

data logger.

The taped data were then digitized and 5-m samples

were obtained starting at minute 10, 30, 50, 70, 90,

and 110. Data were sampled at either 200 or 100 hz,

with all analyses conducted on data sampled at 100 hz.

Data analysis utilized a DEC minicomputer and was

done semiautomatically, in that a skilled analyst applied our computer based algorithms for detecting eye

blinks and saccadic eye movements to the data, and

performed editing functions as necessary.

Most editing involved the deletion ofsaccades not

meeting our "eyeball" criteria for acceptance as saccades. Major reasons for rejecting computer identified saccades were the occurrence of a burst ofmuscle

artifact, in which the algorithm detected an occasional

saccade, saccades followed by a corrective eye movement, and the identification of a slow eye movement

as a saccade (compensatory, pursuit or skin potential

change). Eye movements were only evaluated in the

horizontal plane.

Editing ofeyeblinks was a somewhat more involved

process if the blink occurred in conjunction with a

major eye (and head) movement in the vertical plane.

Such movements occurred when gaze had to be shifted

from the CRT to the response panel located to the

right and on a level with the base of the CRT. Such

movements also occurred with return of gaze to the
Blinks, Saccades, andFixation Pauses

CRT. Our algorithm for detecting blinks includes, as

part of the algorithm, the instruction that if the

voltage level following completion of eye closure does

not return to half amplitude of the closure in a

specified time period (300 msc.) to not consider that

voltage change pattern a blink. The computer thus did

not identify many blinks associated with gaze shifts

from the response panel to the CRT. (An upward

rotation of the eyeball produces a voltage change in

the same direction as a lid closure.) Eye position

higher in the visual plane at the end of a blink than it

was before blink initiation precluded this criterion

from being met. These blinks were manually identified by setting blink initiation atthe same voltage level

obtained after the eyes reopened. These blinks were

thus measured as smaller in amplitude and shorter in

duration than was really the case.

Some aspects of eyelid motion were manually abstracted. One of the criteria for blink identification

requires the operator to set limits to the time between

half closure and half reopening. The limit was 300

msec, for this data analysis. Ifa closure-reopening was

not identified on the basis of this criterion and the

above process took less than half a second, the event

was labeled along closure duration blink (LCD blink)

and independently logged on a data sheet. If the

closure-reopening took more than half a second and

was accompanied by no horizontal eye movements or

slow pendular eye movements, it was identified as a lid

closure and its occurrence and approximate duration

abstracted.

The editing process allows for the inspection of

1000 consecutive data points in a number ofchannels.

The data were thus edited in 10-s chunks, a timeconsuming, but necessary procedure. Five consecutive minutes (or 30 ten second frames) of data were

analyzed; the output of this analysis and summary

statistic printed. Data from the summary statistics

were used for all analyses, except for the blink flurry

analysis and the analyses incorporating manually edited

and added information. The blink flurry analysis was

manually abstracted from the computer print-out of

the raw analyzed data.

Measures abstracted and hypotheses concerning

change as a function of TOT:

1. Blink rate (average number of blinks per minute).

Hypothesis: Significant increase.

2. Blink closing duration (average time from blink

initiations to full closure).

Hypothesis: Significant increase in blink closing

duration.

3. 50% window (average time from lid being half

closed during closing portion of blink to reopening to same level).

Hypothesis: Significant increase.

4. Blink amplitude.

Hypothesis: None.

5. LCD Blinks (blinks with 50% window measure

between 200 and 500 msec).

Hypothesis: Increase in frequency ofsuch blinks.

6. Eye closures (frequency ofclosures in excess of500

msec).

Hypothesis: Increase in frequency.

7. Frequency of flurries (a flurry was defined by the

occurrence of 3 or more blinks in 3 consecutive

seconds).

Hypothesis: None - post hoc measure.

8. Percent of blinks that are part of a flurry. (Blinks

that are part of a flurry divided by all blinks).

Hypothesis: None - post hoc analysis.

9. Saccade rate (average number of saccades per

minute).

Hypothesis: Reduction in rate.

10. Median saccade amplitude.

Hypothesis: None.

11. Median fixation duration.

Hypothesis: Increase in median fixation duration.

12. Fixations in one second time bins.

Hypothesis: Shifttoward longer duration fixations.
Blinks, Saccades, andFixation Pauses

CO

I-

_l D

CO

LU

cr<

i->

ujO

_l2

m<

<u_

i-o<

Z)

CO

3 WAY 1.

Gx Gx

Dx TDx

TOT TOT

o

COo

CM

COO

5q

in

COo

COoCM

COo

2 WAY INTERACTIONS

Gx Gx Gx Dx Dx TDx Dx TOTx

TOT TD D TOT TD TOT F F

oqq

qq

q

COq

ooCOmo

wq

in

COoCOo

5q

oo

q

oq

q

MAIN EFFECTS

G TD D TOT F

oq

oooo

CMoo

o

oo

COo

COoo

CDo

oooooooo

oooo

oq

CMo

inq

oq

oqoq

o

CDq

5

COoo

q

BL RATE

BL CLOSING DURATIONBL 50% WINDOWBL AMPLITUDEBL LCD FREQUENCYEYE CLOSURE FREQUENCY

BL FLURRY FREQUENCY

# BLINKS IN FLURRIESSACCADE RATESACCADE AMPLITUDE

FIXATION DURATIONFIXATION IN 1 SEC TIME PDS

MISSED EVENTSR.T. 1R.T.2
Blinks, Saccades, andFixation Pauses

RESULTS

All analyses utilized ANOVA with 2 within (TOT

and Day) and 2 between subject variables (Time of

Day and Gender).

Table 1 presents summary data for the 12ANOVAs

involving oculometric measures and the 3 analyses

involving aspects ofperformance. Only effects significant at the p < .05 level will be discussed, though

Table 1 presents all effects significant at the p < .10

level and under. Significant TOT effects were obtained for all oculometric and 2 out of the 3 performance variables. As will be described below, these

effects were the most robust, with 10 of the 13

analyses significant beyond the .01 level. There were

3 main effects attributable to Gender, 1 to Time ofD

and 6 to D. Significant 2-way interactions were obtained as follows: 1 for GxTOT, 2 for GxTD and

DxTOT, and 1 for DxTD. Significant 3-way interactions were obtained as follows: 3 for GxTOTxD and

1 for GxTOTxTD.

One of the analyses of oculometric variables, involving the fixation duration variable (F), was somewhat different from the above analyses in that fixation

durations were classified into fixations less than Is in

duration, those between 1 and 2, 2 and 3, and greater

than 3 s in duration. This then became another

within-subject variable for these analyses. A significant F was obtained for TOT and significant 2-way

E

Q.

rr

m

interactions involving the DxF and TOTxFvariables.

The analyses of performance variables averaged these

measures over 4 successive 30-m periods.

For most significant results, we have provided

figures to describe the effect.

I - BLINK RATE

The significant Gender effect (F(l/21)=6.43,

p<.019) is attributable to females blinking significantly more frequently than males. Average blink rate

for females was 19.3, while that for males was 12.3

blinks per minute. The significant D effect (F(2/

42)=5.30, p<.009) is depicted in Figure 1. There is a

significant increase in blink rate over days. Average

blink rate on D 1 is approximately 14.5 blinks per

minute, and on D 3 is somewhat in excess of 17 blinks

per minute.

The significant TOT effect (F(5/105)=12.70,

p<.0001) is accounted for by a consistent increase in

blink rate from the first to the last sample. These

results are presented in Figure 2. It appears that the

slope of the blink rate change between successive

samples is reasonably constant, with the exception of

the shift between the 50-and 70-m samples. The 70-

m sample shows a considerably smaller increase than

one would expect extrapolating from the previous

data points. There were no significant interactions

involving the blink rate measure.

19

Day

Figure 1. Blink rate as a function of Day.

Time from Session Onset (in min)

Figure 2. Biink rate as a function of Time on Task.
Blinks, Saccades, andFixation Pauses

10 30 50 70 90 110

Time from Session Onset (in min)

Figure 3. Biink closing duration as a

function of Time on Task.

1 2 3

Time from Session Onset (in mi

Figure 4. Blink closing duration as

function of Day.

II - BLINK CLOSING DURATION

Blink Closing Duration is the time between blink

initiation and the lowest point reached by the lid

during a blink. There were 2 significant main effects

and no interactions. The TOT effect (F(3.66/

76.94)=5.98, p<.001) is depicted in Figure 3, the D

effect F(l.29/27.18)=4.09, p<.024) in Figure 4. The

major shift in the TOT measure occurs between the

10- and 30-m periods, followed by a smaller but linear

slope increase over the remaining time periods. The

increase over days is, as depicted in Figure 4, small,

but linear.

Ill - BLINK 50% WINDOW

The 50% window measure is the time between the

lid being half-closed during the closing portion ofthe

blink and crossing that same level during the reopening phase. There was a significant TOT effect (F(3.91 /

82.03)=4.87, p<.002). Figure 5 depicts this effect.

There is a linear increase in the 50% window variable.

Average window duration at 10 m into the recording

is approximately 122 ms and 129 ms at time 110 m.

There was a 2-way interaction (significant only at p <

.06 level) in which females show a consistently lower

level over days and less of an increase over days than

males. Though not significant, this interaction is

depicted in Figure 6.

TheD by TOT interaction (F(8.99/188.87)=1.88,

p<.057) is depicted in Figure 7. Ds 1 and 2 start at a

lower level and demonstrate similar patterns, while on D

3, the increase over time blocks is markedly attenuated.

IV - BLINK AMPLITUDE

The significant TOT effect (F(3.52/73.96)=2.70,

p<.044) is displayed in Figure 8. There is a steady

increase in blink amplitude over time blocks, with the

largest increments occurring late in task performance.
Blinks, Saccades, andFixation Pauses

126

118

10 30 50 70 90 110

Time from Session onset (in min)

Figure 5. Blink duration (50% window) as a

function of Time On Task.

o

<D

CO

E,

c

o

3

a

o

c

132

130

128

126

124

122

120

Gender

•e-Male

e-Female

12 3

Day

Figure 6. Blink duration (50% window)

as a function of Day and Gender.

o

c

m

130

128

126

124

o

CD

CO

E,

c

o

■■S 122

3

Q 120

118

116

114

112

JL a

X~^

Days

-e-1

*r2

"Ar3

10 30 50 70 90 110

Time from Session Onset (in min)

Figure 7. Blink duration (50% window) as a

function of Day and Time on Task.

co

750

740

c

3

Q 730

<j.

CD

-1 720 •*-*

Q.

F

< /10

c

m 700

690

10 30 50 70 90 110

Time from Session Onset (in min)

Figure 8. Blink amplitude as a function of

Time on Task.
Blinks, Saccades, andFixation Pauses

The significant 3-way interaction of GxTOTxD

(F(7.37/154.7)=2.19,p<.035) is depicted in Figure 9.

For male subjects, blink amplitude appears to be

consistently smaller, as compared to females, with the

major differences seen on Ds 1 and 3. For females,

there is a general increase in amplitude over days. The

slope of blink amplitude change over time is steeper

for the male than the female groups.

V - LONG CLOSURE DURATION BLINK

FREQUENCY

Long closure duration blinks are identified if the

50% closure duration exceeds 200, butis smaller than

500 ms. Blinks with closure durations in excess of

300ms were manually abstracted. Long closure duration blinks increased significantly as a function of

TOT (F(3.91/82.07)=6.92, p<.001).

As depicted in Figure 10, the greatest increment

occurs between 10 and 30 m, little change between m

30 and 50, and then a return to a continued increase

in such blinks over time.

Figure 11 depicts the results of the significant 3-

way interaction involving TOTxGxTD (F(3.91/

154.19)=2.84, p<.030). The Gender and Time of D

components appear to be principally a function of

males showing the highest incidence of LCD blinks

when run in the afternoon, as compared to the morning. Females, on the other hand, had the highest

incidence ofLCD blinks when run in the morning. It

should be remembered that this effect (AM vs. PM) is

a between subjects effect. The TOT component suggests differences in slope between AM and PM runs

for both males and females. For males, the steepest

slope change occurs in the PM run, while for females,

it is found in the group run in the morning. Females

participating in the afternoon show little change in

this variable over time.

To evaluate whether there was a differential increase in long closure duration blinks compared to the

increase in "normal" blinks as a function ofTOT, we

conducted a further analysis. LCD blinks were expressed as a ratio of all blinks. That data were arc sine

transformed and subjected to the same type ofANOVA

characteristic of other analysis. No significant effects

were obtained, suggesting that the increase in LCD

blinks parallels the increase in blinks obtained as a

function of TOT.

850

In 825

'c 800

3 775

Q

750

725

T3 700

13 675

Q.

E

<

650

625

^c 600

_c 575

m 550

525

10 30 50 70 90 110

Time from Session Onset (in min)

Gender by Day

-e- Males Day 1 -a-Males Day 2

--Females Day 1 -»-Females Day 2

-Males Day 3

-Females Day 3

Figure 9. Blink amplitude as a function of Gender, Day,

and Time on Task.

10
Blinks, Saccades, andFixation Pauses

ftc

<D

3

03

10 30 50 70 90 110

Time from Session Onset (in min)

Figure 10. Frequency of long closure

duration as a function of Time on Task.

10 30 50 70 90 110

Time from Session Onset (in min)

Gender by Time of Day

©-Males AM -«-Males PM

-«-Females AM -»-Females PM

Figure 11. Long closure duration blink frequency as

a function of Gender, Time of Day, and Time on Task.

CO

<D

u.

CO

o

Ü

5.

LU

c

<D

3

o12

10

Time of Day

-e-AM -e-PM

• /

z:\

Day

Figure 12. Frequency of eye closures

as a function of Day and Time of Day.

11
Blinks, Saccades, andFixation Pauses

VI - EYELID CLOSURE FREQUENCY

Eyelid closures were identified whenever the eyelid

remained closed for more than 0.5 seconds. Since lid

closures were relatively infrequently occurring events,

data were analyzed for the first half, vs. second half of

the 2-h session; i.e., data were combined for the 10,

30, and 50 m time samples and for the 70, 90, and 110

m samples. Two significant main effects and 1 significant interaction were found. The significant TOT

effect (F(l/21)=4.49, p<.046) is reflected in an average number of closures during the first hour of 5.07,

and 6.76 during the second h of task performance.

The significant D effect (F(2/42)=4.66. p<.015)

coupled with the significant D x TD interaction (F(2/

42)=5.00,p< .011) is depicted in Figure 12. There is

a significant increase in the frequency ofthese events

over the 3 Ds of task performance. It is principally a

function ofsubjects who participated in the AM.

VII - BLINK FLURRY FREQUENCY

A flurry was tallied if 3 or more successive blinks

occurred with an interval of less than 1 s between

them. AsignificantTOT effect (F(4.85/107.92)=3.40,

p<.008), a significant 2-way interaction involving D

and TOT (F(7.69/161.40)=2.16, p<.035), and a 3-

way interaction involving D, TOT, and Gender

(F(7.69/I6l.40)=2.08,p<.043) were obtained. These

effects are depicted in Figures 13, 14, and 15, respectively. The effect is accounted for principally by

increases in flurry frequency from the first to the

second time block, and again from the fifth to the

sixth one. From 30 m through 90 m, flurry frequency

is asymptotic. The significant DxTOT interaction is

not readily identifiable from the graph. It is most

likely a function ofslope differences between D 1 and

the other 2 Ds. The DxTOTxG interaction is accounted for by the greater incidence offlurry activity

for females, as well as slope differences. For all 18

(3x6) data points, mean flurry activity is greater for

the female, as compared to data sampled at the same

TOT and D for the male subjects.

VIII - NUMBER OF BLINKS IN FLURRIES

(per 5min)

We initially analyzed the total number of blinks in

flurries. The same variables significant in the Blink

Flurry Frequency analysis were again significant. Results for the TOT effect (F(4.78/100.48)=3.00,

p<.0l6) are depicted in Figure 16.

10 30 50 70 90 110

Time from Session Onset (in min)

Figure 13. Frequency of blink flurries

as a function of Time on Task.

io 4

>.

o

tz

§

3

El 2

1

Day

-e-1

e-2

•*-3

10 30 50 70 90 110

Time from Session Onset (in min)

Figure 14. Frequency of blink flurries as a function

of Day and Time on Task.

12
Blinks, Saccades, andFixation Pauses

£ 5

§

4

8" 3

u_

£ 2

10 30 50 70 90 110

Time from Session Onset (in min)

Gender by Day

-©-Males Day 1 -s-Males Day 2

-* Females Day 1 -»-Females Day 2

-Males Day 3

-Females Day 3

Figure 15. Number of blink flurries as a function of

Gender, Day, and Time on Task.

in

c

©

a03

3

10 30 50 70 90 110

Time from Session Onset (in min)

Figure 16. Number of blink flurries as a function

of Time on Task.

13
Blinks, Saccades, andFixation Pauses

Those for the D x TOT interaction (F(7.42/

155.77)=2.17, p<-037) and the D x TOT x Gender

interaction (F(7.42/155.77)=2.22. p<.032) are presented in Figures 17 and 18. Since there were significant effects attributable to flurry frequency and since

we were interested in determining whether there was

not only an increase in flurry frequency, as a function

ofTOT, but also whether there was an increase in the

number ofblinks constituting a flurry as a function of

TOT, a further analysis was undertaken. We calculated the average number of blinks per flurry for each

subject for each time period, excluding blocks where

a subject had no flurries, and a second analysis including such blocks. With these "corrections" to the data,

no significant effects were obtained. Thus, there does

not appear to be an increase in the number of blinks

incorporated in a flurry as a function of TOT.

IX - SACCADE RATE

The only significant effect for saccade rate was a

main effect forTOT(F(5/105)=20.66,p<.001). These

results are graphed in Figure 19. There is a steady

decline in saccade rate from m 10 through m 90, with

the m 110 level not appreciably different from the m

90 level.

X - SACCADE AMPLITUDE

There were 4 significant effects for saccade amplitude, 2 main and two 2-way interactions. The main

effects were for TOT (F(5/105)=15.57, p<.001) and

Gender (F(l/21)=11.71, p<.003). These effects are

graphed in Figures 20 and 21. The saccade amplitude

effect discriminates the first hour of data collection

from the second h in that there is a major increase in

saccade amplitude from the 50 m to the 70 m blocks.

in

?c

CD

am

U_

10 30 50 70 90 110

Time from Session Onset (in min)

Figure 17. Number of blink flurries as a function

of Day and Time on Task.

14
Blinks, Saccades, andFixation Pauses

10 30 50 70 90 110

Time from Session Onset (in min)

-©-Males Day 1

-•-Females Day 1

Gender by Day

-& Males Day 2

- -Females Day 2

•Males Day 3

-Females Day 3

Figure 18. Frequency of blink flurries as a function

of Gender, Day, and Time on Task.

106 310

10 30 50 70 90 110

Time from Session Onset (in min)

Figure 19. Saccade rate as a function

of Time on Task.

en +^

'c

Q

<:

c

o

-a

Q.

E

<

TJa

o

o

0) to

290

270

250

10 30 50 70 90 110

Time from Session Onset (in min)

Figure 20. Saccade amplitude as a function

of Time on Task.

15
Blinks, Saccades, andFixation Pauses

The gender effect involves larger amplitude saccades

for female, as compared to the male subjects. The two

2-way interactions were for Gender x TOT (F(5/

105)=3.14, p<.011) and for G xTD (F(l/21)=7.18,

p<.0l4).

Figure 21 depicts the G x TOT interaction, as well

as the main effect of Gender. The significant interaction effect involves a greater slope change over time

for the female, as compared to the male groups.

The G x TD interaction demonstrates a decrease in

saccade amplitude for females run in the afternoon, as

compared to the morning hours, while the opposite

pattern prevails for the male groups. This effect appears to be quite robust and is graphed in Figure 22.

XI - FIXATION DURATION

Average fixation duration produced a significant

TOT effect (F(5/105) = 10.05, p<.001). The overall

effect is of a steady increase in fixation durations, an

effect to be expected from the prior results of declining saccade frequency. The pattern as depicted in

Figure 23 is a sawtooth one with the 30, 70, and 110

m samples showing an inversion from the prior sample

period.

XII - FIXATIONS IN 1 SECOND TIME BINS

This analysis was concerned with the question of

whether the change in fixation pause duration was

attributable solely to an increase in average fixation

duration, or whether the increase could be attributed

to a change in the distribution. Fixations were categorized as falling in the following windows: less than 1

s, between 1 and 2, 2 and 3, and those longer than 3

s in duration. These analyses thus constituted

4x6x2x2x3 in design. The main effect for fixation

duration (F) (F(1.15/24.25)=288.96, p<.001) comes

as no surprise. By far the greatest number of fixation

pauses are less than 1 s in duration.

These results are depicted in Figure 24. The significant TOT effect (F(5/105)=21.83, p<.001) is depicted in Figure 25. There is a decline in short fixation

pauses (those less than 1 s in duration) and a slow but

steady increase in the other 3 fixation pause duration

bins. TheTOTxF interaction (F(6.10/128.16)=26.29,

p<.001) is also seen in this figure, and is accounted for

by the decrease in short duration and an increase in

longer duration fixations.

The last 2-way interaction involving D and F (F(6/

I6)=2.85,p<.044) is depicted in Figure 26. The effect

is not readily apparent, but is probably attributable to

350

.-§ 330 c

3

310 Q

<r

c

© 290

o

1- 270

<

§ 250

COÜ

I 230

Gender

e-Male

-Female

210

10 30 50 70 90 110

Time from Session Onset (in min)

Figure 21. Saccade amplitude as a function

of gender and Time on Task.

16
Blinks, Saccades, andFixation Pauses

CO +^"c

13

Q

<:

Q.

£

<

tu

T3

CO

Ü

Ü

CO

W

330

300

o 270

Gender

-e-Male

-•-Female

240

210

180

AM PM

Time of Day

Figure 22. Saccade amplitude as a function

of Gender and Time of Day.

ü

CD

CO

E,

c

o

"S

Q

c

g

1

400

10 30 50 70 90 110

Time from Session Onset (in min)

Figure 23. Fixation duration as a function of

Time on Task.

17
Blinks, Saccades, andFixation Pauses

in

o

c

0)

3

0)

(0

X

•

i

440

420

\

400

380 11

50 /

40

30

20

10 (-

0

0-1 1-2 2-3

Fixation Duration (in sec)

>j>

Figure 24. Frequency of fixations in 4 duration bins (0-1 sec,

1-2 sec, 2-3 sec, and > 3 sec).
Blinks, Saccades, andFixation Pauses

10 30 50 70 90 110

Time from Session Onset (in min)

Figure 25. Frequency of fixations in 4 duration bins (0-1

sec, 1-2 sec, 2-3 sec, and > 3 sec) as a function of Time

on Task.

19
Blinks, Saccades, andFixation Pauses

450

440 -

430

420

410

c 400

(0

X

'S 390/;

c

o>3

cr

£

\L 40 \-

30 -

20

10

\

0-1

O Day 1

• Day 2

v Day 3

-V

1-2 2-3 >3

Fixation Duration (in sec)

Figure 26. Frequency of fixations in 4 duration bins (0-1 sec,

1-2 sec, 2-3 sec, and > 3 sec) as a function of Day.

20
Blinks, Saccades, andFixation Pauses

the increase in frequency from D 1 to D 3 offixation

pauses falling below 1 s and greater than 3 s, and the

consequent increase in total number offixation pauses

over days.

XIII - PERFORMANCE

Three measures of performance were utilized. All

demonstrated TOT effects, with 2 also demonstrating

D effects. The measures used were: missed signals, and

2 response latency measures. For all 3 measures, data

were abstracted for successive 30-m periods, each

period containing 11 events requiring a response. The

2 response latency measures were restricted to situations requiring a decision concerning 2 aircraft flying

at the same altitude. There were 8 such events in every

30-m period. The first of these latency measures

involved the time following presentation of 2 aircraft

at the same altitude and the response associated with

recognition of that situation. The second response

latency was measured from the pointin time when the

subject had signaled detection of the problem. It

signaled the operator's decision that the aircraft were

flying toward or away from each other.

Missed events for the conflict-no conflict situation

were identified if the subject did not respond to the

event within 28 s of its initiation. After 28 s had

elapsed, they were "warned" of the event by both an

auditory signal, as well as the highlighting of the 2

aircraft at the same altitude.

Unlike the analysis of oculometric variables, where

TOT effects were based on data sampled at 6 time

intervals; the performance analyses are based on data

for successive 30-m periods (4 periods),

a. Missed events

TheANOVAfor missed events provided 2 significant effects, a significant TOT effect (F(2.34/

49.06)=10.08, p<.0001), and a significant D effect (F(2/42)=9.57, p<.0004). Figure 27 depicts

the average number of events missed per 30-m

period for each of the 3 Ds of task performance.

Two things are readily apparent from this figure:

an increase in missed events as a function ofTOT,

and a reduction in such events as a function ofDs.

Differences are also apparent between theAM and

PM subjects in that the latter missed fewer events

when both TOT and D are taken into consideration. As depicted in Figure 28, for all but 1 ofthe

24 plotted values (4 time periods by 3 Ds by 2

times of D) the average PM value falls below the

AM values.

<n +-»

c

CD>

LL1

o

03

in

A

E

ZJ

0-30 31-60 61-90 91-120

Time from Session Onset (in min)

Figure 27. Number of missed events as a

function of Day, and Time on Task .

21
Blinks, Saccades, andFixation Pauses

b. Latency offirst response

The analysis ofreaction time to the first response

following onset of2 aircraft on a vector atthe same

altitude provided the following significant results.

There was a significant TD effect (F(l/21)=6.50,

p<.019) with PM latencies shorter than those for

AM subjects (AM 17.57 s, PM 15.36 s); a significant D effect (F(2/42)=6.19, p<.004) with decreases in response latencies over Ds (D 1 -17.53,

D 2 - 16.38 and D 3 - 15.62 s); and a significant

TOT effect (F(3/63)=8.15, p<.0001) reflecting

increases in response latency over time. These

results are graphed in Figure 29.

c. Latency ofsecond response

The analysis of time between identification of 2

aircraft at the same altitude and the decision that

they were flying toward or away from each other

provided the following significant results. There

was a significant Gender effect (F(l/21)=6.71,

p<.017), with males responding more rapidly

than females (5.16 vs 6.69 s); a significant Gender

by TD interaction (F(l/21)=8.07, p<.010) accounted for principally by males participating in

the PM sessions responding more rapidly than the

other 3 groups (males participating in the morning and females participating at either time). The

D effect was not significant (F(l/21)=4.04,

p<.057); for males, the AM and PM response

latencies were 6.61 and 3.72 s, respectively; for

females, 6.46 and 6.96. The TOT effect was not

significant. Figure 30 depicts the changes in latency ofthe second response.

§

Ui

TJ©

(0

w

<0

E

3

Z

0-30 31-60 61-90 91-120

Time Blocks (in min)

Day by Time of Day

-© Day 1 AM ^Day 1 PM -*-Day 2 AM

-•-Day 2 PM Day 3 AM -*-Day 3 PM

Figure 28. Missed events as a function of

Day, Time on Task, and Time of Day.

22
Blinks, Saccades, andFixation Pauses

o

CD

CO

CD

E

c

o

+ » u

CO

<D

0-30 31-60 61-90 91-120

Time from Session Onset (in min)

Figure 29. Reaction time of first response

as a function of Day and Time on Task.

0-30 31-60 61-90 91-120

Time from Session Onset (in min)

Figure 30. Reaction time of second response

as a function of Day and Time on Task.

23
Blinks, Saccades, andFixation Pauses

DISCUSSION

Table 1 summarizes the results of all the analyses.

We should point out some possible inflation of effects. A number of measures may be significantly

correlated with each other. We chose to ignore this

confound, since we expected such correlations to be

relatively small. A second reason for not considering

this potential confound in our analysis was the supposition that if variables are significantly correlated,

they should show similar patterns ofchange over time.

This obviously is not the case. For example, one

would expect the pattern ofchange over time for blink

amplitude and blink closing duration to be similar,

since there should be some relationship between amplitude and the time taken by the eyelid to cover that

distance. For the blink closing duration measure, the

greatest change is between the 10- and the 30-m

samples (Figure 3) while for blink amplitude, the

greatest shift occurs between the 90- and 110-m

samples (Figure 8). On the other hand, the relationship between the closing duration and the 50% window duration measure should be closer, since these

measures overlap (Figures 3 and 5). Both show similar

patterns.

I - BLINK RATE

Our finding of a significant increase in blink rate as

a function ofTOT is in agreement with much of the

literature (reviewed in Stern, Boyer, and Schroeder,

1994). That article, which reviews the literature on

blink rate as a measure of "fatigue," concludes that

evidence for a TOT effect is compelling, and that for

fatigue effects a reasonable inference. The best evidence for fatigue effects comes from studies in which

subjects were sleep deprived or in which environmental variables such as lighting were degraded. These

studies further suggestthat fatigue effects are best seen

when the purported measure offatigue (such as blink

frequency) is obtained during prolonged task performance, rather than during "tests" conducted immediately after prolonged performance, especially if these

test sessions are ofshort duration.

Table 2 identifies a few studies demonstrating

increases in blink rate as a function of TOT. In the

present study, blink rate increased from a mean of

13.4 per m during minutes 10-15 oftask performance

to 17.8 per m during minutes 110-115. This is an

increase of approximately 30% over a 1-h and 40-m

period, an increase somewhat less than might have

been expected by extrapolating from the results presented in Table 2. However, our first evaluation

occurred after 10m oftask performance. Had we used

the initial 5 m of task performance as our "baseline"

measure, the results would probably be concordant

with those reported in the literature. It may only be

coincidence, but our results are most similar to the

Mourant (1981) study. Both studies had subjects

working at a CRT, though the tasks performed were

markedly different. It would be nice to be able to say

that the increase in blink rate is purely a TOT or

"fatigue" phenomenon. However, a number of other

variables, also affect blink rate. Before turning to these

other variables we will briefly review studies that have

demonstrated increases in blink frequency as a function of TOT.

Table 2 demonstrates that a variety of tasks, where

duration exceeds half an hour, demonstrates significant increases in blink rate. Thus, driving an automobile, a truck, or airplane simulator, vigilance task

performance (Mackworth Clock test), reading, and

reading from a CRT, all lead to increases in blink

frequency, ranging from 30 to 300%. Studies demonstrating this effect date back at least to 1895, when

Katz, using himselfas subject, demonstrated increases

in blink frequency while reading. He attributed the

increase to "retinal fatigue," but also reported that

requiring the subject to make frequent changes in

accommodation and vergence produced increases in

blinking. Our finding of a 30% increase in blink

frequency over the 1-h 40-m period elapsing between

the first and last 5-m samples evaluated is on the

"conservative" side ofthe changes reported in Table 2.

Not all studies in which subjects were required to

perform for extended periods produced increases in

blink rate. Carmichael and Dearborn (1947) had

subjects read for a 6-h period. No significant increases

in blink rate were reported for either college students

or high school students reading both easy and difficult

text in hard copy or microfilm format. These authors

compared their results to those of Hoffman (1946)

who, in a study preliminary to the Carmichael and

24
Blinks, Saccades, andFixation Pauses

TABLE 2

TIME-ON-TASK, INCREASE IN BLINK RATE

Author Nature of Task Duration (hrs) % increase

Buettger1923 reading 4 200-300

Carpenter 1948 vigilance 2 47

Haider*1976 driving simlulator 4 80-100

Hoffman 1946 reading 1 71

4 268

Luckiesh* 1937 reading, light intensity 1 27

2 79

3 257

Morris GAT-1 flight simlulator 4 268

Mourant* 1981 reading CRT 2 33

Pfaff*1976 driving, auto 3 167

Stern* 1976 driving, simlulator 0.5 31

Tinker 1945 reading 0.5 38

*only the first named author of the article is identified

Dearborn study, had used the same equipment with

students reading for a 4-h period. Hoffman had observed significant increases in blink rate after the

initial hour of reading (see Table 2). The major

difference between these 2 studies was that in the

Carmichael and Dearborn study, tests of comprehension (12-13 per 6-h reading period) occurred after

every 20-25 pages ofreading. Hoffman, on the other

hand, did not utilize comprehension tests; reading

material was easy, pay was less, and students were not

exhorted to do well. Carmichael and Dearborn came

to the conclusion that well motivated subjects do not

demonstrate alterations in blink rate as a function of

TOT.

We suspect that a repeated measure ANOVA design, such as used by Hoffman (1946) would have

found significant increases in blink rate in the

Carmichael and Dearborn data. We also suspect that

the technique used to analyze the data (critical ratios)

was overly sensitive to the large individual differences

in blink rate. Carmichael and Dearborn fortunately

present some of their summary data in tables. Using

their tabled values, we compared blink rate at the start

ofthe experiment with blink rate at 1-hour intervals.

There were 6 such comparisons for all reading "events"

(easy and difficult; text and microfilm; college and

high school subjects). There were thus 2x2x2x6= 48

ratios calculated, 40 ofthese were positive, 8 negative

(negative indicates that blink rate was smaller later, as

compared to the initial 5-m period of reading). If

there was no significant effect, one would have expected half of the 48 values to be positive and half

negative. Forty of the 48 values were positive. The

likelihood ofthis being a chance event is less than .01.

We thus concluded that Carmichael and Dearborn's

data demonstrate TOT effects. The average increase

across all trial blocks was 14%, while the average

decrease for the 8 negative values was 1.6%.

Though not stated, we suspect that they sampled

their blink rates not at 30-m intervals as suggested,

but during the 5 m ofreading immediately preceding

or following each ofthe 13 comprehension tests. This

might well lead to a lower blink rate in its own right,

since Ponder and Kennedy (1927) had demonstrated

that any interruption in task performance would lead

to an alteration in blink rate. We will not review the

25
Blinks, Saccades, andFixation Pauses

acrimonious debate between Tinker (1945) and

Bitterman (1945) on one side, and Luckiesh and Moss

(1937) on the other side, dealing with blink frequency

as a measure offatigue. Luckiesh, a lighting engineer,

presented a considerable amount ofdata demonstrating that fatigue (as well as lighting conditions) affected blink rate, with an increase in blinking as a

function offatigue. Tinker and Bitterman, both psychologists, presented evidence refuting the Luckiesh

findings. Our review ofthe published results leads us

to the conclusion that Luckiesh was correct, while his

critics were in error. (Stern et al., 1994).

Our TOT effect on blink rate is at least suggestive

of fatigue effects. Corroborating evidence will be

elaborated in subsequent sections of this discussion.

Alterations in blink rate also occur as a function of

the nature and difficulty ofthe task being performed.

Results of studies demonstrating this effect are presented in Table 3. There is a relationship between

blink rate for both visual and non-visual tasks, with

the more "perceptually demanding" tasks producing

lower blink rates. Wilson (1993) has demonstrated in

pilots flying the F4 Phantom A/C that the lowest

blink rate and shortest closure duration blinks occur

during high workload segments of the tasks. Many

authors have demonstrated that the blink rate during

reading is significantly lower than during "non-reading" periods (Ponder and Kennedy, 1927). Other

situations, such as solving arithmetic problems (Gille

et al., 1977; Tanaka & Yamaoka, 1992); vocalizing

during vs. quietly solving problems (Schuri and von

Cramon, 1981); social vs. non social perceptual tasks

(von Cranach, 1969); large vs. small angular gaze

displacement (Watanabe et al., 1980); being engaged

in discussion vs. listening (von Cramon, 1980); increased levels ofmuscle tension induced by squeezing

a hand dynamometer (King and Michels, 1957; Lovaas,

1960); and easy vs. difficult auditory tracking task

(Gregory, 1952) all lead to higher blink rates than the

comparison condition.

A number of hypotheses have been invoked to

account for the increase in blink rate as a function of

TOT. The first suggests that visually demanding

tasks, such as reading, lead to blink inhibition. Thus,

the increase in blinking over time is attributable to a

decrease in the ability to maintain such inhibitory

control. A second hypothesis attributes the increase in

blinking to increases in muscle tension and/or overt

motor activity associated with attempts to sit quietly

while performing a task for extended periods oftime.

A third possibility is that, in complex tasks, such as

used in the present study, there is a reduction in task

difficulty (learning effect) as a function of TOT. A

reduction in the allocation of attentional resources to

the task at hand would require less inhibition of

blinking. Not only might one expect the task to

become easier within a day but one would expect some

transfer from day 1 to subsequent days. Such a transfer

of training should be manifest in a significant DAY

effect or an interaction involving DAY. Significant

DAY effects were obtained for 4 oculometric variables: blink rate, blink closing duration, eyelid closure

frequency, missed events and response, as well as the

time to event detection performance measure. Blink

rate, blink closing duration, and eyelid closure frequency increased, while performance measures (the

number ofmissed events and response time to detection

of aircraft at the same altitude) improved over DAYS.

To give credence to any of these hypotheses, it

would have been desirable to have recorded blinks

under "non-task" conditions to determine if task

performance, in fact, led to blink inhibition. There is

considerable variability in "resting" blink rates across

studies. However, the majority ofstudies reviewed in

Table 4 had higher resting blink rates than those

obtained early in task performance in our experiment.

It thus may not be unreasonable to suggest that

performance of the ATC simulation task leads to

blink inhibition. Table 4 summarizes studies in which

blink rates were obtained under "resting" and at least

1 "task" condition.

The inhibition of blinking associated with demanding visual task performance, such as reading or

piloting an aircraft or, in the present context, performing the ATC simulation task, can be accounted

for by the "minimal disturbance hypothesis" suggested by Knorr (1924). This hypothesis takes account ofthe fact that during and bracketing saccades

there is "saccade suppression," i.e., a marked reduction in visual acuity. During and surrounding the

period of a blink there is also suppression of visual

information (Wibbenmeyer, Stern and Chen 1983).

26
Blinks, Saccades, andFixation Pauses

TABLE 3

TASK (DIFFICULTY) AND BLINK RATE

Author Tasks compared Effect obtained

Carmichael 1947 reading N

Clites 1935 logical probs, ment. arith., visual percpetion Y

Gille 1977 # copying, mental arith., solving Y

Gregory 1952 non-visual task Y

Haider 1976 simulator driv. - 3 level diff. Y

Hare 1971 viewing slides-affect Y

Holland 1975 count b'wd, memory, rest Y

King 1957 dynamon, tens, level Y

Lovaas 1960 dynamon, tens, level Y

Luckiesh 1937 light intens., ment. arith., reading, conversation Y

Peterson 1931 reading, unoccupied Y

Poulton 1952 visual tracking Y

Schuri 1981 vocabulary and quiet prob.solv. Y

Stern 1984 pilot vs. copilot aircraft Y

Telford1933 mental arith., rest. Y

v. Cramon 1980 resting, reading, listen, question, discuss Y

v. Cranach 1964 "social," "non-social" Y

Watanabe 1980 angular displacement, target Y

Wilson 1993 pilot high & low workload Y

Wood 1983 problem solving, easy, diff. Y

TABLE 4

BLINK RATE AT REST PLUS OTHER CONDITION

Author Rate rest Task Condition Rate

Gregory 1952 22 stylus maze 19

Harris 1966 22 muscle tension 28

Holland 1975 11 count backwards 5

Martin 1958 18 response to questions 34

Peterson 1931 15 reading 4

v. Cramon 1980 12 discussion 27

27
Blinks, Saccades, andFixation Pauses

The co-occurence of these 2 events, then, would

produce the least interference with the ability to take

in visual information. Knorr found that blinks are

inhibited during reading and that those blinks which

do occur, occur at points in time where they interfere

minimally with information acquisition, such as at

the end of a line of text or at the end of a paragraph.

Orchard and Stern (1991) similarly report that blinks

are more likely to occur in conjunction with line

change saccades, and add regressive saccades and

fixation pauses preceded by regressive saccades to

Knorr's list. In a non-reading context, Watanabe et al.

(1980) required subjects to shift gaze from a centrally

presented LED to LEDs at various eccentricities, with

a return to the central LED after completion of the

required gaze shift. They reported that most blinks

occurred as gaze returned from the peripheral to the

central display, i.e., few blinks occurred while the eyes

were moving to the required location. They also

reported that blinks were more likely to occur with

large, rather than with small angular displacement of

the eyes. Fogarty and Stern (1989) have found similar

results. Subjects required to abstract and respond to

information presented at a peripheral location seldom

blinked as gaze shifted to the target location and were

most likely to blink as gaze returned to the central

location. These authors also noted that the return

from a large amplitude gaze shift (40 degree) was more

likely to lead to a blink during gaze return than a small

amplitude gaze shift (15 degree). Galley (1993) also

found blinks to be non-random events. In a driver

simulation task, he reported that "probability of a

blink is very finely tuned to the end ofthe interfering

inspection of the target field." Our interpretation is

that people suppress blinking behavior the nearer they

approach the critical target period, and release the

blink when target identification is done." (p. 1067).

The breakdown of such inhibitory control or the

breakdown of the tight time locking between blinks

and saccades as a function ofTOT is, in our opinion,

a reasonable hypothesis to entertain further to account for the demonstrated TOT, or perhaps "fatigue" effect. Bartlett (1943), in his lecture on fatigue,

stated:

In other words, he [the fatigued subject] could, within the

limits offatigue set by the experiment, still carry out the local

actions of control as well or better than ever [when task

demands were altered]; but he could not maintain the organized, co-ordinated andtimed responses for more than a short

period. — P. 253

The finding ofgreater blink frequencies for female,

as compared to male subjects, deserves further exploration. It is possible that this is a "real" effect. On the

other hand, it may be secondary, for example, to the

wearing of contact lenses, which may be more frequent in females than males. (We, unfortunately, did

not record whether subjects wore contact lenses.)

Orchard (1993, personal communication) has demonstrated that even soft contact lens wearers engaged

in reading have a higher blink rate than non-contact

lens wearers. Sex differences have occasionally been

described in the literature. Ponder and Kennedy

(1928), in their seminal article on blinking, presented

some data on differences in blink rate between men

and women. Their results indicated no simple relation, but one dependent on the situation in which

blinking was evaluated.

The finding that blink rate increased significantly

over the 3 days ofdata collection suggests a number of

possibilities. The first is that the task becomes easier

over days. Poulton and Gregory (1952), for example,

demonstrated that increasing difficulty of a tracking

task led to a significant decrease in blink frequency.

Stern and Skelly (1984) demonstrated significant

differences in blink frequency between the pilot in

control ofan aircraft (simulator) and the copilot, with

the former blinking significantly less frequently than

the latter. That study also demonstrated that as the

difficulty offlying the aircraft increased, blink inhibition increased. The increase in blink rate over days

may thus be associated with a reduction in task difficulty, i.e., learning. Aside from learning how to perform the task more efficiently, they also may have

learned from their day 1 experience that infrequently

occurring events never occur in rapid successions.

Thus, blink rate following the identification of such

an event may have increased, accounting for the

overall increase.

28
Blinks, Saccades, andFixation Pauses

Clearly, not all types ofincreases in task difficulty

are associated with a decrease in blink rate. In our

studies requiring subjects to make temporal discrimination (visually as well as auditorily presented stimuli),

we have not been able to demonstrate an association

between task difficulty and blink rate, though Poulton

and Gregory (1952) reported differences in blink rate

as a function of difficulty of a tracking task.

It is also possible that "fatigue" or "motivational"

variables contributed to this finding. We think ofthe

ATC task as one requiring the inhibition ofblinking.

Fatigue or a reduction in motivation, i.e., attention to

the task at hand, could well reduce such inhibitory

control. We can discard the hypothesis that motivation to perform the task may have waned over days on

the basis of improved performance over days, i.e.,

subjects missed fewer infrequently occurring events

on D 3 than they did on D 1, as well as fewer on D 2

than D 1, and response times became faster over days.

II and HI -BLINK CLOSING DURATION AND

50% WINDOW

In earlier studies (Goldstein, Walrath, Stern and

Strock, 1985; Bauer, Strock, Goldstein and Stern,

1985; Bauer, Goldstein and Stern 1987), where the

period of task performance was less than 1 hour, we

found that both the 50% closure measure, as well as

the closing duration measures, increased significantly

as a function ofTOT. The effect generally was more

robust for the 50% window measure. This measure is

also affected by task requirements, with visually demanding tasks leading to shorter 50% window durations than visually or auditorily less demanding tasks.

The closing duration measure, like blink rate,

showed a significant effect for both TOT (Figs. 3 and

5), as well as Ds (Figs. 4 and 6) while the 50% window

measure only showed the significant TOT effect. For

both variables, the greatest increase occurred between

samples taken at 10- and 30-m of task performance

following the latter point in time increments in both

closing duration and 50% window, appear to be quite

constant across successive samples. Like blink rate,

which showed a significant increase over days, blink

closing duration also demonstrated such an increase;

the pattern of increase is, however, quite different

suggesting that these 2 measures tap different aspects

of the TOT phenomena.

Recall thatthe closing duration measure is an index

of the time taken from the initiation of lid closure

(associated with a blink) to the pointin time where the

lid is fully closed, while the 50% window measure is

the time between the lid reaching halfthe full closure

and the point in time where the lid returns through

that same level during reopening. We should point

out that our algorithm for the identification of a

closure-reopening as a blink requires that the 50%

window measure be completed in a specifiable time

frame, which in this study, was set at 300 msec. Eye

position shifts during a blink, and quite frequently,

the blink does not return to the 50% level during the

reopening phase. This occurs because eye position on

reopening is higher in the visual field than before

closing (for example, blinks occurring as gaze returns

from the keyboard back to the CRT). Thus, data had

to be edited to allow the computer algorithm to

identify such blinks. Such editing involved setting the

initiation of blink at the level of the eye position

obtained after reopening. Such editing reduced average amplitude, closing duration, and 50% window.

The amount ofsuch editing did not change appreciably across time blocks and, thus, did not contribute to

TOT effects. Our editing, therefore, artificially reduced these variables. Nevertheless, our TOT variable

demonstrates an increase in closing duration and 50%

window as a function of TOT. Thus, it is a robust

effect.

The 50% window duration measure again is sensitive to TOT effects, but like blink rate, and unlike the

closing duration measure, the change over time is a

linear one. Though narrowly failing (at P< .061 and

P< .057) to meet our criteria of acceptability for

statistical significance, we would like to discuss the

two 2-way interactions, both of which involve Ds as

one ofthe variables. The D x Gender interaction (Fig.

6) indicates that, similar to the prior 2 measures, this

one also increases over Ds; however, the increase is

significantly slower for the female, as compared to the

male subjects. The D by TOT interaction (Fig. 7)

29
Blinks, Saccades, andFixation Pauses

indicates that the slope changes over Ds. The major

effect is that on Ds 1 and 2, the 50% closure duration

measure starts from a lower level than for D 3. This

measure attains approximately the same level at minute

110 for all 3 days. We suspect that this measure is

affected both by TOT, as well as task difficulty or

degree of "engagement" ofthe subject in task performance. The relationship between these 2 variables is

not a simple one. If we can take the finding that this

variable discriminates D 3 performance from both Ds

1 and 2 as indexing alterations in perceived difficulty,

then the finding that all 3 Ds end up at the same level

suggests that we are dealing with a ceiling effect or that

after about 2 h, task difficulty is perceived similarly

across Ds.

We (Bauer et al., 1985 and Stern & Skelly, 1984)

have previously demonstrated TOT effects similar to

those obtained here. In the Bauer et al. study, subjects

performed a visual or auditory temporal discrimination task for 45 m. The 50% window measure changed

from 133 msec, early in task performance to 148 msec,

for approximately the last 5 m ofthe 45-m task. The

Stern and Skelly (1984) study, performed in a flight

simulator with Air Force pilots flying a 5-h bomber

mission, also demonstrated significant TOT effects

for this measure, as well as significant effects attributable to differences in task demands on the pilot during

different flight segments. For example, the pilot in

command of the aircraft demonstrated significantly

shorter 50% window durations than the copilot. The

nature of flight maneuvers also significantly affected

average window duration with "weapons delivery"

and "threat avoidance," producing significantly shorter

window durations than cruising at altitude or "nap of

the earth" flying. The present results suggest that

performing the ATC simulation task was more demanding of the participant on D 1 as compared to

successive Ds, or that subjects had to pay closer attention

to task performance early on D 1 than on subsequent Ds.

This measure thus appears to be sensitive to both

TOT and motivational effects. To the extentthat closing

duration and the 50% window measures reflect

momentary reductions in motivation (also considered

as momentary drops in alertness or attention to task),

the monitoring ofthese variables should allow one to

predict that if a brief stimulus requiring a response

occurs in close temporal relationship to such a blink,

then the likelihood of a performance drop-out or

"performance block" (Bills, 1931) is markedly enhanced. We should, however, point out that long

closure duration blinks, unfortunately, do not uniquely

identify the occurrence ofa performance "block." We

have also observed longer closing duration blinks

while subjects stored and rehearsedinformation. (Stern,

1992; Goldstein, Bauer, & Stern, 1992).

IV - BLINK AMPLITUDE

Blink amplitude has, in our previous laboratory

studies, seldom demonstrated TOT effects. The only

major finding dealing with this variable demonstrating TOT effects is a study by Morris (1984), in which

pilots were sleep deprived and required to fly a 4-h

cross country simulated mission the following afternoon. His results demonstrated significant decreases

in blink amplitude over time. These results were

interpreted as suggesting that, late in task performance, the eyes were partially closed, so that the

distance the eyelids had to move during a blink was

markedly attenuated. In the current study, we have a

significant increase in blink amplitude (Fig. 8) over

TOT, and a significant interaction involving Gender,

D, and TOT. One rationalization to account for the

TOT effect is to suggest a change in head position

over time. If there is a significant increase in head

droop, i.e., the chin moving closer and closer to the

chest as a function ofTOT, this would require a wider

opening ofthe palpebral fissure to allow the viewer to

see the upper portion of the CRT. Such a wider

opening ofthe eyes would resultin an increase in blink

amplitude. This change in blink amplitude over time

is equally well seen in Fig. 9, where the data are broken

down by Gender and D. Males, at comparable points

in time, appear to demonstrate smaller amplitude

blinks than the female participants in this study.

Though females showed less variability over days on

this measure, they still demonstrated a reasonably

linear increase over Ds with successive Ds starting out

and ending with greater amplitude blinks than on earlier

Ds. For males, this pattern did not occur.

30
Blinks, Saccades, andFixation Pauses

V - LONG CLOSURE DURATION BLINK

FREQUENCY

Long closure duration blinks again showed consistent changes over the 2-h period, without any suggestion ofreaching an asymptote (Fig. 10). These results

are again concordant with earlier studies from our

laboratory (Stern, Goldstein, andWalrath, 1984). In

the present study, subjects were alerted ifthey missed

an infrequently occurring event. Since these events

occurred relatively infrequently, and since task duration was considerably longer in the present task, there

were many opportunities for the occurrence of long

closure duration blinks and closures. The interaction

between Gender, TD, and TOT (Fig. 11) suggests a

markedly different pattern between males and females

as a function of TD of participation in the experiment. If an increase of long closure duration blinks

over time is symptomatic offatigue effects, then males

participating in the afternoon developed this effect

more strongly than those who participated in the

morning. On the other hand, females who participated in the afternoon run showed a low level ofLCD

blinks and little change in their occurrence over time.

Females who participated in the morning showed a

pattern similar to that seen in males in the afternoon.

What might account for these effects? We know ofno

literature that ascribes a differential effect in complex

task performance to Gender as a function ofTD. The

literature on circadian rhythms, demonstrating differences in performance between subjects identified as

"morning" or "evening" types (on the basis of body

temperature changes over the 24-h cycle), does not

provide us any clues about the Gender effect.

VI - EYELID CLOSURES

In the present study, eye closure frequency also

increased significantly as a function of TOT and D,

supporting the idea that motivational (and/or learning) variables, as suggested earlier for the 50 % window measure, may account for the D effect. These

results also suggest that there may be a significant

correlation between these 2 measures. Are they measuring similar or different effects?

VII - BLINK FLURRY FREQUENCY

The blink flurry measure is a relatively new one for

us, though there is precedence to suggest an increase

in this variable as a function ofTOT. We suspect that

flurries may be associated with a "momentary" and

marked reduction in blink inhibition. It is our impression that such flurries most frequently occur following the identification of infrequently occurring

signals and also prior to, and following, periods of eye

closures. These events both appear to be times when

attention is likely to lapse. In our review of the

literature, we came across 3 comments about flurries.

Carpenter (1948) reported that periods ofblink inhibition in which subjects appeared to be staring, were

sometimes followed by a "burst of blinks." Carpenter

did not comment further about this phenomenon.

Yamada (1992) reported flurries of blinking following

task completion Their observations, thus, are concordant with ours. Frolov (1990) reports as follows:

In tracking rare random visual signals "volleys ofaftereffects"

were noticed which were the ER [eye response] at the time

when the operator ended the tracking [ofa dot moving across

the face ofthe CRT in20 seconds] and was getting ready to

wait for new signals. The "volley" indices were computed for

minute-long intervals, which followed ends of tracking.

These parameters were found sensitive to the complexity of

the task for the subject. — p. 74

It is our impression that the "volley of aftereffects"

is similar to our measure ofblink flurries and that our

observations are concordant. Thus, one can identify 3

separate studies, all of which have observed blink

flurries and have time-locked them to specific events.

As depicted in Fig. 13, the major effect appears between m 10 and 30 and again between m 90 and 110.

Between m 30 and 90, there do not appear to be

consistent changes in this variable. The 2 significant

interactions again suggest that there is greater inhibition of flurries early in task performance, and this

effect is most apparent on D 2. Thus, we again see

changes over D. The major difference for this variable

appears to occur between D 1 and the other 2 Ds.

31
Blinks, Saccades, andFixation Pauses

When the results are broken down further, because of

the triple interaction involving TOT x G x D, it

appears that the changes over time are principally

attributable to the results ofthe female subject group.

Males demonstrate a somewhat random pattern, both

with respect to TOTxD effects. Females appear to not

only demonstrate more flurries than males, but the

pattern offlurry activity discriminates their D 1 from

the other 2 Ds of measurement.

Since an initial analysis ofsimilar data had found

not only an increase in flurry frequency, but also an

increase in the number of blinks constituting a flurry

as a function of TOT, we again evaluated this question in the combined data set. As is apparent from

comparing Fig. 13 with Figs. 16, 14 with 17, and 15

with 18, these 2 measures reflect the same phenomenon. Thus, we concluded that in the present data set,

the TOT effect is restricted to Flurry Frequency. We

have no evidence in this data of an increase in the

number ofblinks constituting a flurry as a function of

TOT.

Why is there an increase in flurries as a function of

TOT? Do flurries occur at random time points or are

they related to other events? It is our impression that

flurries do not occur at random time points. Flurries

are most likely to be seen after the subject has responded to an infrequently occurring event and are

seen (somewhat less frequently) preceding and/or

following eye closures. They are also frequently seen

when the subject realizes that the experimental procedure has been completed. There is inhibition ofblinking during the period ofidentifying and responding to

such events. One possible rationalization for the flurry,

but not our favorite one, is that these blinks occur as

"catch-up phenomena" to make up for blinks missed

while detecting, identifying and responding to an

"event". The rationalization we believe to be correct

suggests that the flurry of blinks occur because the

subject has learned that another "event" will not occur

for some time. Thus, the flurry indexes a period of

reduced alertness or attention to the task. This latter

interpretation is reinforced by the fact that during a

flurry, one also sees little saccadic activity in the

horizontal plane. Another pointin time where flurries

are likely to occur is preceding and following an eyelid

closure. It appears as if a flurry of blinks is an attempt

to ward off an eye closure. We thus suspect that a

measure incorporating the concurrence offlurries and

long duration fixation pauses may be a useful measure

of reduced alertness.

Though not systematically studied by anyone to

date we, as well as others (Yamada 1992), have observed flurries ofblinking at the end of an experimental run. Yamada (1992), for example, had children

perform a number of tasks, ranging from watching a

video-animation (Snoopy and Charlie Brown), performing the STROOP test, and playing a video game

(Nintendo Super-Mario III). He reports "Subjects'

blinking was inhibited just after the initiation ofeach

task. When the given task [was] finished, blinks occurred in bursts" (p. 4).

Results for the blink analyses discussed to this

point suggest that: 1) that a number of blink parameters are sensitive to TOT effects; 2) some of these

effects wash out over Ds; and 3) that males and

femaleshave somewhat different patterns ofresponding.

DC and X - SACCADE RATE AND SACCADE

AMPLITUDE

The second major measure dealt with aspects ofeye

movements. When we use the term "saccade" here, it

should be pointed out that we were liberal in the

definition ofsaccades and included some pursuit eye

movements, especially if they were relatively fast.

Compensatory eye movements, however, were never

identified as saccades. We should point out that

pursuit eye movements constituted only a small proportion of these "saccades." The occurrence of saccades (so defined) decreased linearly from the first to

the 90-m measurement period, with the final measurement at approximately the same level as the 90-m

measure.

Saccade amplitude is affected by TOT. We have

identified 2 studies demonstrating significant amplitude changes as a function of TOT. Malmstrom,

Rändle, and Murphy (1981) obtained a linear decrease in saccade amplitude of 0.29 degrees per m as

well as a significant shift in accommodation (0.11

diopters per m) as subjects performed 2 scanning

tasks, each for a total of approximately 6.5 m. In the

task demonstrating saccade amplitude effects subjects

were required to track a sinusoidally moving target

32
Blinks, Saccades, andFixation Pauses

(horizontal plane at 0.4 hz, 18 degree amplitude) for

13 consecutive 30-s periods. They obtained a 9% loss

of range, with extent of eye movements decreasing

from 15.4 to 14.3 degrees over the 6.5 m period. No

phase lag changes ofeye position with respect to target

position were obtained. The authors concluded that

the significant change in saccade amplitude cannot be

accounted for on the basis of refractoriness of the

muscles controlling eye movements and suggest a

CNS mediated change. Mayetal. (1985) recorded eye

movements while subjects performed non-visual tone

counting tasks differing in complexity. Eye movements were studied under a "free viewing condition,"

i.e., no restriction on eye movements, and a condition

where subjects were required to shift gaze between

LEDs 20 degrees apart, with a gaze shift required every

5 s. Spontaneous (non-stimulus triggered) saccades

decreased in amplitude as a function of TOT. Of

further interest, though not directly relevant here, is

their reporting a decrease in saccade amplitude as a

function of task difficulty (with no effect on saccade

velocity). There was, thus, limited support for evaluating saccade amplitude effects before considering

saccade duration or velocity effects.

We should preface discussion of our results with a

brief comment on other variables that can contribute

to finding changes in saccade amplitude as a function

of TOT. The major variable of concern is the head's

position relative to the CRT. In the studies identified

above, this was controlled by immobilizing the head.

Ifthe head is free to move, as is true ofour experimental situation, then changes in saccade amplitude may

well be secondary to changes in head position. Moving away from the CRT display would decrease the

visual angle subtended by the display, and result in

decreased saccade amplitude. Moving the head closer

to the display would have the opposite effect. The

chair on which our subjects sat was not attached to the

floor, butsubjects were notlikely to move it during an

experiment. Subjects could, however, shift their position on the chair. They could sit up straight and move

their upper body toward the CRT; they could also

slouch in the chair, thus moving their eyes further

away from the CRT. Such body movements might

account for the obtained changesin saccade amplitude as

a function ofTOT. We suspect, however, that this is

not the case. We would consider such movements as

unwanted "noise" in our signal analysis, since there is

no reason for them to be time-locked to events occurring on the CRT, i.e., we would not expect shifts in

body position to occur at specific times, but suspect

that they would occur quite randomly, with perhaps

an increase in frequency over time.

With a decrease in saccade frequency one might, or

might not, expect an increase in saccade amplitude. If

one believes thatthe area searched should decline over

time, one might expect not only a decrease in saccade

frequency, but in amplitude as well. The current

search task, however, did not involve the random

presentation of stimuli across the CRT. It will be

recalled that the subject's principal task was to monitor aircraft moving along 2 vectors. Thus, as long as

subjects are performing this task, one would not

expect a decrease in saccade amplitude. One might

expect no change, or an increase in saccade amplitude

as saccade frequency decreased. The significant changes

in saccade amplitude do not relate well to the changes

in saccade frequency. While the latter changes is a

reasonably linearly fashion as a function ofTOT, the

saccade amplitude measure shows a sudden shift between the 50- and 70-m samples with decreases in

amplitude for other segments. Had we any expectation about this variable, it would have been that it

mirrors saccade frequency changes. This is obviously

not the case. Why there should be a sudden increase in

saccade amplitude between the 50- and 70-m measurement period is a mystery. To evaluate the robustness ofthis phenomenon, we conducted the following

analysis. We tallied the number oftimes that each of

the 25 participants demonstrated an increase or decrease in saccade amplitude from the 10- to the 30-m,

the 30- to the 50-m, the 50- to the 70-m, the 70- to the

90-m and the 90- to the 110- m blocks.

33
Blinks, Saccades, andFixation Pauses

These results were compiled for each ofthe 3 Ds of

analysis and are presented in Table 5. The effect is a

robust one and was obtained on all 3 Ds. Thus, on D

1, 23 ofthe 25 subjects showed an increase in average

saccade amplitude between the 50- and 70-m time

periods. A similar effect is seen for Ds 2 and 3.

We also have no rationale to account for the sustained elevation in saccade amplitude for the last 2

measurement periods. Saccade amplitude increases

are most likely to occur when the operator moves

closer to the display. Why a majority of subjects

should make such a move at about the same time

during task performance is equally mysterious.

The Gender by TOT interaction suggests that the

slope is steeper for females and that saccade amplitude

also discriminates between the sexes. However, we did

not attempt to calibrate eye movement amplitudes to

allow for a test of the hypothesis that the amplitude

differences reflect differences in extent of gaze shift

accomplished. It is quite possible that the obtained

differences simply reflect differences in variables, such

as distance between the eyes, or distance between the

CRT and the eyes.

What is nevertheless perplexing is the significant

Saccade Amplitude x Gender x TD interaction, where

the pattern of saccade amplitude differences as a

function of TD cleanly discriminates between the

sexes. Female subjects run in the morning hours show

the largest amplitude saccades for females, while for

males, the largest amplitude saccades were found in

those participating in the afternoon. This is a between-subjects effect and since the number ofsubjects

per group was small, could be a fortuitous finding.

Future research will have to determine the reality of

this effect.

XI and XII - FIXATION DURATION AND

FEKATIONS IN 1 SECOND TIME BINS

Fixation Pause

There is a paucity ofresearch dealing with changes

in fixation pause duration as a function ofTOT. It is

clear from the literature that in complex tasks, such as

scanning an instrument panel during flight, that neither the search pattern nor the time the eyes dwell at

a particular location are random processes (Ellis and

Stark, 1968). Statistical dependencies, independent

of the placement ofspecific instruments, are the rule

rather than the exception, and most subjects demonstrate specific patterns ofsearch activity while dealing

with component aspects of the task of piloting an

aircraft (Stoliarov personal communication 1991).

A number of studies have used secondary task

technology to evaluate aspects ofwork load associated

with a visual task on scanning performance. The

results ofsuch studies, while tangential to the present

study, provide some relevant information. Tole et al.

(1982), demonstrated, in an aircraft piloting task,

that fixation pause durations increase as a function of

task difficulty of the secondary task, a verbal loading

task. Scanning behavior was also detrimentally affected, with novice pilots showing greater restrictions

ofscanning behavior than skilled pilots as secondary

load increased. Another finding important for us was

that, as loading increased, so did dwell time (fixation

TABLE 5

NUMBER OF SUBJECTS DEMONSTRATING AN INCREASE (+) OR

A DECREASE (-) IN SACCADE AMPLITUDE BETWEEN SUCCESSIVE

DATA BLOCKS.

BLOCK (min) DAY 1 DAY 2 DAY 3

+ - + - + -

10-30 10 15 12 13 11 14

30-50 9 16 12 13 8 17

50-70 23 2 22 3 22 3

70-90 9 16 4 21 8 17

90-110 9 16 11 14 9 16

34
Blinks, Saccades, andFixation Pauses

duration) on each instrument. The increase, in some

cases, was large enough so that these authors referree

to the subject "staring" at the display. Stares are

defined as dwell times in excess of 5 s. The highest

secondary load condition produced increases in stares

ranging from 3.7 to 23.4 percent for the 6 subjects for

whom data are presented (mean increase of 12.7%,

S.D. 7.7).

There is a vast literature dealing with fixation

pauses associated with reading. Hoffman (1946) had

subjects read without interruption for 4 h. He reported a significant decrease in both the number of

fixations and the number oflines read within the first

30 m of reading performance. Variability (SD) of

number of fixations per line increased; the increase

was significant after 2 h of reading. Average fixation

pause duration (interpolated from saccade frequency

data, Fig. 4) was 280 ms. at the first sample and 316

ms. atthe 4-h completion time sample, approximately

a 13% increase (compared to a 6 to 8% increase in our

study).

Carmichael and Dearborn (1947) reported (using

critical ratio statistics) mean differences in saccade

frequency of 3 of 12 comparisons for both of their

reading tasks. The general pattern appears to be one of

high reading efficiency for the first 5 m ofthe reading

task. There is a marked drop atthe 30-m measurement

period with relatively asymptotic performance from

the second through the sixth h ofreading. Itshould be

remembered that reading was interrupted after every

20-25 pages for comprehension checks. Though these

authors minimize the importance of the obtained

TOT effects, to those looking for such effects, there is

abundant suggestive evidence that, had they used

more appropriate statistical tools (available at the

time), they would have obtained a TOT effect.

The specifically useful references for our evaluation

are the Tole et al. studies, which suggest a decrease in

saccades and an increase in fixation pause duration,

especially long pauses as secondary task demands

increase. Ifwe can, instead ofinvoking secondary task

demands (which require the allocation of attentional

resources), conceive ofTOT effects as requiring greater

allocation ofattentional resources to the primary task,

or the allocation ofattentional resources to counteract

boredom, fatigue, physical discomfort, daydreaming,

and other variables associated with TOT (which can

also be conceived of as secondary tasks, albeit not

under the control of the experimenter) we would

expect both a decrease in saccade frequency and an

increase in long duration fixation pauses (Tole's stares).

Two analyses dealing with fixation pause duration

were conducted. The first dealt with median fixation

pause durations. This was, as one would expect from

the saccade frequency results, found to generate significant increases in pause duration as a function of

TOT. The next question asked ofthe data was whether

this increase reflected simply an increase in average

fixation duration or whether it might be accounted for

by a shift in the distribution, i.e., could the increase be

accounted for by a shift in only the frequency of

occurrence of unusually long fixation pauses, such as

might be associated with staring at the display or

occasional difficulties in making decisions? For this

analysis, we partitioned fixations into 4 time windows, namely fixations less than 1 s in duration, those

between 1 and 2, 2 and 3, and longer than 3 s in

duration. As might be expected, the greatest percentage of fixations fell into the less than 1 s window

(87.8%).Though there is a significantincrease in long

duration fixations over time, the most dramatic shift

appears to be for the decrease in frequency offixation

pauses under 1 s. Though the increase in long fixation

pauses as a function ofTOT is, on an absolute basis,

relatively small, we believe thatthe occurrence ofsuch

long fixation pauses, in general, index periods ofnonattention to the task. We say "in general," because

there are times during responding to the occurrence of

2 aircraft at the same altitude (one ofthe infrequently

occurring events) that a long fixation pause may be

appropriate. After identifying that 2 aircraft are at the

same altitude, the observer has to return gaze to the

aircraft to determine whether they are flying away

from, or toward each other. A 6-s period elapses

between displaying 2 aircraft at the same altitude and

the next update ofthe display. Assuming that it took

2 s to identify that 2 aircraft were at the same altitude,

and another second to make the required response to

the detection ofthat event, the viewer has 3 s to return

and fixate on 1 ofthe aircraft, and, if he is fortunate,

in time to detect direction of motion of the second

aircraft during the same sweep. Since such events

35
Blinks, Saccades, andFixation Pauses

occurred at most twice during any of the 5-m sampling periods, they cannot account for the TOT

effects obtained.

XIII - PERFORMANCE

The finding that all 3 performance measures, namely

missed signals, mean response latencies for the first

response, and mean latency to second response demonstrated significant D effects, demonstrate that subjects improved in task performance over days, and

suggests that they maintained a relatively high and

consistent level ofmotivation to perform the task over

the 3 Ds.

The significant TOT effect was limited to the first

2 measures, misses and latency of initial response. In

both cases, we see essentially poorer performance over

time, with an increase in missed signals and an increase in response latency. The lack of a significant

effect for latency of the second response requires

explanation. We assume that the level of alertness

declines from the start to the end of each session, and

that this is reflected in missed signals, as well as longer

initial response latencies. However, once 2 aircraft

have been identified as being at the same altitude, the

subjectis alerted that he next has to determine whether

the 2 are flying toward, or away from each other.

Though not significant, we plotted mean response

latencies as a function of TOT for the second response. On D 1, we see a decrease in response latency

over TOT, with D 2 and 3 asymptotic at about the

same level that characterized the last segment of D 1.

This is, thus, the only measure in which there is

improvement as a function ofTOT. We attribute this

improvementto the probability thatsubjects are learning the location ofthe keyboard switch that has to be

actuated, with alertness at a reasonably constant level,

while for the other responses, alertness is decreasing as

a function of TOT.

The significant TD effect for the first response

latency measure, coupled with the almost significant

effect for missed signals, i.e., fewer signals missed by

subjects participating in the PM suggests that afternoon participants were generally more alert.

The TD effect was limited to latency of first response, with shorter latencies for the PM, as compared

to the AM runs. TheTD effect was marginally significant for missed signals, with fewer missed signals for

the PM runs. What might account for these significant TD effects? Folkard (1983) summarizes the literature dealing with the effect of TD on aspects of

"performance." He points out that the idea that performance on all tasks is equally affected by our "biological clocks" is a mistaken, though popular notion

whose history can be traced back to Kleitman's work

on biological rhythms (1963). In general, simple

visual-motor tasks, i.e., tasks requiring immediate

processing and responding, such as simple reaction

time, letter cancellation, and card sorting tasks show

performance patterns that mirror diurnal variations in

body temperature reasonably well. As body temperature increases from 800 to 2000 hours, there is improved performance on such tasks. However, short

term memory tasks (reading a short paragraph and

responding to questions about the material read)

demonstrate a steady decline in immediate recall

between those hours. Interestingly, delayed recall is

better when material is read in the afternoon. For

working memory, some authors report peak performance about mid-day.

No simple (or even complicated) rationale can be

invoked for better performance in the PM. The task

certainly contains visual-motor, as well as short term

or working memory components. As is apparent from

Folkard's review these variables would lead to opposing results with respect to TD effects. The fact that

PM performance was better than morning performance might suggest that short term memory effects

predominated in our task. They certainly are a major

component of the task, since subjects had to obtain

information about aircraft altitude for up to 8 aircraft

on a vector at any one time, then to "forget" that

information as the subject started to evaluate altitudes

on the second vector. The idea that this task makes

greater demands on short term memory is a post-hoc

inference not easily subjected to empirical verification.

The results for response latency for the second

response (only considered were trials in which the first

response was not cued) demonstrated a different pattern. First of all, there was a significant Gender effect

with males responding more rapidly than females.

The Gender by TD interaction, coupled with the near

significant main effect for TD suggests that the greatest

36
Blinks, Saccades, andFixation Pauses

difference between the AM and PM performance is

attributable to male subjects. Those participating in

the afternoon had mean response latencies approximately halfthe response latencies ofthose participating in the AM. Females, on the other hand, showed

slightly shorter response latencies in the morning.

These are intriguing empirical differences with no

theoretical reason to account for them.

The finding that TD effect is principally found for

male subjects raises another issue that could not be

answered by recourse to the literature. Folkard (1983)

makes no reference to Gender as a variable contributing to TD effects. Hancock (1983), in a chapter in the

same book, deals with the Gender issue. He refers to

one study in which an interaction between gender of

experimenter and subject was obtained. For male

experimenters, same-sex subjects show improvement

in performance efficiency, while females tend to perform more poorly with male experimenters. The

present experiment did not systematically vary gender

of the experimenter. Both male and female experimenters participated.

In summary, our performance measures demonstrated significant improvement over Ds (all 3 measures), significant decrements associated with TOT (2

measures), and suggestive TD and Gender effects.

Performance was better in the PM, as compared to

AM, and males demonstrated this effect to a greater

extent than female participants (1 measure).

GENDER AND TIME OF DAY EFFECTS

There were several oculometric effects involving

the Gender variable. The 2 main effects dealt with a

higher blink rate and larger saccade amplitude in our

group of females. There were 3 significant 2-way

interactions. In the first, a Gender x D interaction was

obtained for the 50% window duration measure.

Females had crisper blinks (shorter 50 % window

duration) than males, and the increase in this measure

over Ds was smaller for the group of women. The

other 2-way interaction dealt with saccade amplitude,

in that there was a Gender by TOT (Fig. 21) and a

Gender by TD interaction (Fig. 22). The G x TOT is

a subtle effect, one most likely accounted for by a

greater increase in saccade amplitude between the 50-

and 70-m data samples for female subjects and, perhaps, the small differences between the time 10-, 30-

and 50-m measures. The Gender by TD interaction is

more dramatic and readily seen in Fig. 22. Females

who participated in the morning session had larger

amplitude saccades, when compared to those participating in the afternoon session, or compared to males

in the morning session. The pattern for males was the

exact opposite.

There were 4 significant 3-way interactions involving Gender. Three ofthese were for the Gender x TOT

x D interactions. The significant blink amplitude

effect is, in part, a function of differences in blink

amplitude, with females showing generally larger blink

amplitudes than males. The larger blink and saccade

amplitudes for females, though not a large effect, is

one deserving further attention. We know of no

literature suggesting differences in EOG variables as a

function of sex. The D component appears to be

principally attributable to females, in that the slope of

this variable over TOT decreases from D 1 to D 3. In

males, if there is a consistent pattern, it is one of a

slope increase from D 1 to D 3.

The Gender by TD interaction for saccade amplitude is a surprising one. Why should males and

females demonstrate opposing patterns (Fig. 22), and

what meaning might be attributed to either pattern?

One variable to consider is the time period during

which data were collected. All of the AM males, and

4 of the PM females data were collected from July

through November 1991; while data for 4 of the 6

males run in the PM, and 6 ofthe females run in the

AM, were collected during approximately the same

time span in 1993. Time ofyear does not appear to be

a variable that could account for these results. A

second possibility is that the sexes differ in circadian

"alertness" rhythms. Initial subjectselection was based

on the assumption ofrandom sampling. That process

generated very few males willing to come to the

laboratory in the afternoon hours, and equally few

females interested in being tested during the morning

hours. Ifthis selection process has anything to do with

circadian rhythms, we would expect "morning" types

to be more likely to volunteer for morning, and

"evening" types for afternoon experiments. One might

also expect morning types to perform better in the

morning and evening types in the afternoon, though

37
Blinks, Saccades, andFixation Pauses

the literature on this point is inconclusive, especially

if one utilizes a relatively complex task, as was done

here. Evaluating performance, in terms ofthe number

of "unusual" events to which a subject had to be cued

because ofnon-responding in the allotted time, we see

little support for a TD effect for male subjects. For

females, we see that performance for those participating in the PM session is better than that for those

serving in the AM, as well as better than the performance ofmales, regardless ofTD. Because ofthe small

sample size, we believe these results need replication

before they can be accepted.

Thus, we have 2 lines of evidence suggesting that

female subjects who participated in the PM runs were

more alert and able to sustain alertness better, than

was true of females participating in the morning. A

recent report by Knipling andWierwille (1993) briefly

reviews the literature on automobile accidents in

which driving while drowsy was implicated in the

Police Accident Report. "Male drivers had an involvement ratio (per km) that was 1.8 times greater and an

involvement likelihood (i.e., involvement per registered driver) that was 3.1 times greater than

females."(p2) They further point out that male drivers, especially young ones, are over-represented in all

automobile accidents. "However, these sex and age

differences are much greater for drowsy driver crashes

than for crashes in general." Our results would suggest

that if TD had been identified in the above accident

statistics, that the involvement ratio calculated for the

afternoon hours would show an even greater discrepancy between males and females than that calculated

for the entire 24-h cycle. This certainly is an area

requiring further study.

One 3-way interaction of Gender x TOT x D

involving blink amplitude, has already been discussed.

The other 2 involved aspects of the blink flurry

measure. Since these 2 measures appear to be redundant, we will limit our discussion to the Flurry count

measure. Females, on the average, engage in more

flurries than males on all days, and show more of an

increasing pattern as a function ofTOT than is true of

males (Fig. 15).

The interactions involving TOT with TD also

deserve some special discussion. The 50% closure

duration measure shows not only an increase as a

function ofTOT, butTD as well. We have previously

shown that this measure is affected by perceptual task

difficulty, with more difficult tasks leading to shorter

closure durations. The lengthening of closure durations over days suggests that the task is either becoming easier for the subjects or that they are less well

motivated on the third day. We can rule out the latter

possibility from the results ofthe performance analysis. Frequency of occurrence of missed signals decreases significantly over days. If motivation lags, we

might expect more missed signals on D 3 than D 1.

What we find is that most subjects improve over days,

thus the learning hypothesis appears to be a more

reasonable one to account for these results.

DAY EFFECTS

Performance data indicated that the number of

missed signals and response latencies decreased over

days. In line with the improvement in performance,

the oculometric measures further suggest that "effort"

expended in task performance decreases over days.

Blink rate increases significantly over days, blink

closing duration becomes longer, the 50% window

measure increases, as does the frequency of eyelid

closures.

Blink rate is affected by perceptual difficulty with

greater inhibition associated with performing more

difficult tasks. It is, of course, also affected by TOT,

with an increase over time. The observed blink rate

increase over Ds may thus indicate that the task is

becoming easier for the subject, i.e., that one has to

allocate fewer attentional resources to task performance. Similarly, blink "crispness," as measured by

the 50% window measure, is associated with task

difficulty and expectancy effects. As one approaches a

point in time where a stimulus is expected, one sees

both a decrease in blink likelihood, as well as a

decrease in the 50% window measure (Goldstein,

Bauer and Stern 1992). Thus, the changes in closing

duration and 50% window suggest that subjects not

only improved in their ability to detect unexpected

infrequently occurring events, but also learned something about the timing of these events. They apparently learned that the likelihood a second event would

occur was small immediately following the occurrence of an "unexpected" event.

38
Blinks, Saccades, andFixation Pauses

The significant increase in eye closures over days is

also ofinterest in this regard. It is our impression that

a large percentage ofsuch closures occurred following

the making ofthe manual responses. This is, again, an

opportune time to be inattentive to the CRT, since

the subject has learned from his D 1 experience, and

reinforced by his D 2 experience that the next event

requiring action on his part will not occur for a minute

or more.

DIAGNOSTICITY OF OCULOMETRIC

MEASURES

To further explore the significant TOT results and

to determine the diagnosticity ofthese measures, i.e.,

how many of the 25 subjects demonstrated the phenomenon, we conducted the following analysis. For

each subject for each successive pair of data points

(10-30, 30-50, 50-70, 70-90. 90-110 m) we determined whether the second value was larger than the

preceding one. Since we had data available for 3 Ds 3

times 5, or 15, such comparisons were made. For each

subject we tallied the total number of events so identified. With 15 measures one would expect that on

average, 7-111 would show an increase, and the same

number a decrease between successive values, unless

there were consistent trends across subjects (as already

demonstrated in the ANOVAs).

Table 6 depicts the results ofthis analysis for the 12

gaze control measures for which significant TOT

results were obtained. It is apparent that 4 of the

measures produced splits significantly better than the

.02 probability level (using the binomial test). These

are: blink rate, closure duration, saccade rate, and

frequency of fixations less than 1 s in duration. Two

others, though notsignificant, are worthy ofmention,

namely 50% window duration and saccade amplitude.

The number of subjects who were appropriately

identified by all 6, by 5, 4, 3, and 2 variables is

depicted in Table 7. Since the saccade measure had

only 1 subject who fell below the median cutoffvalue,

we did a further tally across the remaining 5 variables.

These are also depicted in Table 7.

It is apparent that subjects are reasonably consistent across measures, i.e., ifthey qualify for inclusion

on one variable, they are most likely to qualify on the

other variables as well. Using the 6 measures, we find

5 subjects fully concordant, 11 concordant on 5, 6 on

4, and 3 on 3 or fewer measures. Conducting the same

tally for the 5 variables (excluding the saccade measure), 5 are concordant on all 5, 11 on 4, 6 on 3, and

3 on fewer than 3 variables.

TABLE 6

NUMBER OF SUBJECTS FOR WHOM 7 OR FEWER, OR 8 OR MORE

COMPARISONS OF SUCCESSIVE TIME PERIODS (15) SHOWED AN INCREASE

OR A DECREASE AS A FUNCTION OF TIME ON TASK.

=/<7 =/>8

Blink rate 6 19

Closure duration 6 19

50% window 8 17

Blink Amplitude 12 13

LCD blink frequency 13 12

Flurry frequency 11 14

Sum flurry blinks 10 15

Saccade rate 1 24

Saccade amplitude 16 9

Fixation duration 10 15

Frequency fixations <1 sec 3 22

39
Blinks, Saccades, andFixation Pauses

TABLE 7

NUMBER OF SUBJECTS FOR WHOM SPECIFIED NUMBER

OF MEASURES DISCRIMINATED

Using all 6 Gaze Measures Using only 5 Gaze Measures

No. Measures No. Subjects No. Measures No Subjects

6 5 5 5

5 11 4 11

4 6 3 6

3 2 2 3

2 1 1 0

1 0 0 0

0 0 25 25

SUMMARY

The present analysis allowed for the partitioning of

effects attributable to TOT, TD, and Gender, as well

as experience with the task. It also included information about 3 performance measures. From the results

ofthe performance analysis we can infer that motivation to perform the task was maintained at similar

levels over the 3 Ds of the experiment. Though subjects demonstrated an increase in missed signals and

reaction time as a function of TOT, they demonstrated a reduction in the number of missed events

and reaction times over the 3 Ds. Thus, changes in the

oculometric variables are not likely to be attributable

to a decline in motivation to perform the task over

days. Not only is there improvement in performance

over days, but blink rate (increases), blink closing

duration (increases), 50% window duration (increases)

and eyelid closure frequency (increases) all demonstrated changes, suggesting that the task must have

become easier to perform over days or that subjects

were developing "strategies" to deal with the task. We

suspect that one ofthe strategies involved the realization that "infrequently occurring events," i.e., those

requiring a response, did not occur in close contiguity

to each other. The interval between any 2 such events

ranged between 1.5 and 4 m, with most having an

interevent interval of at least 2 m. They, thus, may

have learned that lapses in attention were not likely to

affect task performance if they occurred shortly after

responding to an infrequently occurring event. It was

our impression that eyelid closures and blink flurries

were most likely to occur in the minute immediately

following a response. If these impressions can be

substantiated, and the data are available to do this, we

would be on more solid ground with respect to this

aspect of learning.

Whether the increase in blink rate, closing duration, and 50% window duration also follow this

pattern is also ofinterest. We suspect thatthe changes

in these variables are more likely to reflect the possibility that the task became easier for our subjects as a

function of experience.

The observed Gender effect came as somewhat of a

surprise. Though the differences are not statistically

significant, there was a trend for females to perform

better, i.e., miss fewer infrequently occurring events

than was true for male subjects. Significant Gender

effects included saccade and blink amplitude, with

females generally showing larger amplitude responses

than males.

Gender effects of greatest interest were those in

which Gender interacted with TD. Such interactions

were obtained for the Long Closure Duration blink

frequency and the saccade amplitude measures. For

the LCD Blink measure, the interaction also includes

TOT. The results suggest that the TOT effect is

greater for PM males and for AM females, with more

modest increases in this variable for AM males, and

little or no increase for PM females. We interpret

these increases in LCD blink frequency as suggestive

of "fatigue" effects. It is unclear why females should

40
Blinks, Saccades, andFixation Pauses

show such an effect principally for morning participants, while males demonstrate the greatest effect

when participating in the afternoon, with modest

changes for those participating in the morning. Replicating this gender effect would appear to be highly

desirable because of the implication of these results

for shift work.

The TOT effects obtained for all variables clearly

demonstrates the potential utility ofthese oculometric

measures for defining periods of impaired performance potential. A word of caution is in order, since

for some ofthe variables, the TOT effect was reduced

over Ds. Specifically, for the 50% window blink

duration measure, the slope becomes shallower over

Ds so that by D 3, there appears to be no longer a TOT

effect. A similar effect is seen in the blink flurry

measure. These, however, were the only measures for

which this cautionary comment is applicable.

REFERENCES

Andresen, B., Seifert, R., Lammers, O, and Thorn, E.

1988. Spontaneous blink rate, schizophrenia, and

schizotypal personality. Journal ofPsychophysiology,2, 129-130.

Appel, V.H., McCarron, L.T. and Manning, B.A. 1968.

Eyeblink rate: Behavioral index ofthreat? Journal

ofConsulting Psychology, 15_, 153-157.

Bagley, J. and Maneus, L. 1979. Effect of awareness as

an indication of cognitive load. Perception and

Motor Skills, 42, 591-594.

Bahill, H.T. and Stark, L. 1975. Overlapping saccades

and glissades are produced by fatigue in the saccadic eye movement system. ExperimentalNeurology, 48, 95-106.

Bartlett, F.C. 1943. Fatigue following highly skilled

work Proceedings ofthe Royal Society, JjJ, 131,

247-257.

Bauer, L.O., Goldstein, R., andStern,J.A. 1987. Effects

ofinformation processing demands on physiological response patters. Human Factors, 22., (2), 213-

234.

Bauer, L.O., Strock, B.D., Goldstein, R., Stern, J.A. &

Walrath, L.C. 1985. Auditory discrimination and

the eyeblink. Psychophysiology, 22, 636-641.

Bills, A.G. 1931. Blocking: A new principle of mental

fatigue. American Journal ofPsychology, 43_, 230-

245.

Bills, A.G. 1943. ThePsychology ofEfficiency,NewYork:

Harper and Brothers Publication.

Bills, A.G. and McTeer, W. 1932. Transfer of fatigue

and identical elements. Journal of Experimental

Psychology, 15_, 23-36.

Bitterman, M.E. 1945. Heart rate and frequency of

blinking as indices of visual efficiency. Journal of

Experimental Psychology, 25., 279-292.

Buettger, W. 1923. Concerning blink frequency as a

measure of visual fatigue: effect oflight intensity

and color on fatigue. Dissertation abstract: Medical Faculty Georg-August University, Gottingen,

Germany.

Carpenter, A. 1948. The rate of blinking during prolonged visual search. Journal ofExperimental Psychology, 18, 587-591.

Carmichael, L. and Dearborn, W.F. 1947. Reading and

VisualFatigue. Boston: Naughton Mifflin Co.

Clites, M.S. 1935. Certain somatic activities in relation

to successful and unsuccessful problem solving.

Journal ofExperimental Psychology, 18, 708-724.

Collewijn, H., van der Steen, J. and Steinman, R.M.

1985. Human eye movements associated with

blinks and prolonged eyelid closure. Journal of

Neurophysiology, $4, 11-27.

Crommelinck, M. and Roucoux, A. 1976. Characteristics of cat's eye saccades in different states of

alertness. Brain Research, 103. 574-578.

Dichgans, J., Nauck, B. and Wolpert, E. 1973. The

influence ofattention, vigilance and stimulus area

on optokinetic and vestibular nystagmus and voluntary saccades. In Zikmund, V. (Ed.), Oculomotor

Systems and Brain Functions. London: Butterworth,

280-294.

Dwelshauvers, G. 1928. The education of automatism.

Psychologie et vie, \, 11-14.

Ellis, S.R. and Stark, L. 1986. Statistical dependency in

visual scanning. Human Factors, 28, 421-438.

Evinger.C, Shaw, M., Peck, C.K., Manning, K.A. and

Baker,R. 1984. Blinking and the associated eye

movements in humans, guinea pigs, and rabbits.

Journal of Neurophysiology, 52, 323-339.

41
Blinks, Saccades, andFixation Pauses

Fisk, A.D., and Schneider, W. 1981. Central and automatic processing during tasks requiring sustained

attention: A new approach to vigilance. Human

Factors, 23., 737-750.

Fogarty, C. and Stern, J.A. 1989. Eye movements and

blinks: Their relationship to higher cognitive processes. InternationalJournal or Psychophysiology, 8,

35-42.

Frank, M.C., and Kuhlo, W. 1970. The effect ofalcohol

on saccadic eye movements in man. Archive

Psychiatrie undNervenkranken, 213, 238-245.

Frolov, M.V. 1990. Monitoring thefunctional state ofa

human operator. Moscow: Nauka Publishers.

Galley, N. 1993. The evaluation of the EOG as a

psychophysiological measuring instrument in the

study of driver behavior. Ergonomics, $6, 1063-

1070.

Giambra, L.M., and Quilter, R.E. 1987. A two-term

exponential functional description of the true

course ofsustained attention. Human Factors, 29,

635-643.

Goldstein, R, Bauer, L.O., and Stern, J.A. 1992. Effect

of task difficulty and interstimulus interval on

blinking and cardiac activity.International Journal

ofPsychophysiology, 13_, 111-117.

Goldstein, R., Walrath, L.C., Stern, J.A., and Strock,

B.D. 1985. Blink activity in a discrimination task

as a function ofstimulus modality and schedule of

presentation. Psychophysiology, 22, 629-635.

Gregory, R.L. 1952. Variations in blink rate during

non-visual tasks. Quarterly Journal Experimental

Psychology, A, 165-169.

Hacker, W. 1962. On modifications of the so-called

spontaneous eye blink during work. Probleme und

Ergebnisse der Psychologic 1, 7-43.

Haider, E. and Rohmert, W. 1976. Blink frequency

during 4 hours ofsimulated truck driving. European Journal of Applied Psychology, 3_5_, 137-147.

Hare, R., Wood, K., Britain, S., Shadman, J. 1970.

Autonomie responses to affective visual stimulation. Psychophysiology, Z, 408-417.

Harris, C.S., Thackray, R.L, and Shoenberger, R.W.

1966. Blink rate as a function of induced muscle

tension and manifest anxiety. Perceptual andMotor Skills, 22, 155-160.

Hoffman, A.C. 1946. Eye movements during prolonged

reading. Journal ofExperimental Psychology, 36,

95-118.

Holland, M.K. and Tarlow, G. 1975. Blinking and

thinking. Perceptual and Motor Skills, A\, 403-

406.

Jantti, V., Lang, A.H., Keskinen, E., Lehtinen, I., and

Pakkanen, A. 1983. Acute effects ofLV alcohol on

saccadic eye movements and subjective evaluation

ofintoxication. Psychopharmacology, 22., 251-255.

Katz, R. 1895. Blinks as a measure of visual fatigue.

Klinische Monatblaetterfur Augenheilkunde, 154-

157.

Kecklund, G. andAkerstedt, T. 1993. Sleepiness in long

distance truck driving: An ambulatory EEG study

of night driving. Ergonomics, $6, 1007-1017.

Kennard, D.W. and Glaser, G.H. 1964. An analysis of

eyelid movements. Journal of Nervous andMental

Disorders, 139,31-48.

King, D.C., and Michels, K.M. 1957. Muscular tension

and the human blink rate. Journal ofExperimental

Psychology,^., 113-116.

Kleitman, N. 1963. Sleep and wakefulness. Chicago:

University of Chicago Press.

Knipling, R.R. and Wierwille, W.W. 1993. U.S. IVHS

Research: Vehicle-based drowsy driver detection.

Paper presented at the Vigilance and Transport

Conference Lyon, France; December 9-10. Sponsored by INRETS and USTRB.

Knorr, A. 1924. Contributions to knowledge about eye

blinks under normal and pathological conditions.

Abhandlungen an der Gesamtgeboten der Medizin,

25, in Hacker.

Lille, F. and Cheliout, F. 1982. Variations in diurnal

and nocturnal waking state in air traffic controllers. European Journal ofApplied Physiology, 49.

319-328.

Lobb, M.L. and Stern, J.A. 1986. Pattern of eyelid

motion predictive ofdecision errors during drowsiness: oculomotor indices ofaltered states. Internationaljournal ofNeuroscience, 3_Q, 17-22.

Lovaas, I.O. I960. The relationship ofinduced muscular tension, tension level and manifest anxiety in

learning. Journal ofExperimental Psychology, 59.

145-152.

42
Blinks, Saccades, andFixation Pauses

Luckiesh, M. and Moss, F. 1937. The Science ofSeeing.

New York: D. van Nostrand Co., Inc.

Mackworth, J.F. 1964. Performance decrement in vigilance, threshold, and high-speed perceptual motor

tasks. Canadian Journal of Psychology, 18, 209-

223.

Manzer, C.W. 1927. An experimental investigation of

rest pauses. Archives Psychology, 9_Q, 1-84.

Martin, I. 1958. Blink rate and muscle tension. Journal

of'Mental Science, 104. 123-132.

Malmstrom, F.V., Rändle, R.J., Murphy, M.R., Reed,

R.E., Weber, R.J. 1981. Visual fatigue: The need

for an integrated model. Bulletin ofthePsychonomic

Society, 11, 183-186.

May, J.G., Kennedy, R.S., Williams, M.C., Dunlap,

W.P. and Brannan, J.R. 1985- Eye movements as

an index of mental work load. Final Report Contract F49620-85-C-0121, Phase I, Topic # 174.

Morris, T.L. 1984. EOG indices of fatigue-induced

decrements in flying related performance.

USAFSAM Technical Report, Brooks AFB.

Mourant, R.R., Lakshmanan, R., and Chantadisai, R.

1981. Visual fatigue and CRT display terminals.

Human Factors, 23, 529-540.

Orchard, L. & Stern, J.A. 1991. Blinks as an index of

cognitive activity during reading.IntegrativePhysiological and Behavioral Science, 26, 108-116.

Oswald, I. 1962. Sleeping and Waking, Amsterdam:

Elsevier.

Peterson, J. and Allison, I.W. 1931. Control of the eye

wink mechanism. Journal of ExperimentalPsychology,!^, 144-154.

Pfaff, V, Fruhstorfer, H. and Peter, J.H. 1976. Changes

in eye blink duration and frequency during car

driving. Pflueger Archive, 362: R21.

Ponder, E. & Kennedy, W.P. 1927. On the act of

blinking. Quarterly Journal ofExperimental Physiology, 18,89-110.

Poulton, E.C. and Gregory, R.L. 1952. Blinking during

visual tracking. Quarterly Journal ofExperimental

Psychology, 4, 57-65.

Simonov, P.V. and Frolov, M.V 1984. Psychophysiological control of the functional state of the

human operator. Soviet Psychology, 23., 68-87.

Stave, A. 1977. The effects of cockpit environment on

long-term pilot performance. Human Factors, 19.

503-514.

Stern, J.A. 1992. The eye blink: Affective and cognitive

influences. In Forgay, D., Sosnowski, T.,

Wrzesniewski, K. (Eds.) Anxiety: Recent Developments in Cognitive, Psychophysiological and Health

Research. Washington, D.C: Hemisphere Publishing Co.

Stern, J.A., Beideman, L. and Chen, S.C. 1976. Effects

ofalcohol on visual search and motor performance

during complex task performance. In M. Horvath

(Ed.) Adverse Effects ofEnvironmental Chemicals

and Psychotropic Drugs. Amsterdam: Elsevier Scientific, 2, 53-68.

Stern, J.A., Boyer, D., Schroeder, DJ., Touchstone,

R.M., and Stoliarov, N. (1994). Blinks, saccades,

and fixation pauses during vigilance performance.

Federal Aviation Administration, Office ofAviation Medicine Report no. DOT/FAA/AM-94/26.

Available from: National Technical Information

Service, Springfield, VA 22161; ordering no.

ADA290600.

Stern, JA., Bremer, D.A., and McClure, J.N.Jr. 1974.

Analysis of eye movements and blinks during

reading: Effects of valium. Psychopharmacologia,

40, 170-175.

Stern, J.A., Goldstein, R. and Walrath. L.C. 1984.

Phasic inattention: Electrooculographic markers.

In M.G.H. Coles, J.R. Jennings and J.A. Stern

(Eds)., Psychophysiological perspectives: Festschrift

for Beatrice and John Lacey. New York: Van

Nostrand Co., Inc. 157-170.

Stern,J.A. andSkelly,J.J. 1984. The eye blink and work

load considerations. In Proceedings ofthe Human

Factors Society 28thAnnual Meeting, SantaMonica,

CA: Human Factors Society, 942-944.

Stern, J.A, Walrath, L.C. and Goldstein, R. 1984. The

endogenous eyeblink. Psychophysiology, 21,22-33.

Tanaka, Y., Yamaoka, K. 1992. Blink activity and task

difficulty. Paper presented at Pavlovian Society of

North America, October 9-12, Westwood, CA.

Thackray, R.I. and Touchstone, R.M. 1989. Effects of

high visual taskload on the behaviours involved in

complex monitoring. Ergonomics, 3.2, 27-38.

43
Blinks, Saccades, andFixation Pauses

Tinker, M.A. 1945. Reliability of blinking frequency

employed as a measure of readability. Journal of

Experimental Psychology, 3_5_, 418-424.

Tole, J.R., Stephens, A.T., Harris, R.L.Sr., and Eprath,

A.R. 1982. Visual scanning behavior and mental

work load in aircraft pilots. Aviation, Space, and

Environmental Medicine, 5_2, 54-61.

vonCramon, D. 1980. Spontaneous eyeblink: mirror of

psychosomatic activity. Medizinische Psychologie,

6, 123-139.

von Cranach, M., Schmid,R. and Vogel, M.W. 1969.

Concerning the relationship between blinks and

gaze shifts. Psychologische Forschung, 22. 68-78.

Watanabe, Y., Fujita, T. and Gyoba, J. 1980. Investigation ofblinking contingent upon saccadic eye movements. Tohoku Psychologica Folia, 39, 121-129.

Weiner, E.A. and Concepcion, P. 1975. Effects of

affective stimuli on eye blink rate and anxiety.

Journal ofClinical Psychology, 21, 256-259.

Wibbenmeyer, R., SternJ.A, and Chen, S.C. 1983.

Elevation of visual threshold associated with

eyeblink onset. International Journal of Neurosciences, 18, 279-286.

Wilson, G. 1993. Air-to-ground training mission: a

psychophysiological work load analysis. Ergonomics, 20, 1071-1087.

Williams, H., Lubin, A. and Goodnow, JJ. 1959. Impaired performance with acute sleep loss. Psychological Monographs, 7_2, 484.

Wood, J. and Hassett, J. 1983. Eye blinking during

problem solving. Psychophysiology, 20, 18-20.

Yamada, F. 1992. Inhibition of blink rate and enhancement of Fm-theta (frontal mid line) rhythm during playing a video game. Paper presented at Annual Meeting of Society for Psychophysiological

Research, October 16, San Diego, CA.

Zee, D., Chu, R., Leigh, J., Savino, P.J., Schatz, N.,

Reingold, D. and Cogan, D.G. 1983. Blink-saccade synkinesis. Neurology, 22. 1233-1236.

44 &US. GOVERNMENT PRINTING OFFICE: 1996 - 760-082/20069