1

MULTI-DIMENSIONAL CHARACTERIZATIONS OF OPERATOR STATE: 

A VALIDATION OF OCULOMOTOR METRICS

INTRODUCTION

 The present experiment represents one component of a larger project, the ultimate goal of which 

is to develop an oculomotor-based system capable 

of real-time alertness/attention monitoring in the 

fi eld; specifi cally in aviation operational situations. 

The general strategy underlying this effort is to 

elaborate the relationships between the activity of 

the eyes and performance (as well as the specifi c 

signal processing algorithms that best capture this 

relationship) in laboratory/simulator settings prior 

to actual fi eld testing.

However, a number of factors, though not insurmountable, complicate the general issue of how to 

relate oculomotor activity to momentary periods of 

inattention and/or lapses in vigilance. The foremost 

problem concerns validation. Ideally, one would 

like an alertness index, which can be directly related 

to some aspect of performance. A major diffi culty 

arises from the existence of substantial individual 

differences in the ability to perform different kinds 

of tasks at different levels of alertness. Thus, a perfect 

measure of alertness might predict performance 

quite well for one individual, while another person 

may show no overt signs of impairment even though 

alertness is at unacceptably low levels. Conversely, 

the same person may be able to perform well on a 

perceptual motor task at low alertness levels but be 

incapable of performing a more demanding mental 

arithmetic task. 

One might ask why, in the case of no obvious 

performance impairment, the level of alertness 

should be considered unacceptable. The answer 

to this question lies in the ability to deal with rare 

but cognitively challenging events. Consider, for 

example, an airplane pilot during a long-duration 

fl ight. Due to the high level of automation in the 

cockpit and the routine nature of the demands 

placed upon the pilot, adequate performance, as 

measured by any parameter of aircraft activity 

(e.g., altitude/course deviations) can be maintained 

at even low levels of alertness. The performance 

consequences of impaired alertness may only be 

evident when a critical equipment failure or freak 

atmospheric event increases the demands upon the 

pilot, for example.

In short, the ultimate goal of an alertness-monitoring system is to detect operator states associated 

with an increased probability of making a catastrophic error (either in detection or judgment). The 

validation problem, then, concerns how to prove 

that a system is capable of reducing the occurrence 

of catastrophic, yet extremely rare, events. While 

fi eld studies provide the best information concerning feasibility, reliability and usability, the validity 

of a device cannot be ascertained in controlled 

fi eld settings – while equipment can be stressed to 

the failure point, this strategy cannot be employed 

when assessing human operators.

Clearly, the best assessment of the effi cacy of any 

device can be obtained after the system has been 

ubiquitously employed in the fi eld for a substantial 

period of time. Then fi ve-year accident rates before 

and after implementation can be evaluated to determine whether a substantial effect upon safety has 

occurred. In the interim, validity-testing must come 

from controlled laboratory and simulator environments where the state of the operator, as well as the 

demands of the task can be varied to include levels 

that would be prohibitively dangerous in applied 

settings. While there are some problems of generalization, increased errors and slowed reaction times 

in the laboratory have been found to predict an 

increased likelihood of catastrophic performance in 

the fi eld. The present study represents an attempt 

to improve the ability to link oculomotor behavior 

to performance in two ways.

First, traditional measures of “overt” performance 

have been augmented by the inclusion of “covert,” 

physiological behavioral measures. These measures 

provide a richer description of operator state against 

which to check the validity of putative metrics such 

as measures of oculomotor activity. Thus, because 

the causes underlying performance lapses are inherently multi-dimensional (encompassing such factors 

as fatigue and inattention) a multi-dimensional 

characterization of alertness is desirable.
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The second strategy for improving the ability to 

detect relationships between eye-movements and 

performance is to recognize that the transition from a 

state of high alertness to drowsiness does not occur in 

a smooth (or graded) fashion. Instead, the frequency 

with which relatively stable periods of performance 

are punctuated by transient attentional drop-outs 

increases. Thus, while similar average performance 

levels may be maintained, performance variability 

about the mean is likely to increase as a function 

of both fatigue and lowered levels of attentiveness 

(Bills, 1934). Attempts to relate alertness measures 

to behavior should not be restricted to predicting 

mean performance levels but should also explore 

whether the measure is premonitory of increased 

performance variability.

To this end, a modifi ed Bakan Continuous Performance Task (CPT) was employed. Subjects were 

required to monitor sequentially displayed digits 

and produce a response when either three odd or 

three even digits were presented in succession. This 

task was chosen for several reasons. First, information processing demands varied considerably on a 

stimulus-by-stimulus basis allowing for momentary 

shifts in processing effi cacy to be evaluated. Second, this task could be presented for long enough 

periods of time to allow for the manifestation of 

Time on Task (TOT) effects. Finally, this paradigm 

produces an environment rich in eye-movements 

that can be systematically related to factors such as 

fatigue as well as the expectancies and strategies of 

individual subjects. 

METHODS

 Subjects

Twenty individuals ranging in age from 19 to 

28 years participated in the study. All subjects 

were right-handed, in good health, and possessed 

normal or corrected-to-normal vision and hearing. 

The subjects were screened for previous history of 

head trauma or neurological disorders. Monetary 

remuneration ($30.00) was provided upon completion of the experiment. Data from one of the 

subjects were discarded due to inadequate levels of 

responding (error rates in excess of 50%); while the 

electro encephalographic (EEG) records of three of 

the subjects contained either clinical EEG abnormalities or unacceptably high levels of movement 

and/or electrodermal contamination. Thus, the 

analyses described below were conducted upon a 

database consisting of 16 subjects (eight male and 

eight female).

Tasks/Stimuli

A modifi ed Bakan running-memory task was 

employed. A total of 1,200 stimuli was presented 

at four predictable locations along a horizontal row 

separated by 0, 7, 14 and 21 degrees of visual angle 

from the left-most location. These stimuli, consisting of yellow numerical digits (0.9) fl anked by “Xs” 

(e.g., X6X) presented against a blue background, 

traversed the screen from left to right and back 

again with a constant Stimulus Onset Asynchrony 

(SOA) of 2,511 msec. The central digit was replaced 

with an “X” (e.g., XXX), after an initial exposure 

duration of 300 msec. Subjects were instructed to 

press a mouse button with one hand following the 

presentation of three consecutive odd digits, while 

three consecutively displayed even digits required a 

button press with the opposite hand. Subjects were 

further instructed that four consecutive even/odd 

digit sequences would never occur.

Two pseudo-random stimulus lists (A and B) were 

created, differing only in that the relative positions 

of even and odd digits were reversed in the two 

lists. The last 600 stimuli in each list were generated by reversing the order of the fi rst 600 stimuli. 

Of the 1,200 stimuli, 12.25% required a response 

(evenly distributed across hands). Target density 

was controlled so that the number of imperative 

stimuli was either two or three during each minute 

of task performance.

Eight groupings created by the various digit (even/

odd) by response hand (left/right) by stimulus list 

(A/B) mappings were equally represented within the 

male (n=8) and female (n=8) subject groups. The 

words “EVEN” and “ODD” (oriented vertically) 

were continuously displayed along the appropriate 

left/right extremes of the display screen in order 

to inform subjects which response hand was to be 

utilized for a given stimulus category.

Apparatus

EEG activity was recorded continuously, from 

17 standard sites (Fz, F3, F4, F7, F8, Cz, C3, C4, 

T3, T4, T5, T6, Pz, P3, P4, O1 and O2) according to the International 10/20 system (see Jasper, 

1958), and from two non-standard sites (C3’ and 

C4’) located 4 cm to the left and right of Cz, re-
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spectively. The Ag/AgCl scalp EEG electrodes were 

all referred to the two earlobes linked through 5 

Kohm resistors. A ground electrode was attached 

to the subject’s forehead.

Horizontal electrooculographic (EOG) activity 

was recorded from electrodes placed on the outer 

canthus of each eye. For two of the subjects, vertical 

EOG measurements were obtained from a bipolar 

montage located on the outer canthus of the left eye 

oriented perpendicularly to the plane of horizontal 

recording. For the remaining subjects, traditional 

vertical EOG placements above and below the left 

eye and centered on the pupil were employed.

Electrocardiographic (ECG) activity was recorded 

from two electrodes — one located immediately 

below the heart and the other symmetrically placed 

with respect to midline on the right side of the 

chest. Thoracic and abdominal respiratory movements were monitored via Grass PT 5A volumetric 

pressure transducers. All electrode impedances were 

maintained at or below 5 kohms.

The physiological signals were amplifi ed by 

Grass Model 8-24E amplifi ers with a time constant 

of 10 sec and a high frequency cutoff of 70 Hz. 

Continuous paper tracings of the polygraph output 

were obtained for each session. The data were also 

continuously sampled at 200 Hz and stored to disk 

by a PDP11-based data acquisition and recording 

system. The presentation of visual stimuli and the 

recording of responses were under the control of 

a 386-based personal computer. A background of 

white noise was continuously presented through 

E.A.R. Tone 3A insert headphones.

Procedure

Each subject participated in a single session that 

always began at 1:00 pm to capitalize on the early 

to mid-afternoon dip in alertness (see Broughton, 

1994 for a review). Upon arrival at the laboratory, 

subjects fi lled out an informed consent form and 

then endorsed an item on the Stanford Sleepiness 

Scale (SSS – Hoddes et al., 1973) indicating their 

current level of drowsiness. A personal history questionnaire, that included items concerning current 

medical and medication status, recent history of 

food and alcohol consumption, typical and current sleep patterns, as well as standard requests for 

information on age, gender and handedness, was 

then administered.

The next hour consisted of a thorough briefi ng 

on the purpose of the experiment; a discussion 

of the tasks to be performed, as well as various 

concerns related to the recording of physiological 

signals; and the actual attachment of the electrodes 

and respiration transducers. Subjects were then 

seated in a dimly lit, sound-attenuated and shielded 

chamber, and the sleepiness scale was administered 

once again. Earplugs, through which white noise 

was continuously presented during all recording 

conditions, were inserted at this time.

Five minutes of data were subsequently recorded 

under eyes-open resting conditions during which the 

subjects were asked to minimize eye movements by 

maintaining fi xation on the center of a CRT placed 

directly in front of them. This condition was followed by an eye-movement calibration procedure 

that required subjects to visually track the movements of a fi xation spot, that horizontally traversed 

the display screen in discrete steps.

Subjects next received fi ve minutes of practice on 

the running memory task. Instructions emphasizing 

both speed and accuracy were delivered (i.e.,, the 

subjects were to respond as quickly as possible but 

not at rates that would generate substantial numbers 

of errors). After providing an additional self-report 

of drowsiness, subjects experienced 50 minutes of 

continuous performance on the running memory 

task. During this block, experimenter/subject interactions were not permitted. A fi nal eyes-closed 

resting condition (10-min) then occurred. In addition to the initial SSS administration upon arrival 

at the laboratory (SS1), additional measures were 

obtained just prior to the 5-min eyes-open resting 

condition (SSS2), the 50-min Bakan task (SSS3), 

and before and after the 10 min eyes-closed resting 

conditions (SSS4 and SSS5).

Analyses

Questions concerning the consequences of increased Time-On-Task (TOT) were explored by 

segmenting the 50 min of continuous performance 

as follows: The fi rst 2 min of the task (considered a 

“warm-up” period) were not analyzed, the remaining 

48 min were divided into three consecutive 16-min 

blocks, designated “Early,” “Middle,” and “Late.” A 

total of 1,147 stimuli were presented during these 

three segments.

Unless otherwise stated, the stimuli were further 

subdivided into fi ve mutually exclusive categories. 
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“Imperative” stimuli (i.e., a third consecutively presented 

odd or even digit) required a button-press response. The 

stimulus immediately preceding an imperative stimulus 

(i.e., the second consecutively presented odd or even 

number) was labelled “2nd-Digit”. A stimulus was termed 

a “disconfi rmation” if it was preceded by a 2nd-Digit 

stimulus (leading to an expectancy to respond) but then 

alternated (i.e., an even digit preceded by two odd digits 

or an odd digit preceded by two even digits).

Stimuli immediately following imperative stimuli, 

labeled “post-imperative,” were singled out because 

subjects could always predict whether they would be 

even or odd (four consecutive even/odd sequences never 

occurred). Finally, all digits (except for the special-case 

disconfi rmation stimuli) that represented an alternation 

(e.g., even preceded by odd and vice versa) were categorized as “1st-Digit” stimuli. Of the 1,147 total stimuli 

analyzed, 385 were 1st-Digit, 310 were 2nd-Digit, 141 

were imperative, 141 were post-imperative, and 170 

constituted disconfi rmations.

Unless otherwise stated, the stimulus type and TOT 

effects were statistically evaluated by a repeated measures 

2-factor (5 stimulus type x 3 task periods) Analysis of 

Variance (ANOVA). Planned contrasts of hypotheses 

concerning specifi c cell means were conducted using a 

modifi ed Bonferonni technique (Keppel, 1991) only in the 

presence of signifi cant main effects and/or interactions.

In general, tests of the following two hypotheses were 

planned: 1) increased TOT would be associated with 

overt performance decrements and physiological response 

patterns indicative of decreased alertness during later task 

segments; and 2) the distinct information processing 

demands and anticipation levels imposed by stimuli in 

the categories detailed above would be associated with 

differing physiological response patterns.

RESULTS AND DISCUSSION

Subjective Sleepiness Ratings

Data summarizing the SSS estimates of drowsiness 

at different periods during the experimental session are 

presented in Figure 1. Higher scores on this scale are 

indicative of increasing drowsiness. Subjective reports of 

sleepiness clearly differed during the experimental session 

[F(4,60)=26.76; p<0.0001]. Follow up testing revealed 

that while SSS ratings remained constant during the 

three administrations prior to the vigilance block (times 

1, 2, and 3), elevated reports of sleepiness were obtained for 

both of the tests, that followed 50 min of continuous task 

performance (times 4 and 5).

Overt Performance Data

Measures of response speed, accuracy and operator sensitivity (Calderia, 1980 or Wickens, 1984, 

for a discussion of using A’ as a non-parametric 

analog of d’) are presented in Table 1. While several 

of these measures showed trends towards poorer 

performance at later task periods, none of these 

differences reached statistical signifi cance. Clearly, 

subjects were able to maintain comparable mean 

performance levels throughout the 50-min task 

block. However, inspection of the standard deviation scores associated with these measures indicated, 

1) considerable between-subject variability and 2) 

increased within-subject variability during later task 

blocks (especially during the middle period).

For these reasons, an analysis strategy was developed that would minimize between-subject differences in baseline response levels and at the same time 

be sensitive to changes in performance variability 

as well as central tendency. For each subject, mean 

RT and percent correct measures were computed 

for every minute of the task. This distribution was 

then z-transformed, and local mean and standard 

deviations of the transformed scores were computed 

for each minute of the task. These local estimates of 

central tendency and variability were then averaged 

across the early, middle, and late task periods.

The z-scores for the RT distributions were multiplied by -1 so that for any given task segment, 

mean z-scores signifi cantly more negative than the 

expected value of zero indicate poorer than average 

Figure 1. Stanford Sleepiness Scale Ratings.
5

performance with respect to the task as a whole. 

Similarly, positive segment means indicate better 

than average performance during the segment. 

In a similar fashion, segment standard deviations 

signifi cantly less than or greater than the expected 

value of 1 indicate reduced and increased performance variability, respectively. One-tailed t-tests 

were performed to test the hypothesis that mean 

performance levels would be higher and less variable 

during the early task segment and would deteriorate 

and become more variable during the middle and 

late task segments.

The results of these analyses are displayed in 

Figure 2. Just as the non-transformed measures of 

response speed and accuracy did not differ as a function of task segment, the mean values of the z-score 

distributions (see Panel A) did not differ from the 

expected value of 0, even though there was a trend 

towards poorer performance in later task segments. 

However, Panel B indicates that performance variability (as indexed by average local performance 

standard deviations) was lowest during the early 

task segment for both RT (t=-2.70; p<0.02) and 

percent correct (t=-1.82; p<0.05), while RT variability alone increased during the middle segment 

of the task (t=2.08; p<0.03).

An additional analysis was performed to ascertain 

whether the reason that the measures of central tendency failed to discriminate performance during the 

various task segments was due to differences in the 

manner in which individual subjects may have opted 

to trade response speed for accuracy (Wickelgren, 

1977). To this end, a composite Performance Index 

(PI) was created by averaging the means of the ztransformed RT and percent correct measures.

Note that high values of the PI require fast reactions and high accuracy while low PI scores refl ect 

slow responses and low accuracy. Intermediate 

values can be produced either by speed/accuracy 

tradeoffs (e.g., fast/inaccurate or slow/accurate) or 

by intermediate response levels along both dimensions. Mean PI levels for the 3 task segments are 

presented in Figure 3. PI scores exceeded expected 

levels for the task as a whole during the early task 

segment (t=1.89; p<0.04) – when response speed 

and accuracy are considered simultaneously, performance did, in fact, degrade with increasing time 

on task. Thus, the performance data indicate that the 

increased subjective reports of drowsiness induced by 

the vigilance task were associated with both poorer 

performance levels and increasing performance variability during middle and late task segments. 

Table I. Mean (and Standard Deviation) of Performance 

Metrics by Time Segment. 

Task Segment 

Measures Early Middle Late

Reaction Time (msec) 647 (136.8) 667 (147.2) 655 (126.5) 

A' 0.978 (0.0215) 0.964 (0.0439) 0.966 (0.0339) 

Percent Correct 98.4 (1.61) 97.7 (2.36) 97.9 (1.87) 

Target Hit Percentage 91.8 (8.38) 86.3 (16.55) 86.7 (13.08) 

False Alarm Frequency 2 (4.0) 3 (3.0) 2 (2.0) 

Figure 2. Mean (Panel A) and Std. Dev. 

(Panel B) of local performance measures 

as a function of task segment. 
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Blink Metrics

Blinks in the vertical EOG were automatically 

scored by a computer algorithm if all of the following criteria were met: a) a slope deviation in excess 

of 30 μVolts of appropriate polarity in a 30 msec 

window could be determined (initial point labelled 

as blink onset); b) an infl ection point (termed blink 

peak) occurred within 200 msec of blink onset; c) 

the voltage difference between the blink onset and 

peak points was at least 50 μVolts (termed blink 

amplitude); and fi nally, d) the vertical EOG recovered (i.e., returned towards baseline) 30% of the 

blink amplitude value within 75 msec.

Upon detection of a blink, the 50% closure duration was defi ned as the time difference between the 

point on the ascending limb of the blink (when 50% 

of the blink amplitude was exceeded) and a similar 

point on the descending limb. The performance of 

the automated scoring algorithm was monitored by 

an experimenter who was responsible for rejecting 

noise artifacts incorrectly identifi ed as blinks, as 

well as for manually identifying blinks that the 

computer failed to detect.

On average, subjects produced 23.66 (s.d. 

= 11.96) blinks/min during the vigilance task, 

which represents a signifi cant increase over the 

15.78 (s.d. = 9.40) blinks/min generated during 

the 5 min eyes-open no task condition (t=4.1593; 

p=0.0004). Figure 4 displays the blink proportions (#blinks/#stimuli) associated with the various 

stimuli presented during the three task segments. 

Blink proportions (rather than frequencies) are 

presented to correct for the unequal trial sizes in 

the different stimulus categories. Blink production 

was not sensitive to either the TOT [F(2,30)=0.51; 

p=0.5330] or stimulus category [F(4,60)=0.88; 

p=0.1015] manipulations. Despite an apparent 

trend towards decreased blinking following imperative stimuli during the early task segment, the 

TOT x stimulus type interaction also did not reach 

statistical signifi cance [F(8,120)=2.09; p=0.1015]. 

Thus, while the vigilance task increased the pressure 

to produce blinks with respect to the non-task condition; equivalent blink rates were associated with all 

task periods and stimulus types.

In contrast, blink durations (see Figure 5) did 

vary as a function of both TOT [F(2,30)=9.53; 

p=0.0035] and stimulus type [F(4,60)=12.67; 

p=0.0001]. The lack of a signifi cant interaction [F(8,120)=0.30; p=0.737] allowed follow-up tests for each of these independent 

variables to be collapsed across levels of the 

other factor. These tests confi rmed that a) 

blink durations increased during the middle 

and late task segments; and b) blink duration 

was also sensitive to the differing information 

processing demands associated with the various 

stimulus categories. Several stimulus category 

effects emerged, all of which are consistent with 

the hypothesis that blink durations would decrease 

with decreasing temporal proximity to periods 

Figure 4. Blink proportions as a function of stimulus where an imperative stimulus was anticipated.

type and task segment.

Figure 3. Mean Performance Index Scores as a 

function of Task Segment (* indicates p<0.05).
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Thus, blinks following disconfi rmation stimuli 

(which were at least two digits removed from the 

next potential imperative stimulus) were longer 

than the blinks following all other stimuli. Similarly, blinks associated with 1st-digit stimuli were 

longer than blinks following 2nd-digit and imperative stimuli (which did not, in themselves, differ). 

Post- imperative stimulus blinks were longer than 

blinks following imperative stimuli but shorter 

than disconfi rmation-related blinks.

The results of the analysis of blink amplitudes are 

displayed in Figure 6. Blink amplitude did not vary 

with TOT [F(2,30)=1.77; p=0.1980] but was sensitive to stimulus category [F(4,60)=5.81; p=0.0015]. 

These factors did not interact [F(8,120)=1.11; 

p=0.3619]. The analysis of stimulus effects collapsed across the three task segments revealed that 

the blinks following post-imperative stimuli were 

larger than the 1st-digit, 2nd-digit and imperative 

stimulus blinks, while the disconfi rmation-related 

blinks could be discriminated only from the 2nddigit blinks. Taken together, the amplitude and duration results are consistent with the hypothesis that blink 

morphology adapts in order to minimize processing 

“down time” due to closure of the eye with increases 

in the anticipation of a target stimulus.

Additional analyses were conducted to explore 

how the timing of blinks may be related to the 

TOT and stimulus category manipulations. Mean 

latencies to the fi rst blink following a given stimulus 

type are displayed in Figure 7. The blink latency 

measure was sensitive to both TOT [F(2,30)=12.28; 

p=0.0008] and stimulus type [F(4,60)=23.95; 

p=0.0001]. The interaction of these factors was 

also signifi cant [F(8,120)=3.78; p=0.0226]. Follow-up tests confi rmed that blink latencies were 

considerably delayed following the imperative 

stimulus during all three task segments. Additionally, blink latencies tended to decrease with 

increased TOT.

The signifi cant interaction effect results because 

the TOT effects were not equivalent across stimulus categories. Thus, fi rst blink latencies increased 

from early to middle to late following the imperative and 1st-digit stimuli. For the disconfi rmation 

stimuli, the middle and late segments did not 

differ from each other but were associated with 

reduced latencies, in comparison with the early 

segment. Blink latency following post- imperative 

stimuli discriminated only between the early and 

late segments. Finally, no TOT related latency 

Figure 5. Blink durations as a function of 

stimulus type and task segment.

Figure 6. Blink amplitude as a function of stimulus and 

task segment.

Figure 7. First blink latency as a function of stimulus 

type and task segment. 
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differences reached statistical signifi cance for the 

blinks following 2nd-digit stimuli.

Figure 8 displays the results of a different analysis 

of blink timing in which the post-stimulus distribution of blinks was calculated for consecutive 500 msec 

bins. Clearly, blinks were not randomly distributed 

throughout the interval between stimuli as indicated 

by a signifi cant main effect of bin [F(4,60)=58.77; 

p=0.0001]. The blink distributions also varied as a function of stimulus type giving rise to a signifi cant bin x 

stimulus type interaction [F(16,240)=6.12; p=0.0016]. 

A post-hoc testing strategy was performed, comparing 

the distribution of blinks following imperative and 

disconfi rmation stimuli with the blink distributions, 

collapsed across all the other stimulus categories. These 

tests were conducted to examine the following hypotheses: a) blinks would be inhibited as a function of the 

anticipation of a target; b) this inhibition would last 

longer for the imperative stimuli that actually required 

a decision to respond.

Both hypotheses were confi rmed. Blinks were signifi -

cantly less likely to occur following both the imperative 

and disconfi rmation stimuli during the fi rst 500 msec 

post-stimulus. In the 500-1,000 msec interval, the release 

from inhibition resulting from the recognition that the 

anticipation of a response had been disconfi rmed resulted 

in an increase in blinking relative to the other stimulus 

categories. Finally, a delayed release from inhibition 

following the imperative stimuli produced elevated 

blink rates during the 1,000-1,500, 1,500-2,000, and 

the 2,000-2,500 msec post-stimulus intervals.

The relationship between the blink distributions 

following imperative and disconfi rmation stimuli and 

the latency distribution of post-imperative stimulus 

button press responses is displayed in greater detail 

in Figure 9. In this fi gure, the data have been binned 

in 50 msec epochs for each of the early, middle and 

late task segments. Solid bars indicate bin proportions 

signifi cantly different from zero.

Blinks were almost completely inhibited during the 

fi rst 500 msec following stimulus presentation. Blinks 

were also extremely rare during the 1,000 msec interval 

preceding the presentation of the next stimulus. The 

distribution of blinks following disconfi rmation stimuli 

closely parallels the distribution of button press responses 

following imperative stimuli. The much less sharply peaked 

distribution of imperative stimulus blinks clearly refl ects 

the delayed release from inhibition associated with the 

requirement to respond.

The bottom Panel of Figure 9 displays difference scores calculated by subtracting the imperative 

distribution from the disconfi rmation proportions. 

The bi-phasic nature of the resulting distribution 

(signifi cantly positive during the 500-750 msec 

interval and signifi cantly negative during the 750-

1,000 msec interval) supports the hypothesis that 

blink inhibition would be prolonged following 

imperative stimuli.

Figure 8. Post-stimulus blink distributions as a function of 

stimulus type and task segment. 

Figure 9. Imperative blink (panel A), button 

press response (panel B), Disconfirmation 

blink (panel C), and Disconfirmaiton – 

Imperative distributions (panel D).
9

Single Trial Relationships Between Blink Parameters 

and Performance

The sensitivity of the blink closure duration 

and latency measures to the differing information 

processing demands associated with the various 

stimulus categories suggests that variability in these 

metrics may be systematically related to variance 

in single trial measures of performance. Several 

additional analyses were conducted to explore this 

possibility.

The non-random blink distribution displayed in 

Figure 8 suggests that blinks are produced during 

periods when subjects momentarily suspend the 

active intake and processing of information and 

are inhibited during other times. Thus, increased 

processing times on trials where the subject was having trouble making the decision to respond should 

signifi cantly delay blink initiation latencies. Note 

that especially long processing delays will begin 

to encroach upon the interval normally associated 

with an inhibition of blinking in anticipation of 

the presentation of the next stimulus and therefore decrease the probability that a blink will be 

produced. This logic leads to the prediction that 

imperative trials containing no blinks should be 

associated with an increase in response time and/or 

a decrease in response accuracy in comparison with 

trials containing blinks.

To test this hypothesis, performance measures 

were sorted as a function of whether or not an associated blink was produced by each subject. This 

analysis could not be performed on one subject, 

due to an exceptionally low overall blink rate (6.5 

blinks/min). On average, 120 (s.d. = 26.14) of 

the 147 imperative stimuli contained blinks. The 

mean RT and percent correct measures associated 

with the blink present vs. blink absent trials are 

displayed in Figure 10. The results are clear – RTs 

on trials containing blinks were approximately 

80 msec faster than on trials where blinks were 

absent [F(1,14)=6.80; p=0.0207], while performance accuracy remained constant [F(1,14)=0.02; 

p=0.8946].

Subsequent analyses were undertaken to determine whether additional relationships between 

performance and both the timing and morphology of blinks could be demonstrated. As indicated 

above, blink latency and duration signifi cantly 

varied as a function of both stimulus type and TOT. 

To examine whether variability in these measures 

could be related to single trial performance variability, imperative stimuli containing blinks were 

classifi ed as a function of a) blink-duration quartile, 

and b) blink latency quartile. RTs and percent correct scores, as a function of these blink parameters, 

were then computed.

The performance measures associated with progressively longer blink durations are presented in 

Figure 11. RT did not vary as a function of blink 

duration [F(1,14)=2.49; p=0.1096]. However, a 

signifi cant relationship between blink duration and 

performance accuracy was obtained [F(1,14)=4.33; 

p=0.0375]. Follow-up testing indicated that this 

effect was due to a signifi cant decrease in percent 

correct scores for the quartile containing the longest duration blinks, as compared with all other 

quartiles.

Figure 12 displays the results of an identical analysis on the performance data sorted as a function of 

blink latency quartiles. As with the blink duration 

analysis, the latency of associated blinks clearly 

affected performance accuracy [F(3,42)=9.19; 

p=0.0017]. The shortest latency blink quartile was 

associated with poorer accuracy measures than all of 

Figure 10. RT (panel A) and performance 

accuracy (panel B) in the presence and 

absence of a concurrent blink 
10

the other quartiles. In addition, the second shortest 

quartile could also be discriminated from the fourth 

(longest latency) quartile.

The main effect of latency quartile upon RT 

was not signifi cant [F(3,42)=2.89; p=0.0777]. 

A separate test excluding the quartile containing 

the shortest latency blinks was conducted for two 

reasons. First, as indicated above, this quartile was 

associated with signifi cantly poorer performance 

accuracy; and second, a separate analysis indicated 

that this quartile was associated with signifi cantly 

increased RT variability compared with the other 

quartiles (t=2.05; p=0.0298). The analysis of latency 

quartiles 2 through 4 did indicate a signifi cant effect 

upon RT [F(2,28)=6.26; p=0.0109] – responses were 

signifi cantly delayed for the 4th (longest latency) 

quartile compared with quartiles 2 and 3.

These analyses clearly support the hypothesis that 

variability in blink parameters could be related to 

trial-to-trial performance variance. In the absence of 

a blink, RTs were substantially delayed. On those trials 

containing blinks, if the blink latency was very short 

(indicating that the blink occurred during times when 

the subject should have been engaged in active information processing), errors increased and RTs became 

more variable. If the blink latency was particularly 

long, RT was long, consistent with the hypothesis that 

increased processing times on these trials were associated 

with increased intervals of blink inhibition. Finally, 

trials containing particularly long duration, presumably fatigue-related blinks, were also associated with 

decreases in performance accuracy.

Saccade Metrics

Saccadic activity in the horizontal EOG was 

automatically scored by a computer algorithm described elsewhere (see Oster and Stern, 1980). The 

performance of the algorithm was monitored by 

an experimenter who was responsible for rejecting 

noise artifacts incorrectly identifi ed as saccades, as 

well as for manually identifying saccades that the 

computer failed to detect.

Figure 13 displays the post-stimulus saccade 

proportions (#saccades/#stimuli) associated with 

the various stimulus categories presented during 

the three task segments. While horizontal saccade production was not directly affected by TOT 

[F(2,30)=0.58; p=0.5153], signifi cant differences 

as a function of stimulus category [F(4,60)=24.85; 

p<0.0001], were mediated by a signifi cant interaction with TOT [F(8,120)=3.98; p=0.0003].

Separate tests of stimulus category differences 

were conducted for each of the three task segments 

to elucidate the nature of this interaction. These tests 

revealed several relationships that were stable across 

task segment. Namely, the saccade proportion values 

obtained following 2nd-Digit stimuli were always 

larger than the saccadic proportions following the 

Figure 11. RT (panel A) and performance 

accuracy as a function of blink duration quartile. 

Figure 12. RT (panel A) and performance accuracy 

(panel B) as a function of blink latency quartile.
11

1st-Digit, imperative and disconfi rmation stimuli. 

Thus, the inter-stimulus interval immediately preceding a potential imperative stimulus was associated 

with the greatest level of saccadic activity. Similarly, 

the periods immediately following a potential target 

(i.e., the post-imperative and post-disconfi rmation 

intervals) were associated with a reduction in saccadic activity at each task segment.

The interaction between stimulus category and 

TOT resulted from variability in saccadic activity following post-imperative stimuli. During the 

early task segments, the saccadic activity following 

these stimuli was equivalent to 1st-Digit stimuli. 

An increase during the middle segment produced 

a post-imperative level indistinguishable from that, 

following 2nd-Digit stimuli. Finally, the level of saccades following post-imperative stimuli during the 

late task segment was comparable to the imperative 

and disconfi rmation stimulus levels.

All of the above results are consistent with the hypothesis that the production of saccades is infl uenced 

by the expectancy for a target event. Saccade production was increased in anticipation of a target and was 

decreased immediately following either a response or 

a disconfi rmation of this expectancy. Finally, saccades 

following the predictable post-imperative stimuli were 

associated with the most variable pattern of saccadic 

activity. This non-random distribution of saccades supports the hypothesis that subjects will adopt different 

patterns of eye movements as a function of changes in 

the information processing demands of the task. In 

particular, the above results indicate that subjects move 

their eyes to the location of an anticipated stimulus 

more frequently when the probability is high that 

this stimulus will require a response.

The total distribution of saccades was divided 

into three sub-classes to more precisely determine 

how patterns of eye movements are related to information processing and performance. The fi rst 

two categories were labeled reactive and anticipatory saccades. “Reactive” saccades, or saccades 

triggered by the presentation of a stimulus, serve 

to center the stimulus on the fovea. A saccade 

was classifi ed as reactive if it occurred between 

100 and 300 msec following stimulus onset. 

“Anticipatory” saccades were defi ned as movements which brought the eye towards a stimulus 

location in advance of the actual presentation of 

the stimulus.

The third category, labeled “re-check” saccades, 

consisted of relatively large-amplitude lateral eyemovements that returned after a short pause to a 

position close to the original point of regard. The 

rectangular-shaped waves produced by re-check saccades were readily identifi able in the EOG records 

and presumably refl ect the re-acquisition of information concerning the mapping of odd- and even-digit 

responses to the respective correct response hand. 

This information was continuously displayed at the 

extreme lateral locations on the screen, as described 

in the methods section.

Figures 14-17 display the mean values of the 

metrics computed for both anticipatory and reactive saccades associated with each stimulus type at 

all three task segments. Note that the anticipatory 

saccade measures displayed in these fi gures refer 

to activity before the associated stimulus, while 

the reactive metrics, refer to saccades that followed

stimulus onset. Also note that the imperative and 

disconfi rmation stimulus categories have been collapsed in these analyses because subjects could not 

distinguish between these two events during the 

anticipatory (pre-stimulus) and reactive (within 300 

msec of stimulus presentation) time intervals.

The production of both anticipatory [F(3,45)=16.42; 

p<0.0001] and reactive [F(3,45)=33.54; p<0.0001] 

saccades varied as a function of stimulus type — but 

in an inverse fashion (see Figure 14). Anticipatory 

saccades were less likely and reactive saccades more 

frequent for the highly predictable post-imperative 

stimuli (except that the post-imperative reactive 

saccade proportions could not be reliably discriminated from the 2nd-Digit stimuli). Conversely, 

Figure 13. Post-stimulus saccade proportions as a function 

of stimulus type and task segment. 
12

Figure 14. Anticipatory (panel A) and reactive 

(panel B) saccade proportions as a function 

of stimulus type and task segment. 

Figure 15. Anticipatory (panel A) and 

reactive (panel B) saccade durations as a 

function of stimulus type and task segment. 

Figure 16. Anticipatory (panel A) and reactive 

(panel B) saccade amplitudes as a function of 

stimulus type and task segment. 

Figure 17. Anticipatory (panel A) and 

reactive (panel B) saccade velocities as a 

function of stimulus type and task segment. 
13

anticipatory behavior was the best and reactivity the 

lowest when an imperative stimulus was expected. 

For both the anticipatory and reactive sub-types, 

saccade proportions associated with 1st and 2ndDigit stimuli were equivalent.

While anticipatory saccade production remained 

relatively stable [F(2,30)=0.91; p=0.3827], reactive 

saccades were infl uenced by TOT [F(2,30)=28.79; 

p<0.0001] with fewer reactive saccades occurring 

during the middle and late segments than during the 

early portion of the task. The interaction between 

TOT and stimulus type was not signifi cant for either 

anticipatory [F(6,90)=0.44; p=0.6669] or reactive 

[F(6,90)=1.77; p=0.1580] saccades.

Stimulus type did not reliably affect the duration 

(see Figure 15) of either anticipatory [F(3,45)=2.74; 

p=0.0648] or reactive [F(3,45)=3.07; p=0.0572] 

saccades. Reactive saccade duration was also unaffected by TOT [F(2,30)=0.30; p=0.6925]. Anticipatory saccade duration was infl uenced by TOT 

[F(2,30)=7.35; p=0.0082] with increased saccade 

durations associated with the middle and late task 

segments. This effect did not interact with stimulus 

type [F(6,90)=1.80; p=0.1705].

Differences in saccade amplitude and velocity as 

a function of TOT were also found (Figures 16 and 

17). The signifi cant main effects of TOT upon the 

amplitude [F(2,30)=4.32; p=0.0378] and velocity 

[F(2,30)=15.74; p<0.0001] of anticipatory saccades 

were due to the generation of smaller and slower 

saccades during the middle and late task segments. 

A similar pattern at these task segments also resulted 

in signifi cant TOT effects upon measures of reactive saccade amplitude [F(2,30)=17.83; p<0.0001] 

and velocity [F(2,30)=34.04; p<0.0001]. Anticipatory saccade amplitudes were not infl uenced by 

stimulus type [F(3,45)=1.87; p=0.1716]. However, 

this factor did affect anticipatory saccade velocity 

[F(3,45)=8.16; p=0.0002]. Post-imperative and 

imperative stimuli were both associated with higher 

saccade velocities than 1st and 2nd-Digit stimuli.

For reactive saccades, both the amplitude [F(3,45)=9.17; p=0.0007] and velocity 

[F(3,45)=5.98; p=0.0063] measures were sensitive 

to stimulus type. Post-imperative reactive saccades 

were larger and faster than all other stimulus types. 

Imperative stimulus reactive saccades were smaller 

and slower than for all other stimulus types (the sole 

exception being that the 2nd-Digit reactive velocities 

could not be reliably discriminated). There were no 

signifi cant TOT x stimulus type interaction effects 

upon saccade amplitude or velocity for either the 

reactive or anticipatory saccade categories (p > 0.05 

in all cases).

Reactive saccade latencies (see Figure 18) were 

sensitive to TOT [F(2,30)=7.86; p=0.0053] but 

not to stimulus type [F(3,45)=1.31; p=0.2819], 

and these factors did not interact [F(6,90)=2.04; 

p=0.1329]. The TOT effect resulted from a latency 

increase of approximately 7 msec during the middle 

and late task segments. In contrast, no signifi cant 

main effects of either TOT or stimulus type upon anticipatory saccade latencies were obtained (p < 0.05). 

However, a signifi cant interaction [F(6,90)=4.72; 

p=0.0049] resulted from the fact that during the 

early task segment, anticipatory saccades occurred 

closer to the time of stimulus presentation when 

an imperative stimulus was expected than for the 

post-imperative and 1st-Digit stimuli. No latency 

differences related to stimulus type were found for 

the middle and late segments.

Taken as a whole, these data provide compelling 

support for the claim that saccade timing and morphology are sensitive to information processing demands 

and TOT. Subjects moved their eyes to an anticipated 

information source with greater frequency (as evidenced 

Figure 18. Pre-stimulus anticipatory (panel A) 

and post-stimulus reactive saccade latencies as 

a function of stimulus type and task segment.
14

by increased anticipatory saccade proportions) 

and with greater accuracy (as indexed by reduced 

reactive saccade proportions, amplitudes and velocities) when expecting an imperative stimulus. 

Similarly, in advance of the highly predictable 

post-imperative stimuli anticipatory behavior 

was poorer (reduced anticipatory proportions, 

increased reactive proportions, amplitudes and 

velocities). These effects were largely impervious 

to TOT. However, the morphology of saccades did 

vary with TOT. Both anticipatory and reactive 

saccades became smaller in amplitude and slower 

in velocity during middle and late task segments. 

The duration of anticipatory saccades and the 

latency of reactive saccades both increased during 

these segments as well.

Data concerning the fi nal saccade sub-type 

– recheck saccades – are presented in Figures 19-21. 

Recheck saccades could be reliably detected in 14 of 

the 16 subjects. The proportion of recheck saccades 

was clearly higher following imperative stimuli (see 

Figure 19). Suffi cient numbers of recheck saccades to 

enable a factorial analysis of stimulus type (restricted 

to 1st-Digit, 2nd-Digit and imperative stimuli) by 

task segment were present for nine of the subjects. 

The results of such an analysis (see Figure 20) indicated that the proportion of recheck saccades was 

sensitive to stimulus type [F(2,16)=7.36; p=0.0208] 

but not to TOT [F(2,16)=1.34; p=0.2860]. Follow-up testing confi rmed that the stimulus type 

effect was due to a greater proportion of re-check 

saccades following imperative stimuli (0.839) than 

following 1st-Digit (0.339) and 2nd-Digit (0.455) 

stimuli. This effect did not interact with TOT 

[F(4,32)=0.65; p=0.5356].

Because the anticipatory and reactive saccade 

analyses had indicated signifi cant TOT effects upon 

amplitude and velocity measures, recheck saccade 

morphology was also evaluated along these dimensions. However, since the termination point of these 

saccades was constrained by the distance required 

to travel to a continuously displayed information 

source, it was predicted that the amplitude of reFigure 19. Recheck saccade proportions as a function of 

stimulus type. 
15

check saccades should not be affected by either TOT 

or stimulus type. In contrast, the speed with which the 

eye moved to obtain peripherally displayed information 

was not similarly constrained so that TOT effects upon 

velocity were expected.

The data confi rmed both of these predictions (see 

Figure 21). Recheck saccade amplitudes remained constant as a function of TOT [F(2,16)=0.49; p=0.5525] 

and stimulus type [F(2,16)=1.24; p=0.3045]. Recheck 

saccade velocities, on the other hand, were infl uenced 

by TOT [F(2,16)=4.50; p=0.0291] but not by stimulus 

type [F(2,16)=1.11; p=0.3278]. This TOT effect, which 

did not interact with stimulus type [F(4,32)=0.26; 

p=0.7613], was due to decreased recheck velocities at 

the middle and late task segments (as was the case for 

the anticipatory and reactive saccades).

Single Trial Relationships Between Saccade 

Parameters and Performance

The sensitivity of the anticipatory and reactive saccade measures to the differing information processing 

demands associated with the various stimulus categories 

suggests that variability in these metrics may be systematically related to variance in single-trial measures 

of performance. Therefore, several additional analyses 

were conducted to explore this possibility.

The non-random saccade distributions detailed 

above indicate that response expectancies held by 

subjects are refl ected in the proportions of anticipatory 

and reactive saccades elicited by imperative stimuli. 

The following analyses were conducted to determine 

whether imperative trials with accurate anticipatory 

saccades (indexed by the absence of a reactive saccade) 

were associated with a decrease in response time and/or 

response accuracy in comparison to trials containing 

reactive saccades. One subject was excluded from the 

analysis due to a virtually complete absence of reactive 

saccades following imperative stimuli.

On average, 55 (s.d. = 27.24) of the 147 imperative 

stimuli contained reactive saccades. The mean RT and 

percent correct measures associated with the anticipatory saccade only vs. reactive trials are displayed in 

Figure 22. RTs on trials containing reactive saccades 

were approximately 100 msec longer than on trials 

where reactive saccades were absent [F(1,14)=48.06; 

p<0.0001]. Performance accuracy was also affected 

[F(1,14)=39.96; p<0.0001] — poorly anticipated trials 

resulted in increased misses. Furthermore, between subject differences in miss rates were signifi cantly correlated 

with reactive saccade latency (r=0.44). Thus, subjects 

with long reactive saccade latencies to imperative stimuli 

were more likely to generate misses.

Subsequent analyses were undertaken to determine 

whether additional within-subject relationships between 

performance and both the timing and morphology of 

reactive saccades could be demonstrated. Imperative 

stimuli containing reactive saccades were classifi ed as 

a function of median splits computed on the latency 

and size of the saccade. RTs based upon these splits 

were then computed (insuffi cient misses occurred to 

reliably estimate median split response accuracies for 

most subjects).

RT was not systematically related to reactive 

saccade latency [F(1,14)=2.10; p=0.1691). On 

the other hand, the amplitude of the reactive saccade did have a signifi cant effect [F(1,14)=12.10; 

p= 0.0037]. RTs following small reactive saccades 

(mean=696, s.d. = 37.29) were approximately 30 

msec faster than if the saccade was large (mean=726, 

s.d.=40.75).

Ten of the subjects produced suffi cient numbers 

of recheck saccades on imperative stimuli to enable 

an analysis of how recheck behavior was related to 

performance variability. A comparison of performance on trials with and without recheck saccades 

is displayed in Figure 23. On average, 54 (s.d. = 

44.76) of the 147 imperative stimuli contained 

Figure 22. RT (panel A) and performance 

accuracy (panel B) in the presence and 

absence of anticipatory saccades.
16

recheck saccades. RTs on trials containing recheck 

saccades were approximately 70 msec longer than 

on trials where recheck saccades did not occur 

[F(1,9)=7.60; p<0.0222]. A tendency towards 

improved performance accuracy following recheck 

saccades failed to achieve statistical signifi cance 

[F(1,9)=2.92; p<0.1219].

Imperative stimuli containing recheck saccades were classifi ed as a function of median splits 

computed on the velocity and size of the saccade. 

RTs based upon these splits were then computed. 

Once again, insuffi cient misses occurred to reliably 

estimate median split response accuracies for most 

subjects. As predicted, RT did not vary as a function of recheck saccade amplitude [F(1,9)=1.71; 

p=0.2236). However, the velocity of the reactive 

saccade did have a signifi cant effect [F(1,14)=9.09; 

p= 0.0146]. RTs following fast recheck saccades 

(mean=683, s.d. = 220.04) were approximately 45 

msec faster than if the saccade was slow (mean=731, 

s.d.=221.84).

These analyses clearly support the hypothesis that 

variability in saccade parameters can be related to 

trial-to-trial performance variance. In the absence of 

a reactive saccade, RTs were substantially faster and 

accuracy was higher. Furthermore, those trials requiring 

large reactive saccades in order to foveate an imperative stimulus resulted in increased reaction times. The 

abstraction of peripherally presented information following the presentation of an imperative stimulus also 

occurred at a cost – trials containing recheck saccades 

were associated with increased reaction time, and the 

more slowly the eye moved to the peripheral location, 

the greater the RT cost.

Electroencephalographic (EEG) Transients

Transient EEG events have been associated with 

increasing drowsiness (Santamaria and Chiappa, 

1987). The occurrence of 25 categories of events were 

scored by a trained clinical electroencephalographer 

using impressionistic criteria from the paper record. 

While many of these events were either infrequent, 

unevenly distributed across subjects, or both, two of 

the categories, labelled fl at EEG and EEG slowing, 

occurred with suffi cient frequency across subjects 

to enable an analysis of TOT effects.

The fl at EEG metric refl ects prolonged periods 

of high frequency, low-voltage EEG activity characterized by an absence of delta, theta and alpha 

waves. EEG slowing transients were characterized 

by periods where an initial burst of activity at one 

frequency (generally alpha) continuously decreased 

in frequency (generally into the theta range) within 

a 1 to 2 sec interval.

The combined frequency of the EEG slowing and 

fl atness metrics varied signifi cantly as a function of 

TOT [F(2,30)=4.41; p=0.0229]. Signifi cantly more 

of these transients occurred during the late task 

segment (mean = 1.94; s.d. = 0.551) than during 

the early portion of the task (mean = 0.43; s.d. = 

0.223). The frequency of such transients during the 

middle task segment (mean = 1.31; s.d. = 0.553) 

could not be discriminated from either the early 

or the late period.

Event-Related Brain Potential (ERP) Metrics

The ERP is characterized by a transient series 

of voltage oscillations in the EEG time-locked to 

the occurrence of discrete events. Thus, the ERP 

refl ects phasic changes in the brain associated with 

the processing of specifi c events. The analysis of 

ERPs is complicated by two factors. First, ERP 

components typically range in amplitude between 

Figure 23. RT (panel A) and performance 

accuracy (panel B) in the presence and 

absence of a recheck saccade. 
17

1 to 30 microvolts, while the background EEG may 

contain voltages in excess of 50 to 100 microvolts 

— resulting in a poor signal-to-noise ratio. The second complicating factor results from the artifactual 

contamination of the EEG produced by positional 

shifts of the polarized eye ball. Thus, both saccades 

and blinks produce electrical currents that propagate 

along the scalp, generating substantial voltages at 

the EEG recording locations.

The following strategy was employed to overcome 

these diffi culties. Periods containing excessive levels of artifact (due to muscle activity for example) 

were identifi ed from the paper records and data 

collected during these periods were excluded from 

all analyses of brain electrical activity. Second, conventional signal averaging techniques, which reduce 

the contribution of noise by averaging across epochs 

associated with similar events, were employed.

The issue of eye movement contamination (a 

particular problem in this eye-movement rich 

task) was also addressed. Epochs of EEG activity, time-locked to the occurrence of saccades and 

blinks during the practice session, were extracted 

and averaged. The contributions of specifi c EOG 

events to the EEG recorded at each electrode were 

estimated via correlational analyses conducted upon 

these averages. Finally, blink and saccade propagation factors produced by these analyses were used to 

remove the estimated contribution of the electrical 

activity generated by blinks and saccades from each 

time point in the EEG records.

The average ERPs (recorded at Pz) elicited by 

the events in an imperative stimulus sequence are 

presented in Figure 24. Two components of the ERP 

signature are particularly noticeable in this fi gure. 

The P300 response (the large positive peak 

at approximately 500 msec post-stimulus 

in this Figure) has been shown to be sensitive to a number of factors related to the 

relevance, salience, and probability of the 

eliciting stimuli (Johnson, 1986) as well 

as the cognitive demands of the task. The 

latter sensitivity has been demonstrated 

in both laboratory (Sirevaag et al., 1984) 

as well as simulator (Kramer et al., 1987; 

Sirevaag et al., 1993) environments. The 

Contingent Negative Variation (CNV), 

which can be seen as a slow build-up in 

negativity preceding stimulus presentation 

(especially prior to the imperative stimulus in Figure 24) appears to consist of at least two 

separate processes: an orienting component and a 

response preparation component (Rohrbaugh and 

Gaillard, 1983).

Measures of the CNV and P300 were abstracted 

from averaged epochs recorded at the Fz, Cz and Pz 

electrode locations. P300 amplitude was computed 

as the voltage difference between the largest positive 

defl ection occurring in a latency window extending 

between 300 and 650 msec post-stimulus and the 

mean voltage of the 100 msec pre-stimulus baseline. 

P300 latency was defi ned as the time post-stimulus at 

which the peak amplitude was detected. Contingent 

Negative Variation (CNV) amplitude was quantifi ed as the difference between the area under the 

curve in two 50-msec windows. The fi rst window 

began 1,000 msec prior to stimulus presentation, 

the second window was centered around the time 

of stimulus onset.

The P300 amplitude and latency results for the 

Pz electrode location are presented in Figures 25 and 

26. The amplitude [F(2,30)=0.99; p= 0.3704] and 

latency [F(2,30)=2.25; p=0.1333] of the P300 were 

unaffected by TOT. However, both the amplitude 

[F(4,60)=50.34; p<0.0001] and latency [F(4,60)=4.16; 

p=0.0261] measures were infl uenced by stimulus type. 

P300s were larger following imperative stimuli than 

when following all other stimuli, while the disconfi rmation-related P300s were larger than the 1st-Digit, 

2nd-Digit, and post-imperative stimuli (which did 

not themselves differ). P300 latencies following the 

imperative and disconfi rmation stimuli were delayed 

with respect to the latencies following post-imperative 

stimuli. There were no interactions between the TOT 

Figure 24. Grand average event-related brain potentials recorded 

at Pz in response to the stimuli in an imperative response 

sequence.
18

and stimulus type factors for either the amplitude 

or the latency measures.

The amplitude of the CNV component at Cz 

is displayed in Figure 27. Note that, because this 

measure was computed prior to stimulus presentation, the imperative and discon fi rmation stimuli 

have been collapsed into a single category. As was 

the case for the P300 data, CNV amplitude was not 

sensitive to TOT [F(2,30)=1.19; p=0.3098] but did 

vary with stimulus type [F(3,45)=7.92; p=0.0051] 

with no interaction. The CNVs generated in anticipation of an imperative stimulus were larger than 

CNVs for all the other stimulus types.

The amplitude of the P300 elicited by imperative 

stimuli was signifi cantly correlated with individual 

differences in RT (r = -0.65). Thus, subjects producing the largest P300s also responded more quickly. 

CNV amplitude was not systematically related to 

between subject reaction time differences.

Event-Related Power Metrics

Stimulus locked modulations of power in traditionally defi ned bands (e.g., alpha, theta, etc.) were 

evaluated by a technique similar to that proposed by 

Pfurtscheller (1989): 1) The continuously recorded, 

eye-movement-corrected EEG was subjected to a 

Finite Impulse Response (FIR) narrow band-pass 

fi lter to exclude all activity other than the band of 

interest; 2) the voltages recorded at each time point 

were squared (power is proportional to voltage 

squared); 3) signal averaging algorithms were employed to compute stimulus-locked event-related 

modulations in power in a manner identical to 

that employed for ERPs.

Event-related activity in the theta (passband=4.5-

7.5 Hz), gamma (passband=35.0-45.0 Hz), low 

(passband=7.5-8.5 Hz), medium (passband=9.5-

10.5), and high (passband=11.5-12.5) alpha bands 

recorded at the O2 electrode is displayed in Figure 

28. The theta band response (see Panel A) consists 

of a stimulus-locked increase in power roughly 

contemporaneous with stimulus presentation. 

Close inspection of Panel A, reveals stimulus related 

differences in the interval extending between 600-

900 msec post-stimulus. While imperative stimuli 

are associated with a decrease in theta power during this period, the other stimuli exhibit a small 

increase in power.

Theta modulations during these two epochs 

were quantifi ed by fi rst obtaining a baseline 

Figure 25. P300 amplittudes at Pz as a function of stimulus 

type and task segment. 

Figure 26. P300 latencies at Pz as a function of stimulus 

type and task segment. 

Figure 27. CNV amplitudes at Cz as a function of stimulus 

type and task segment. 
19

estimate of theta activity, computed as the area 

under the curve in the 300 msec period preceding 

stimulus onset. This baseline estimate was then 

subtracted from similar area measures computed 

during the 0-300 and 600-900 msec post-stimulus 

intervals, respectively. Mean theta power modulations as a function of TOT and stimulus type are 

presented in Figure 29.

Panel A displays the results for the early 0-300 

msec interval. While the initial increase in theta 

activity did not vary as a function of stimulus 

type [F(4,60)=0.62; p=0.5925], signifi cant TOT 

effects were obtained [F(2,30)=7.03; p=0.0044]. 

The surge in theta power during stimulus presentation decreased during the middle and late 

task segments. This effect did not interact with 

stimulus type.

In contrast, theta activity during the 600-900 

msec interval (see Panel B of Figure 29) varied signifi cantly as a function of stimulus type 

[F(4,60)=13.13; p<0.0001] but remained constant 

during the different task segments [F(2,30)=0.13; 

p=0.8703]. Late theta activity was signifi cantly 

depressed following imperative stimuli compared 

with all other stimulus types. Interestingly, the 

stimuli associated with the next highest level of 

response expectancy (i.e., 2nd-Digit stimuli) were 

associated with reduced theta power, compared 

with the 1st-Digit and post-imperative stimuli. 

Thus, while the presentation of a stimulus induced 

an initial burst of theta activity that decreased with 

TOT, the requirement to respond resulted in a suppression of subsequent theta activity during all three 

task segments.

Inspection of the event-related activity in the three 

alpha bands displayed in Panels B,C and D of Figure 

28 reveals a number of interesting modulations. Five 

epochs of interest were identifi ed, and a measure of 

average area under the curve was computed for each. 

The 1st period (epoch1) refl ects the average alpha 

power in the 300 msec interval beginning 1,000 msec 

before the presentation of the stimulus. The 2nd 

period (epoch2) measures alpha activity just prior to 

and including the period of stimulus onset and was 

computed as the average area under the curve in the 

300 msec interval beginning 200 msec before stimulus 

presentation. The average area in the 100-300 msec 

post-stimulus period was defi ned as epoch3. The 4th 

period (epoch4) spanned 500 msec beginning 300 msec 

post-stimulus. The fi nal period that was quantifi ed 

Figure 28. Event-related modulations at O2 of 

activity in the Theta (panel A), low alpha (panel B), 

medium alpha (panel C), high alpha (panel D) and 

gamma (panel E) frequency ranges. 

Figure 29. Early (panel A) and late (panel B) theta 

power modulations as a function of stimulus type and 

task segment. 
20

(epoch5) subtended the interval from 800-1,900 

msec post-stimulus.

The continuous decline in alpha power in 

anticipation of stimulus presentation, which 

can be seen most clearly in the waveform refl ecting medium band alpha activity (Panel C 

of Figure 28) will subsequently be referred to 

as pre-stimulus alpha desynchronization. This 

activity was quantifi ed by subtracting epoch1 

from epoch2. A similar period of reduced alpha 

power following the stimulus (hereafter labelled 

post-stimulus alpha desynch ronization) was 

computed as the difference between the epoch2 

and epoch4 measures. The difference between 

epochs 4 and 5 was used to index the large increase 

in alpha power seen to peak at approximately 

1100 msec in Panel C of Figure 28 (termed late 

alpha hyper synchronization). Finally, the early 

hypersynch ronization most clearly seen in the low 

alpha band (Panel B of Figure 28) was measured 

as the difference between epochs 2 and 3.

Figures 30-32 display the mean values of these 

measures as a function of TOT and stimulus type 

for the low-, medium-, and high-frequency alpha 

bands. To facilitate inter-band comparisons, 

identical scales were used in all three fi gures. 

As can be seen in Figure 30, signifi cant prestimulus alpha desynchronizations were not 

present in the low alpha band (refl ected in the 

positive values displayed in Panel A). Similarly, 

the late hyper synchronization, though detectable, was negligible in this band (see Panel D). 

Neither of these components of event-related 

low alpha band modulation varied signifi cantly 

as a function of either TOT or stimulus type (all 

p-values > 0.05). However, both components of 

the bi-phasic response occurring immediately 

following the presentation of the stimulus were 

infl uenced by task demands. The early hypersynchronization (see Panel B) varied as a function of TOT [F(2,30)=7.18; p=0.0049], with 

reductions in the amplitude of this response 

during the middle- and late-task segments. This 

effect mirrors the reductions in the early theta 

response during these task segments.

The measures of post-stimulus low alpha desynchronization refl ected in Panel C evidence a 

signifi cant effect of stimulus type [F(4,60)=5.45; 

p=0.0042]. Greater desynch ronization occurred 

following the presentation of an imperative 

Figure 30. Low alpha band pre-stimulus (panel A), and 

post-stimulus (panel B) desynchronizations, early (panel C) 

and late (panel D) hypersynchronizations at 02 as a 

function of stimulus type and task segment. 

Figure 31. Medium alpha band pre- (panel A), and poststimulus (panel B) desynchronizations, early (panel C) and 

late (panel D) hypersynchronizations at 02 as a function of 

stimulus type and task segment. 
21

stimulus than for all the other stimulus types (except 

for the high expectancy level, 2nd-digit stimuli). No 

other low alpha band, main effects or interactions 

were obtained.

A different picture emerges with respect to the 

medium alpha band measures displayed in Figure 31. 

Now the post-stimulus hypersynchronization is not in 

evidence (witness the negative values in Panel B). The 

large pre-stimulus desynch ronizations depicted in Panel 

A did vary as a function of stimulus type [F(4,60)=3.87; 

p=0.0211]. Larger desynchronizations occurred prior 

to the presentation of an imperative or disconfi rmation 

stimulus than in anticipation of either a 1st-Digit or 

post-imperative (highly predictable) stimulus.

The post-stimulus middle alpha band desynchronization (see Panel C) also varied with stimulus type 

[F(4,60)=6.17; p=0.002]. Note that, in contrast to the 

pre-stimulus period, subjects can now discriminate between the imperative and the discon fi rmation stimuli. 

Consequently, the post-stimulus desynchronization was 

smaller following disconfi rmations than for all other 

stimulus types. Increased desynchronizations following 

imperative stimuli, as compared with 1st-Digit and 

post-imperative stimuli, were only marginally signifi -

cant (p-values obtained via the modifi ed Bonferonni 

procedure < 0.06).

Differences in the amplitude of the late hypersynchronization in the middle alpha band (see Panel 

D), as a function of stimulus type, were also present 

[F(4,60)=11.69; p<0.0001]. More alpha power was 

present during later portions of the inter-stimulus 

interval following the removal of the expectation to 

respond (i.e., following imperative and disconfi rmation 

stimuli). None of the components of the middle alpha 

band response, displayed a sensitivity to TOT effects 

or stimulus type by TOT interactions.

The responses in the high alpha frequency band 

are displayed in Figure 32. The overall pattern is quite 

similar to that obtained for the middle alpha band 

modulations. Once again, no early hypersynchronization was evident and no main effects of TOT were 

obtained for any of the components. The magnitude 

of the pre-stimulus high alpha desynchronization was 

affected by stimulus type [F(4,60)=3.50; p=0.0313]. 

However, the specifi c stimulus-related differences, 

were somewhat different than for the middle band 

– while the greatest desynchronizations still occurred 

in anticipation of the imperative and disconfi rmation 

stimuli, these levels differed from those associated 

with the 1st- and 2nd-Digit stimuli (rather than the 

1st-Digit and post-imperative levels discriminated by 

the middle band).

The magnitude of the post-stimulus desynchronization was also infl uenced by stimulus type 

[F(4,60)=5.84; p=0.0042] in the high alpha range. 

Desynchronizations associated with the dis confi rmation 

stimuli were smaller than those following the 2nd-Digit, 

imperative, and post-imperative stimuli. While the late 

hypersynchronization was also sensitive to stimulus 

type [F(4,60)=13.54; p<0.0001], these effects were 

not equivalent during the three task segments, resulting in a signifi cant TOT by stimulus type interaction 

[F(8,120)=2.74; p=0.0195]. Some relationships were 

constant — the hypersynchronizations following imperative and disconfi rmation stimuli were always larger 

than following 1st- and 2nd-Digit stimuli.

The locus of the interaction effect stems from 

the fact that, while imperative stimulus hypersynchronizations increased with TOT, discon fi rmation 

hyper synchronizations tended to decrease. Thus, during 

the early task period, imperative hypersynchronizations were smaller than for disconfi rmations; during 

the middle period they were equivalent, and late in 

task performance the imperative responses were larger 

than the dis confi rmation hypersynchronizations. Finally, while post-imperative stimuli produced smaller 

Figure 32. High alpha band pre- (panel A), and poststimulus (panel B) desynchronizations, early (panel C) 

and late (panel D) hypersynchronizations at 02 as a 

function of stimulus type and task segment 
22

hyper synchronizations than the imperative stimuli 

at middle- and late-task segments, these levels could 

be distinguished from the disconfi rmation responses 

only during the early and late periods. The slightly 

more complex pattern of results obtained for the high 

alpha band modulations than for the middle band are 

most likely because the decreased statistical power, 

resulting from smaller component amplitudes in the 

high band, yielded decreased effect sizes and poorer 

signal-to-noise ratios.

Figure 33 displays the results of analyses exploring the 

effects of TOT and stimulus type upon evoked gamma 

band activity. As depicted in Panel D of Figure 28, two 

distinct periods of increased gamma power can be distinguished – a sharply peaked response during the period 

of stimulus presentation, and a later, broader response 

particularly evident following imperative stimuli. These 

responses were extremely small – and a somewhat different quantifi cation strategy was employed to deal with 

the poor signal-to-noise ratios, that resulted.

The early response was measured via a base-to-peak 

procedure similar to that employed for the P300 component of the ERP. Thus, the mean of the 1,000 msec baseline 

was subtracted from the maximum power value detected 

in a window extending from 75 to 200 msec post-stimulus 

onset. Mean early gamma response power values as a function of stimulus type and TOT are presented in Panel A 

of Figure 33. Signifi cant TOT [F(2,30)=4.55; p=0.0192] 

and stimulus type [F(4,60)=11.66; p=0.0178] effects were 

obtained. The stimulus type by TOT interaction term 

was also signifi cant [F(8,120)=2.87; p=0.0178].

Follow-up analyses revealed that, during the early 

and middle task segment, the gamma response to the 

post-imperative stimulus was enhanced, relative to all 

other stimulus types. During the middle-task segment, 

a reduction in 1st-digit elicited gamma power could be 

detected, in addition to the increase following post-imperative stimuli. Finally, no stimulus-related effects were 

present during the late task blocks.

Late gamma activity was measured as the difference 

between the mean of the baseline and the mean power 

in a window extending from 500 to 1,000 msec poststimulus (see Panel B). Gamma power late in the stimulus 

epoch was infl uenced by stimulus type [F(4,60)=5.03; 

p=0.0137]. Imperative stimuli generated increased late 

gamma power, as compared with 1st-Digit, 2nd-Digit and 

disconfi rmation stimuli. There were no TOT, or stimulus 

type by TOT interaction effects upon this measure.

The event-related power analyses present a complex, yet 

coherent, picture of how modulations in specifi c frequency 

bands of the EEG are related to the demands of the task. The 

interval preceding the presentation of a stimulus was associated with a gradual reduction in middle- and high-frequency 

alpha power – and the degree of desynchronization increased 

in the anticipation that a response would be required (i.e., 

prior to imperative and disconfi rmation stimuli).

The presentation of a stimulus elicited a transient increase 

in power in the theta, low alpha, and gamma bands. The 

response in all three of these bands diminished during the 

middle- and late-task segments (however, in the gamma 

band this reduction was restricted to the burst following 

post-imperative stimuli). The period immediately following 

termination of the stimulus was characterized by an additional desynchronization in all three of the alpha bands. 

In general, though the details differed from band to band, 

this desynchronization was smallest for those stimuli farthest 

removed from a potential response.

Finally, the middle portion of the inter-stimulus interval 

was associated with reduced theta power and increased 

gamma power following imperative stimuli. While the postresponse decision periods following the imperative (go) and 

disconfi rmation (no-go) stimuli contained large middle- and 

high-alpha band hypersynchronizations.

Figure 33. Early (Panel A) and late (panel B) gamma 

band responses at 02 as a function of stimulus type 

and task segment. 
23

Electrocardiographic (ECG) Metrics

Individual R-waves in the ECG were detected 

via an algorithm similar to that employed for 

the detection of blinks. Thus, a potential Rwave onset was defi ned as the point at which 

the slope of ten successive time points exceeded 

a pre-set criteria. R-wave detection was confi rmed if a) a peak was detected within 100 

msec of the onset point, b) the amplitude of 

the R-wave fell within a pre-defi ned range, 

and c) the duration of the R-wave fell within 

predefi ned parameters. The actual values of 

the slope, amplitude, and latency criteria were 

tailored for each subject to maximize detection 

likelihood. The performance of the automated 

scoring algorithm was monitored by an experimenter who was responsible for rejecting noise 

artifacts incorrectly identifi ed as R-waves, as well as 

for manually inserting R-waves that the computer 

failed to detect.

Due to the 200 Hz sampling rate employed in 

this study, the time at which the peak of the R-wave 

occurred could only be estimated with an accuracy 

of ± 5 msec. To improve the temporal accuracy 

of this measure, an algorithm was developed that 

identifi ed the points around the peak where the 

slope of the ECG signal transitioned from negative 

to positive and re-calculated the time of the peak 

by interpolation.

Data samples consisting of the time between 

consecutive R-waves, were interpolated to create 

equidistant time series in the manner prescribed by 

Mulder (1980). Following the removal of DC offset 

and linear trend, the data were smoothed using a 

modifi ed cosine function, zero-padded, and submitted to a Fast Fourier Transform (FFT) analysis. This 

procedure yielded estimates of the power in spectral 

bands 0.01 Hz wide, ranging from 0 to 0.50 Hz. 

The power spectra from 3 consecutive, 5-min periods 

during the early, middle and late task periods were 

averaged together to produce stable estimates of ECG 

variability during the different task segments.

 The ECG power spectrum averaged over task 

periods and subjects is presented in Figure 34. Four 

distinct peaks in the spectral density function are 

clearly visible. The physiological mechanisms underlying three of these peaks are relatively well known 

(see Aasman et al., 1987). Activity in the range 

from 0.02 to 0.06 Hz is related to vasomotor activity involved in the regulation of body temperature. 

 Energy in the range from 0.07 to 0.14 Hz (hereafter 

referred to as the 0.10 Hz component) is related 

to the short term regulation of arterial blood fl ow. 

Previous research has demonstrated the sensitivity 

of the 0.10 Hz component (which is thought to 

be primarily infl uenced by sympathetic autonomic 

activity) to a variety of variables including (but not 

limited to) manipulations of cognitive workload 

(Mulder, 1980; Van Dellen et al. 1985).

Frequencies above 0.14 Hz predominantly refl ect 

the infl uence of respiration upon the distribution of 

cardiac inter-beat intervals. Two respiration-related 

peaks in the spectral density function can be identifi ed. The fi rst, which peaks at approximately 0.25 

Hz, corresponds to the traditionally defi ned Respiratory Sinus Arrhythmia (hereafter termed Spectral 

RSA). In this study, Spectral RSA was quantifi ed 

as the energy in the bands from 0.14 to 0.34 Hz. 

Previous research has suggested that the Spectral 

RSA component provides a measure of vagal tone 

in the Autonomic Nervous System (ANS) and can, 

therefore, be used as a sensitive index of operator 

state (Porges, 1984).

A smaller peak at approximately 0.40 Hz was 

found to result from a tendency on the part of some 

subjects to time-lock respiratory cycles to the rate of 

stimulus presentation (an ISI of 2511 msec = 0.40 

Hz). This component, labelled Stimulus RSA, was 

computed as the total spectral power in the range 

from 0.39 to 0.42 Hz. The absolute power in the 

0.10 Hz, Spectral and Stimulus RSA bands were 

submitted to a log transform to diminish the effect 

of individual differences present in the absolute 

power scores.

Figure 34. Spectral density (power) function for cardiac IBI 

variability averaged over subjects and task segments. 
24

Several measures of cardiovascular function during the early-, middle-, and late-task segments are 

displayed in Table 2. Mean Interbeat-Interval (IBI) 

varied considerably across subjects (minimum = 

670 msec, maximum = 1,113 msec) but did not 

vary across task segments (p>0.05). The 0.10 Hz 

component of IBI variability, on the other hand, 

was sensitive to TOT (F(2,30)=10.35; p=0.0015). 

Power in this band was greater during the middleand late-task segments.

In contrast, the two RSA components did not 

signifi cantly vary with TOT (p-values > 0.05). A 

4th spectral measure, presumed to index the relative 

balance between the sympathetic and parasympathetic branches of the ANS was computed as the 

ratio of 0.10 Hz power to spectral RSA power 

(termed S/P ratio). This ratio was sensitive to TOT 

(F(2,30)=5.44; p=0.0106) and, like the 0.10 Hz 

component, signifi cantly increased during the 

middle and late task segments.

A major drawback of the spectral technique outlined above is that the measures refl ect the activity 

in 5-min segments. An alternative “peak to valley” 

technique for estimating RSA (see Grossman, van 

Beek, & Wientjes, 1990) allows quantifi cation 

of this component on a minute-to-minute basis. 

To this end, the RSA2001 program (Uijtdehaage, 

1994) was employed to compute Peak-Valley RSA. 

Across subjects, the Peak-Valley estimates were 

highly correlated with absolute Spectral RSA power 

(r=0.91).

 Peak-Valley RSA scores for each of the three task 

segments are also presented in Table 2. As was the 

case for Spectral RSA, mean levels of Peak-Valley 

RSA were unaffected by TOT. However, because 

estimates of RSA and mean heart rate were available 

for each minute of the task, an analysis similar to 

that conducted on the performance data could be 

performed. Thus, for each subject the heart rate and 

RSA scores were z-transformed and the standard 

deviations of the z-scores within the early-, middle-, 

and late-task segments were obtained.

The results of these analyses (termed heart rate 

variability and RSA variability) are included in Table 

2. Heart rate variability was signifi cantly infl uenced 

by TOT (F(2,30)=5.17; p=0.0139). Increased variability was present during the late task segment in 

comparison with the early period of task performance. The intermediate middle segment values 

could not be distinguished from either the early or 

the late segment. RSA variability was also sensitive 

to TOT [F(2,30)=4.72; p=0.0311]. This measure 

was lower for both the early and middle segments 

than late in the task.

Table 2. Mean (and Standard Deviation) of Cardiovascular 

Metrics by Time Segment 

Task Segment 

Measures Early Middle Late

Heart Rate 

(beats/min) 

71 (11.20) 71 (10.59) 71 (10.39) 

0.10 Hz 

Log(Power) 

9.93 (1.215) 10.21 (1.107) 10.49 (0.930) 

Spectral RSA 

Log(Power)

9.40 (1.199) 9.45 (1.103) 9.52 (0.976) 

Stimulus RSA 

Log (Power)

8.71 (1.110) 8.63 (0.860) 8.77 (0.822) 

S/P Ratio 1.035 (0.1266) 1.077 (0.1403) 1.099 (0.1160) 

Peak-Valley RSA 89.11 (52.20) 89.30 (60.43) 90.53 (47.36) 

Heart Rate

Variability

0.73 (0.144) 0.81 (0.211) 0.93 (0.169) 

RSA

Variability

0.85 (0.255) 0.80 (0.202) 1.05 (0.281) 
25

Event-Related Cardiac Activity

Concurrent with the identifi cation of R-waves, an 

output fi le was created containing the instantaneous 

inter-beat interval every 5 msec. This enabled cardiac 

responses preceding and following specifi c stimuli 

to be evaluated via signal-averaging techniques 

identical to that employed for ERPs. The average 

event-related cardiac response to imperative digit 

sequences (collapsed across subjects) is presented 

in Figure 35.

A large heart rate deceleration (refl ected in an 

increase in the inter-beat interval) can be seen to 

precede the presentation of an imperative stimulus, 

continuing for approximately 1 sec thereafter. The 

magnitude of the cardiac deceleration associated 

with each stimulus type was quantifi ed as the difference between the maximum IBI in the post-stimulus 

epoch between 0 and 1,500 msec and the mean IBI 

in a 500 msec period beginning 1000 msec prior 

to stimulus presentation.

Figure 36 displays mean IBI difference scores 

as a function of stimulus type and task period. 

While this variable was not directly affected by 

TOT [F(2,30)=2.26; p=0.1438] the absence 

of decelerative responses associated with 2ndDigit and post-imperative stimuli generated 

a signifi cant main effect of stimulus type 

[F(4,60)=44.29; p<0.0001]. The existence of 

a signifi cant TOT by stimulus type interaction 

term [F(8,120)=2.68; p=0.0367] prompt ed 

separate examinations of TOT effects at the 

three stimuli for which cardiac decelerations 

were present.

No TOT effects were present for either 

the imperative or the discon fi rmation 

stimuli (p-values < 0.05). However, the 

decelerations associated with 1st-Digit 

stimuli did vary with TOT [F(2,30)=5.19; 

p=0.0159]. Greater decelerations in 

response to 1st-Digit stimuli occurred 

during the middle and late portions of 

the task.

Exploratory Modeling

Multiple regression techniques were 

employed to explore the extent to which 

performance variability both between 

and across subjects could be predicted 

by measures of oculographic, encepha lographic, 

and cardiovascular function. Models containing 

up to 15 variables in each of these measurement 

domains were tested for “optimality” on the basis 

of the Mallow C(p) statistic. The terms that were 

subsequently found to contribute signifi cantly to 

the model were retained.

The results of these analyses are displayed in 

Table 3. Oculomotor variables accounted for approximately 50% of the variance in both A’ and 

RT. Event-related potentials and frequency band 

modulations were much better at predicting RT 

than A’, while the reverse was true for the transient 

EEG measures. Cardiovascular measures in and of 

themselves, were poor predictors of both RT and 

A’.

A “Full” psychophysiological model was developed by entering the best parameters from each 

measurement domain found in the above analyses. 

Figure 35. Event-related cardiac IBI response to stimuli in an 

imperative response sequence. 

Figure 36. Cardiac decelerations (maximum IBI – baseline IBI) 

as a function of stimulus type and task segment. 
26

Once again, only parameters signifi cantly contributing to the optimal model were retained. Eleven 

parameters were identifi ed as predictive of the A’ 

index of performance accuracy. Seven of these 

parameters were indexes of oculomotor behavior 

(2nd-Digit and imperative stimulus blink amplitudes and durations, post-imperative stimulus 

reactive saccade amplitudes and velocities, and 

2nd-Digit reactive saccade latencies). The remaining 

parameters were the amplitude of the late gamma 

response following imperative stimuli, generalized 

theta EEG transients, and two cardiovascular parameters related to RSA.

Eight parameters were contained in the optimal 

model for the prediction of RT – 1st Digit blink 

latency, P300 latencies following imperative and 

disconfi rmation stimuli, two measures of event-related alpha modulation, the amplitude of the early 

gamma response following post-imperative stimuli, 

the S/P cardiovascular ratio, and spectral RSA.

Table 4 displays the actual and predicted A’ and 

RT scores for two high-performance (Subjects 16 

and 20) and two low-performance (Subjects 6 and 

17) individuals at all three task segments. Note 

that the “Full” psychophysiological models do a 

reasonable job of predicting both between as well 

Table 3. Performance Modeling 

A Prime RT 

Measures # Var. R-Square # Var. R-Square

Oculomotor Activity 5 0.53 4 0.51 

Event-Related Brain Activity 4 0.27 7 0.73 

Transient Brain Activity 4 0.61 1 0.14 

Cardiovascular Activity 3 0.19 4 0.30 

"Full" Psychophysiological Model 11 0.90 8 0.70 

Table 4. Model Predictions for Low vs. High Performance Subjects 

Subject

ID

Task 

Segment 

Predicted

A' 

Actual

A' 

Predicted

RT

Actual

RT

16 Early 568 584 0.998 .994 

16 Middle 517 523 1.004 1.000 

16 Late 493 533 1.001 1.000 

20 Early 472 444 1.003 0.999 

20 Middle 468 484 0.999 1.000 

20 Late 490 461 0.995 1.000 

6 Early 710 759 0.992 0.970 

6 Middle 799 872 0.926 0.934 

6 Late 796 781 0.953 0.946 

17 Early 739 826 0.934 0.932 

17 Middle 924 877 0.864 0.854 

17 Late 749 812 0.907 0.908 
27

as within subject performance variability. Also, 

note that the predictions are somewhat better 

for performance accuracy than for RT.

CONCLUSIONS

The data presented here provide compelling 

evidence that psychophysiological metrics can 

be used to infer the relative level of alertness 

and/or attentiveness of human operators during the performance of complex information 

processing tasks. Figure 37 summarizes the 

responses across a variety of measurement 

domains to individual stimuli in the present 

task. Note that for the sake of simplicity, alpha 

activity in this fi gure has been collapsed across 

the low-, medium-, and high-frequency alpha 

bands, and that heart rate, rather than mean 

IBI, is displayed.

The period immediately preceding the 

presentation of a stimulus was characterized 

by, an inhibition of blinks, the production of 

anticipatory saccades and a suppression of alpha 

band power. Prior to imperative stimuli, a heart 

rate deceleration coupled with a substantial increase in CNV amplitude was also present. The 

post-stimulus period also generated a variety of 

responses. The immediate post-stimulus period 

was associated with the presence of reactive 

saccades as well as a continued suppression of 

blinking. This period also produced bursts of 

activity in the theta, low alpha, and gamma 

frequency bands.

Most responses, occurred approximately 500-

600 msec post-stimulus. This period also marked the 

period of maximum likelihood for the production 

of a blink, as well as the elicitation of a large alpha 

hypersynchronization. Importantly, the latencies of 

blinks following imperative stimuli were delayed, 

and these stimuli also generated larger P300 and 

late gamma responses.

A number of interesting relationships between the 

activity in these response domains and performance 

emerged. While mean performance levels were fairly 

consistent across early, middle, and late segments 

of the task, increased TOT did result in increased 

performance variability. Similar TOT effects were 

obtained for many physiological measures, including: blink closure durations and latencies; reactive 

saccade frequencies and latencies; anticipatory and 

reactive saccade amplitudes and velocities; recheck 

saccade velocities; early theta, low-frequency alpha 

and gamma band hypersynchronizations; and 

power in the 0.10 Hz component of cardiac IBI 

variability.

Of greater interest are the many relationships 

between oculomotor measures and patterns of 

within-subject performance variability. In the 

absence of a blink, RTs were substantially delayed 

— consistent with the hypothesis of an increased 

period of blink inhibition associated with lengthier 

and less effi cient decision-making processes on these 

trials. Furthermore, when a blink was present, if 

the blink latency was too short (and encroached 

upon intervals associated with stimulus identifi cation and classifi cation processes) errors increased 

and RTs became more variable. Particularly long 

blink latencies were associated with increased RTs. 

Finally, if a long closure duration blink followed the 

Figure 37. Summary of responses in a variety of 

systems to the stimuli in an imperative stimulus 

sequence. Note the increase response lability 

associated with an imperative stimulus. 
28

presentation of an imperative stimulus, the target 

was more likely to be missed. Saccadic activity 

was also predictive of within-subject performance 

variability. Imperative stimuli followed by reactive 

saccades were responded to with decreased accuracy 

levels and increased RTs compared with stimuli that 

were effectively anticipated. Furthermore, the larger 

the reactive saccade, the greater the increase in RT. 

Slow-velocity recheck saccades were also associated 

with increased manual response latencies.

Finally, exploratory multi-variate analyses revealed a number of interesting relationships between 

overt performance and physiology. Eye-movements 

alone could account for over 50% of the variance in 

performance accuracy and response speed. Eventrelated potentials and modulations in specifi c 

frequency bands of the EEG were much better 

at predicting RT than accuracy. Interestingly, the 

reverse was true for the measures of more transient 

EEG events. Cardiovascular measures, as such, 

were poor predictors of both RT and A’. “Full” 

psychophysiological models, containing measures 

from all of the physiological systems, accounted for 

substantial variance from both within and between 

subjects, with respect to both the accuracy (R2

 = 

0.90) and RT (R2

 = 0.70) measures.

In conclusion, the present study demonstrated a 

number of intriguing relationships between overt 

performance and covert psychophysiological variables. These fi ndings will substantially contribute to 

attempts to develop oculometric, real-time alertness 

monitoring systems by providing a) an improved 

understanding of the relationship between the activity of the eyes and ongoing performance; and b) a 

richer, multi-variate space against which to validate 

the oculomotor metrics.

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