Original Article

Post-saccadic Eye Movement Indices Under Cognitive
Load: A Path Analysis to Determine Visual Performance

Marzieh Salehi Fadardi1, MOptom, PhD; Javad Salehi Fadardi2,3,4, PhD; Monireh Mahjoob5, PhD; Hassan
Doosti6, PhD

1Department of Optometry & Vision Sciences, University of Melbourne, Melbourne, Australia
2Department of Psychology, Ferdowsi University, Mashhad, Iran

3Claremont Graduate University, California, USA
4School of Psychology, Bangor University, Bangor, UK

5Health Promotion Research Center, Department of Optometry, Rehabilitation Faculty, Zahedan University of Medical Sciences,
Zahedan, Iran

6Department of Statistics, Macquarie University, Sydney, Australia

ORCID:
Marzieh Salehi Fadardi: https://orcid.org/0000-0002-5422-7695

Monireh Mahjoob: https://orcid.org/0000-0001-9455-0721

Abstract

Purpose: The evidence on the linear relationship between cognitive load, saccade,
fixation, and task performance was uncertain. We tested pathway models for degraded
task performance resulting from changes in saccadic and post-saccadic fixation under
cognitive load.
Methods: Participants’ (n = 38) eye movements were recorded using a post-saccadic
discrimination task with and without arithmetic operations to impose cognitive load,
validated through recording heart rate variability and subjective measurement.
Results: Results showed that cognitive load led to longer latencies of saccade and
fixation; more inaccurate responses and fewer secondary saccades (P < 0.001). Longer
saccade latencies influenced task performance indirectly via increases in fixation
latency, therefore, longer reaction times and higher response errors were observed due
to limited fixation duration on desired target.
Conclusion: We suggest that latency and duration of fixation indicate efficiency of
information processing and can predict the speed and accuracy of task performance
under cognitive load.

Keywords: Eye Movement; Saccades; Task Performance

J Ophthalmic Vis Res 2022; 17 (3): 397–404

Correspondence to:

Monireh Mahjoob, PhD. Department of Optometry,
Rehabilitation Sciences Faculty, Zahedan University of
Medical Sciences, Zahedan 9816743463, Iran.
E-mail: mahjoob_opt@yahoo.com
Received: 28-07-2021 Accepted: 19-02-2022

Access this article online

Website: https://knepublishing.com/index.php/JOVR

DOI: 10.18502/jovr.v17i3.11578

This is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.

How to cite this article: Fadardi MS, Fadardi JS, Mahjoob M, Doosti
H. Post-saccadic Eye Movement Indices Under Cognitive Load: A
Path Analysis to Determine Visual Performance. J Ophthalmic Vis
Res 2022;17:397–404.

© 2022 Fadardi et al. THIS IS AN OPEN ACCESS ARTICLE DISTRIBUTED UNDER THE CREATIVE COMMONS ATTRIBUTION LICENSE | PUBLISHED BY KNOWLEDGE E 397

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Visual Performance Under Cognitive Load; Fadardi et al

INTRODUCTION

Saccadic eye movements are rapid and conjugate
eye movements that voluntarily move the eyes from
one target to another. Several parts of the cerebral
cortex, such as the frontal eye field and the parietal
eye field, play an important role in performing a
saccade.[1]

An individual’s performance of visual tasks can
be degraded due to task conditions such as
cognitive load and mental stress.[2–8] Cognitive
load refers to the amount of information that
working memory deals with at a time. Visual tasks
under high cognitive load conditions can limit the
application of top–down processes and result in
poor task performance.[2–8] Distracted minds can
also lead to the degradation of an individual’s
performance during daily activities where both
accuracy and speed are required to deal with visual
targets under high load situations such as driving.
In order to see a target, individuals require both to
get the eyes on the desired target (fixation) that
is to “collect” the sensory visual inputs-, and then
to “process” the consequent visual information.
However, this is not always the case, even for
normally sighted individuals. In some instances, it
has been reported that individuals have looked at
but failed to see a visual target or saw it very late
while driving and doing another task at the same
time.[9]

Previous investigations in normally sighted
individuals have reported that cognitive
demands of concurrent tasks can affect saccade
eye movements and individuals’ visual task
performance.[2–7] Fadardi and Abel found that
both latencies of saccade and fixation on the
desired target increased and task performance
decreased when participants were required to
perform a post-saccadic task concurrently with
an arithmetic task.[8] They suggested that such
changes in saccades may give rise to late eye
fixation which itself may further limit fixation
duration and efficiency of information processing
under time-restricted situations.[8] However, the
cause–effect relationship between the changes in
saccades and post-saccadic task performance is
not yet clear.

Path analysis is an extension of multiple
regression statistical analysis that is used to
evaluate causal models by examining the
relationships between several dependent
variables and independent variables. This

method as a methodological tool can estimate
the magnitude and strength of effects in causal
connections between variables. In addition, path
analysis is useful for comparing different causal
models to examine the best fit with the data.[10]

Here, we aim to investigate the cause–
effect relationship between changes in saccade
characteristics, fixation duration, accuracy, and
speed of task performance within subjects. We
have hypothesized that task-induced changes
in saccades give rise to the degradation of an
individuals’ performance of post-saccadic tasks
indirectly by imposing changes in eye fixation on
the desired target. The results of this study have
agreed with this theory and has also suggested
that the timing aspects of fixation (latency and
duration) which may give rise to poor visual task
performance may also be utilized in its prediction.

METHODS

Participants

Thirty-two graduate students from the University
of Melbourne (24 females and 8 males) with
a mean age of 30.81 ± 12.5 years participated
in the experiment. All participants had normal
general health condition and a visual acuity ≥6/9
in each eye. Participants were requested to avoid
sedatives and alcohol consumption the night
before the tasks.

Apparatus and Procedures

All procedures contributing to the current study
conformed to the Declaration of Helsinki and
were approved by the institutional review board
of the University of Melbourne. All participants
volunteered for the experiment, and the signed
written informed consent was obtained from them.

The tasks were presented using the SR
Research Experiment Builder 1.6.2 (SR Research,
Mississagua, Ontario) and a 1024×728 NEC-WT610
projector with a screen placed at a distance of 160
cm from participants. Participants were required
to look at a fixation point presented at the central
gaze position until it disappeared within a random
time between 1500—3000 ms. The fixation point
was followed by a Tumbling E target presented
randomly across ±25º horizontally away from
center in 5º steps. Participants were required to

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Visual Performance Under Cognitive Load; Fadardi et al

look at and promptly discriminate the direction
of the E target (right or left) using a game player
console. Participants were highly encouraged to
do the tasks with the highest accuracy and speed
as much as possible. The submission of manual
responses was followed by the fixation target
re-appearing and participants were required to
look back to the center and wait for another E
target to be presented. The size of the Tumbling
E and fixation targets were the same; that is, 0.3
Log MAR. Each eccentric gaze position was tested
every five times.

The task was repeated with a concurrent
arithmetic task to impose a high level of mental
load within participants. For the arithmetic task,
the participants were required to continuously
subtract 7 from a number between 100 and 200
and verbalize the arithmetic responses in 0.5 s.
Participants’ performance on the arithmetic task
was recorded and was qualitatively monitored
after the experiment was terminated. Task
performance was measured using participants’
reaction time to the E targets and the percent rate
of accurate responses was manually submitted.
The time-related variables including participants’
reaction time and latency of eye movements were
measured using the onset time of each target on
the screen; that was determined by installing a
photocell in a corner of the screen which recorded
luminance changes.

Eye movements were recorded during the tasks
using an EyeLink II high-speed head mounted
video eye tracker (SR Research, Mississagua,
Ontario) at a sampling rate of 500 Hz in
pupil tracking mode with an accuracy of 0.5º
or better. Head movements were minimized
during the tasks using an adjustable supportive
headrest. Participants’ head movements were
also qualitatively evaluated offline by plotting the
positions of the head markers of the EyeLink II.

Concurrent heart rate recordings and a
retrospective subjective rating score using a
computer version of NASA TLX (Task Load Index)
were used to confirm within-subjects’ variation in
level of cognitive load across the task conditions.

Eye Movements Analysis

Using the analogue output from the EyeLink II
(resampled at 1000 Hz) allowed us to synchronize
the time of the eye movements with the target

timing. The digital output from EyeLink II was used
for the analysis of eye position in MATLAB R2012a
after being digitally low-pass filtered with a cut-off
at 70Hz. A bidirectional filter (using the Matlab filtfilt
command) was applied to the data to avoid time
delays. The filter used a mean squares algorithm to
produce the least error signal; that is, the difference
between the desired and the actual signal. Velocity
data were derived from the position data and
smoothed using a second-order differentiator with
a cut-off frequency of 62.5 Hz.

Saccades were selected using a custom
written MATLAB program that presented potential
saccades for manual review and selection. The
program used the criteria of 10º/s and 8000º/s[2]
for saccade onset and offset after the target step.
Trials associated with blinks, predictive saccades,
or saccades with directional error or gain of
<0.5 or >1.5 were not included in the analysis.[11]
Asymptotic peak velocity, saccade latency, and
gain were measured under each task condition.
Occurrence of any secondary saccade was also
marked for each trial.

Latency of initial eye fixation, also termed target
acquisition time,[8] was determined as the time
between the target onset on the screen and the
initial placement of the eye gaze within 0.5º of the
target of interest which was typically located at the
end of the saccadic eye movements. Trials with
fixation losses due to saccades larger than 1º were
not included in the analysis. Fixation duration was
measured as the time of duration of fixating on the
target before a manual response to the target of
either right or left, was submitted.

Data Analysis

Task conditions were compared within participants
to determine whether any significant effects on
task performance exist, and to monitor saccade
eye movements; that is, latency, gain, and velocity
of pre-saccades, the probability of the occurrence
of secondary saccades, and the latency and
duration of eye fixation on the desired target.
Correlation and regression tests investigated
potential relations among participants’ changes
in multiple variables including task performance,
cognitive load, and eye movement variables. The
Chi-square and its corresponding P-value of 0.05
were used as the criteria to exclude a variable
from the model. Values >0.05 were excluded from

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Visual Performance Under Cognitive Load; Fadardi et al

the analysis. Exclusion or replacing the unaffected
variables did not show to have any significant effect
on the pathway models. During the next step, path
analysis (standardized regression coefficients
and correlation between variables) was used to
develop a model to assess how the changes in
load indicators affected visual task performance
through possible alterations in saccades and/or
fixation eye movements. An acceptable model
was determined using the criteria of 0.90–1.00
for Comparative Fit Index (CFI) and 0.00–0.06
for Root Mean Square Error of Approximation
(RMSEA). The models with the largest CFI and the
smallest RMSEA were determined as the final path
models selected to analyze the effects of cognitive
load on participants’ reaction time and response
errors submitted for the discrimination task. The
IBM SPSS with AMOS Graphics Versions 24 (IBM
Corporation, Somers, NY) was used to analyze
data.

RESULTS

Both rating scores of mental load and heart rate
significantly increased among participants during
the task with mental arithmetic when compared
to the task without arithmetic (mean difference
(SE); for the load score: 15.77 (1.17), P < 0.001;
for the heart rate: 10.25 (1.17) bpm, P < 0.001).
It was confirmed that the level of cognitive
load significantly increased during the task with
arithmetic. Figure 1 shows the changes in heart rate,
saccade variables, and task performance from the
low to high load within participants. Latencies of
saccade and fixation increased significantly during
the high load when compared to the low load (for
saccade latency: 37.37(32.14) ms, P < 0.001; for
fixation: 40.86(8.85) ms, P < 0.001). The saccade
gain was just significantly greater for the high
load when compared to the low load (0.029(0.011),
P = 0.042). The probability of the occurrence of
the secondary saccades was less under the high
load than under the low load (0.123(0.024), P
< 0.001). Inaccurate responses submitted on the
discrimination task significantly increased under
the high load when compared to the low load
(10.8% vs 0.97%; Wilcoxon Signed Ranks Test: P <
0.001). Fixation duration tended to decrease with
the high load, however, the changes in fixation
duration and participants’ reaction time to the
targets were not significant with cognitive load
(fixation duration under low load: 391.33(40.36) ms;

and under high load: 348.29(11.95) ms; participants’
reaction time under low load: 766.49(10.92) ms; and
under high load: 785.04 (35.89)).

An inverse correlation was found between
changes in fixation duration and response errors
from the low to high load (r = –0.54, P =
0.004). A significant correlation was found between
participants’ reaction time to the E targets and
latency of fixation on the desired target (r =
0.313, P = 0.015). The increase in the response
errors submitted for the discrimination task was
significantly associated with less probability of the
occurrence of secondary saccades (r = –0.401, P
= 0.001). The correlation between the changes in
task performance metrics, that is, reaction time and
accuracy of manual responses, from the low to the
high load just failed to be significant (r = –0.503, P
= 0.05).

Path analyses were employed to investigate
potential relationships among the task-induced
changes in heart rate, MTE score, task
performance, and eye movement variables, that
is, latency, gain and velocity of saccades, latency
and duration of fixation, occurrence of secondary
saccades. Figure 2 illustrates the best path models
for the effects of task condition on performance
metrics including participants’ reaction time (Chi-
square: 0.098, df = 2, P = 0.952, RMSEA: 0.000, NFI
= 0.031), and response errors (Chi-square: 6.334,
df = 10, P = 0.786; RMSEA: 0.000, NFI = 0.737). For
both models (the model of reaction time and model
of response errors), changes in the task condition
was best represented by heart rate and negative
effects shown on an individuals’ task performance
via changes in saccade latency. Changes in
heart rate, used as the metric of cognitive load,
showed an increasing effect on saccade latency
(standardized regression weight [β= 0.07]). There
was an inverse relationship between saccade
latency and the probability of the occurrence of
secondary saccades (β = –0.18). The increase in
saccade latency was found to delay fixation on
the desired target (β = 0.18) which showed an
increasing effect on participants’ reaction time (β
= 0.08). The delay in fixation was found to limit
fixation duration (β = –0.32). Shorter duration of
fixation was found to contribute in the probability of
the submission of inaccurate responses increased
on discrimination tasks (β = –0.59).

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Visual Performance Under Cognitive Load; Fadardi et al

 

Figure 1. Heart rate (a), saccade latency (b), saccade gain (c), latency of target acquisition; i.e., fixation on target of interest (d),
participants’ reaction time (e), and response errors (f) in post-saccadic discrimination task under low and high cognitive loads.
Black lines represent changes from low to high cognitive load for each participant. Red circles with vertical lines express the
mean ± SD under the low and high loads; except for response errors that the red circles express median values. Red diagonal
lines represent changes in the means for (a–e) and changes in the medians for (f) from low to high cognitive load.

DISCUSSION

Results obtained by the current study showed
cognitive load can indirectly affect an individuals’
task performance at the post-saccadic position via
changes in eye movements. Increase in saccade
latency with high cognitive load can degrade visual
task performance indirectly by an increase in the
fixation latency and a decrement in the duration of
fixation on the desired target.

Consistent with previous studies, our findings
showed that cognitive demands affect saccade
latency.[1, 5–12, 15–17] Post saccadic visual tasks,
such as discrimination, have been found to
increase the probability of secondary saccades
occurring.[13] However, the probability of secondary
saccades developing decreased under the high
load possibly because attention allocated to the
discrimination task was reduced by the concurrent
arithmetic task. Although the nature of secondary
saccades is reflexive,[14] the controlled execution

of saccades can suppress reflexive saccades
under high load conditions.[5, 15] In addition, the
programming of secondary saccades is done
prior to their execution and during the latency
of the initial saccades.[16] Like initial saccades,
secondary saccades contribute toward the target
acquisition time and final eye position.[17] Under
cognitive conditions, cost-effective programing
of the saccades (trade of between timing and
accuracy of saccadic task) might encourage the
saccade system to fixate on the desired target
more by using a single saccade rather than
saccade sequences. Despite changes in the
occurrence of secondary saccades with the high
load, we did not find any effect of secondary
saccades on visual task performance.

Longer saccade latency resulted in the decrease
in task performance brought about mainly by an
increase in the latency of fixation on the desired
target. According to Figure 2, a delay in eye
fixation on the target can follow a delay in an

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Visual Performance Under Cognitive Load; Fadardi et al

Figure 2. Illustration of final Path Models for the indirect effects of the changes in the cognitive load indicated by heart rate, from
low to high load on subjects’ reaction time and submission of inaccurate responses.

individual’s reaction time to the target; that is, being
slow in reacting to visual stimulus. Our results are
consistent with the findings of Wang et al who
examined eye movements in a group of patients
with involuntary continuous ocular oscillations,
whose complaints of delayed visual capture were
common (infantile nystagmus).[18] Delayed visual
capture can also occur among normally sighted
individuals, for example, when drivers report that
they looked at a target but either did not see an
object or saw it late.[9] “Being slow to see” has been
identified clinically as prolonged visual recognition
time is predicted as the result of an increase in the
latency of fixation; which has been demonstrated
by our results that expressed changes in the
timing of eye movements and the participant’s
reaction time during high compared low cognitive
loads [Figure 2]. Investigations of eye movements
and reaction times in both of these types of
patients, those with involuntary continuous ocular
oscillations versus normally sighted individuals,
concluded that longer times taken to respond to
a visual target at post-saccadic positions is mainly
as a result of the increased time taken to fixate
on a desired target rather than longer saccade
latency or the duration of fixation on the target.[8, 19]
Thus, an increase in recognition time is predicted
to follow an increase in the latency of fixation;
which has been demonstrated by our results that
expressed changes in the timing of eye movements
and the participant’s reaction time during low to
high cognitive loads [Figure 2].

Despite associations between changes in task
performance and latency of eye movements, our

results did not show any significant differences in
the task conditions as it relates to participants’
reaction times and fixation durations. Previous
investigations have suggested two behavioral
profiles for the discrimination tasks with mental
arithmetic; those include focusing on either the
accuracy or speed of responses at the expense of
the other.[20] One possible reason for no significant
change in our participants’ reaction time can be
the perceived task urgency which might result
in the participants becoming more concerned
about the speed of their performance rather than
the accuracy of their responses. Task urgency
and accuracy also affect saccade latency albeit
via different mechanisms;[3, 21, 22]that is, perceptual
urgency – the extent to which the participant
feels the time is restricted – can lower the
threshold of saccade execution while the other
one (accuracy) can affect saccade latency via the
information processing system of the brain.[23] It
seems that the level of the task urgency perceived
by our participants was not large enough to avoid
increasing the latency of saccades with mental
arithmetic.

According to previous investigations, prolonged
visual recognition time is unlikely due to the
slow speed of information processing.[8, 19] Using
a simulated driving task concurrently with phone
conversing, Recarte and Nunes measured longer
latency of fixation, and more individuals’ response
errors with no significant change in the participants’
reaction time.[25] Consistently, Fadardi and Abel
have suggested that efficiency of information
processing, despite no significant change in its

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Visual Performance Under Cognitive Load; Fadardi et al

duration, gives rise to resolving target details
including both visual inputs and processing of the
information during fixation.[8] Previous results have
suggested that performance was not degraded
due to the time restriction to perform a visual task
but resulted from poor information processing.[8, 25]
According to our results, a decrement in fixation
duration predicted more response errors in an
individual’s task performance [Figure 2]. In other
words, a subject who showed longer fixation
duration is expected to submit a correct response
to a post-saccadic visual task. Our results are
consistent with previous findings by Tsai et al who
used auditory arithmetic evaluations concurrently
with a simulated driving task.[26] Their results
showed that although fixation duration did not
change across cognitive levels, they could predict
upcoming errors when it decreased just before
an error was made in the arithmetic task.[26] The
results obtained through the scene viewing studies
have shown that information extracted during
fixation affects the onset timing of the subsequent
saccade essentially terminating that fixation.[27]
Another study showed that increased cognitive
level of a scene viewing task resulted in longer
duration of fixation.[27]The task design and the
comparisons within subjects that were used in the
current study make it unlikely for the target features
to affect our results obtained for fixation duration.
The efficiency of the information processing can
be reflected in the participants’ task performance
in terms of both the speed and accuracy of the
responses.[24] According to our results, it seems
that providing a correct response to the visual
target requires longer time in the presence of a
limited capacity of information processing or the
quality of processing being degraded despite the
presence of healthy oculomotor systems.

The current study showed that varied levels
of cognitive load changed individuals’ response
errors and reaction times indirectly through
changes in saccade latency. Increased saccade
latency predicts longer time requiring for getting
the eyes on the desired target. If the allocated
time duration between the onset of visual onset
and providing response to the target is restricted,
a delay in eye fixation on the target can further
limit fixation duration before the subject submits
the response to the visual target. Our data showed
that saccade latency can be used as an indicator of
cognitive load, but also flags degraded efficiency
of information processing which can demand

longer fixation duration to protect accuracy of the
subject’s response to the visual target. Therefore,
latencies of saccade and fixation can be used to
monitor forecasted performance especially when
both time and accuracy matter, for example, when
driving.

Ethical approval

All procedures performed in studies involving
human participants were in accordance with
the ethical standards of the institutional and/or
national research committee and with the 1964
Helsinki declaration and its later amendments or
comparable ethical standards.

Acknowledgements

Same data of the eye movement recorded for some
participants attended in this study were used in
another study by Fadardi and Abel (2018)[8] as the
control data collected for task performance and
eye movement reaction time including saccadic
latency.

Financial Support and Sponsorship

This work was supported by the University of
Melbourne.

Conflicts of Interest

No conflicting relationship exists for any author.

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