Pupil Size and Mental Load
Benjamin Stone (bpstone@psychology.adelaide.edu.au)
Department of Psychology, North Terrace, SA 5005 Australia

Michael Lee (michael.lee@psychology.adelaide.edu.au)
Department of Psychology, North Terrace, SA 5005 Australia

Simon Dennis (simon.dennis@psychology.adelaide.edu.au)
Department of Psychology, North Terrace, SA 5005 Australia

Ted Nettelbeck (ted.nettelbeck@psychology.adelaide.edu.au)
Department of Psychology, North Terrace, SA 5005 Australia

Abstract
This experiment explores whether the size of human pupil
dilation is a measure that can discriminate between easier andharder intellectual tasks undertaken by the participant when
information is displayed on a computer monitor. While it has
been previously found that a participant’s pupil dilation will
be larger during harder intellectual tasks, these experiments
have not generally been conducted under the environmental
condition of light radiated from a computer monitor. The
findings of this experiment indicate that computer monitor’sradiance did not interfere with the researcher’s ability todiscriminate successfully between the participant’s taskrelated pupil dilation.

Introduction
The call for a measure of cognitive processing load
A common request that spans several research disciplines
has been for the identification, development, or evaluationof good measures of cognitive processing load. These
disciplines include decision making research (Dennis,
Bruza, & McArthur, 2002, p.132; Russo & Dosher, 1983,
p.695) and human computer interaction (Christie, 1985,
pp177-179). Several methods have been developed andimplemented with varying success in response to this need.
Russo and Dosher (1983) contend, that in process-oriented
decision research that uses eye-tracking devices, eyefixation time can be a suitable measure of cognitive effort(p. 682). In contrast, Payne and colleagues (1988) calculateelementary information processes (EIPs) associated withdecision strategies. This measure of effort is derived bydemarcating a decision strategy into its theoretically-based
component parts; the sum of these parts is assumed toprovide an effort score that can be compared between
strategies (pp. 534-535). More recently, Dennis et al. (2002)
employed a dual task methodology to measure cognitive
load during web browser searches of the internet.
Participants in their study were required to concurrently
browse the internet while also monitoring an auditory
signal. Accuracy ratings on the auditory task were used as ameasure of cognitive load. While these methods have been

employed with mixed success, HCI researchers havecontinued to discuss the need for physiological measures ofmental load (Ward & Marsden, 2003; Christie, 1983).

Pupil dilation as a measure of cognitive load
Historically, the pupil has been considered a window
through which researchers may glimpse the cognitive
processes of the human brain. Bumke, in his 1911 review of
the relevant German literature, proposed that “in generalevery active intellectual process...produces pupil
enlargement” (cited by Hess, 1972, p.492). Bumke’s
assertion has been supported by the work of many
contemporary researchers. More recently, in an experimentthat compared pupillary responses during three different
mathematical-based tasks (no task, add 1 task, and subtract
7 task), researchers found that pupil diameter and thelatency of the pupil constriction response to lightstimulation were positively correlated with the level ofproblem difficulty (Steinhauer, Condray, & Kasparek,
2000); that is, both diameter and latency increased withdifficulty. Moreover, mathematical task difficulty has
consistently been associated with degree of pupil dilation(Hess & Polt, 1964; Boersma, Wilton, Barham, & Muir,
1970; Ahern and Beatty 1979; Schaefer, Ferguson, Brinton,
Klein & Rawson, 1968; Steinhauer, Conray & Kasparek,
2000). In short, these studies found that greater pupillary
dilations are observed when task difficulty is increased.

Pupil dilation as a measure of cognitive load in eyetracking experiments that visually present stimuli
on CRT monitors
HCI eye-tracking research has employed the pupil as ameasure of cognitive load with mixed success. Lin, Zhang
and Watson (2003) failed to find significant differences
between the sizes of participants’ pupil dilations when
operating different computer-based interfaces for thermalhydraulic processing plant systems. However, theirexperiment may have been hindered by at least two
considerations. First, the experiment was conducted underfluorescent lights, which pulse, and could consequently
create noise for the pupil data (Veitch & McColl, 1995).
Second, the researchers examined the mean pupil dilation

for each participant; however, mean pupil dilation size isaffected by extremes cause by participants’ blinks, and istherefore unreliable. The median size of pupil dilation is amore appropriate measure. Furthermore, the researchers
acknowledged that, in at least some task comparisons, it
could be argued that the two tasks were not “more difficult
or more demanding” than each other, which is consistent
with the pupil-based results that they obtained (p.859). In
contrast to these findings, a study conducted by the
Netherlands Aerospace laboratory utilized pupil dilation as ameasure of user’s cognitive load when comparing theeffects of the automation of interfaces for Air Traffic
Control systems (Jorna, 1997). They found that pupil
dilation was a sensitive measure of cognitive load during
tasks of increasing difficulty. Also, the pupil dilation results
were consistent with other measures of cognitive load usedin this study, such as a dual task condition.
It is possible that light radiated from the CRT monitor isinterfering with researchers ability to utilize the pupil as ameasure of cognitive load. The screen pulsating as it
refreshes its display may further confound this situation.
The mixed nature of HCI results indicates a need for further
experimentation to identify the validity of the pupil as anindicator of cognitive load during tasks that are visualdisplayed on computer monitors.

Experimental research hypotheses
The aforementioned issues raise several research hypotheses
that will be addressed in this thesis. These hypotheses will
test under ‘adverse’ lighting conditions the robust finding,that more difficult mathematical tasks will elicit a greater
magnitude of pupil dilation than easier mathematical tasks(Hess & Polt, 1964; Schaefer, et al., 1968; Bradshaw, 1968;
Ahern & Beatty, 1979; Steinhauer et al., 2000). Therefore, itis predicted that when a stimulus ‘X’ is visually presented
on a CRT monitor, on average, participants will produce
significantly greater pupil width during SUB7 task whencompared an easier ADD1 task. Furthermore, it is predictedthat this result will be unaffected by the position of thestimulus ‘ ‘X’ in the CRT monitor’s display. To this end,
the CRT screen is delineated into five rows and columns
corresponding to a 5x5 matrix. It is predicted that, when thestimulus ‘X’ is presented in each of the rows and columns inthis experimental matrix, on average the participants’ pupil
width will be greater during an SUB7 task compared to aneaser ADD1 task.

sighting test was used to determine each participant’s
dominant eye, 40 percent of participants were left eye
dominant while 60 percent were right eye dominant.
Selection Criteria. Potential participants were excluded ifthey reported any diagnosed ophthalmologic condition (forexample, an astigmatism in the eye). Participants were also
excluded if they reported either having a neck/back injury orthat they were currently using of prescribed or nonprescribed medicines.

Apparatus & Procedure
Participants were seated in front of a Hitachi CM772 17
inch CRT Monitor set to refresh at 75 hertz. Screen
resolution was set at 1024 x 768 pixels. Illuminance at theapproximate position of the participant’s right eye whenfacing the monitor (see Item B in Figure 1) was recorded at
24 lux when the monitor switched off and 58 lux when themonitor was displaying the experimental program. The
ViewPoint EyeTracker PC-60 QuickClamp System wasused to record participants eye-movements and pupil
dynamics during the experimental tasks.
Participants consented to two types of testing: eyedominance and cognitive load. All testing was completed
within 30 minutes.
First, the Rosenbach (1903) sighting test was used todetermine each participant’s dominant eye. This is a simple
test that requires the participant to point at a distant object.
While using their finger as a sight, the participant is thenasked to close one eye, then reopen it, and then repeat thisprocess with their other eye. The closing of their dominant
eye will cause their finger seem offset from the target(Kommerell et al., 2003).

Methodology
Participants
Source, age-range, and eye-dominance. 50 participants
were recruited from the Adelaide University Psychology
Department, 41 females and 9 males. Of these participants,
36 were First Year Psychology Students who were willingto engage in voluntary psychological testing in return forpartial course credit. There were no restrictions placed onparticipants’ age, which ranged from 16 to 53 (M = 22 years
1 month, SD = 6 years 5 months). The Rosenbach (1903)

Figure 1: The layout of the experimental room.
Secondly, a View Point Eye-Tracking device was used torecord the magnitude of the pupillary response inparticipants’ dominant eye during mathematical tasks
selected to that create different levels of cognitive load.
There were two mathematical task conditions: ADD 1 and
SUB7. The ADD1 condition required participants tocontinually add one to a randomly generated integerbetween 1 and 499. In the SUB7 task participants wereasked to continually subtract seven from a randomly
generated integer between 500-999. The participants wereasked to do both tasks silently (in their heads) while visuallyfollowing a ‘X’ that is presented on a computer monitor (seeFigure 2).

The ViewPoint Eye-Tracker measures pupil width in “pixels
normalized with respect to the EyeCamera window”(Arlington Research, Inc., 2002, p.25). In the results section,
for convenience, this measurement has been multiplied byten. Participants’ pupil dilation widths were recorded whenthe eye-tracking software recorded that the co-ordinates ofthe eye were in the same region of interest as theexperimental stimulus ‘X’. This method generated several
thousand pupil width measurements for each participant
during each task. The median of these measurements(referred hereafter as ‘Median Pupil Width’) was used tocombat confounding effects of extreme scores generated
from participant’s blinking and moving

Results
Figure 2: The visual stimulus is randomly moved to anothercell in the experimental display every five seconds.
The visual display is divided into 25 invisible cells or
regions of interest (ROI) that are arranged into a five by fivegrid. For each experimental task, the ‘X’ passes through 24of these cells (all except the top-left cell), stopping in eachcell for ten seconds. The sequence through these 24 cells is
randomly generated using a C++ function that is ‘seeded’
with an integer from the computer clock, thereby generating
a quasi-random sequence and guaranteeing that participants
did not receive the stimuli in the same order. An average
pupillary response to each task condition for each subject
was calculated over the 24 cells. Also, average pupillary
responses to each task condition for each subject were
calculated over the rows and columns contained in theexperimental grid.

This results section is delineated into three parts. The firstsub-section reports overall findings when participants’ pupil
width was compared for both the easy (ADD1) and harder(SUB7) mathematical tasks. The second and third sections
report findings when participants’ pupil widths werecompared during these tasks across the rows and columns of
the experimental grid, respectively.
Descriptive statistics derived from the participants’pupillary responses during the mathematical tasks arepresented in Table 1. In all comparisons, participants’
Median Pupil Width was found to be larger during the
SUB7 task when compared to the easier ADD1 task. For
each of these comparisons, a related sample t-test has beenused to compare means. These findings were statisticallysignificant in nearly all of the experimental locations used
for these comparisons.

Table 1: Descriptive statistics of the participants’ Median Pupil Width
Overall
Rows:

R0
R1
R2
R3
R4

Columns:
C0
C1
C2
C3
C4

Task
ADD1
SUB7

N
50
50

Range
116.00138.00Min.
112.00118.00Max.
228.00
256.00M
170.82186.02

SD
26.49
30.94

Skew
-0.19
-0.21

SE
0.34
0.34

Kurtosis
-0.36
-0.33

SE
0.66
0.66

ADD1
SUB7
ADD1
SUB7
ADD1
SUB7
ADD1
SUB7
ADD1
SUB7

50
50
50
50
50
50
50
50
50
50

128.00144.00128.00150.00112.00141.00112.00125.00109.00129.00118.00124.00112.00118.00109.00118.00106.00115.00112.00121.00246.00268.00240.00268.00221.00259.00218.00240.00221.00250.00182.09197.61
175.12191.55167.90182.20165.16179.14168.00183.10

30.59
34.07
28.30
32.37
25.60
30.00
25.65
28.92
25.54
32.16

-0.101
-0.17
-0.14
-0.28
-0.28
-0.05
-0.23
-0.39
-0.27
0.15

0.34
0.34
0.34
0.34
0.34
0.34
0.34
0.34
0.34
0.34

-0.58
-0.43
-0.25
-0.20
-0.48
-0.07
-0.39
-0.44
-0.40
-0.57

0.66
0.66
0.66
0.66
0.66
0.66
0.66
0.66
0.66
0.66

ADD1
SUB7
ADD1
SUB7
ADD1
SUB7
ADD1
SUB7
ADD1
SUB7

50
50
50
50
50
50
50
50
50
50

328.00156.00112.00138.00116.00129.00112.00135.00100.00128.00115.00118.00112.00121.00112.00121.00109.00118.00115.00115.00443.00274.00224.00259.00228.00250.00221.00253.00215.00243.00185.90196.33174.50189.56
171.79186.20168.17181.65163.24177.16

54.56
34.19
26.13
32.24
26.95
31.21
27.04
30.22
25.18
30.09

3.18
-0.09
-0.36
-0.18
-0.10
-0.27
-0.24
-0.23
-0.14
-0.08

0.34
0.34
0.34
0.34
0.34
0.34
0.34
0.34
0.34
0.34

12.93
-0.10
-0.40
-0.63
-0.44
-0.43
-0.64
-0.41
-0.69
-0.40

0.66
0.66
0.66
0.66
0.66
0.66
0.66
0.66
0.66
0.66

Overall

300

300
11

200

Pupil Width in Pixels(x10)

Increased task difficulty (ADD1 to SUB 7) was paralleledby an amplification of mean Median Pupil Width (170.82,
186.02, respectively). Overall, the distributions of theMedian Pupil Width were normal in both mathematicaltasks. The significant differences between average SUB7
and ADD1 Median Pupil Width (t(49)= -12.31, p< .001)
indicated a medium effect size (d= -0.53). The differences
between these means are graphically displayed in Figure 3.

100

0

R4

R4

R3

R3

R2

R2

R1

R1

R0

R0

Pupil Width in Pixels (x10)

50

B7
SU

50

1
DD

50

A

1
DD

1
DD

50

B7
SU

A

50

A

50

B7
SU

50

1
DD

1
DD

50

B7
SU

A

50

A

50

B7
SU

N=

200

Task & Row
100

Figure 4: A Boxplot illustration of the differences between
participants’ average SUB7 and ADD1 Median Pupil
Widths in each of the 5 rows in the experimental grid.

0
N=

50

50

ADD 1

SUB 7

Task

Figure 3: A Boxplot illustration of the differences between
participants’ overall average SUB7 and ADD1 Median
Pupil Widths.

Rows
In comparisons of row data, increases in task difficulty
corresponded with enlarged Median Pupil Widths (see Table1). The related sample t-tests shown in Table 2 indicate thatthe differences between means associated with theseincreases were statistically significant in all comparisons.
Table 2: Results of related samples t-tests used to compare
average Median Pupil Widths recorded while participants
were viewing the ‘X’ Stimulus in rows 0 to 4.
Comparison
ADD1-SUB7Median Pupil
Width
t
d.f.
p
R0
-9.60
49
<0.001R1-10.5149<0.001
R2-9.81
49<0.001
R3-11.0049<0.001R4-8.16
49<0.001Moderate effect sizes were found when comparing theADD1 and SUB7 row data (d= -0.48, d= -0.54, d= -0.51, d=-0.51 and d= -0.52) in rows 0 to 4, respectively. All row
data were normally distributed and are graphically display
in Figure 4.

Columns
In all column comparisons increases in task difficulty
corresponded with enlarged Median Pupil Widths (see Table1). Related sample t-tests found that the differences between
means associated with these increases were statisticallysignificant in 4 of 5 comparisons (see Table 3).
Table 3: Results of related samples t-tests used to compare
average Median Pupil Widths recorded while participants
were viewing the ‘X’ Stimulus in columns 0 to 4.
Comparison
ADD1-SUB7Median Pupil
Width
t
d.f.
p
C0
-1.78
49
n.s.
C1
-9.07
49
<0.001
C2-8.59
49<0.001C3-9.76
49<0.001C4-8.90
49<0.001The data gathered when participants were viewing stimuli inthe leftmost column (C0) was not associated with significant
statistical differences in the Median Pupil Width during theADD1 and SUB7 tasks. Moderate effect sizes were foundwhen comparing the ADD1 and SUB7 row data (d= -0.51,
d= -0.49, d= -0.47 and d= -0.50) in columns 1 to 4,
respectively.
With the exception of C0, all column data were normally
distributed, and can be viewed in Figure 5. The data in C0 is
positive skewed by of two outliers that are 3.74 and 4.71
standard deviations from the mean (see Figure 6). Whenthese outliers are removed the difference between ADD1
and SUB7means in C0 is statistically significant (t(47)= 8.91, p< .001), revealing a medium effect size (d= 0.60).

500
22

Pupil Width in Pixels (x10)

400

25

300

200

100

0
50

B7
SU

1
DD

50

A

A

50

B7
SU

50

1
DD

50

B7
SU

50

1
DD

50

A

1
DD

1
DD

50

B7
SU

A

50

A

50

B7
SU

N=

C4

C4

C3

C3

C2

C2

C1

C1

C0

C0

Task & Column

Figure 5: A Boxplot illustration of the differences between
participants’ average SUB7 and ADD1 Median Pupil
Widths in each of the 5 columns in the experimental grid.

Discussion
This report has focused on one broad question. This
question asks, is it possible to distinguish between tasks ofdifferent cognitive difficulty based on participants’ pupil
widths when stimuli are visually presented on a CRT
monitor? It was argued that presence of the CRT monitors
illumination, and the pulsating nature of the screen
refreshing, may have confounded the use of participants’pupil dynamics as a measure of cognitive load. Supporting
the first research hypothesis, overall the results of thisresearch suggest that CRT monitors can successfully be
used to visually present information to participants inexperiments that utilize pupil dynamics as a measure ofcognitive load.
This finding is consistent when data is recorded from theinformation display area as a whole and when this display is
divided into rows. However, in contrast to the second
research hypothesis, the results were not always statisticallysignificant when the display area was delineated intocolumns. While it was possible to statistically correct orremove the erroneous column data, this result suggests thatcalibration errors occurred during the experiment. Theseerrors may have been introduced by participant’s moving or
fidgeting during the experiment. That is, movement of theeye-ball away from its calibrated line of sight may haveincreased the area defined as pupil beyond its normalbounds when the participant was viewing some areas of thescreen. One way of addressing this problem would be toincorporate monitoring software into these experiments thatalerts the experimenter when eye-tracking calibration has
been compromised.
There are several other limitations present in this research.
This article has not examined the results gathered for theindividual cells in the experimental matrix. To extend the

Figure 6: A histogram displaying the relative position of theoutliers in the distribution of ADD1 in C0.
reporting of this research to level of the individual cells wasconsidered to be beyond the scope of this article.
Furthermore, the pupil’s discriminatory power needs to be
more fully explored. While pupil dynamics can be used todiscern between SUB7 and easier ADD1 tasks, can theyalso be used in finer discriminations between cognitive
tasks? This is a question that should be addressed in future
research.
The robust effect studied in this experiment indicates thatthe processes subserving pupil dilation, whether they be thesympathetic innervation (Hess, 1972) or the vasculature of
the iris (Appenzeller & Oribe, 1997), may be affected, either
directly or indirectly, by the information processing
demands placed on the human brain during mathematicalproblem solving. Several researchers have suggested that
pupil dilation is part of the orienting reflex (or response)(Sokolov, 1963; Kahneman, 1973), and others havespeculated as to its functional significance. For example,
Spinks and Siddle (1983) have tentatively suggested that
increased pupil dilation during arousal will allow more lightinto the eye and may facilitate clearer perception of focused
objects of interest, whilst simultaneously blurring
distracting peripheral visual cues.
In summary, while the level of information processing
required by humans during mathematical tasks appears to
produce a robust effect, displayed in the magnitude ofpupillary dilation, the functional significance of thisphenomenon, if any, remains a matter for speculation. In thecurrent experiment, this effect was not noticeably
confounded by light emitted from the CRT monitor.
However, pupil data accuracy appeared to be affected byeither stimulus placement and/or participant movement.
Even though both the physiological and functional basis of
this phenomenon remain controversial, it appears thatparticipants’ pupil dilation can be exploited by researchers
as a measure of cognitive load.

Acknowledgments
We would like to thank the University of Adelaide
Psychology department for their ongoing support of thisresearch.

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