Evaluating Blink Rate as a Dynamic Indicator of Mental Workload in

a Flight Simulator

Simon Schwerd

and Axel Schulte

Institute of Flight Systems, University of the Bundeswehr Munich, Germany

Keywords: Blink Rate, Mental Workload, Flight Simulato.

Abstract: This study assesses blink rate as a potential indicator for mental workload (MWL) in a dual task scenario in

a flight simulator. Prior research indicated that blink rate decreases as mental workload increases across

various tasks and domains. In our study, we aimed to determine if these findings are consistent in a dual task

environment within a fast jet simulator. Furthermore, we evaluated blink rate fluctuations caused by the

dynamic shifts in MWL as tasks are executed, switched, or completed. To investigate this, we executed a

flight simulator experiment involving ten participants. They were tasked with two distinct activities: first,

classifying air and ground targets, and second, maintaining a specific flight altitude. The results validated that

blink rate decreases with increasing task difficulty. However, when a secondary task imposes significant

workload, blink rates did not reliably indicate the primary task's difficulty. We also found that the timing of

spontaneous blinks was influenced by task completion and switches. Specifically, blink rates surged

immediately after decision-making points and during transitions between tasks.

1 INTRODUCTION

In recent years, there has been growing interest in

measuring cognitive states, especially when humans

control dynamic systems. Accurate assessment of

these states not only provides insights into human-

machine performance but also offers new possibilities

for enhancing human-machine interface (Feigh et al.,

2012). For example, displays or assistance systems

that adjust to their users’ cognitive state could

improve interaction and promote a cooperative

relationship between users and machines.

A central focus of this research is the concept of

mental workload (MWL). It can be described as the

extent to which a limited set of cognitive resources

are engaged over time while processing a task (for a

full explanation, see Longo et al. (2022)).

Understanding MWL is crucial because it directly

affects human performance, especially in tasks that

require continuous attention. In the context of

adaptive systems, MWL measurements were

successfully applied in assisting users based on their

workload level (Brand & Schulte, 2021; Hajek et al.,

2013).

https://orcid.org/0000-0001-6950-2226

https://orcid.org/0000-0001-9445-6911

Rather than relying on user feedback, MWL can

be objectively gauged using physiological sensors.

This subject has been extensively researched using

various metrics, including heart rate, pupil dilation,

and EEG alpha waves (Ayres et al., 2021; Charles &

Nixon, 2019). Of these metrics, blink rate (BR) has

emerged as an effective measure due to its balance

between simplicity of measurement and sensitivity to

MWL, as reported by several studies (Da Tao et al.,

2019). Compared to more complex sensors such (e.g.,

fNIRS, EEG), BR can be easily obtained with a basic

camera and image processing. In the following

section, we review relevant studies dealing with the

relationship between BR and MWL.

1.1 Blink Rate and MWL

In an early study, Holland and Tarlow (1972)

demonstrated in a memory and mental arithmetic test

that blink rate decreased with increasing task

difficulty. Interestingly, they observed that BR

increased before participants made mistakes.

Boehm-Davis et al. (2000) showed in a simulated

radar track classification task that blink rate decreased

362

Schwerd, S. and Schulte, A.

Evaluating Blink Rate as a Dynamic Indicator of Mental Workload in a Flight Simulator.

DOI: 10.5220/0012319100003660

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024) - Volume 1: GRAPP, HUCAPP

and IVAPP, pages 362-368

ISBN: 978-989-758-679-8; ISSN: 2184-4321

in a time frame of 1.5s before a classification event

compared to a baseline. This suggested that blink rate

rebounds after high MWL situations and could also

be an indicator of task progress.

Faure et al. (2016) reported in a driving task

experiment that blink frequency decreased with

increasing task load of the primary driving task but

within a fixed driving task difficulty, the addition of

auditory secondary tasks increased blink frequency.

Therefore, it is not clear if blink frequency is only

sensitive to tasks with visual demands.

In addition, there are some challenges when using

blink rate as an indicator for MWL. First, blinks are

not continuous signals, which complicates processing

(Cho, 2021; Siegle et al., 2008). Second, blink rate

was also reported as an indicator of fatigue and time

on task, which could be a confounding factor for

MWL measurement (Maffei & Angrilli, 2018; Stern

et al., 1994). However, it could also be argued that

blink rate does not measure MWL but rather

activation and engagement in a visual task which

happens to correlate well with performance and

reported MWL in visual tasks.

1.2 Contributions

In this study, we aimed to evaluate if blink rate for

MWL measurement is applicable to adaptive systems

in a cockpit environment. This entails the following

research questions:

What is the general relationship between BR

and MWL in single and dual visual task settings?

Based on the results of other studies, we expect

BR to be sensitive to MWL in a cockpit task

environment. However, no reviewed study has tested

the relationship between MWL and blink rate in a

dual task setting with two visual tasks. Since most

cockpit tasks are visual, we aim to evaluate (1) the

sensitivity of BR as a MWL measurement and (2) if

the BR-MWL correlation still holds in single

compared to a dual task setting.

Can blink rate be associated with changing

MWL due to task progress? We evaluate if the

timing of spontaneous blinks is related to the

dynamics of the task environment and associated

changes in MWL. Boehm-Davis et al. (2000) reported

that, in a single-task experiment, blink rate rebounds

after a task has been completed. Therefore, we would

expect to observe a fluctuating BR during task

execution, such as rebounds after task completion or

at switching between two tasks. If there is a valid

relationship, this could be used to improve the timing

of adaptations in adaptive systems, which is difficult

to determine. Adaptations at the wrong moment can

heavily disrupt the workflow of the user. Also,

physiological measures with high time constants

(measures that react slowly to changes in the task

environment, e.g., heart rate) can not provide

cognitive state estimation in a timely manner.

To address these questions, we conducted a flight

simulator study. In the following, we describe the

experimental design and subsequently discuss our

results.

2 EXPERIMENT

The experiment was conducted in a research fighter

jet simulator at the HuMiCS Lab (“Humans,

Missions, and Cognitive Systems Laboratory”) of the

University of the Bundeswehr in Munich (see Figure

1). The experimental design was inspired by an early

study by Boehm-Davis et al. (2000) and transferred

into a military aviation domain. Furthermore, we

added a secondary task condition to evaluate our

research questions.

Figure 1: Jet simulator cockpit at the HuMiCS Lab. Setup

consists of a throttle, stick, three touch display and a

projected outside view with a head-up display overlay.

2.1 Design

We created a 2x2x2 design with the following

conditions:

Primary Task Type. In the first task type (T1),

participants classified air tracks based on altitude and

velocity as hostile versus not hostile (see Figure 2).

Participants had a decision matrix that indicated high

speed and high altitude to be hostile, while all other

combinations should be classified as not hostile. The

second task type (T2) was the classification of ground

targets based on incoming sensor images. Images of

military vehicles should be classified as hostile as

Evaluating Blink Rate as a Dynamic Indicator of Mental Workload in a Flight Simulator

363

opposed to civilian vehicles (see Figure 3 for an

example).

Primary Task Difficulty. Difficulty was varied

by the ambiguity of the targets. At low difficulty

(Low), target classes could be easily identified, e.g., a

single tank on the sensor image in T1 or instant high

speed and high altitude in T2. At high difficulty

(High), target classes were ambiguous, and sensor

data were cluttered by distractors, e.g., multiple

different vehicles on the sensor data and accelerating

air targets in speed and altitude. Note that the

difficulty was only varied by how easily a target could

be classified and not by a higher number of targets.

Figure 2: Display for air track classification in T1.

Secondary Task Present. As a secondary task,

participants were asked to fly a fighter jet at a

specified altitude of 4000ft MSL. The primary task

was briefed to be more important than the secondary

task. There were two conditions: Secondary task

present (Dual-Task, DT) or absent (Single-Task, ST).

Figure 3: Sensor Picture for T2 with high difficulty

containing several different vehicles.

2.2 Participants & Procedure

Ten participants took part in the study (1 female,

mean age=24.5y). All participants were students of

aerospace-related studies at the University of the

Bundeswehr in Munich but had no prior experience

in flying with the used research simulator. At the

beginning, participants were briefed about the

experimental procedure and provided their consent

with data collection in written form.

After that, eye-tracking cameras for blink

detection were adjusted and calibrated using a

standard point calibration procedure. Then, the

participants conducted two training missions

encountering task types T1 and T2 at both levels of

difficulty. In the third training mission, the secondary

task was trained without any other task present.

Participants were encouraged to ask questions during

training since no questions were allowed in the

subsequent experimental tasks. In total, each training

mission lasted 15 minutes.

After training, the experimental tasks were

conducted in sequence. After each mission,

participants were asked to fill out a NASA-TLX

questionnaire. Order of conditions was randomized

and different for each participant to compensate both

effects of training and fatigue influence. The Total

duration of the experiment was approximately 2

hours.

2.3 Data Analysis

During the missions, we logged the following data:

Gaze Tracking. We measured gaze with a

commercial camera-based eye-tracking system

(SmartEye 4-camera system, 0.3 MP). The system

measures at a frequency of 60 Hz and classifies gaze

samples into either fixation, saccade, or blink. For this

study, blinks were analyzed in post-processing.

Saccades were used to measure gaze switches

between cockpit displays and outside windows.

Subjective Workload. Participants answered a

simplified NASA-TLX to report subjective workload.

In the simplified NASA-TLX, no weights are

assigned to the different dimensions.

Task Progress. Time points of classification

were logged when participants pressed the

corresponding button in the cockpit. Time points of

task switches were logged in the DT condition when

participants gaze switched from inside the cockpit to

outside.

The data were analyzed using Python Pandas. The

plots were generated using the Seaborn library and

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error bars always indicate the standard deviation

divided by the square root of the sample size

3 RESULTS

We first evaluate the general relationship between

blink rate and MWL and proceed to compare ST to

DT conditions. Then, we present the results on the

relationship between blink rate and task situations.

3.1 Blink Rate, Task Difficulty and

Mental Workload

Figure 4 shows the participants’ subjective rating of

MWL via the NASA-TLX questionnaire across all

experimental conditions. The figure displays the non-

weighted mean values of all TLX dimensions for each

participant, with error bars. In general, the task

difficulty aligns well with the experimental design,

with exception from the “DT High” condition for the

air track classification task (T1), which was rated as

causing a lower workload than the “DT Low”

condition. Generally, participants reported lower

MWL in the air track task T1.

Figure 4: Mean NASA-TLX results over all conditions.

Figure 5 shows the mean BR (average blink rate

per mission) across all conditions (n=10 per

condition). The mean blink rate dropped considerably

when comparing ST to DT conditions in both task

types, T1 and T2. The primary task difficulty slightly

reduced mean BR within ST (T1: -0.8, T2: -0.99), but

there was no change within DT conditions.

https://seaborn.pydata.org/tutorial/error_bars.html

Figure 5: Mean BR over experimental conditions.

The correlation between reported MWL and blink

rate is displayed in Figure 6, and shows a strong

negative correlation (r = -0.9, p < 0.003) between

mean NASA-TLX scores and mean blink rate in each

experimental condition.

Figure 6: Regression plot of mean subjective MWL rating

versus blink rate. Data points refer to the mean of one

experimental condition over all participants.

These results indicate that there is a negative

correlation between mean BR and workload. Since

the study only had 10 participants, we also analyzed

the BR for each participant across the experimental

conditions, which is shown in Figure 7 containing the

individual mean BR per trial.

Evaluating Blink Rate as a Dynamic Indicator of Mental Workload in a Flight Simulator

365

Figure 7: Individual BR in T1 and T2 of all participants.

The mean BR values in the low difficulty

conditions vary greatly among individuals from 2 to

almost 16 blinks per minute. This variance is

decreasing with increasing difficulty. A second

observation is that the change in BR is not consistent

for each participant. There is one outlier (P3) who has

an inverse relationship between BR and task

difficulty in T2. Another outlier is P7, whose BR

change “Low” and “High” difficulty in the ST

condition is also inverse compared with the expected

trend. Although, the data of these two participants is

not clear, all other participants show the expected

relationship between the mean values of BR and

MWL.

3.2 Dynamic Changes of MWL

Our second investigation focused on how BR and

changing MWL due to task progress (e.g., task

completion) are associated. For this, we chose to

analyze two distinct time points. We start with

classification events, during which participants

assigned an ID to targets by pressing a button on the

cockpit interface.

Figure 8 and Figure 9 show a comparison of

different blink rates for each condition in both tasks.

“Average” represents the mean overall BR, which

was already discussed in the previous section. As a

relevant event, we chose the moment of classifying a

target as hostile or not-hostile. Based on this, we

computed blink rate for the following time frames: 5

seconds preceding classification (“Before

Classification”) and 5 seconds post-classification

(“After Classification”).

Figure 8: Comparison of average BR to BR in timeframes

near classification (t±5s) in the air track task T1.

The results in the Low ST condition for Task T1

(see Figure 8) indicate that there is a large difference

in blink rate before and after classification, which

confirms the results from Boehm-Davis et al. (2000).

However, the BR in the other three conditions shows

that this difference decreases as overall task difficulty

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increases. In the High DT condition, there is no

difference in BR before and after classification.

Figure 9: Comparison of average BR to BR in timeframes

near classification (t±5s) in the ground track task T2.

In task T2 (see Figure 9), the difference in time

before and after a classification exists in all

conditions, but it also decreases with increasing task

difficulty and the presence of a secondary task.

Secondly, we conducted an analysis for task

switches in the dual task scenario. The time of a task

switch 𝑡



was defined as the moment the

participants’ saccades between cockpit screen and the

outside view. We used this switch of focus to identify

the current task. Outside view was associated with the

altitude tracking task while focus on the cockpit

screen was linked to the primary task.

Figure 10: Comparison of average BR to BR in timeframes

near task switch (𝑡1𝑠) in all DT conditions.

Figure 10 shows the average blink rate in the

vicinity of a task switch (time frame 1𝑠, compared

to the cumulative average blink rate throughout each

trial. The data underscore that blinks are frequent

during task transitions, supporting the notion that

blinks predominantly occur post-task completion or

during switches.

3.3 Discussion

In the following, we discuss our general findings and

the limitations of our experiment.

3.3.1 General Findings

The results from the experiment confirmed the

findings of the reviewed studies: blink rate decreases

with increasing task load. The strongest effects were

observed when comparing ST and DT scenarios.

Within the DT settings, BR did not reflect the

changing difficulty of the primary task. This suggests

that there is a limit to the sensitivity in cases where

the visual task load is high and participants’ blink rate

does not decrease further. It remains an open question

whether this corresponds to a MWL limit in visual

tasks. Individual BR data also showed that baseline

BR is different among participants. Therefore,

individual calibration should be considered for the

design of robust measurement systems.

BR also showed effects regarding the dynamic

changes of MWL due to task progress. In the low

difficulty ST conditions of T1 and T2, there was a

large difference between average blink rate before

and after a classification event. Similarly, BR was

significantly higher within a short time frame at task

switches, also indicating that spontaneous blinks are

inhibited during task execution and rebound in the

moments between tasks. This relationship could be

utilized in adaptive systems to identify opportune

moments to interrupt a user. The moment a user

finishes a task might be an optimal point to disrupt

them, since they are not committed to another task in

the cockpit yet.

3.3.2 Limitations

Our experimental design did not perfectly align with

the subjective MWL reports. The air track task T1 at

high difficulty was regarded as almost equally

workload-inducing as at the easy difficulty level.

Another confounding factor of the experimental

design was that the DT conditions took significantly

longer than the ST conditions. This could be

problematic as BR increases with time-on-task and

fatigue. In addition, we were not able to design a

completely counterbalanced study with the low

number of participants. Individual BR results showed

that the correlation between BR and difficulty was not

present for some participants. A possible explanation

is the different order of experimental conditions or

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367

individual capabilities. In conclusion, future

experimental design should therefore focus on equal

time-on-task and a higher number of participants as

well as a sufficient training before the experimental

trials.

4 CONCLUSIONS

This study demonstrated that blink rate is indeed a

sensitive measure for MWL and should be considered

as a reliable measure in visual task settings. Using BR

has the great advantage, that blinks can be robustly

detected with low-tech equipment, presenting a good

trade-off between effort and sensitivity for MWL

estimation in real-world applications. Apart from

mean MWL measurement, our results also indicated

that the moments of blinking are not necessarily

random but rather indicate task progress, which could

be valuable for the application in adaptive systems.

Future research should focus on integrating BR

estimation into an adaptive policy by evaluating

strategies that act upon both MWL estimation and

dynamic changes of user BR. For this, the main

challenge is two-fold: First, we must show that the

BR measurement is robust enough to allow for a

reliable classification of MWL across a broad

spectrum of situations and users. Second, we must

evaluate, if adapting a system based on this measure

is useful to the user.

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