EPOS BrainAdapt™
A Dual Task Study
The effect of noise attenuation on listening effort, efficiency,
and reaction time, while performing a dual task

Scientific Whitepaper2023

Scientific Whitepaper

Scientific Whitepaper

Introduction
The clinical study presented
in this scientific whitepaper
evaluated the potential
benefits of noise attenuation
in audio devices while people
performed a dual task
consisting of understanding
speech while reacting as fast
as possible to a visual task.

2

People often perform more than one task at a time, without even realizing it. Many
situations in everyday life involve understanding speech and, at the same time, solving
a task visually. For example, in a professional context, one may need to listen to acolleague speaking while identifying information on a screen. Similarly at home, one
may want to listen to someone talking while doing other tasks at the same time. Despite
being common situations, performing more than one task at a time is often challenging,and one may need to prioritize one task over the other(s) to maintain a certain level of
performance. This is because performing one or more tasks requires using cognitive
capacity. Since cognitive capacity is a limited commodity, allocating additional
resources to one task will reduce the resources available to perform the other tasks.
Though we may not realize it, understanding speech when there is poor audio and noise
requires additional cognitive resources. This, in turn, may limit the amount of cognitive
resources that can be used to perform other tasks.
The study presented in this scientific whitepaper evaluated the potential benefits ofnoise attenuation in audio devices while people performed a dual task consisting ofunderstanding speech while reacting as fast as possible to a visual task.
This type of in-depth scientific research has been a key element in the developmentof audio solutions built on EPOS BrainAdapt™ technology, which supports the brain’s
natural way of processing sound (Christiansen and Ng, 2022). The custom-madecomponents, acoustics and sophisticated algorithms that go into EPOS solutions
ensure optimized cognitive performance, even in challenging sound environments.

3

Scientific Whitepaper

Scientific Whitepaper

EPOS product innovation is driven by psychoacoustic research
In the EPOS product innovation process, we start by defining the sound profile that
supports the best cognitive performance in specific use cases. To ensure that EPOS
solutions provide the most balanced, clear, and natural soundscape possible, we draw on
more than a decade of research into how the brain perceives sound and how the brain’s
cognitive load can be lowered in various sound situations.

Scientific BackgroundAs we move into product development, we implement fine-tuned acoustics andsophisticated algorithms into our solutions. We look at the product holistically, integratetechnical features and the right set of custom-made components to provide the best
conditions for your brain.

In a dual task paradigm, two tasks (a primary and a secondary one) are performed alone
and simultaneously. Each individual has a certain amount of cognitive resources available.
If the total resources required to perform both tasks exceed the maximum resources
available, the participant will need to prioritize the primary task and a decrease inperformance will be observed on the secondary task.

Finally, before launch, we conduct psychoacoustic research in collaboration with the
Demant Group to validate that EPOS users obtain the maximum cognitive benefit
intended. The following pages present the results of a recent study that will help EPOS
continue deliver superior audio and video solutions with audio designed for the brain.
The technology tested in this study is featured in the following EPOS products:• IMPACT 1060 ANC• IMPACT 1060T ANC• IMPACT 1061 ANC• IMPACT 1061T ANC• ADAPT 600 Series• GTW 270 Hybrid• H3PRO Hybrid• H6PRO Closed

This decrease in performance on the dual task relative to the task performed in isolation
is used to index the cost of performing two tasks simultaneously. This difference inperformance can be considered an indirect estimate of listening effort (Gagné etal., 2017).
Using a dual task paradigm, consisting of a speech recognition task and a visual reaction
time task, Sarampalis et al. (2009) demonstrated that listeners with normal hearing
reacted faster in the visual reaction time task when noise attenuation was activated,compared to when it was not. This finding suggests that noise attenuation frees up
cognitive resources that would otherwise have been needed for recognizing speech,
to be used for other tasks (i.e., to react faster in the visual task). In other words, noise
attenuation decreases listening effort (Ng et al., 2013, 2015; Sarampalis et al., 2009).
In the study presented here, we expand on the findings by Sarampalis et al. (2009),
by combining the dual task paradigm with subjective ratings of listening effort andpupillometry. Pupillary responses measured during a speech recognition task have beenshown in previous studies to be a physiological indicator of listening effort (Ohlenforst
et al., 2018; Wendt et al., 2017). In this study, pupil dilation is measured during the dual
task; hence, it reflects the cognitive resources overall allocated to perform a speech
recognition task as well as a visual reaction time task.

45

Scientific Whitepaper

Scientific Whitepaper

Aim of the Study

Materials and Methods

The aim of the study was to evaluate the potential benefits of noise attenuation on
reaction time (RT), accuracy, speech-in-noise recognition, listening effort, and overall
cognitive resource allocation during a dual task, consisting of a primary auditory task
and a secondary visual task (Bianchi et al., 2023).

Participants
Twenty-six participants (15 males; 11 females) with self-reported normal hearing were
included in this study. The mean age of the participants was 35 years (range: 23-51 years,
SD = 9.2 years).
Assessment Tools
Three measures were included in the current study: the dual task, the subjective listening
effort rating and pupillometry.
1. Dual Taska. Primary speech-in-noise recognition task
Target sentences (Danish Hearing in Noise Test, HINT; Nielsen & Dau, 2011) were
presented in a background noise composed of four-talker (4T) babble. The
HINT sentences have a duration of approximately 1.5 seconds (s) and consist ofrecordings of a male speaker, while the 4T babble consists of recordings of two
female and two male speakers reading different newspaper passages. The 4T
babble started 3 s prior to a sentence and continued for 3 s after the sentence.
The task was to verbally repeat the target sentences as accurately as possible
after the offset of the 4T babble.

6

7

Scientific Whitepaper

Scientific Whitepaperb. Secondary visual reaction time task
Concomitantly with target sentence onset, a digit between 1 and 8 appeared on ascreen, either on the right- or left-hand side of the screen. The task was to press
the arrow key on a keyboard as fast as possible towards the digit if it was an evennumber, and away from the digit if it was an odd number. Hence, the correct arrow
key depended on both the digit appearing on the screen (i.e., even vs. odd) andthe positioning of the digit on the screen (i.e., right vs left). The participant had topress the arrow key as fast as possible and before the end of 4T babble. Figure 1
shows an illustration of one trial of the dual task.

Sentence
presentation& visual task

Sentence
reconstruction

2. Subjective Listening Effort Rating
The subjective listening effort rating consisted of three questions that theparticipants filled out after each condition of the dual task (see Figure 3).
The three questions were related to invested listening effort, self-perceived
performance in speech recognition, and tendency to give-up understanding:2)

How many of the words do you think you understood correctly?

3)

How often did you give up understanding the sentence?

The answers are marked at any location of the following scale:

0%
3s

~1.5s

3s

20%

30%

40%

50%

60%

70%

80%

90%

100%

3. Pupillometry
Time (s)Figure 1: Illustration of the structure and timing of one dual task trial.

10%

Time (s)7~4.5s: Number on the screen and button press

8

How much effort did it require from you to hear the sentences?

Sentence
repetition

Auditory task

Visual task

1)

Pupil dilation was measured while the participants performed the dual task, aswell as during the visual task performed in isolation pre- and post-test (see test
procedure and Figure 3). Pupillary responses have been found to be a sensitive
measure to capture changes in listening effort measured during speech-in-noise
paradigms (Ohlenforst et al., 2018; Wendt et al., 2017), as well as a physiologicalmarker for visual target detection (Privitera et al., 2010).

9

Scientific Whitepaper

Scientific Whitepaper

Experiment Set-up

Test Conditions

The participants were seated in a sound treated room and were instructed to fixate theirgaze on a focus point in front of them. The HINT sentences and the 4T babble wereplayed through a pair of headphones. The digits were presented on a screen mounted ona wall in front of the participants. The keyboard was placed in front of the participant. The
pupillary responses were recorded using the iView X RED (Senso-Motoric Instruments)
eye-tracker, which continuously tracks both eye and head movement via an infrared
camera. Figure 2 shows the experimental set-up.

The HINT sentences were presented at two speech levels, 60 dB SPL and 64 dB SPL.
Two noise conditions were tested: unprocessed 4T babble presented at 70 dB SPL(No Attenuation condition), resulting in signal-to-noise ratios (SNRs) of -10 dB and -6 dB,
and 4T babble pre-processed with passive damping (Attenuation condition).

Keyboard and screen for visual task

Pupil camera

Figure 2: Illustration of the experimental set-up.

10

11

Scientific Whitepaper

Scientific Whitepaper

Test Procedure

Statistical Analysis

The test session started with training on the visual task only, which consisted of 25 trials.
This was followed by a training on the dual task with unprocessed noise, which consistedof two lists of 25 HINT sentences (i.e., 50 trials in total).

The models Linear mixed effects models were used for the statistical analyses. The
models contained speech level and noise attenuation condition as fixed effects and test
participant (N = 26) as the random effect. One model was built for each of the outcomesof interest: speech recognition performance, accuracy, reaction time, efficiency, as well
as for each question of the subjective listening effort rating. Pupillary responses wereanalyzed using temporal Bayesian analysis of pupil responses (Hershman et al. 2022).
Note that the pupil data were analyzed for 25 participants as a technical error occurred
during the measurement of the last participant and no pupil data was saved.

After training, 25 trials of the secondary visual task were performed as a baseline
condition (visual pre-test). This was followed by the dual task, which consisted of 50
trials for each of the four tested conditions (two speech levels and two noise attenuation
conditions). The noise attenuation conditions were presented in a blocked design: alltrials with noise attenuation in one block and all trials without noise attenuation noise inanother block. Half of the participants started with the block with noise attenuation, andhalf of the participants started with the block without noise attenuation. The order of the
speech levels was randomized within each block. After each condition of the dual task,
the participants filled out the subjective rating of effort. After the dual task, another 25
trials of the secondary visual task were performed (visual post-test). Figure 3 illustrates
the test procedure.

Dual-task(60 dB SPL)No Attenuation
50 trials

Visualtraining
25 trials

Dual-task
training
50 trials

Dual-task(64 dB SPL)No Attenuation
50 trials

Dual-task(60 dB SPL)
Attenuation
50 trials

Dual-task(64 dB SPL)
Attenuation
50 trials

Visualpre-test

Visualpost-test

25 trials

25 trials

Dual-task(60 dB SPL)
Attenuation
50 trials

Dual-task(64 dB SPL)
Attenuation
50 trials

Rating of effort

Dual-task(60 dB SPL)No Attenuation

Rating of effort

50 trials

Dual-task(64 dB SPL)No Attenuation

Rating of effort

50 trials

Rating of effort

Time

Speech levels (60 and 64 dB SPL) randomized

Figure 3: Illustration of the test procedure.

12

13

Scientific Whitepaper

Scientific Whitepaper

Results
Speech-in-noise Recognition
Table 1 and Figure 4 show the mean speech-in-noise recognition performance for eachspeech level and noise attenuation condition. In the No Attenuation condition, the mean
speech recognition performance was of 51.6% when the speech level was 60 dB SPL(SNR = -10 dB) and 84.4 % when the speech level was 64 dB SPL (SNR = -6 dB). The
percentage refers to how many words the participants could repeat correctly out of
the all the sentences played for that condition. In the Attenuation condition, speech
recognition performance increased significantly (p < 0.0001) up to 99.8% and 99.9%,
for speech levels of 60 and 64 dB SPL, respectively. In other words, applying noise
attenuation led to a significant improvement in speech recognition, on average, up to
48.2 percentage points (99.8% - 51.6%).

Speech level = 60 dB SPL

Speech level = 64 dB SPL

No Attenuation

51.6% [ SD = ±10.8 ] 84.4% [ SD = ±8.5 ]Attenuation

99.8% [ SD = ±0.1 ]Speech understanding [% words correct]***
Dual task Attenuation

***
100

Dual task No Attenuation

***

80

60

40

20

0

64 dB
SPL

60 dB
SPL

64 dB
SPL

60 dB
SPL

Figure 4: Mean speech-in-noise recognition performance for each test condition of the dual task. Error bars indicate the standard
error of the mean. The asterisks indicate a significant difference between conditions (*** p < 0.0001).

Reaction Time
Figure 5 shows the mean reaction time for each test condition of the dual task, as wellas during the visual task performed in isolation pre- and post-test as baseline condition.
When performing the visual task alone, participants pressed the arrow key, on average,
710 ms after the visual stimulus presentation, with no significant difference between
pre- and post-test conditions. When adding the speech recognition task to the visualtask, reaction time was significantly higher (p = 0.0001) for the No Attenuation condition(on average, 900 ms and 1000 ms, at 64 and 60 dB SPL, respectively).
***

99.9% [ SD = ±0.6 ]1.5Dual task Attenuation***

Table 1: Mean speech-in-noise recognition performance in percentage at each speech level and in each noise attenuation condition.
The standard deviations are indicated in brackets.

Reaction time [s]*Dual task No Attenuation*

Visual1

0.5

0VisualpreVisual
post

64 dB
SPL

60 dB
SPL

64 dB
SPL

60 dB
SPL

Figure 5: Mean reaction time for each test condition of the dual task, as well as during the visual task performed in isolation
pre- and post-test as baseline condition. Error bars indicate the standard error of the mean. The asterisks indicate a significant
difference between conditions (*** p <= 0.0001; * p < 0.05).

14

15

Scientific Whitepaper

Scientific Whitepaper

This finding indicates that additional cognitive resources needed to be diverted from thereaction time task (leading to a longer reaction time) to understand the sentence for the
No Attenuation condition. In the Attenuation condition, reaction time was, on average,
680 ms and 700 ms, at 64 and 60 dB SPL, respectively, which was significantly lower(p < 0.0001) relative to the reaction time in the No Attenuation condition, while it did not
significantly differ to the reaction time in the visual only task.
In other words, when the background noise was attenuated, there were enough cognitive
resources available to perform both tasks at a very high level of performance (speech
recognition almost at 100% and similar reaction time as in the visual only task). Hence,in the Attenuation condition, there was no “cost” of performing dual task, because
performance did not decrease relative to the visual task performed in isolation.
In conclusion, applying noise attenuation led to significantly faster reaction time (i.e., thearrow key was pressed significantly faster), on average, up to 23.9% (300 ms) relative to
No Attenuation.

Subjective listening effort rating
The outcomes of the subjective listening effort rating are presented in figure 6. The
outcomes show that invested listening effort (on average, 17.6% and 19.0% in the
Attenuation condition, 71.4% and 86.5% in the No Attenuation condition, at speech levelsof 64 and 60 dB SPL, respectively) and tendency to give up (on average, 1.4% and 1.1% in
the Attenuation condition, 23.7% and 54.5% in the No Attenuation condition, at speech
levels of 64 and 60 dB SPL, respectively) were significantly lower with attenuation than
without (p < 0.0001). Hence, applying noise attenuation led to a significant decreasein perceived listening effort, on average, up to 67.5 percentage points relative to No
Attenuation (86.5% - 19.0%).
Additionally, the perceived performance was significantly higher (p < 0.0001) in the
Attenuation condition (on average, 97.1% and 97.8%, at speech levels of 64 and 60 dB
SPL, respectively) than for the No Attenuation condition (on average, 65.6% and 39.3%,
at speech levels of 64 and 60 dB SPL, respectively).

Accuracy
Accuracy on the visual task (i.e., pressing the correct arrow key) was very high for all
conditions, ranging from 97.2% when the visual task was performed in isolation to 98.4%
when the visual task was performed within the dual task (both in the Attenuation and No
Attenuation conditions).

Efficiency
Reaction time and accuracy were also combined in one unique measure of efficiency,
which was calculated as the inverse of the Inverse Efficiency Score (IES; see Bruyer andBrysbaert, 2011). For each participant and condition, the mean button press accuracy
was divided by the mean reaction time of the correct responses. On average, noise
attenuation increased efficiency up to 40.6% relative to the No Attenuation condition(p < 0.0001 ).

Efficiency =

16

1
IES

=

Button press accuracy

Perceived performance

Tendency to give-up

***

***

***

100

Percentage [%]Since there was no difference in accuracy between Attenuation and No Attenuation
conditions, one can conclude that applying noise attenuation led to faster reaction times
without any compromise on accuracy.

Invested effort

***

***

***

80

60

40

20

0

64 dB
SPL

60 dB
SPL

64 dB
SPL

60 dB
SPL

64 dB
SPL

Attenuation

60 dB
SPL

64 dB
SPL

60 dB
SPL

64 dB
SPL

60 dB
SPL

64 dB
SPL

60 dB
SPL

No Attenuation

Figure 6: Mean subjective effort ratings for each question in each test condition. Error bars indicate the standard error of themean. The asterisks indicate a significant difference between conditions (*** p < 0.0001).

Reaction time of correct responses

17

Scientific Whitepaper

Scientific Whitepaper

Pupillometry
Figure 7 shows the mean pupil curves for the Attenuation (mint curve) and No
Attenuation (petrol curve) conditions of the dual task, as well as for the visual task
performed in isolation (gray curve). The horizontal bars indicate what conditions are
significantly different for each time sample.
The curves for the dual task show a larger pupil dilation than for the visual task within the
dual task window, indicating the extra cost of adding a listening task to the visual task.
Specifically, for the No Attenuation condition, the extra effort for listening to the sentence
in noise is allocated immediately because it is important to understand the first part of thesentence. The visual task is then performed afterwards (average button press between
0.9 and 1 s). However, for the Attenuation condition, the background noise is low, and thesentence can be easily understood. Hence, the participants can perform the visual task
immediately (average button press around 0.7 s) and process the sentence afterwards.
Baseline

Dual task

Finally, a large difference in pupil dilation can be observed in the “Noise window”, i.e.,
when the participants need to keep the sentence in working memory, rehearse it, andpossibly reconstruct those words that were not heard, in order to repeat as many words
as possible after 4.5 s. The pupil dilation in this time window is an indicator of how many
cognitive resources are needed to prepare the response. The results show a rapid releaseof effort for the Attenuation condition and for the Visual condition (i.e., the pupil quickly
drops below zero), suggesting that the sentence was fully understood and there was noneed to allocate resources to reconstruct it. However, a significantly larger pupil dilation
was observed for the No Attenuation condition, indicating that extra cognitive resources
needed to be allocated for sentence reconstruction when the noise was not attenuated.
Hence, when the background noise was high (No Attenuation condition), not only extra
cognitive resources needed to be allocated immediately for understanding the speech
signal, thereby delaying reaction time to the visual task, but also a more sustained use ofcognitive resources was needed afterwards to reconstruct the sentence (see illustrationin Figure 8).

Sentence
reconstruction

Sentence
repetition

Allocation of extra resources
to reconstruct the sentence:“long-term” effect of noise~52%-84%
correct

Sentence presentation& visual task

Noise

0.1

No
Attenuation

Pupil Dilation [mm]0.05Visual task
is postponed
when there ishigh noise

0

-0.05

Attenuation

-0.1

~100%
correct

Visual task
is prioritizedwhen there islittle noise

-0.15
-1

0

Visual1

Dual-Task Attenuation

2

3

4

Time (s)Dual-Task No Attenuation

Figure 7: Mean pupil curves as a function of time. Time zero indicates the onset of the visual task as well as the start of thesentence (“Dual task window” for the Attenuation and No Attenuation conditions). After 1.5 s the sentence stops while thenoise continues for 3 additional seconds (until 4.5 s). Between 1.5 s and 4.5 s (“Noise window”) the participant needs toprepare for repeating the sentence back after 4.5 s. The Attenuation and No Attenuation curves are averaged across the two
speech levels, and the Visual curve is averaged across pre- and post-conditions. The horizontal bars indicate what conditions
are significantly different for each time sample.

18

0

0.7

1

1.5

Resources mostly allocated to listen/process/reconstruct the sentence3

Resources mostly allocatedto solve visual task

4.5

Time (s)Button press

Figure 8: Illustration of cognitive resource allocation during the dual task.

19

Scientific Whitepaper

Conclusions

Scientific Whitepaper

The Benefits of EPOS Noise
Attenuation

The results of this study indicate that noise attenuation provides several benefits forparticipants with normal hearing when performing a dual task is required.
When noise was attenuated during a dual task, speech-in-noise recognition improved
up to 48.2 percentage points and reaction time on the visual task improved up to 23.9%(i.e., the key was pressed 300 ms faster) without compromising accuracy, leading to anincrease in efficiency up to 40.6%.
Additionally, applying noise attenuation led to a significant decrease in perceived listening
effort, on average, up to 67.5 percentage points relative to No Attenuation, as reflected by
the subjective listening effort ratings.
The pupil dilation results showed that when noise was not attenuated, extra cognitive
resources needed to be allocated immediately for understanding speech, thereby
delaying reaction time to the visual task. Attenuating the noise led to a significantly lower
pupil dilation while listening to the beginning of the sentence (i.e., significantly reduced
listening effort). Freeing up cognitive resources allowed the participants to quickly andaccurately perform the visual reaction time task. Pupillometry results also indicated thatafter listening to the sentence, there was a rapid release of effort in the Attenuation
condition, while pupil dilation was significantly higher for the No Attenuation condition,
indicating that there was a longer and more sustained allocation of cognitive resources toreconstruct the sentence and prepare a response with no attenuation.

48%

67%

People can understand speech better when
the noise is attenuated.

Users spend less effort listening, and therefore, havemore available cognitive capacity for other tasks.

40%

23%

Noise attenuation increases efficiency by improving
reaction time without loss of precision.

With less energy spent listening, users can act faster.

better recognition of speech-in-noise*

reduction of listening effort**

These findings demonstrate that noise attenuation reduces listening effort when
individuals with normal hearing are required to understand speech in background noise.
This, in turn, frees up cognitive resources that would have been needed for understanding
speech in background noise to be used for other tasks (e.g., the visual reaction time task).
This study is just one example of decades of psychoacoustic research conducted by the
Demant Group, of which EPOS is proud to be a part. The learnings collected in this study
and others, including a recent study of the benefits of noise dampening, are applied inongoing development of EPOS BrainAdapt™ solutions, which are designed to support
the brain’s natural way of processing sound.

increase in efficiency***

improved reaction time****To learn more about EPOS solutions and the science behind them,visit eposaudio.com/brainadapt

20*48% better recognition of speech-in-noise as indicated by percentage of correctly repeated words.
**Up to 67% reduction of listening effort, on average, as reflected by the subjective listening effort ratings.
***Up to 40% increase in efficiency, as reflected by the ratio between accuracy and reaction time for the correct responses.
****Up to 300 ms faster reaction to a visual-cognitive task without loss of precision.

Scientific Whitepaper

Scientific Whitepaper

Implications for gamers
Gaming requires people to perform several tasks simultaneously, such as communicatingwith team members while solving challenging visual and motoric tasks, as accurately andquickly as possible. Communicating with a team member in noisy environments increases
listening effort, which may result in fewer available cognitive resources to be allocated tothe actions required in the game.
This can slow down and impair the gaming performance. This effect is illustrated in
section A of Figure 9 (adapted from Lunner et al., 2009). An effective noise attenuation
system is able to reduce the cognitive resources used for listening – hence, allowing thegamer to both communicate with team members and gaming without experiencing a dual
task cost.
Implications for business professionals
In calls and hybrid meetings, business professionals often need to listen to a colleague
speaking while reviewing and processing information being presented on a screen.
Noisy environments, such as open offices, make it harder for the brain to perform
simultaneous audio and visual tasks. This reduces call efficiency and productivity as
people have a lower cognitive capacity to both communicate and solve tasks at thesame time.

Cognitive resources allocated to:

Listening

Other tasks

Person in quiet

A
Person in noise

B

Person in noisewith noise attenuation

Figure 9: Section A in the above chart shows the tradeoff between resources allocated for listening and for other tasks, dependingon what type of sound environment the person is in. In a very noisy environment, a person spends more listening effort, resulting inless cognitive resources that can be used on other tasks. Section B in the chart shows that noise attenuation decreases listening
effort - hence, freeing up cognitive resources for other tasks.

Advanced noise attenuation technology effectively blocks out disturbing noise, allowing
people to listen, process information, and efficiently perform tasks, enabling professionals
to make the most of their workday.

22

23

Scientific Whitepaper

References• Bruyer, R., & Brysbaert, M. (2011). Combining speed and accuracy in cognitive psychology:
Is the inverse efficiency score (IES) a better dependent variable than the mean reaction time (RT)and the percentage of errors (PE)?. Psychologica Belgica, 51(1), 5-13.
• Bianchi, F., Bjergskov Larsen, E., Jonsson, S., Svensson, C., Micula, A., Christiansen, T., Ng, E. H. N.
Effect of noise attenuation on reaction time and listening effort during a dual task. Speech inNoise Workshop, January 2023, Split, Croatia.
• Christiansen, T. & Ng, E. H. N. (2022). The effects of noise attenuation on listening effort andarousal. EPOS Scientific Whitepaper.
• Gagne, J. P., Besser, J., & Lemke, U. (2017). Behavioral assessment of listening effort using adual-task paradigm: A review. Trends in hearing, 21, 2331216516687287.
• Hershman, R., Milshtein, D., & Henik, A. (2023). The contribution of temporal analysis of pupillometry• measurements to cognitive research. Psychological Research, 87(1), 28-42.
• Lunner, T., Rudner, M., & Rönnberg, J. (2009). Cognition and hearing aids. Scandinavian
Journal of Psychology, 50(5), 395–403. https://doi.org/10.1111/j.1467-9450.2009.00742.x• Ng, E. H. N., Rudner, M., Lunner, T., Pedersen, M. S., & Rönnberg, J. (2013). Effects of noise andworking memory capacity on memory processing of speech for hearing-aid users. International
Journal of Audiology, 52(7), 433–441. https://doi.org/10.3109/14992027.2013.776181• Nielsen, J. B., & Dau, T. (2011). The Danish hearing in noise test. International Journal of Audiology,
50(3), 202–208. https://doi.org/10.3109/14992027.2010.524254• Ohlenforst, B., Wendt, D., Kramer, S. E., Naylor, G., Zekveld, A. A., & Lunner, T. (2018). Impact of
SNR, masker type and noise reduction processing on sentence recognition performance andlistening effort as indicated by the pupil dilation response. Hearing Research, 365, 90–99.
https://doi.org/10.1016/j.heares.2018.05.003• Privitera, C. M., Renninger, L. W., Carney, T., Klein, S., & Aguilar, M. (2010). Pupil dilation during visualtarget detection. Journal of Vision, 10(10), 3–3. https://doi.org/10.1167/10.10.3• Sarampalis, A., Kalluri, S., Edwards, B., & Hafter, E. (2009). Objective Measures of Listening Effort:
Effects of Background Noise and Noise Reduction. Journal of Speech, Language, and Hearing
Research, 52(5), 1230–1240. https://doi.org/10.1044/1092-4388(2009/08-0111)• Wendt, D., Hietkamp, R. K., & Lunner, T. (2017). Impact of Noise and Noise Reduction on Processing
Effort: A Pupillometry Study. 38(6).
• Zénon, A., Sidibé, M., & Olivier, E. (2014). Pupil size variations correlate with physical effort
perception. Frontiers in Behavioral Neuroscience, 8.

Editors of the issue:
Federica Bianchi, Senior Researcher, Centre for Applied Audiology Research, Oticon A/STorben Christiansen, Director of Technology, EPOS

24