=Paper=
{{Paper
|id=Vol-3866/paper3
|storemode=property
|title=UX Design: the Impacts on Physiological Responses
|pdfUrl=https://ceur-ws.org/Vol-3866/short3.pdf
|volume=Vol-3866
|authors=Nataša Miletić,Matjaž Kljun,Klen Čopič Pucihar
|dblpUrl=https://dblp.org/rec/conf/hci-si/MileticKP24
}}
==UX Design: the Impacts on Physiological Responses==
https://ceur-ws.org/Vol-3866/short3.pdf
UX Design: the Impacts on Physiological Responses
Nataša Miletić1 , dr. Matjaž Kljun2 and dr. Klen Čopič Pucihar2
1
University of Primorska, The Faculty of Mathematics, Natural Sciences and Information Technologies, Glagoljaška ulica
8, 6000 Koper
Abstract
Dark patterns are deceptive design techniques used to trick and manipulate users into taking actions
they did not initially intend, often exploiting cognitive biases and obscuring true choices to benefit the
designer or service provider. The study described is an initial investigation into impacts of dark patterns
on physiological responses during interactive tasks. We used a combination of questionnaires and
biometric data to asses user frustration, engagement, and emotional responses to various dark patterns.
We measured anger, disgust, joy, and engagement through facial expression analysis as well ad galvanic
skin response (GSR). The research seeks to contribute to further understanding of the implications of
dark patterns on users’ physiological responses.
Keywords
dark patterns, physiological responses, UX design
1. Introduction
1.1. Problem Statement
Conventional web design and development practices often incorporate user experience (UX)
techniques aimed at enhancing usability and satisfaction. However, some techniques, known
as dark patterns, intentionally manipulate users into taking actions they might not otherwise
take [? ]. Dark patterns can include misleading information, forced continuity, and hidden costs,
among others. While experienced users may recognize and avoid these tactics, novice users
are more susceptible to them, which can lead to frustration, mistrust, and ethical concerns [?
]. Understanding how dark patterns affect users, such as stress levels and eye movement, can
provide insights into the immediate impact of these practices on user engagement and well
being. Recent research highlighted the need for more user-centered design approaches that
prioritize user welfare over manipulative tactics. As such, there is a need to better understand
the impact of dark patterns, combining both subjective experiences and objective physiological
data.
1.2. Research Questions and Objectives
In this study, we tried to answer the following research questions (RQ):
HCI SI 2024: Human-Computer Interaction Slovenia 2024, November 8th, 2024, Ljubljana, Slovenia
Envelope-Open 89191146@student.upr.si (N. Miletić); matjaz.kljun@upr.si (dr. M. Kljun); klen.copic@famnit.upr.si
(dr. K. Č. Pucihar)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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http://ceur-ws.org
ISSN 1613-0073
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RQ1 Will the presence of dark patterns in UX design have an impact on physiological responses?
RQ2 Will the presence of different types of dark patterns have a different influence on different
users?
To this end we have run two user studies. In Study A participants completed a questionnaire
about their experiences with and perceptions of dark patterns, specifically which ones they find
most annoying. In Study B participants navigated a different web pages designed to feature a
variety of dark patterns. During Study B we captured eye-tracking, GSR and heart rate, and
participants had to fill in a couple of questionnaires.
2. Study A
2.1. Method
To investigate user perceptions and annoyance levels associated with various dark patterns, a
structured questionnaire was developed and made available online. The questionnaire included
11 distinct types of dark patterns selected based on its common occurrence in digital interfaces
and in the academic literature [? ]. Dark patterns included in the study are:
• Disguised Ads: Making advertisements look like part of the page content.
• Social Pyramid: Convincing users to share contacts that will be used for marketing
purposes.
• Scaremongering or Toying with emotion: Making users believe they are in danger.
• Deceptive Wording: Using complex wording to obscure the final action as a subset of
the pattern called hidden information or aesthetic manipulation.
• Sneak into Basket: Convincing users to add more products to their basket to increase
spending.
• Forced Action or Pre-selection: Offering pre-selected expensive or firm friendly options
first.
• Price Comparison Prevention: Combining prices in a complex manner to prevent easy
comparison of products.
• Bait and Switch: Offering an apparent bargain with the intention of substituting inferior
or more expensive goods.
• Confirmshaming or “FOMO” (Fear of Missing Out): Influencing users’ decisions by
triggering uncomfortable emotions about missing out.
• Hidden Costs: Not showing the full price upfront.
• Privacy Zuckering: Tricking users into sharing more information about themselves
than they would otherwise.
The study involved 39 participants that were regular users of digital interfaces, encompassing a
wide range of ages, professions, and levels of technical proficiency recruited through social media
invitations. Participants were presented with a detailed description and a visual representation
of each dark pattern as seen in ??. The visual aids were designed to mimic real-life scenarios in
Figure 1: ’Bait and Switch’ visual representation
which these dark patterns commonly appear, thereby providing participants with a realistic
context for their ratings.
Each participant was asked to rate their level of annoyance for each dark pattern on a scale
from 1 to 10, where 1 indicated “not annoying” and 10 indicated “very annoying”. All questions
were present on one page so participants could change their answers anytime before submitting
the form. They were informed beforehand about the nature of the questions and the purpose of
their answers. Additionally, they were provided with contact information in case they were
interested in the final results of the study.
2.2. Results
Participants in the study faced no time constraints and were able to complete the questionnaire at
their own pace. The responses were based on their prior interactions with various dark patterns
encountered during their web usage. The results reflect a range of experiences. The descriptive
statistics for the ratings of the 11 dark patterns are presented in ??. Privacy Zuckering received
the highest average annoyance rating (𝑥 ̄ = 8.92, SD = 1.88), indicating a strong consensus
among participants regarding its irritation. Similarly, Hidden Costs was rated highly annoying
(𝑥 ̄ = 8.85, SD = 1.93). In contrast Sneak into Basket (𝑥 ̄ = 6.62, SD = 2.60) and Confirmshaming or
FOMO (𝑥 ̄ = 6.72, SD = 2.74) were rated lower on average, suggesting they are perceived as less
annoying or not encountered often. However, the higher standard deviations for these patterns
indicate a wider range of opinions, making them less universally disliked.
Table 1
Descriptive statistics of dark patterns from Study A
Dark Pattern Mean Median Standard Deviation Minimum Maximum
Disguised Ads 6.92 7.00 2.64 1 10
Social Proof Spoofing 8.18 9.00 2.37 1 10
Scaremongering 8.28 10.00 2.74 1 10
Deceptive Wording 8.31 9.00 1.95 3 10
Sneak into Basket 6.62 7.00 2.60 1 10
Forced Action 6.77 7.00 2.69 1 10
Price Comparison Prevention 6.97 7.00 2.73 1 10
Bait and Switch 7.15 7.00 2.68 1 10
Confirmshaming or FOMO 6.72 7.00 2.74 1 10
Hidden Costs 8.85 10.00 1.93 2 10
Privacy Zuckering 8.92 10.00 1.88 3 10
3. Study B
3.1. Method
In this study we conducted a controlled experiment to measure physiological responses of
participants exposed to various dark patterns. Users had to complete 5 tasks on different
web pages. After completing a predefined task on each web page, participant answered the
NASA Task Load Index (NASA-TLX)1 , which measures the perceived workload in order to
assess a task performance. The instructions given for each task were intentionally minimal to
replicate a realistic user experience. No additional information or hints were provided, ensuring
that participants relied solely on their skills to navigate, search, compare products through
potentially misleading or irrelevant data, provided they remained on the same website. This
allowed us to evaluate participant responses to realistic examples of dark patterns.
This study involved nine participants, all students from different fields of study (computer
science, biopsychology, management) and aged between 21 and 24 years (𝑥 ̄ = 22.88, SD = 1.09).
These participants were regular computer users, ensuring that the sample was representative
of typical student user. Participants were fully informed about studies’ objectives, provided
with consent forms, and assured of their right to withdraw at any time without repercussions.
Participants were briefed on the general purpose of the study, though specific details about the
dark patterns or expected outcomes were not disclosed to prevent any bias in their interactions.
Contact information was provided for participants interested in the final results.
3.2. Tasks
The study consisted of five distinct tasks, each needed to be completed on an existing web
page. We used existing web pages to simulate real-life scenarios, ensuring that the participants’
reactions would be genuine and reflective of their typical user behavior. Some of the web pages
were also hosting illegitimate content but were selected due to the high number of dark patterns
used.
1
NASA-TLX https://en.wikipedia.org/wiki/NASA-TLX
• Task A: Participants were required to navigate APK Mirror2 , website hosting downloads
of various software Android application packages (APKs). Participants were instructed to
locate and download a specific version of the YouTube application (19.16.39). This task
assessed their ability to discern legitimate links from disguised ads and irrelevant results.
• Task B: Participants were required to navigate to DaddyLive3 , a website streaming various
sports events and TV channels. The specific objective was to find and stream a particular
channel, Arena Sport Serbia, and ensure the video was fullscreened and unmuted. This
task focused on dealing with a stream of pop-up windows.
• Task C: The task required participants to navigate the Amazon UK4 ,website to find
and identify the cheapest physical copy of the book “Harry Potter and the Order of
the Phoenix” (either paperback or hardcover) in any language. This task tested their
proficiency in using e-commerce filters and search functions among misleading listings.
• Task D: The task involved navigating The Pirate Bay5 torrent site, finding and down-
loading a torrent file for participants’ favorite video game. This task aimed to assess the
ability to identify the correct download link among various misleading elements and
gather data on user reactions to site responsiveness and redirect behaviors.
• Task E: The fifth task involved navigating the MovieWatcher6 website, where participants
were asked to locate and play any of the movies from “The Lord of the Rings” trilogy.
This task was designed to evaluate their persistence and time to abandonment when faced
with often unresponsive and at times non-functional website.
3.3. Measured Parameters
Several physiological parameters were measured to assess the participants’ responses besides
the NASA-TLX perceived workload questionnaire. We used the iMotions software to capture
the following data:
• Eye Tracking: Tobii Pro Spectrum screen was used to capture eye tracking data including
pupil dilation.
• Heartbeat and Galvanic Skin Response: Shimer3 GSR+ sensor on participant’s wrist
captured the heartbeat and skin conductivity as indicators of physiological stress.
• Facial Emotion Analysis: High-resolution camera recorded participants’ facial expressions
with Affectiva AFFDEX [? ] embedded in iMotions software to detect emotional positive
and negative responses.
It needs to be noted that we did not compare the group exposed to dark patterns with a
group not exposed to them and so we do not use comparative statistics. For facial expression
we aggregated the data for negative and positive emotions as in [? ]. From the eye tracking
data we focused in particular on Pupil Dilation [? ]. Pupil dilation refers to the enlargement
2
APK Mirror https://www.apkmirror.com/
3
DaddyLive https://daddylive.watch/
4
Amazon UK https://www.amazon.co.uk/
5
The Pirate Bay https://thepiratebay.org/
6
MovieWatcher https://moviewatcher-to.lol/
of the pupils and can provide insights into attention, interest, emotion, mental workload, and
arousal. For NASA-TLX we used the Wilcoxon signed-rank [? ] test to compare subjective
workload scores between different tasks.
3.4. Results
In Task A (APK Mirror) participants often clicked on misleading ads disguised as download
buttons, leading to frustration and increased time spent on the task. The cognitive load was
higher (see ??), possibly due to constant re-evaluation of links, making this task one of the more
mentally demanding. In Task B (DaddyLive) the pop-up-heavy nature of the website introduced
forced action patterns, where users had to close numerous pop-ups before accessing the desired
stream. This task caused significant annoyance and distraction, especially when participants
struggled to return to the main content. Users showed heightened levels of heartbeat (see ??)
due to the persistence of pop-ups, but once they managed to complete the task, the frustration
diminished quickly.
Task C (Amazon UK) focused on price comparison and search filters. Participants faced
obfuscation through misleading listings and non-relevant search results. However, the familiar
interface of Amazon helped mitigate extreme frustration, and participants managed to complete
the task with moderate cognitive load. The emotional responses here were intense with positive
emotions prevailing. Some participants reported being overwhelmed by the sheer number of
listings.
Task D (Pirate Bay Torrent Search) made participants face false links and numerous redirects,
that caused significant frustration and most negative responses across tasks. Users struggled to
find the correct download link amidst misleading ads, leading to heightened stress levels. This
task was particularly demanding due to the site’s aggressive dark patterns, including constant
redirects and fake download buttons, which resulted in a longer task completion time.
Task E (MovieWatcher Website): This task proved to be the most frustrating due to completely
unresponsive and non-functional elements. The MovieWatcher website was riddled with dead
ends and forced continuity, as users were bombarded with ads and reloading pages that prevented
them from completing the task. Most participants abandoned the task, showcasing its high
emotional toll.
Overall, Task E was reportedly very frustrating due to severe usability issues and dark patterns
that rendered the website practically unusable. Task D and B followed closely, with deceptive
elements frustrating users to a high degree. Task B, though disruptive, was manageable once
users navigated past the pop-ups, while Task C posed moderate challenges with less emotional
strain. Task A, though complex, did not elicit the same level of frustration as tasks that involved
more aggressive dark patterns like those in Task D and Task E.
3.4.1. NASA-TLX
Task E (MovieWatcher) was consistently perceived as more demanding than Tasks A (APKMir-
ror), and Task C (Amazon), which could suggest that Task E involves higher cognitive or physical
demands. This may be attributed to Task E’s complexity or the specific skills required, leading
to increased mental or physical strain compared to the other tasks. Task B and Task E have
Table 2
Mean (Standard Deviation) for each task from Study B
Task Negative Emotions in % (SD) Positive Emotions in % (SD) Heart Rate Pupil Dilation
A 27.21 (10.04) 10.90 (4.88) 97.81 (7.55) 2.96 (0.41)
B 24.67 (8.07) 17.62 (4.90) 72.39 (12.91) 3.52 (1.12)
C 41.65 (12.51) 69.84 (11.98) 88.57 (10.65) 2.89 (1.46)
D 59.42 (9.42) 12.64 (8.10) 89.65 (12.49) 2.24 (0.43)
E 28.10 (10.60) 14.05 (6.27) 101.99 (34.89) 4.19 (1.96)
similar perceived workloads. This could be relevant for acknowledging that tasks are designed
to be of similar difficulty or require similar skill sets.
Frustration ratings show an upward trend across tasks, reaching higher levels in later tasks.
As tasks become more difficult, participants experience greater frustration. This suggests
that the increasing complexity or challenges of the tasks contribute to a heightened sense of
frustration.
Temporal demand ratings show a rising trend from Task A through Task E, indicating an
increasing sense of urgency or pressure to complete tasks. This reflects that participants feel
more pressured to manage their time effectively as they progress through the tasks.
The overall performance ratings across tasks tend to cluster around the 50 percentage mark.
This consistent midpoint suggests that participants generally felt they performed at an average
level, regardless of the task’s complexity or difficulty.
Table 3
Mean ratings for each measure across tasks from study B
Measure Task A Task B Task C Task D Task E
Mental Demand 26.67 47.78 29.44 37.78 43.33
Physical Demand 11.11 18.89 20.56 27.78 32.78
Temporal Demand 30.56 46.67 36.11 51.11 63.33
Performance 48.89 45.00 57.22 51.11 47.78
Effort 25.00 55.00 35.00 52.22 62.22
Frustration 32.22 68.89 32.78 60.00 81.11
Overall 28.24 53.89 35.28 51.28 54.89
4. Discussion
RQ1 Will the presence of dark patterns in UX design have an impact on physiological responses,
such as stress levels and pulse?
During the experiments, we observed significant fluctuations in GSR conductance levels,
particularly when users encountered dark patterns. These patterns often involve misleading or
obstructive elements in the user interface designed to manipulate user behavior, such as repeti-
tive pop-up ads or misleading navigation elements. Participants exhibited notable increases
in GSR conductance when they struggled to complete tasks on the first attempt. For instance,
users who had to repeatedly close pop-up ads experienced elevated stress levels. The need
to frequently interact with these deceptive elements caused frustration, which was reflected
in their physiological responses. Waiting for pages to load or when faced with delays in the
interface was also accompanied by increases in GSR conductance, suggesting that waiting and
delays contributed to heightened stress and decreased user satisfaction.
Participants were observed to smile even when they were visibly frustrated, such as when
they could not locate a desired button or feature. This reaction underscores a complex interplay
between emotional responses and physiological arousal. The incongruity between facial ex-
pressions and physiological data highlights how users’ external behavior may not always align
with their internal stress levels.
We noticed that participants often fixed their gaze on the upper right corner of the window,
anticipating that a pop-up ad would have an ’X’ button for closing. This fixation point was a
behavioral indicator of their expectation and frustration with the dark patterns in the interface.
RQ2 Will the presence of different types of dark patterns have a different influence on different
users?
The findings indicate that the impact of dark patterns on users varies significantly depending
on the nature of the task and users’ experience with the web and existing dark patterns. Tasks
involving complex interactions or repetitive attempts revealed more pronounced physiological
and emotional responses. A notable observation was that many participants smiled while
expressing frustration when repeatedly encountering various pop-up ads. This reaction could be
attributed to the lab setting, where participants might have been more conscious of their behavior
or felt a heightened sense of self-awareness compared to their typical at-home environment.
These variations highlight the need for a nuanced understanding of how different dark
patterns affect user behavior, underscoring the importance of continued research into their
effects on user trust and stress.
5. Limitations and Future Work
We are not comparing the use of dark patterns to any baseline where there are no dark patterns.
So we report only on the responses for particular tasks completed on particular web sites.
Further investigation should provide a baseline and compare it to the design with dark patterns.
Due to time constraints, our study was conducted with a relatively small sample size. All
participants were constant computer users, which may have introduced a bias in their responses,
as their habitual interaction with technology could influence their reactions to dark patterns
differently compared to occasional users. Additionally, the controlled lab environment may not
fully capture the complexities of users’ interactions with dark patterns in their natural settings.
6. Conclusion
Given the ubiquity of dark patterns on the web [? ] we run two user studies exploring attitudes
towards dark patterns and their physiological responses when encountering them. Study
A included a questionnaire of 11 dark patterns and asked participants how annoying was a
particular pattern on a scale from 1 (not at all) to 10 (very annoying). In study B we asked
participants to complete 5 tasks, each on a different website featuring a variety of dark patterns
Through selected tasks, we observe distinct variations in users responses to different types of
dark patterns, underscoring the complex interplay between design elements and user behavior.
The results highlight that misleading design practices can exaggerate user frustration and stress.
The task/specific results highlighted how users frustration and stress were particularly
pronounced in scenarios involving repeated obstacles or misleading interface elements, such as
those encountered during searches or interactions with unresponsive websites. These finding
emphasize that dark patterns significantly disrupt user experience, and increase cognitive load.
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