Defining and identifying attention capture deceptive designs in digital interfaces

Abstract

Many tech companies exploit psychological vulnerabilities to design digital interfaces that maximize the frequency and duration of user visits. Consequently, users often report feeling dissatisfied with time spent on such services. Prior work has developed typologies of damaging design patterns (or dark patterns) that contribute to financial and privacy harms, which has helped designers to resist these patterns and policymakers to regulate them. However, we are missing a collection of similar problematic patterns that lead to attentional harms. To close this gap, we conducted a systematic literature review for what we call ‘attention capture damaging patterns’ (ACDPs). We analyzed 43 papers to identify their characteristics, the psychological vulnerabilities they exploit, and their impact on digital wellbeing. We propose a definition of ACDPs and identify eleven common types, from Time Fog to Infinite Scroll. Our typology offers technologists and policymakers a common reference to advocate, design, and regulate against attentional harms.

Conclusion

As tech companies design their digital services to maximize time spent and daily visits, there is a need to better understand the design patterns that “steal” attention and harm digital wellbeing. To pursue this goal, we presented the results of a systematic literature review that provided a comprehensive overview of attention capture damaging patterns (ACDPs). Our typology of 11 ADCPs complements existing collections of damaging patterns that have been proposed in the domains of finance and privacy and is a call for designers and regulators to seriously consider attentional harms and the design patterns that promote them. We hope that the definition, criteria, and typology of ADCPs in this work serves as a common reference for technologists and policymakers aiming to align technology design with digital wellbeing.

Content

Abstract

User dissatisfaction is increasing as many companies exploit psychological vulnerabilities to maximize visit frequency and time spent.
Existing research on ‘damaging design patterns’ or dark patterns mainly focuses on financial harm or privacy harm.
This study focuses on attentional harms. This is called attention capture damaging patterns (ACDP).
By analyzing 43 papers, we identified the characteristics of ACDP and classified 11 commonly used types.

1. Introduction

Due to ‘dark patterns’, many people feel that they use digital devices for too long or are using them compulsively. Some complain of addiction.
In order to avoid using ‘dark’ in a negative sense, terms such as deceptive or damaging were suggested as alternatives. In this paper, ‘damaging’ is adopted.
Although there have been many studies over the past 10 years, research on attentional harms is relatively lacking. Of Brignull’s 12 original patterns, only “Disguised Ads” is related to attention.
Attention capture damaging patterns (ACDP) were defined through a systematic literature review (Systematic literature review) and a related website was created: attentioncapture.com/
- The name of the podcast hosted by Tristan Harris with Aza Raskin is “Your Undivided Attention” —ak

2. Methods

A literature review was conducted according to PRISMA guideline.

PRISMA?
- “PRISMA is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses” —doi.org/10.1371/journal.pmed.1000100
- Preferred Reporting Items for Systematic reviews and Meta-Analyses
- One of several improvements proposed since QUOROM (Quality of Reporting of Meta-analysis) in 1999.
- Consists of 27 checklist items and a four-stage flowchart. Methods and procedures to improve the quality of literature studies and meta-analyses, including reducing bias and increasing Reproducibility
Analyzed 43 out of 1334 retrieved documents

3. Systematic Literature Review of Attention Capture Deceptive Designs

3.1. Definition and Criteria

Definition of ACDP:

A recurring pattern in digital interfaces that a designer uses to exploit psychological vulnerabilities and capture attention, often leading the user to lose track of their goals, lose their sense of time and control, and later feel regret. Slot machines? —ak

Criteria:

C1. Exploit Psychological Vulnerabilities
C2. Automate the User Experience:
- Automated UX design that induces meaningless normative dissociation (everyday, rather than pathological, dissociation: daydreaming, immersion, being absorbed in a film, etc.).
- What is automated UX? Skipping the user’s autonomous decision-making process. Example: An infinite supply of content, bypassing the decision-making process of “let’s look at the next page.”
- Aza Raskin’s infinite scroll is also the result of Jef Raskin’s research. Jef Raskin argues that since human attention is limited, the user’s attention should be kept as much as possible directed to the task itself rather than the interface, and only then can one fall into a state of immersion. —ak
C3. Lose Track of Goals for Use: Causes people to forget their original purpose and do something else.
C4. Lost Sense of Time and Control: Similar to C2.
C5. Sense of Regret: Unlike flow experiences, people often feel regret after experiencing everyday dissociation. A typical symptom of addiction. —ak

3.2. A Typology of Attention Capture Damaging Patterns

Typology? Similar in meaning to taxonomy. The authors chose it because 1) they wanted to emphasize “ideal types,” and 2) unlike taxonomy, it does not require strictly exclusive membership.

Three important decisions:

Academic name vs. Everyday Name: They chose everyday names in the hope of creating a ‘living’ language, in keeping with Christopher Alexander’s aims.
Which patterns to include: They chose to focus on patterns that produce ‘outcomes’ that are associated with attentional harm. Example: “Countdown timer” is a distraction, but its purpose is financial harm, so not included.
How much to consider context: The same UI may or may not be harmful depending on the context. Therefore, they decided to include context (what situations are most likely to lead to attentional harm) when describing the pattern. CA’s pattern language is also in the context-problem-solution format. Because no problem or solution exists without context. —ak

Typology:

P1. Infinite Scroll: As the user scrolls down a page, more content automatically and continuously loads at the bottom.
P2. Casino Pull-to-refresh: When the user swipes down on their smartphone, there is an animated reload of the page that may or may not reveal new appealing content.
P3. Never-ending Autoplay: A new video is automatically played when the current one finishes. There is never a point for the user to stop and reflect, and the option to turn of autoplay is hidden or non-existent.
P4. Guilty Pleasure Recommendations: Personalized suggestions that prey on individual consumer frailty to target user’s guilty pleasures and increase use time.
- A type of recommendation that presents content that provides a somewhat enjoyable experience, but leaves you feeling regretful after the experience. Particularly dangerous for users with poor impulse control. YouTube viral video recommendations, etc.
- Because it learns based on what users click, it becomes increasingly better at capturing users. “Never-ending Autoplay” is not much different: it too learns from when users stop scrolling, when they scroll again, and so on. —ak
P5. Disguised Ads and Recommendations: Advertisements and recommendations, e.g., posts and sponsored pages, that are disguised as normal content into social networks’ newsfeeds.
P6. Recapture Notifications: Notifications that are deliberately sent to recapture users’ attention and have them start a new usage session, e.g., notifications with recommended content or notifications about content the user has never interacted with.
P7. Playing by Appointment: Users are forced to use a digital service at specific times as defined by the service, otherwise the user may lose points and achievements.
P8. Grinding: Users are forced to repeat the same process several times to unlock an achievement, e.g., a new level in a video game or a badge on a social network.
P9. Attentional Roach Motel: Registering to and accessing attention-capture digital services is easy, while operations like logout or canceling an account are painfully difficult.
P10. Time Fog: A pattern through which designers reduce users’ awareness of time spent, e.g., by hiding the smartphone’s clock.
P11. Fake Social Notifications: The platform sends messages pretending to be another user or social notifications about some content the user has never interacted with.

4. Discussion

4.1. Damaging Design Patterns: A New Name for Dark Patterns

In an effort to use Inclusive language, ‘deceptive’ patterns and ‘damaging patterns’ were proposed instead of ‘dark’ patterns.
Following Caroline Sinders’ suggestion, the authors adopt damaging patterns as an alternative.
It has been pointed out that the ‘dark’ in dark patterns is ambiguous, but the authors think that this is actually a strength. They prefer ‘damaging’ for the same advantage.

4.2. Deception and Seduction

Two categories of ACDP:

Deception
- Example: Disguised Ads and Recommendations, Fake Social Notifications
- Often mentioned as a subject of regulation.
Seduction
- Example: Guilty Pleasure Recommendations
- Not well covered in academia. According to Mathur et al, ‘seduction’ is included in only one of the 19 definitions.
- It is not easy to distinguish how much temptation is ethically appropriate. A/B testing is sometimes suggested, but in the case of ACDP, it is not easy. why? This is because it is difficult to know whether an increase in time spent means an increase in harm. Qualitative evaluation must be conducted together. A typical problem with engagement metrics. It is generally difficult to distinguish positive from negative engagement. —ak

4.3. ACDPs Across Domains and Interfaces

Most existing studies focus on games and social media. This is because there is great concern about the dangers of games and social media. In the authors’ opinion, ACDP is already widely used in other fields.
Most existing studies are focused on GUI. However, ACDP can be fully used in other fields such as VUI.

4.4. Implications for Designers

Assess whether an existing design causes attentional harm
Propose alternative designs that respect users’ attention
Creating tools for digital self-control. Example: Apple’s Screen Time and Focus modes —ak

4.5. Limitations and Future Work

As this is a field that changes so rapidly, new papers other than those included in this study will be added quickly.
Keywords for search were selected subjectively. However, since all keywords and selection process have been disclosed (PRISMA), evaluation/reproducibility is possible.
The literature is limited to Computer science (HCI, etc.). It would be good to expand to other fields such as law or social science.

5. Conclusions

ACDP was defined, standards were created, and 11 types that met the standards were classified.
The authors hope the definition, criteria, and types will be useful to technologists and policymakers.