arXiv subjects

Rainer Malaka

Publications and source records attributed to Rainer Malaka.

At least 19 recordsLinked to original sources

arXiv2026

Tactile Search: Enhancing Targeting in 3D Space

Visual search is crucial in daily life, from scanning for relevant information to spotting signs of danger. When sensory channels are overloaded or degraded, cognitive tasks can be supported by crossmodal information representations through vibrotactile cues. We introduce Tactile Search, an approach that uses modulation of frequency and amplitude of vibrations to the hands, for guiding attention to the location of objects in 3D space. We evaluated this approach in a competitive VR game where participants searched for targets using both vision and touch. Across two studies -- an in-the-wild demonstration (n=55) and a controlled laboratory experiment (n=28) -- we found that vibrotactile feedback significantly improved performance and increased user confidence. In the combined haptic condition, performance did not differ across target heights. We further analyzed participants' subjective experiences and search strategies highlighting the benefits of the tactile cues. Our findings suggest that Tactile Search can enhance interaction and provide design considerations for integrating haptic search into interactive systems.

cs.HC↗

arXiv2026

Not All Trust is the Same: Effects of Decision Workflow and Explanations in Human-AI Decision Making

A central challenge in AI-assisted decision making is achieving warranted, well-calibrated trust. Both overtrust (accepting incorrect AI recommendations) and undertrust (rejecting correct advice) should be prevented. Prior studies differ in the design of the decision workflow - whether users see the AI suggestion immediately (1-step setup) or have to submit a first decision beforehand (2-step setup) -, and in how trust is measured - through self-reports or as behavioral trust, that is, reliance. We examined the effects and interactions of (a) the type of decision workflow, (b) the presence of explanations, and (c) users' domain knowledge and prior AI experience. We compared reported trust, reliance (agreement rate and switch rate), and overreliance. Results showed no evidence that a 2-step setup reduces overreliance. The decision workflow also did not directly affect self-reported trust, but there was a crossover interaction effect with domain knowledge and explanations, suggesting that the effects of explanations alone may not generalize across workflow setups. Finally, our findings confirm that reported trust and reliance behavior are distinct constructs that should be evaluated separately in AI-assisted decision making.

cs.HC↗

arXiv2025

Breathe with Me: Synchronizing Biosignals for User Embodiment in Robots

Embodiment of users within robotic systems has been explored in human-robot interaction, most often in telepresence and teleoperation. In these applications, synchronized visuomotor feedback can evoke a sense of body ownership and agency, contributing to the experience of embodiment. We extend this work by employing embreathment, the representation of the user's own breath in real time, as a means for enhancing user embodiment experience in robots. In a within-subjects experiment, participants controlled a robotic arm, while its movements were either synchronized or non-synchronized with their own breath. Synchrony was shown to significantly increase body ownership, and was preferred by most participants. We propose the representation of physiological signals as a novel interoceptive pathway for human-robot interaction, and discuss implications for telepresence, prosthetics, collaboration with robots, and shared autonomy.

cs.RO↗

arXiv2025

Can AI Explanations Make You Change Your Mind?

In the context of AI-based decision support systems, explanations can help users to judge when to trust the AI's suggestion, and when to question it. In this way, human oversight can prevent AI errors and biased decision-making. However, this rests on the assumption that users will consider explanations in enough detail to be able to catch such errors. We conducted an online study on trust in explainable DSS, and were surprised to find that in many cases, participants spent little time on the explanation and did not always consider it in detail. We present an exploratory analysis of this data, investigating what factors impact how carefully study participants consider AI explanations, and how this in turn impacts whether they are open to changing their mind based on what the AI suggests.

cs.HC↗

arXiv2025

Finding Uncommon Ground: A Human-Centered Model for Extrospective Explanations

The need for explanations in AI has, by and large, been driven by the desire to increase the transparency of black-box machine learning models. However, such explanations, which focus on the internal mechanisms that lead to a specific output, are often unsuitable for non-experts. To facilitate a human-centered perspective on AI explanations, agents need to focus on individuals and their preferences as well as the context in which the explanations are given. This paper proposes a personalized approach to explanation, where the agent tailors the information provided to the user based on what is most likely pertinent to them. We propose a model of the agent's worldview that also serves as a personal and dynamic memory of its previous interactions with the same user, based on which the artificial agent can estimate what part of its knowledge is most likely new information to the user.

cs.AI↗

arXiv2025

Bot App\'etit! Exploring how Robot Morphology Shapes Perceived Affordances via a Mise en Place Scenario in a VR Kitchen

This study explores which factors of the visual design of a robot may influence how humans would place it in a collaborative cooking scenario and how these features may influence task delegation. Human participants were placed in a Virtual Reality (VR) environment and asked to set up a kitchen for cooking alongside a robot companion while considering the robot's morphology. We collected multimodal data for the arrangements created by the participants, transcripts of their think-aloud as they were performing the task, and transcripts of their answers to structured post-task questionnaires. Based on analyzing this data, we formulate several hypotheses: humans prefer to collaborate with biomorphic robots; human beliefs about the sensory capabilities of robots are less influenced by the morphology of the robot than beliefs about action capabilities; and humans will implement fewer avoidance strategies when sharing space with gracile robots. We intend to verify these hypotheses in follow-up studies.

cs.RO↗

arXiv2025

MetaMorph -- A Metamodelling Approach For Robot Morphology

Robot appearance crucially shapes Human-Robot Interaction (HRI) but is typically described via broad categories like anthropomorphic, zoomorphic, or technical. More precise approaches focus almost exclusively on anthropomorphic features, which fail to classify robots across all types, limiting the ability to draw meaningful connections between robot design and its effect on interaction. In response, we present MetaMorph, a comprehensive framework for classifying robot morphology. Using a metamodeling approach, MetaMorph was synthesized from 222 robots in the IEEE Robots Guide, offering a structured method for comparing visual features. This model allows researchers to assess the visual distances between robot models and explore optimal design traits tailored to different tasks and contexts.

cs.RO↗

arXiv2025

The Wilhelm Tell Dataset of Affordance Demonstrations

Affordances - i.e. possibilities for action that an environment or objects in it provide - are important for robots operating in human environments to perceive. Existing approaches train such capabilities on annotated static images or shapes. This work presents a novel dataset for affordance learning of common household tasks. Unlike previous approaches, our dataset consists of video sequences demonstrating the tasks from first- and third-person perspectives, along with metadata about the affordances that are manifested in the task, and is aimed towards training perception systems to recognize affordance manifestations. The demonstrations were collected from several participants and in total record about seven hours of human activity. The variety of task performances also allows studying preparatory maneuvers that people may perform for a task, such as how they arrange their task space, which is also relevant for collaborative service robots.

cs.RO↗

arXiv2025

A Comparative Study of How People With and Without ADHD Recognise and Avoid Dark Patterns on Social Media

Dark patterns are deceptive strategies that recent work in human-computer interaction (HCI) has captured throughout digital domains, including social networking sites (SNSs). While research has identified difficulties among people to recognise dark patterns effectively, few studies consider vulnerable populations and their experience in this regard, including people with attention deficit hyperactivity disorder (ADHD), who may be especially susceptible to attention-grabbing tricks. Based on an interactive web study with 135 participants, we investigate SNS users' ability to recognise and avoid dark patterns by comparing results from participants with and without ADHD. In line with prior work, we noticed overall low recognition of dark patterns with no significant differences between the two groups. Yet, ADHD individuals were able to avoid specific dark patterns more often. Our results advance previous work by understanding dark patterns in a realistic environment and offer insights into their effect on vulnerable populations.

cs.HC↗

arXiv2025

How Metacognitive Architectures Remember Their Own Thoughts: A Systematic Review

Background: Metacognition has gained significant attention for its potential to enhance autonomy and adaptability of artificial agents but remains a fragmented field: diverse theories, terminologies, and design choices have led to disjointed developments and limited comparability across systems. Existing overviews remain at a conceptual level that is undiscerning to the underlying algorithms, representations, and their respective success. Methods: We address this gap by performing an explorative systematic review. Reports were included if they described techniques enabling Computational Metacognitive Architectures (CMAs) to model, store, remember, and process their episodic metacognitive experiences, one of Flavell's (1979a) three foundational components of metacognition. Searches were conducted in 16 databases, consulted between December 2023 and June 2024. Data were extracted using a 20-item framework considering pertinent aspects. Results: A total of 101 reports on 35 distinct CMAs were included. Our findings show that metacognitive experiences may boost system performance and explainability, e.g., via self-repair. However, lack of standardization and limited evaluations may hinder progress: only 17% of CMAs were quantitatively evaluated regarding this review's focus, and significant terminological inconsistency limits cross-architecture synthesis. Systems also varied widely in memory content, data types, and employed algorithms. Discussion: Limitations include the non-iterative nature of the search query, heterogeneous data availability, and an under-representation of emergent, sub-symbolic CMAs. Future research should focus on standardization and evaluation, e.g., via community-driven challenges, and on transferring promising principles to emergent architectures.

q-bio.NC↗

arXiv2024

Level Up or Game Over: Exploring How Dark Patterns Shape Mobile Games

This study explores the prevalence of dark patterns in mobile games that exploit players through temporal, monetary, social, and psychological means. Recognizing the ethical concerns and potential harm surrounding these manipulative strategies, we analyze user-generated data of 1496 games to identify relationships between the deployment of dark patterns within "dark" and "healthy" games. Our findings reveal that dark patterns are not only widespread in games typically seen as problematic but are also present in games that may be perceived as benign. This research contributes needed quantitative support to the broader understanding of dark patterns in games. With an emphasis on ethical design, our study highlights current problems of revenue models that can be particularly harmful to vulnerable populations. To this end, we discuss the relevance of community-based approaches to surface harmful design and the necessity for collaboration among players/users and practitioners to promote healthier gaming experiences.

cs.HC↗

arXiv2024

Finding a Way Through the Social Media Labyrinth: Guiding Design Through User Expectations

Social networking services (SNS) have become integral to modern life to create and maintain meaningful relationships. Nevertheless, their historic growth of features has led to labyrinthine user interfaces (UIs) that often result in frustration among users - for instance, when trying to control privacy-related settings. This paper aims to mitigate labyrinthine UIs by studying users' expectations (N=21) through an online card sorting exercise based on 58 common SNS UI features, teaching us about their expectations regarding the importance of specific UI features and the frequency with which they use them. Our findings offer a valuable understanding of the relationship between the importance and frequency of UI features and provide design considerations for six identified UI feature groups. Through these findings, we inform the design and development of user-centred alternatives to current SNS interfaces that enable users to successfully navigate SNS and feel in control over their data by meeting their expectations.

cs.HC↗

arXiv2024

Hell is Paved with Good Intentions: The Intricate Relationship Between Cognitive Biases and Dark Patterns

Throughout the past decade, research in HCI has identified numerous instances of dark patterns in digital interfaces. These efforts have led to a well-fostered typology describing harmful strategies users struggle to navigate. However, an in-depth understanding of the underlying mechanisms that deceive, coerce, or manipulate users is missing. We explore the interplay between cognitive biases and dark patterns to address this gap. To that end, we conducted four focus groups with experts (N=15) in psychology and dark pattern scholarship, inquiring how they conceptualise the relation between cognitive biases and dark patterns. Based on our results, we constructed the "Relationship Model of Cognitive Biases and Dark Patterns" which illustrates how cognitive bias and deceptive design patterns relate and identifies opportune moments for ethical reconsideration and user protection mechanisms. Our insights contribute to the current discourse by emphasising ethical design decisions and their implications in the field of HCI.

cs.HC↗

arXiv2024

The Interaction Fidelity Model: A Taxonomy to Distinguish the Aspects of Fidelity in Virtual Reality

Fidelity describes how closely a replication resembles the original. It can be helpful to analyze how faithful interactions in virtual reality (VR) are to a reference interaction. In prior research, fidelity has been restricted to the simulation of reality - also called realism. Our definition includes other reference interactions, such as superpowers or fiction. Interaction fidelity is a multilayered concept. Unfortunately, different aspects of fidelity have either not been distinguished in scientific discourse or referred to with inconsistent terminology. Therefore, we present the Interaction Fidelity Model (IntFi Model). Based on the human-computer interaction loop, it systematically covers all stages of VR interactions. The conceptual model establishes a clear structure and precise definitions of eight distinct components. It was reviewed through interviews with fourteen VR experts. We provide guidelines, diverse examples, and educational material to universally apply the IntFi Model to any VR experience. We identify common patterns and propose foundational research opportunities.

cs.HC↗

arXiv2024

Listening to the Voices: Describing Ethical Caveats of Conversational User Interfaces According to Experts and Frequent Users

Advances in natural language processing and understanding have led to a rapid growth in the popularity of conversational user interfaces (CUIs). While CUIs introduce novel benefits, they also yield risks that may exploit people's trust. Although research looking at unethical design deployed through graphical user interfaces (GUIs) established a thorough understanding of so-called dark patterns, there is a need to continue this discourse within the CUI community to understand potentially problematic interactions. Addressing this gap, we interviewed 27 participants from three cohorts: researchers, practitioners, and frequent users of CUIs. Applying thematic analysis, we construct five themes reflecting each cohort's insights about ethical design challenges and introduce the CUI Expectation Cycle, bridging system capabilities and user expectations while considering each theme's ethical caveats. This research aims to inform future development of CUIs to consider ethical constraints while adopting a human-centred approach.

cs.HC↗

arXiv2023

Defending Against the Dark Arts: Recognising Dark Patterns in Social Media

Interest in unethical user interfaces has grown in HCI over recent years, with researchers identifying malicious design strategies referred to as ''dark patterns''. While such strategies have been described in numerous domains, we lack a thorough understanding of how they operate in social networking services (SNSs). Pivoting towards regulations against such practices, we address this gap by offering novel insights into the types of dark patterns deployed in SNSs and people's ability to recognise them across four widely used mobile SNS applications. Following a cognitive walkthrough, experts (N=6) could identify instances of dark patterns in all four SNSs, including co-occurrences. Based on the results, we designed a novel rating procedure for evaluating the malice of interfaces. Our evaluation shows that regular users (N=193) could differentiate between interfaces featuring dark patterns and those without. Such rating procedures could support policymakers' current moves to regulate deceptive and manipulative designs in online interfaces.

cs.HC↗

arXiv2023

About Engaging and Governing Strategies: A Thematic Analysis of Dark Patterns in Social Networking Services

Research in HCI has shown a growing interest in unethical design practices across numerous domains, often referred to as ``dark patterns''. There is, however, a gap in related literature regarding social networking services (SNSs). In this context, studies emphasise a lack of users' self-determination regarding control over personal data and time spent on SNSs. We collected over 16 hours of screen recordings from Facebook's, Instagram's, TikTok's, and Twitter's mobile applications to understand how dark patterns manifest in these SNSs. For this task, we turned towards HCI experts to mitigate possible difficulties of non-expert participants in recognising dark patterns, as prior studies have noticed. Supported by the recordings, two authors of this paper conducted a thematic analysis based on previously described taxonomies, manually classifying the recorded material while delivering two key findings: We observed which instances occur in SNSs and identified two strategies - engaging and governing - with five dark patterns undiscovered before.

cs.HC↗

arXiv2022

"Seeing the Faces Is So Important" -- Experiences From Online Team Meetings on Commercial Virtual Reality Platforms

During the Covid-19 pandemic, online meetings became common for daily teamwork in the home office. To understand the opportunities and challenges of meeting in virtual reality (VR) compared to video conferences, we conducted the weekly team meetings of our human-computer interaction research lab on five off-the-shelf online meeting platforms over four months. After each of the 12 meetings, we asked the participants (N = 32) to share their experiences, resulting in 200 completed online questionnaires. We evaluated the ratings of the overall meeting experience and conducted an exploratory factor analysis of the quantitative data to compare VR meetings and video calls in terms of meeting involvement and co-presence. In addition, a thematic analysis of the qualitative data revealed genuine insights covering five themes: spatial aspects, meeting atmosphere, expression of emotions, meeting productivity, and user needs. We reflect on our findings gained under authentic working conditions, derive lessons learned for running successful team meetings in VR supporting different kinds of meeting formats, and discuss the team's long-term platform choice.

cs.HC↗