Aalto computer scientists in CHI 2024

Authors

Franziska Babel, Linda Miller, Sam Thellman, Robin Welsch, Philipp Hock, and Tom Ziemke 

Abstract

Increasing encounters between people and autonomous service robots may lead to conflicts due to mismatches between human expectations and robot behaviour. This interactive online study (N = 335) investigated human-robot interactions at an elevator, focusing on the effect of communication and behavioural expectations on participants' acceptance and compliance. Participants evaluated a humanoid delivery robot primed as either submissive or assertive. The robot either matched or violated these expectations by using a command or appeal to ask for priority and then entering either first or waiting for the next ride. The results highlight that robots are less accepted if they violate expectations by entering first or using a command. Interactions were more effective if participants expected an assertive robot which then asked politely for priority and entered first. The findings emphasize the importance of power expectations in human-robot conflicts for the robot's evaluation and effectiveness in everyday situations.

Authors

Agnes M. Kloft, Robin Welsch, Thomas Kosch, and Steeven Villa 

Abstract

Heightened AI expectations facilitate performance in human-AI interactions through placebo effects. While lowering expectations to control for placebo effects is advisable, overly negative expectations could induce nocebo effects. In a letter discrimination task, we informed participants that an AI would either increase or decrease their performance by adapting the interface, when in reality, no AI was present in any condition. A Bayesian analysis showed that participants had high expectations and performed descriptively better irrespective of the AI description when a sham-AI was present. Using cognitive modeling, we could trace this advantage back to participants gathering more information. A replication study verified that negative AI descriptions do not alter expectations, suggesting that performance expectations with AI are biased and robust to negative verbal descriptions. We discuss the impact of user expectations on AI interactions and evaluation.

Received an Honorable Mention

Authors

Solip Park, Perttu Hämäläinen, and Annakaisa Kultima

Abstract

This paper reports the research method of the “Game Expats Story (GES)” project that used qualitative longitudinal research (“QLR”) incorporated with art-based research (“ABR”) in the context of game research. To facilitate greater participant engagement and a higher retention rate of longitudinal participants, we created comic artwork simultaneously while researching the case of migrant/expatriate game developers (“game expats”) in Finland 2020-2023 in two phases: (i) art creation as part of the qualitative data analysis to supplement the researcher’s inductive abstraction of the patterns, and (ii) artwork as a communication and recall tool when periodically engaging with the informants over the multi-year project span. Our findings suggest that the method of QLR-ABR helps game research as it positively influences the researcher’s abstractions of longitudinal data and participants’ continuous engagement with a high retention rate of 89%. We conclude that incorporating artistic methods provides new opportunities for ethnographic research on game development.

Received a Best Paper Award

Authors

Jaakko Väkevä, Elisa D. Mekler, and Janne Lindqvist

Abstract

Videogames can transform the perspectives and attitudes of players. Prior discussion on this transformative potential has typically been limited to non-entertainment videogames with explicit transformational goals. However, recreational gaming appears to hold considerable potential for igniting deeply personal experiences of profound transformation in players. Towards understanding this phenomenon, we conducted an explorative autoethnographic study. For this, the first author played five narrative-driven videogames while collecting self-observational and self-reflective data of his experience during and outside gameplay. Our findings offer intimate insights into the trajectory and emotional qualities of personally meaningful and transformative videogame experiences. For example, we found that gameplay experiences that were initially perceived as bewildering or disorienting could evolve into more harmonious experiences laden with personal meaning. This shift in experience developed through different forms of subsequent re-engagement with initially discrepant game encounters.

Authors

Don Samitha, Regina Bernhaupt, Armağan Karahanoğlu, Carine Lallemand, Daniel Harrison, Dennis Reidsma, Maria F. Montoya 
Dees Postma, Elise van den Hoven, Florian Daiber, Lars Elbæk, Lisa Anneke Burr, Rakesh Patibanda, Michael D Jones, Paolo Buono, Perttu Hämäläinen, Xipei Ren, Robby van Delden, Vincent van Rheden, Fabio Zambetta, and Florian ‘Floyd’ Mueller

Abstract

The field of Sports Human-Computer Interaction (SportsHCI) investigates interaction design to support a physically active human being. Despite growing interest and dissemination of SportsHCI literature over the past years, many publications still focus on solving specific problems in a given sport. We believe in the benefit of generating fundamental knowledge for SportsHCI more broadly to advance the field as a whole. To achieve this, we aim to identify the grand challenges in SportsHCI, which can help researchers and practitioners in developing a future research agenda. Hence, this paper presents a set of grand challenges identified in a five-day workshop with 22 experts who have previously researched, designed, and deployed SportsHCI systems. Addressing these challenges will drive transformative advancements in SportsHCI, fostering better athlete performance, athlete-coach relationships, spectator engagement, but also immersive experiences for recreational sports or exercise motivation, and ultimately, improve human well-being.

Authors

Yue Jiang, Changkong Zhou, Vikas Garg, and Antti Oulasvirta

Abstract

Present-day graphical user interfaces (GUIs) exhibit diverse arrangements of text, graphics, and interactive elements such as buttons and menus, but representations of GUIs have not kept up. They do not encapsulate both semantic and visuo-spatial relationships among elements. To seize machine learning's potential for GUIs more efficiently, Graph4GUI exploits graph neural networks to capture individual elements' properties and their semantic-visuo-spatial constraints in a layout. The learned representation demonstrated its effectiveness in multiple tasks, especially generating designs in a challenging GUI autocompletion task, which involved predicting the positions of remaining unplaced elements in a partially completed GUI. The new model's suggestions showed alignment and visual appeal superior to the baseline method and received higher subjective ratings for preference. Furthermore, we demonstrate the practical benefits and efficiency advantages designers perceive when utilizing our model as an autocompletion plug-in.

Authors

Beatriz Mello, Robin Welsch, Marissa Christien Verbokkem, Pascal Knierim, and Martin Johannes Dechant

Abstract

For individuals with Social Anxiety (SA), interacting with others can be a challenging experience, a concern that extends into the virtual world. While technology has made significant strides in creating more realistic virtual human agents (VHA), the interplay of gaze and interpersonal distance when interacting with VHAs is often neglected. This paper investigates the effect of dynamic and static gaze animations in VHAs on interpersonal distance and their correlation to SA. A Bayesian analysis shows that static-centered and dynamic-centering gaze led participants to stand closer to VHAs than static-averted and dynamic-averting gaze, respectively. In the static gaze conditions, this pattern was found to be reversed in SA: participants with SA traits kept larger distances for static-centered gaze than for averted gaze VHAs. These findings update theory, elucidate how nuanced interactions with VHAs must be designed, and offer renewed guidelines for pleasant VHA interaction.

Authors

Sebastian Berns, Vanessa Volz, Sam Snodgrass, and Christian Guckelsberger

Abstract

Similarity estimation is essential for many game AI applications, from procedural generation of distinct assets to automated exploration with game-playing agents. While similarity metrics often substitute human evaluation, their alignment with our judgement is unclear. Consequently, the result of their application can fail human expectations, leading to e.g. unappreciated content or unbelievable agent behaviour. We alleviate this gap through a multi-factorial study of two tile-based games in two representations, where participants (N=456) judged the similarity of level triplets. Based on this data, we construct domain-specific perceptual spaces, encoding similarity-relevant attributes. We compare 12 metrics to these spaces and evaluate their approximation quality through several quantitative lenses. Moreover, we conduct a qualitative labelling study to identify the features underlying the human similarity judgement in this popular genre. Our findings inform the selection of existing metrics, and highlight requirements for the design of new similarity metrics benefiting game development and research.

Authors

Amel Bourdoucen, and Janne Lindqvist

Abstract

Users need to configure default apps when they first start using their devices. The privacy configurations of these apps do not always match what users think they have initially enabled. We first explored the privacy configurations of eight default apps Safari, Siri, Family Sharing, iMessage, FaceTime, Location Services, Find My and Touch ID. We discovered serious issues with the documentation of these apps. Based on this, we studied users' experiences with an interview study (N=15). We show that: the instructions of setting privacy configurations of default apps are vague and lack required steps; users were unable to disable default apps from accessing their personal information; users assumed they were being tracked by some default apps; default apps may cause tensions in family relationships because of information sharing. Our results illuminate on the privacy and security implications of configuring the privacy of default apps and how users understand the mobile ecosystem.

Authors

Markus Laattala, Roosa Piitulainen, Nadia M. Ady, Monica Tamariz, and Perttu Hämäläinen

Abstract

Dance games are one of the most popular game genres in Virtual Reality (VR), and active dance communities have emerged on social VR platforms such as VR Chat. However, effective instruction of dancing in VR or through other computerized means remains an unsolved human-computer interaction problem. Existing approaches either only instruct movements partially, abstracting away nuances, or require learning and memorizing symbolic notation. In contrast, we investigate how realistic, full-body movements designed by a professional choreographer can be instructed on the fly, without prior learning or memorization. Towards this end, we describe the design and evaluation of WAVE, a novel anticipatory movement visualization technique where the user joins a group of dancers performing the choreography with different time offsets, similar to spectators making waves in sports events. In our user study (N=36), the participants more accurately followed a choreography using WAVE, compared to following a single model dancer.