Evaluating In-Car Tasks’ Distraction Effects with Drive-In LabExisting measurements of driver distraction in laboratory settings lack construct and ecological validity, and therefore, cannot provide reliable estimates of in-car tasks’ distraction effects. In this paper, we operationalize driver distraction in a novel way with the help of Drive-In Lab, where any passenger car can be connected to a driving simulation. The operationalization is based on drivers’ headway maintenance during in-car tasks as compared to baseline driving, while accommodating situational and driver-specific variables, such as brake response times. Realistic visual looming cues enable evaluation of distraction effects on cognitive processes crucial for safe driving. Validation studies with two 2024 car models indicate that the method can reliably differentiate distraction effects between cars, in-car tasks, and drivers as large, medium, small, or no effect on crash potential. The method supports design of in-car interactions by providing valid means to reveal the worst and best practices in in-car user interface design.2025TKTuomo Kujala et al.University of Jyväskylä, Cognitive ScienceAutomated Driving Interface & Takeover DesignIn-Vehicle Haptic, Audio & Multimodal FeedbackCHI
Generative AI and News Consumption: Design Fictions and Critical AnalysisThe emergence of Generative AI features in news applications may radically change news consumption and challenge journalistic practices. To explore the future potentials and risks of this understudied area, we created six design fictions depicting scenarios such as virtual companions delivering news summaries to the user, AI providing context to news topics, and content being transformed into other formats on demand. The fictions, discussed with a multi-disciplinary group of experts, enabled a critical examination of the diverse ethical, societal, and journalistic implications of AI shaping this everyday activity. The discussions raised several concerns, suggesting that such consumer-oriented AI applications can clash with journalistic values and processes. These include fears that neither consumers nor AI could successfully balance engagement, objectivity, and truth, leading to growing detachment from shared understanding. We offer critical insights into the potential long-term effects to guide design efforts in this emerging application area of GenAI.2025JKJoel Kiskola et al.Tampere University, Faculty of Information Technology and CommunicationGenerative AI (Text, Image, Music, Video)Content Moderation & Platform GovernanceDesign FictionCHI
CRTypist: Simulating Touchscreen Typing Behavior via Computational RationalityTouchscreen typing requires coordinating the fingers and visual attention for button-pressing, proofreading, and error correction. Computational models need to account for the associated fast pace, coordination issues, and closed-loop nature of this control problem, which is further complicated by the immense variety of keyboards and users. The paper introduces CRTypist, which generates human-like typing behavior. Its key feature is a reformulation of the supervisory control problem, with the visual attention and motor system being controlled with reference to a working memory representation tracking the text typed thus far. Movement policy is assumed to asymptotically approach optimal performance in line with cognitive and design-related bounds. This flexible model works directly from pixels, without requiring hand-crafted feature engineering for keyboards. It aligns with human data in terms of movements and performance, covers individual differences, and can generalize to diverse keyboard designs. Though limited to skilled typists, the model generates useful estimates of the typing performance achievable under various conditions.2024DSDanqing Shi et al.Aalto UniversityKnowledge Worker Tools & WorkflowsComputational Methods in HCICHI
Supporting Task Switching with Reinforcement LearningAttention management systems aim to mitigate the negative effects of multitasking. However, sophisticated real-time attention management is yet to be developed. We present a novel concept for attention management with reinforcement learning that automatically switches tasks. The system was trained with a user model based on principles of computational rationality. Due to this user model, the system derives a policy that schedules task switches by considering human constraints such as visual limitations and reaction times. We evaluated its capabilities in a challenging dual-task balancing game. Our results confirm our main hypothesis that an attention management system based on reinforcement learning can significantly improve human performance, compared to humans’ self-determined interruption strategy. The system raised the frequency and difficulty of task switches compared to the users while still yielding a lower subjective workload. We conclude by arguing that the concept can be applied to a great variety of multitasking settings.2024ALAlexander Lingler et al.University of Applied Sciences Upper AustriaPrivacy by Design & User ControlNotification & Interruption ManagementCHI
Simulating Emotions With an Integrated Computational Model of Appraisal and Reinforcement LearningPredicting users' emotional states during interaction is a long-standing goal of affective computing. However, traditional methods based on sensory data alone fall short due to the interplay between users' latent cognitive states and emotional responses. To address this, we introduce a computational cognitive model that simulates emotion as a continuous process, rather than a static state, during interactive episodes. This model integrates cognitive-emotional appraisal mechanisms with computational rationality, utilizing value predictions from reinforcement learning. Experiments with human participants demonstrate the model's ability to predict and explain the emergence of emotions such as happiness, boredom, and irritation during interactions. Our approach opens the possibility of designing interactive systems that adapt to users' emotional states, thereby improving user experience and engagement. This work also deepens our understanding of the potential of modeling the relationship between reward processing, reinforcement learning, goal-directed behavior, and appraisal.2024JZJiayi Eurus Zhang et al.University of JyväskyläBrain-Computer Interface (BCI) & NeurofeedbackGenerative AI (Text, Image, Music, Video)Visualization Perception & CognitionCHI
Developing a Conversational Interface for an ACT-based Online Program: Understanding Adolescents’ Expectations of Conversational StyleA preventative approach is crucial for adolescents’ mental well-being, as problems often arise at a young age. Acceptance and Commitment Therapy (ACT) is an evidence-based intervention approach used to enhance psychological flexibility, a central factor in adolescents’ mental well-being. Conversational interfaces are recently being experimented with in mental health promotion. Their conversational style plays a significant role in creating meaningful experiences to achieve positive intervention outcomes. In this study, our objective was to understand adolescents’ expectations of the conversational style of a text-based virtual coach being developed as part of an ACT-based online program to support intervention engagement. We evaluated eight conversation scripts by collecting qualitative and quantitative data through an online survey from over 200 adolescents. Our findings provide insights on preferred conversational interface features regarding conversational style, including language use, artificiality, and empathy in the domain of adolescent mental well-being.2023JPJohanna Peltola et al.Conversational ChatbotsMental Health Apps & Online Support CommunitiesCUI
Computational Rationality as a Theory of InteractionHow do people interact with computers? This fundamental question was asked by Card, Moran, and Newell in 1983 with a proposition to frame it as a question about human cognition -- in other words, as a matter of how information is processed in the mind. Recently, the question has been reframed as one of adaptation: how do people adapt their interaction to the limits imposed by cognition, device design, and environment? This paper synthesizes advances toward an answer within the theoretical framework of computational rationality. The core assumption is that users act in accordance with what is best for them, given the limits imposed by their cognitive architecture and their experience of the task environment. This theory can be expressed in computational models that explain and predict interaction. The paper reviews the theoretical commitments and emerging applications in HCI, and it concludes by outlining a research agenda for future work.2022AOAntti Oulasvirta et al.Aalto UniversityComputational Methods in HCICHI
Modelling Drivers' Adaptation to Assistance SystemsHuman factors research and engineering of advanced driving assistance systems (ADAS) must consider how drivers adapt to their presence. The major obstruction to this at the moment is poor understanding of the details of the adaptive processes that the human cognition undergoes when faced with such changes. This paper presents a simulation model that predicts how drivers adapt to a steering assistance system. Our approach is based on computational rationality, and demonstrates how task interleaving strategies adapt to the task environment and the driver's goals and cognitive limitations. A supervisor controls eye movements between the driving and non-driving tasks, making this choice on the basis of maximising expected joint task utility. The model predicts that with steering assistance, drivers' in car glance durations increase. We also show that this adaptation leads to risky driving in cases where the reliability of the system is compromised.2021JJJussi P. P. Jokinen et al.Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)Eye Tracking & Gaze InteractionAutoUI