Computational Rationality as a Theory of Interaction
Antti Oulasvirta, Jussi P. P. Jokinen, Andrew Howes
Abstract
How 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? The 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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers19
- Bridging the Gulf of Envisioning: Cognitive Challenges in Prompt Based Interactions with LLMsHariharan Subramonyam, Roy Pea, Christopher Lawrence Pondoc, Maneesh Agrawala et al.CHI 2024 · 137 citations
- Breathing Life Into Biomechanical User ModelsAleksi Ikkala, Florian Fischer, Markus Klar, Miroslav Bachinski et al.UIST 2022 · 33 citations
- "AI enhances our performance, I have no doubt this one will do the same": The Placebo effect is robust to negative descriptions of AIAgnes Mercedes Kloft, Robin Welsch, Thomas Kosch, Steeven VillaCHI 2024 · 32 citations
- UIClip: A Data-driven Model for Assessing User Interface DesignJason Wu, Yi-Hao Peng, Xin Yue Amanda Li, Amanda Swearngin et al.UIST 2024 · 29 citations
- CRTypist: Simulating Touchscreen Typing Behavior via Computational RationalityDanqing Shi, Yujun Zhu, Jussi P. P. Jokinen, Aditya Acharya et al.CHI 2024 · 26 citations
Builds on4
- Touchscreen Typing As Optimal Supervisory ControlJussi Jokinen, Aditya Acharya, Mohammad Uzair, Xinhui Jiang et al.CHI 2021 · 105 citations
- Predicting Mid-Air Interaction Movements and Fatigue Using Deep Reinforcement LearningNoshaba Cheema, Laura A. Frey-Law, Kourosh Naderi, Jaakko Lehtinen et al.CHI 2020 · 66 citations
- An Adaptive Model of Gaze-based SelectionXiuli Chen, Aditya Acharya, Antti Oulasvirta, Andrew HowesCHI 2021 · 38 citations
- Forgetting of Passwords: Ecological Theory and DataXianyi Gao, Yulong Yang, Can Liu, Christos Mitropoulos et al.USENIX Security 2018 · 31 citations
Related papers
- Rediscovering Affordance: A Reinforcement Learning PerspectiveYi-Chi Liao, Kashyap Todi, Aditya Acharya, Antti Keurulainen et al.CHI 2022 · 22 citations
- Interaction Knowledge: Understanding the 'Mechanics' of Digital ToolsMiguel A. Renom, Baptiste Caramiaux, Michel Beaudouin-LafonCHI 2023 · 10 citations
- More than Irrational: Modeling Belief-Biased AgentsYifan Zhu, Sammie Katt, Samuel KaskiAAAI 2026 · 1 citation
- Do People Appropriately Rely on AI-Advice? An Analytical Review of HCI Research on Human-AI Decision-MakingMuhammad Raees, Vassilis-Javed Khan, Ioanna Lykourentzou, Konstantinos PapangelisCHI 2026 · 6 citations
- Simulating Emotions With an Integrated Computational Model of Appraisal and Reinforcement LearningJiayi Eurus Zhang, Bernhard Hilpert, Joost Broekens, Jussi P. P. JokinenCHI 2024 · 8 citations
