Journal
PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22)
Volume -, Issue -, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3491102.3517739
Keywords
Cognitive modeling; computational rationality; interaction; reinforcement learning; adaptation; individual differences
Categories
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Funding
- Finnish Center for AI
- Academy of Finland
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This paper examines how people interact with computers and proposes using computational rationality as a theoretical framework to find answers. Research suggests that users' interaction behavior can be explained and predicted through computational models.
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.
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