Adaptive Interaction: A Utility Maximization Approach to Understanding Human Interaction with Technology

Stephen J. Payne · Andrew Howes

Hun 2022 · Springer Nature

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This lecture describes a theoretical framework for the behavioural sciences that holds high promise for theory-driven research and design in Human-Computer Interaction. The framework is designed to tackle the adaptive, ecological, and bounded nature of human behaviour. It is designed to help scientists and practitioners reason about why people choose to behave as they do and to explain which strategies people choose in response to utility, ecology, and cognitive information processing mechanisms. A key idea is that people choose strategies so as to maximise utility given constraints. The framework is illustrated with a number of examples including pointing, multitasking, skim-reading, online purchasing, Signal Detection Theory and diagnosis, and the influence of reputation on purchasing decisions. Importantly, these examples span from perceptual/motor coordination, through cognition to social interaction. Finally, the lecture discusses the challenging idea that people seek to find optimal strategies and also discusses the implications for behavioral investigation in HCI.

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Stephen Payne is professor of human-centric systems in the Department of Computer Science at the University of Bath. Before moving to Bath, Payne was a Professor of Psychology in Cardiff University and (briefly) a Professor in Manchester Business School. Previously he worked at IBM T.J. Watson Research Center. Payne has worked on cognitive approaches to Human-Computer Interaction since his PhD on Task-Action Grammars, awarded in 1985.Andrew Howes is professor of Human-Computer Interaction in the School of Computer Science at the University of Birmingham, UK. He has previously held posts as Professor of Cognitive Systems at Manchester Business School, University of Manchester, and before that as Senior Lecturer in the School of Psychology at the University of Cardiff. Howes is interested in computational theories of the strategies that people choose given psychological constraints.