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Patterns of Ruliness in HCI

Bibliographical data, abstract, contents links and bibliography

In: Beale, R. and Finlay, J. (eds), Neural Networks and Pattern Recognition in Human-Computer Interaction, ch 15. Ellis Horwood, Chichester, England, 1992.

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To model users, or learn about them, we need to have good representations of situations and actions from a human, cognitive point of view, which involves the difficult but important question of what information people are using. Here, a method of using machine learning to evaluate and compare different representations is described, with examples of the results from applying this method to human control data from a simulation game. This method was used to show the significance of contextuality in the analysis of the human data, which opens up great possibilities for further work based around the idea of contexts. If problems can be overcome, concerning uncaptured data and analogue variables, one can look forward to potential applications in interface design and redesign, and in the study of human learning of cognitive skills.

Links to sections of the paper


A fundamental problem in complex HCI




Experiments on ruliness and representation


Outline of analytical methods



Contextuality in human control

Outlook for further developments

General problems

Possible applications



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