Information-Theoretic Analysis of Human Performance for Command Selection
Abstract
Selecting commands is ubiquitous in current GUIs. While a number of studies have focused on improving rapid command selection through novel interaction techniques, new interface design and innovative devices, user performance in this context has received little attention. Inspired by a recent study which formulated information-theoretic hypotheses to support experimental results on command selection, we aim at explaining user performance from an information-theoretic perspective. We design an ad-hoc command selection experiment for information-theoretic analysis, and explain theoretically why the transmitted information from the user to the computer levels off as difficulty increases. Our reasoning is based on basic information-theoretic concepts such as entropy, mutual information and Fano’s inequality. This implies a bell-shaped behavior of the throughput and therefore an optimal level of difficulty for a given input technique.
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