A quantitative measure of information complexity remains very much desirable in HCI field, since it may\naid in optimization of user interfaces, especially in human-computer systems for controlling complex objects. Our\npaper is dedicated to exploration of subjective (subject-depended) aspect of the complexity, conceptualized as\ninformation familiarity. Although research of familiarity in human cognition and behaviour is done in several fields,\nthe accepted models in HCI, such as Human Processor or Hick-Hyman�s law do not generally consider this issue. In\nour experimental study the subjects performed search and selection of digits and letters, whose familiarity was\nconceptualized as frequency of occurrence in numbers and texts. The analysis showed significant effect of\ninformation familiarity on selection time and throughput in regression models, although the R2 values were somehow\nlow. Still, we hope that our results might aid in quantification of information complexity and its further application\nfor optimizing interaction in human-machine systems.
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