an article by Carlos Nakamura and Qing Zeng-Treitler (Department of Biomedical Informatics, University of Utah, USA) published in International Journal of Human-Computer Studies Volume 70 Issue 8 (August 2012)
Abstract
Predicting whether the intended audience will be able to recognize the meaning of an icon or pictograph is not an easy task. Many icon recognition studies have been conducted in the past. However, their findings cannot be generalized to other icons that were not included in the study, which, we argue, is their main limitation.
In this paper, we propose a comprehensive taxonomy of icons that is intended to enable the generalization of the findings of recognition studies. To accomplish this, we analysed a sample of more than eight hundred icons according to three axes: lexical category, semantic category, and representation strategy. Three basic representation strategies were identified: visual similarity; semantic association; and arbitrary convention. These representation strategies are in agreement with the strategies identified in previous taxonomies.
However, a greater number of subcategories of these strategies were identified. Our results also indicate that the lexical and semantic attributes of a concept influence the choice of representation strategy.
Highlights
► Icons can be created by visual similarity, semantic association, and by convention.
► Only concrete concepts (e.g., “car”) can be represented by visual similarity.
► Abstract concepts (e.g., “pain”) can only be represented by semantic association.
► Semantic association is the most diverse and the most used representation strategy.
► Icons created by convention are only understood if the reader already knows the code.
Hazel’s comment:
How many careers libraries are still using the icons developed for CRCI?
Do the users understand them (do you understand them?
Actually I think the question should have been “Does anyone have a careers library in these days of austerity?)
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