4 research outputs found
Empirically measuring soft knowledge in visualization
In this paper, we present an empirical study designed to evaluate the hypothesis that humans’ soft knowledge can enhance
the cost-benefit ratio of a visualization process by reducing the potential distortion. In particular, we focused on the impact of
three classes of soft knowledge: (i) knowledge about application contexts, (ii) knowledge about the patterns to be observed (i.e.,
in relation to visualization task), and (iii) knowledge about statistical measures. We mapped these classes into three control
variables, and used real-world time series data to construct stimuli. The results of the study confirmed the positive contribution
of each class of knowledge towards the reduction of the potential distortion, while the knowledge about the patterns prevents
distortion more effectively than the other two classes