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Item does not contain fulltext Keyboard shortcuts are generally accepted as the most efficient method for issuing commands, but previous research has suggested that many people do not use them. In this study we investigate the use of keyboard shortcuts further and explore reasons why they are underutilized by users. In Experiment 1, we establish two baseline findings: (1) people infrequently use keyboard shortcuts and (2) lack of knowledge of keyboard shortcuts cannot fully account for the low frequency of use. In Experiments 2 and 3, we furthermore establish that (3) even when put under time pressure users often fail to select those methods they themselves believe to be fastest and (4) the frequency of use of keyboard shortcuts can be increased by a tool that assists users learning keyboard shortcuts. We discuss how the theoretical notion of ‘satisficing’, adopted from economic and cognitive theory, can explain our results. 13 p.

Uncertainty visualizations are increasingly used in communications to the general public. A well-known example is the weather forecast. Rather than providing an exact temperature value, weather forecasts often show the range in which the temperature will lie. But uncertainty visualizations are also deployed in graphical forecasts that are used for decision-making in many other different areas like agriculture, flood management, health care, and finance. Visualization appears to be an intuitive way to communicate uncertainty. In principle, uncertainty visualizations enable users to make better decisions by enhancing their awareness of the inherent uncertainty in the data. However, in practice many people (even experts) frequently misunderstand both the concept of uncertainty and its visualizations. We are currently investigating how the visual form and width of the graphical representation of uncertainty ranges affect how people interpret the underlying uncertainty distribution. www.humanfactors.nl

Multiday weather forecasts often include graphical representations of uncertainty. However, visual representations of probabilistic events are often misinterpreted by the general public. Although various uncertainty visualizations are now in use, the parameters that determine their successful deployment are still unknown. At the same time, a correct understanding of possible weather forecast outcomes will enable the public to make better decisions and will increase their trust in these predictions. We investigated the effects of the visual form and width of temperature forecast visualizations with uncertainty on estimates of the probability that the temperature could exceed a given value. The results suggest that people apply an internal model of the uncertainty distribution that closely resembles a normal distribution, which confirms previous findings. Also, the visualization type appears to affect the applied internal model, in particular the probability estimates of values outside the depicted uncertainty range. Furthermore, we find that perceived uncertainty does not necessarily map linearly to visual features, as identical relative positions to the range are being judged differently depending on the width of the uncertainty range. Finally, the internal model of the uncertainty distribution is related to participants’ numeracy. We include some implications for makers or designers of uncertainty visualizations.

Treemaps are a well known and powerful space-filling visualisation method for displaying hierarchical data. Many alternative treemap algorithms have been proposed, often with the aim being to optimise performance across several criteria, including spatial stability to assist users in locating and monitoring items of interest. In this paper, we demonstrate that spatial stability is not fully captured by the commonly used "distance change” (DC) metric, and we introduce a new "location drift” (LD) metric to more fully capture spatial stability. An empirical study examines the validity and usefulness of the location drift metric, showing that it explains some effects on user performance that distance change does not. Next, we introduce "Hilbert” and "Moore” treemap algorithms, which are designed to achieve high spatial stability. We assess their performance in comparison to other treemaps, showing that Hilbert and Moore treemaps perform well across all stability metrics.