Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations.

Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations. Visual saliency models are typically based on the properties of the human visual cortex and predict which areas of a scene have visual features (e.g. color, luminance, edges) that are likely to draw a viewer's attention. While these models can accurately predict where viewers will look in a natural scene, they typically do not perform well for abstract data visualizations. In this paper, we discuss the reasons for the poor performance of existing saliency models when applied to data visualizations. We introduce the Data Visualization Saliency (DVS) model, a saliency model tailored to address some of these weaknesses, and we test the performance of the DVS model and existing saliency models by comparing the saliency maps produced by the models to eye tracking data obtained from human viewers. Finally, we describe how modified saliency models could be used as general tools for assessing the effectiveness of visualizations, including the strengths and weaknesses of this approach.

Practice makes imperfect: Working memory training can harm recognition memory performance.

There is a great deal of debate concerning the benefits of working memory (WM) training and whether that training can transfer to other tasks. Although a consistent finding is that WM training programs elicit a short-term near-transfer effect (i.e., improvement in WM skills), results are inconsistent when considering persistence of such improvement and far transfer effects. In this study, we compared three groups of participants: a group that received WM training, a group that received training on how to use a mental imagery memory strategy, and a control group that received no training. Although the WM training group improved on the trained task, their posttraining performance on nontrained WM tasks did not differ from that of the other two groups. In addition, although the imagery training group's performance on a recognition memory task increased after training, the WM training group's performance on the task decreased after training. Participants' descriptions of the strategies they used to remember the studied items indicated that WM training may lead people to adopt memory strategies that are less effective for other types of memory tasks. These results indicate that WM training may have unintended consequences for other types of memory performance.