Examining Limits of Small Multiples: Frame Quantity Impacts Judgments with Line Graphs.

Small multiples are a popular visualization method, displaying different views of a dataset using multiple frames, often with the same scale and axes. However, there is a need to address their potential constraints, especially in the context of human cognitive capacity limits. These limits dictate the maximum information our mind can process at once. We explore the issue of capacity limitation by testing competing theories that describe how the number of frames shown in a display, the scale of the frames, and time constraints impact user performance with small multiples of line charts in an energy grid scenario. In two online studies (Experiment 1 n = 141 and Experiment 2 n = 360) and a follow-up eye-tracking analysis (n = 5), we found a linear decline in accuracy with increasing frames across seven tasks, which was not fully explained by differences in frame size, suggesting visual search challenges. Moreover, the studies demonstrate that highlighting specific frames can mitigate some visual search difficulties but, surprisingly, not eliminate them. This research offers insights into optimizing the utility of small multiples by aligning them with human limitations.

Numerical and Visual Representations of Uncertainty Lead to Different Patterns of Decision Making.

Although visualizations are a useful tool for helping people to understand information, they can also have unintended effects on human cognition. This is especially true for uncertain information, which is difficult for people to understand. Prior work has found that different methods of visualizing uncertain information can produce different patterns of decision making from users. However, uncertainty can also be represented via text or numerical information, and few studies have systematically compared these types of representations to visualizations of uncertainty. We present two experiments that compared visual representations of risk (icon arrays) to numerical representations (natural frequencies) in a wildfire evacuation task. Like prior studies, we found that different types of visual cues led to different patterns of decision making. In addition, our comparison of visual and numerical representations of risk found that people were more likely to evacuate when they saw visualizations than when they saw numerical representations. These experiments reinforce the idea that design choices are not neutral: seemingly minor differences in how information is represented can have important impacts on human risk perception and decision making.

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.

Retrieval speeds context fluctuation: why semantic generation enhances later learning but hinders prior learning.

In recent work, retrieval has been shown to enhance memory for events following that retrieval. In this set of experiments, we examined the effects of interleaved semantic retrieval on both previous and future learning within a multilist learning paradigm. Interleaved retrieval led to enhanced memory for lists learned following retrieval. In contrast, memory was impaired for lists learned prior to retrieval (Experiment 1). These results are consistent with recent work in multilist learning, directed forgetting, and list-before-last retrieval, all of which indicate a crucial role for retrieval in enhancing mental list segregation. This pattern of results follows clearly from a theoretical perspective in which retrieval drives internal contextual change and in which contextual overlap between study and test promotes better memory. Consistent with that perspective, a 15-min delay before the final test eliminated both effects (Experiment 2). Experiment 2 replicated the results of Experiment 1 with materials and assessments more appropriate for educational settings: Interleaved semantic retrieval led learners to be more able to answer questions correctly about texts studied after a retrieval event but less able to do so for texts studied earlier.