To interpret data visualizations, people must determine how visual features map onto concepts. For example, to interpret colormaps, people must determine how dimensions of color (e.g., lightness, hue) map onto quantities of a given measure (e.g., brain activity, correlation magnitude). This process is easier when the encoded mappings in the visualization match people's predictions of how visual features will map onto concepts, their inferred mappings. To harness this principle in visualization design, it is necessary to understand what factors determine people's inferred mappings. In this study, we investigated how inferred color-quantity mappings for colormap data visualizations were influenced by the background color. Prior literature presents seemingly conflicting accounts of how the background color affects inferred color-quantity mappings. The present results help resolve those conflicts, demonstrating that sometimes the background has an effect and sometimes it does not, depending on whether the colormap appears to vary in opacity. When there is no apparent variation in opacity, participants infer that darker colors map to larger quantities (dark-is-more bias). As apparent variation in opacity increases, participants become biased toward inferring that more opaque colors map to larger quantities (opaque-is-more bias). These biases work together on light backgrounds and conflict on dark backgrounds. Under such conflicts, the opaque-is-more bias can negate, or even supersede the dark-is-more bias. The results suggest that if a design goal is to produce colormaps that match people's inferred mappings and are robust to changes in background color, it is beneficial to use colormaps that will not appear to vary in opacity on any background color, and to encode larger quantities in darker colors.

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.

The Tularosa study was designed to understand how defensive deception-including both cyber and psychological-affects cyber attackers. Over 130 red teamers participated in a network penetration task over two days in which we controlled both the presence of and explicit mention of deceptive defensive techniques. To our knowledge, this represents the largest study of its kind ever conducted on a professional red team population. The design was conducted with a battery of questionnaires (e.g., experience, personality, etc.) and cognitive tasks (e.g., fluid intelligence, working memory, etc.), allowing for the characterization of a "typical" red teamer, as well as physiological measures (e.g., galvanic skin response, heart rate, etc.) to be correlated with the cyber events. This paper focuses on the design, implementation, data, population characteristics, and begins to examine preliminary results.

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.

Studies of pitch perception often involve measuring difference limens for complex tones ͑DLCs͒ that differ in fundamental frequency ͑F0͒. These measures are thought to reflect F0 discrimination and to provide an indirect measure of subjective pitch strength. However, in many situations discrimination may be based on cues other than the pitch or the F0, such as differences in the frequencies of individual components or timbre ͑brightness͒. Here, DLCs were measured for harmonic and inharmonic tones under various conditions, including a randomized or fixed lowest harmonic number, with and without feedback. The inharmonic tones were produced by shifting the frequencies of all harmonics upwards by 6.25%, 12.5%, or 25% of F0. It was hypothesized that, if DLCs reflect residue-pitch discrimination, these frequency-shifted tones, which produced a weaker and more ambiguous pitch than would yield larger DLCs than the harmonic tones. However, if DLCs reflect comparisons of component pitches, or timbre, they should not be systematically influenced by frequency shifting. The results showed larger DLCs and more scattered pitch matches for inharmonic than for harmonic complexes, confirming that the inharmonic tones produced a less consistent pitch than the harmonic tones, and consistent with the idea that DLCs reflect F0 pitch discrimination.