VMC: A Grammar for Visualizing Statistical Model Checks.

Visualizations play a critical role in validating and improving statistical models. However, the design space of model check visualizations is not well understood, making it difficult for authors to explore and specify effective graphical model checks. VMC defines a model check visualization using four components: (1) samples of distributions of checkable quantities generated from the model, including predictive distributions for new data and distributions of model parameters; (2) transformations on observed data to facilitate comparison; (3) visual representations of distributions; and (4) layouts to facilitate comparing model samples and observed data.

What Can Interactive Visualization Do for Participatory Budgeting in Chicago?

Participatory budgeting (PB) is a democratic approach to allocating municipal spending that has been adopted in many places in recent years, including in Chicago. Current PB voting resembles a ballot where residents are asked which municipal projects, such as school improvements and road repairs, to fund with a limited budget. In this work, we ask how interactive visualization can benefit PB by conducting a design probe-based interview study (N=13) with policy workers and academics with expertise in PB, urban planning, and civic HCI.

EVM: Incorporating Model Checking into Exploratory Visual Analysis.

Visual analytics (VA) tools support data exploration by helping analysts quickly and iteratively generate views of data which reveal interesting patterns. However, these tools seldom enable explicit checks of the resulting interpretations of data-e.g.

Causal Support: Modeling Causal Inferences with Visualizations.

Analysts often make visual causal inferences about possible data-generating models. However, visual analytics (VA) software tends to leave these models implicit in the mind of the analyst, which casts doubt on the statistical validity of informal visual "insights". We formally evaluate the quality of causal inferences from visualizations by adopting causal support-a Bayesian cognition model that learns the probability of alternative causal explanations given some data-as a normative benchmark for causal inferences.

Visual Reasoning Strategies for Effect Size Judgments and Decisions.

Uncertainty visualizations often emphasize point estimates to support magnitude estimates or decisions through visual comparison. However, when design choices emphasize means, users may overlook uncertainty information and misinterpret visual distance as a proxy for effect size. We present findings from a mixed design experiment on Mechanical Turk which tests eight uncertainty visualization designs: 95% containment intervals, hypothetical outcome plots, densities, and quantile dotplots, each with and without means added.

Boba: Authoring and Visualizing Multiverse Analyses.

Multiverse analysis is an approach to data analysis in which all "reasonable" analytic decisions are evaluated in parallel and interpreted collectively, in order to foster robustness and transparency. However, specifying a multiverse is demanding because analysts must manage myriad variants from a cross-product of analytic decisions, and the results require nuanced interpretation. We contribute Baba: an integrated domain-specific language (DSL) and visual analysis system for authoring and reviewing multiverse analyses.

The time course of different surround suppression mechanisms.

What we see depends on the spatial context in which it appears. Previous work has linked the suppression of perceived contrast by surrounding stimuli to reduced neural responses in early visual cortex. This surround suppression depends on at least two separable neural mechanisms, "low-level" and "higher level," which can be differentiated by their response characteristics.

Glutamatergic facilitation of neural responses in MT enhances motion perception in humans.

There is large individual variability in human neural responses and perceptual abilities. The factors that give rise to these individual differences, however, remain largely unknown. To examine these factors, we measured fMRI responses to moving gratings in the motion-selective region MT, and perceptual duration thresholds for motion direction discrimination.

In Pursuit of Error: A Survey of Uncertainty Visualization Evaluation.

Understanding and accounting for uncertainty is critical to effectively reasoning about visualized data. However, evaluating the impact of an uncertainty visualization is complex due to the difficulties that people have interpreting uncertainty and the challenge of defining correct behavior with uncertainty information. Currently, evaluators of uncertainty visualization must rely on general purpose visualization evaluation frameworks which can be ill-equipped to provide guidance with the unique difficulties of assessing judgments under uncertainty.

Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data.

Animated representations of outcomes drawn from distributions (hypothetical outcome plots, or HOPs) are used in the media and other public venues to communicate uncertainty. HOPs greatly improve multivariate probability estimation over conventional static uncertainty visualizations and leverage the ability of the visual system to quickly, accurately, and automatically process the summary statistical properties of ensembles. However, it is unclear how well HOPs support applied tasks resembling real world judgments posed in uncertainty communication.

Sex Differences in Visual Motion Processing.

The importance of sex as a biological variable has recently been emphasized by major funding organizations [1] and within the neuroscience community [2]. Critical sex-based neural differences are indicated by, for example, conditions such as autism spectrum disorder (ASD) that have a strong sex bias with a higher prevalence among males [51, 3]. Motivated by this broader context, we report a marked sex difference in a visual motion perception task among neurotypical adults.

Luminance-dependent long-term chromatic adaptation.

There is theoretical and empirical support for long-term adaptation of human vision to chromatic regularities in the environment. The current study investigates whether relationships of luminance and chromaticity in the natural environment could drive chromatic adaptation independently and differently for bright and dark colors. This is motivated by psychophysical evidence of systematic difference shifts in red-green chromatic sensitivities between contextually bright- versus dark-colored stimuli.