VisxVision: Workshop on Novel Directions in Vision Science and Visualization Researchbookmark_border

When estimating the average value of a data series in line graphs, do viewers systematically overweight regions of higher variability? This is the question that Moritz et al. [14] set out to answer, and across two preregistered experiments they reported evidence for a bias they termed variability overweighting. Using their released data and code, I first reproduce their reported analysis, then ask whether their inference survives an equally valid analytic choice: measuring estimation error against the average of the displayed marks of each stimulus, rather than against a reference series that, for one mark type, is never shown. Under this alternative operationalization, the effect is substantially reduced and, for one mark type, reverses. Splitting the analysis by whether variability appears in the upper or lower region of the graph further reveals an asymmetry that the original direction-corrected analysis collapses away. I argue that the deeper issue is what verbal theories leave unspecified. When a theory is silent about which operationalization it requires, the metric ends up doing the theory’s arguing for it. I close with a discussion of what reproduction and reanalysis are for: not verdicts on truth, but instruments for making analytic contingency visible and for forcing theories to say more.

Most knowledge of graphical perception comes from behavioral studies. Understanding from a neural perspective is much more limited due in part to neuroimaging studies' expensiveness and difficulty to conduct. In this paper, we evaluate whether Meta's TRIBE v2 neural encoding model can recover neural contrasts from a visualization fMRI study. Specifically, we evaluate it through a conceptual replication of the visualization-viewing component of a prior comparison of Bubble charts and three-dimensional Surface charts in color and grayscale. We generate TRIBE-predicted cortical responses for the original stimuli and compare the resulting contrasts with those reported in the human study. The model reproduced the direction of 11 of 14 reported cortical effects, with agreement concentrated in visual-processing regions. This agreement characterizes the model's alignment with the prior human-generated fMRI results rather than independently confirming them. We discuss the limitations encountered when working with this model for in-silico replication and hope to encourage future work exploring this new avenue for neuroimaging studies in visualization. Supplemental materials are available at https://osf.io/8a96x/.

Opacity is a widely used channel in data visualization, but it remains less well understood compared to channels such as color, length, size, etc. Recent work from Meng et al. investigated the impact of opacity across competing color schemes, finding that certain color schemes were associated with better participant accuracy. We examine these effects further in a controlled two-alternative forced-choice setup to determine whether opacity differences are truly equal across possible opacity comparison ranges. In a within-subjects study with 96 trials, including two competing color schemes (best and worst) from Meng et al. and 48 opacity pairs, we find little differences between color schemes but larger individual differences in accuracy. Further, results show stable performance in middle opacity ranges, with more errors occurring when comparing extreme values. We discuss potential implications for design guidelines and further study and make our study materials, analysis scripts, and data available at https://osf.io/zv9dx/overview?view_only=38d03cea1b3d42788593e3e6b1016cfd

Visual data communication in digital media is increasingly characterized by short attention spans and snapshot-based viewing, often employing line charts to convey trends and patterns. Among all visual elements, titles are crucial elements that can shape how viewers interpret visual information and form chart takeaways. In this study, we examine how title characteristics, particularly title word count and intended message, influence people’s pattern identification in single-class line charts. Participants viewed 50 line charts collected from online news media and identified the pattern they perceived. Our results demonstrate that both title word count and intended message significantly influence viewers’ pattern identification. Our findings highlight the importance of title framing in shaping quick-view pattern takeaways and supporting effective visualization communication.

Human vision organizes what it sees into wholes: same-colored points group into series, similar marks cohere into categories, and shapes complete into recognizable objects. These are the Gestalt operations that visualization design builds on. Whether vision models organize visual content this way has not been systematically tested. We introduce a behavioral battery that scores models against human data from prior perception studies on four grouping tasks: mark-color odd-one-out, color-series counting, silhouette recognition, and object odd-one-out. We apply it to 45 models across five training families: supervised, self-supervised, and contrastive vision-language encoders, open-weight VLMs, and closed foundation models. The battery reveals that agreement with human responses captures aspects of perceptual organization that conventional performance metrics fail to distinguish, with several closed models exhibiting substantially lower alignment than their benchmark accuracy would suggest. Scoring against published perception data therefore gives visualization research a reusable yardstick, requiring no new user study, for auditing whether the models now entering visualization pipelines organize what they see the way their human audience does.

Color discrimination is a fundamental aspect of visualization as it influences how people interpret visual encodings. Many visualization guidelines are informed by perceptual studies, yet relatively few have been replicated. Acknowledging that the interaction between human perception, visual tasks, and display technology can change over time, we replicate two crowdsourced color discrimination studies conducted 10 years earlier. Specifically, we replicated a visualization-focused color discrimination task (N=144) and a more general perceptual discrimination task (N=394). In both studies, our results reproduced the original perceptual effects. We further use the replication to investigate whether color-related practice influences color discrimination. Specifically, we extended our replication studies by adding questions about participants' engagement with color practices. We then examined whether diverse color-related practices (e.g., artistic hobbies, knowledge of color theory, and cosmetic makeup use) influenced color discrimination. We found no significant difference between participants who reported engaging in color-related practices and those who did not, suggesting that design guidance regarding color discrimination may generalize across viewers regardless of their regular color practice.

Eye-catching graphics, such as circular figure labels and word-scale graphics, are increasingly being placed directly within long-form text paragraphs. Some research has claimed that inline visualiza tions can help readers understand data-rich passages more clearly. However, research in the science of reading calls into question the introduction of images within the flow of text. In this work, we conduct an exploratory study of eye movement in both the pres ence and absence of visual embellishments. Using a high-resolution eye-tracker (EyeLink Portable Duo), we observed small mean in creases in vertical and cross-column saccade rates among six par ticipants, with substantial variation among readers and no detected difference in comprehension accuracy. Mean subjective ratings for the data-light circular-glyph passage indicated it is more distract ing than its unembellished comparison passage. These exploratory observations differ from prior findings and motivate a larger, fully crossed study of inline graphics.