14 research outputs found
Average Estimates in Line Graphs Are Biased Toward Areas of Higher Variability
We investigate variability overweighting, a previously undocumented bias in
line graphs, where estimates of average value are biased toward areas of higher
variability in that line. We found this effect across two preregistered
experiments with 140 and 420 participants. These experiments also show that the
bias is reduced when using a dot encoding of the same series. We can model the
bias with the average of the data series and the average of the points drawn
along the line. This bias might arise because higher variability leads to
stronger weighting in the average calculation, either due to the longer line
segments (even though those segments contain the same number of data values) or
line segments with higher variability being otherwise more visually salient.
Understanding and predicting this bias is important for visualization design
guidelines, recommendation systems, and tool builders, as the bias can
adversely affect estimates of averages and trends
Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis
Today's AI systems for medical decision support often succeed on benchmark
datasets in research papers but fail in real-world deployment. This work
focuses on the decision making of sepsis, an acute life-threatening systematic
infection that requires an early diagnosis with high uncertainty from the
clinician. Our aim is to explore the design requirements for AI systems that
can support clinical experts in making better decisions for the early diagnosis
of sepsis. The study begins with a formative study investigating why clinical
experts abandon an existing AI-powered Sepsis predictive module in their
electrical health record (EHR) system. We argue that a human-centered AI system
needs to support human experts in the intermediate stages of a medical
decision-making process (e.g., generating hypotheses or gathering data),
instead of focusing only on the final decision. Therefore, we build SepsisLab
based on a state-of-the-art AI algorithm and extend it to predict the future
projection of sepsis development, visualize the prediction uncertainty, and
propose actionable suggestions (i.e., which additional laboratory tests can be
collected) to reduce such uncertainty. Through heuristic evaluation with six
clinicians using our prototype system, we demonstrate that SepsisLab enables a
promising human-AI collaboration paradigm for the future of AI-assisted sepsis
diagnosis and other high-stakes medical decision making.Comment: Under submission to CHI202
Evaluating the Effect of Timeline Shape on Visualization Task Performance
Timelines are commonly represented on a horizontal line, which is not
necessarily the most effective way to visualize temporal event sequences.
However, few experiments have evaluated how timeline shape influences task
performance. We present the design and results of a controlled experiment run
on Amazon Mechanical Turk (n=192) in which we evaluate how timeline shape
affects task completion time, correctness, and user preference. We tested 12
combinations of 4 shapes -- horizontal line, vertical line, circle, and spiral
-- and 3 data types -- recurrent, non-recurrent, and mixed event sequences. We
found good evidence that timeline shape meaningfully affects user task
completion time but not correctness and that users have a strong shape
preference. Building on our results, we present design guidelines for creating
effective timeline visualizations based on user task and data types. A free
copy of this paper, the evaluation stimuli and data, and code are available at
https://osf.io/qr5yu/Comment: 12 pages, 5 figure
Doctor of Philosophy
dissertationGiven the widespread use of visualizations and their impact on health and safety, it is important to ensure that viewers interpret visualizations as accurately as possible. Ensemble visualizations are an increasingly popular method for visualizing data, as emerging research demonstrates that ensembles can effectively and intuitively communicate traditionally difficult statistical concepts. While a few studies have identified drawbacks to ensemble visualizations, no studies have identified the sources of reasoning biases that could occur with ensemble visualizations. Our previous work with hurricane forecast simulation ensemble visualizations identified a misunderstanding that could have resulted from the visual features of the display. The current study tested the hypothesis that visual-spatial biases, which are biases that are a direct result of the visualization technique, provide a cognitive mechanism to explain this misunderstanding. In three experiments, we tested the role of the visual elements of ensemble visualizations as well as knowledge about the visualization with novice participants (n = 303). The results suggest that previously documented reasoning errors with ensemble displays can be influenced both by changes to the visualization technique and by top-down knowledge-driven processing
Effects of ensemble and summary displays on interpretations of geospatial uncertainty data
Abstract Ensemble and summary displays are two widely used methods to represent visual-spatial uncertainty; however, there is disagreement about which is the most effective technique to communicate uncertainty to the general public. Visualization scientists create ensemble displays by plotting multiple data points on the same Cartesian coordinate plane. Despite their use in scientific practice, it is more common in public presentations to use visualizations of summary displays, which scientists create by plotting statistical parameters of the ensemble members. While prior work has demonstrated that viewers make different decisions when viewing summary and ensemble displays, it is unclear what components of the displays lead to diverging judgments. This study aims to compare the salience of visual features – or visual elements that attract bottom-up attention – as one possible source of diverging judgments made with ensemble and summary displays in the context of hurricane track forecasts. We report that salient visual features of both ensemble and summary displays influence participant judgment. Specifically, we find that salient features of summary displays of geospatial uncertainty can be misunderstood as displaying size information. Further, salient features of ensemble displays evoke judgments that are indicative of accurate interpretations of the underlying probability distribution of the ensemble data. However, when participants use ensemble displays to make point-based judgments, they may overweight individual ensemble members in their decision-making process. We propose that ensemble displays are a promising alternative to summary displays in a geospatial context but that decisions about visualization methods should be informed by the viewer’s task
Decision making with visualizations: a cognitive framework across disciplines
Abstract Visualizations—visual representations of information, depicted in graphics—are studied by researchers in numerous ways, ranging from the study of the basic principles of creating visualizations, to the cognitive processes underlying their use, as well as how visualizations communicate complex information (such as in medical risk or spatial patterns). However, findings from different domains are rarely shared across domains though there may be domain-general principles underlying visualizations and their use. The limited cross-domain communication may be due to a lack of a unifying cognitive framework. This review aims to address this gap by proposing an integrative model that is grounded in models of visualization comprehension and a dual-process account of decision making. We review empirical studies of decision making with static two-dimensional visualizations motivated by a wide range of research goals and find significant direct and indirect support for a dual-process account of decision making with visualizations. Consistent with a dual-process model, the first type of visualization decision mechanism produces fast, easy, and computationally light decisions with visualizations. The second facilitates slower, more contemplative, and effortful decisions with visualizations. We illustrate the utility of a dual-process account of decision making with visualizations using four cross-domain findings that may constitute universal visualization principles. Further, we offer guidance for future research, including novel areas of exploration and practical recommendations for visualization designers based on cognitive theory and empirical findings
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Decision making with visualizations: a cognitive framework across disciplines.
Visualizations-visual representations of information, depicted in graphics-are studied by researchers in numerous ways, ranging from the study of the basic principles of creating visualizations, to the cognitive processes underlying their use, as well as how visualizations communicate complex information (such as in medical risk or spatial patterns). However, findings from different domains are rarely shared across domains though there may be domain-general principles underlying visualizations and their use. The limited cross-domain communication may be due to a lack of a unifying cognitive framework. This review aims to address this gap by proposing an integrative model that is grounded in models of visualization comprehension and a dual-process account of decision making. We review empirical studies of decision making with static two-dimensional visualizations motivated by a wide range of research goals and find significant direct and indirect support for a dual-process account of decision making with visualizations. Consistent with a dual-process model, the first type of visualization decision mechanism produces fast, easy, and computationally light decisions with visualizations. The second facilitates slower, more contemplative, and effortful decisions with visualizations. We illustrate the utility of a dual-process account of decision making with visualizations using four cross-domain findings that may constitute universal visualization principles. Further, we offer guidance for future research, including novel areas of exploration and practical recommendations for visualization designers based on cognitive theory and empirical findings