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Tiffany Raber

dblp:283/6612 · also Tiffany R. Raber · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics
uncertainty visualization
0.712023
Practice Improves Performance of a 2D Uncertainty Integration Task Within and Across Visualizations · IEEE Trans. Vis. Comput. Graph. 2023
Human-AI interaction › human decision-making
decision making under uncertainty
0.212023
Practice Improves Performance of a 2D Uncertainty Integration Task Within and Across Visualizations · IEEE Trans. Vis. Comput. Graph. 2023

Methods — techniques the papers use, named apart from their topics

user study · 1.3practice with feedback · 1.3equivalence testing · 1.3
YearPublicationVenuePosition
2023 Practice Improves Performance of a 2D Uncertainty Integration Task Within and Across Visualizations
abstract
Information uncertainty is ubiquitous in everyday life, including in domains as diverse as weather forecasts, investments, and health risks. Knowing how to interpret and integrate this uncertain information is vital for making good decisions, but this can be difficult for experts and novices alike. In this study, we examine whether brief, focused practice can improve people's ability to understand and integrate bivariate Gaussian uncertainty visualized via ensemble displays, summary displays, and distributional displays, and we examine whether this is influenced by the complexity of the displayed information. In two experiments (N=118 and 56), decision making was faster and more accurate after practice relative to before practice. Furthermore, the performance improvements transferred to use of display types that were not practiced. This suggests that practice with feedback may improve underlying skills in probabilistic reasoning and provides a promising approach to improve people's decision making under uncertainty.
Sarah Kusumastuti, Kimberly A. Pollard, Ashley H. Oiknine, Bianca Dalangin, Tiffany Raber, Benjamin T. Files
IEEE Trans. Vis. Comput. Graph.5