Publicação
Uncertainty in Data Visualizations: A neuroscience experiment on uncertainty visualizations
| Resumo: | Recent times have seen a huge increase in data driven decision making. With the emergence of Big Data and Business Intelligence, user reports contain more data than ever before creating challenges in visualizing uncertainty in data. We present a comparison study where the traditional uncertainty visualizations error bars and violin plots compare to Hypothetical Outcome Plots. Hypothetical Outcome Plots is a visualization technique drawing animated samples from a distribution, visualizing uncertainty throughout the distribution of samples. Using neuroscience practices, we have conducted an eye-tracking experiment tracing the comparison between Hypothetical Outcome Plots and traditional uncertainty visualizations. We test Hypothetical Outcome Plots in different transitions across multiple visualization designs. The results show that static visualizations were easier for the participants to use as decision aid. While HOP had a lower total score, HOP worked better when visualized in a bar chart state and with bigger transitions between each draw. Moreover, eye-tracking metrics exhibit the difference in difficulty between the visualizations, indicating that participants familiarity with the visualization highly affected their ability to make the best decision. |
|---|---|
| Autores principais: | Graff, Simen Markveien |
| Assunto: | Data Visualization Uncertainty Hypothetical Outcome Plots Neuroscience Eye tracking SDG 4 - Quality education SDG 9 - Industry, innovation and infrastructure |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Recent times have seen a huge increase in data driven decision making. With the emergence of Big Data and Business Intelligence, user reports contain more data than ever before creating challenges in visualizing uncertainty in data. We present a comparison study where the traditional uncertainty visualizations error bars and violin plots compare to Hypothetical Outcome Plots. Hypothetical Outcome Plots is a visualization technique drawing animated samples from a distribution, visualizing uncertainty throughout the distribution of samples. Using neuroscience practices, we have conducted an eye-tracking experiment tracing the comparison between Hypothetical Outcome Plots and traditional uncertainty visualizations. We test Hypothetical Outcome Plots in different transitions across multiple visualization designs. The results show that static visualizations were easier for the participants to use as decision aid. While HOP had a lower total score, HOP worked better when visualized in a bar chart state and with bigger transitions between each draw. Moreover, eye-tracking metrics exhibit the difference in difficulty between the visualizations, indicating that participants familiarity with the visualization highly affected their ability to make the best decision. |
|---|