Examining the Types of Predictions People Make When Using Visualizations of Historical Data: Are Individuals Optimistic, Pessimistic, or Neutral?

Nika Banaei

Authors:  Nika Banaei, Dr. Wayne Giang

Faculty Mentor:  Dr. Wayne Giang

College:  Herbert Wertheim College of Engineering


Individuals are often asked to make predictions of the future value of a variable. For example, dispatchers use estimates of patient transfer times to support medical triage and operational decisions. Presenting decision-makers with historical data may help with this task, but the best way to present this information in a decision-aid is still unknown. Furthermore, decision-makers often have contextual information about the current situation that may influence how they interpret these decision-aids. Previously, we compared two types of visualizations of historical data (median-only vs. boxplot) and three types of contextual information (no context information, consequence information, and likelihood information) in an online experimental study of 79 participants. This previous study showed that prediction behavior was significantly impacted by the type of contextual information provided. In this poster, we provide an analysis of the reasons why participants chose these predictions by examining participant self-reports of the type of prediction that they chose (i.e., optimistic, average, or pessimistic cases). We then describe the design of a new experiment using eye-tracking and a think-aloud methodology to further explore participants prediction strategies when using visualizations of historical data. The results of these studies can help us design future decision-aids that present uncertainty information.

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Wayne Giang
Wayne Giang (@guest_1020)
1 year ago

Good job with the poster!

Nika Banaei
Nika Banaei (@guest_1338)
Reply to  Wayne Giang
1 year ago

Thank you Dr. Giang for your support.

Nika Banaei
Nika Banaei (@guest_1572)
1 year ago

I will be hosting a Zoom meeting to discuss any questions: https://ufl.zoom.us/j/973940352

Nina Rudd
Nina Rudd (@guest_2684)
1 year ago

Great poster and presentation Nika!

m.eskandar (@guest_3308)
1 year ago

Very informative Poster, congratulations.

Jeffrey Chen
Jeffrey Chen (@guest_4202)
1 year ago

Great job on the poster, its very interesting that 70% of the participants strategies remained unknown.

Nika Banaei
Nika Banaei (@guest_5760)
Reply to  Jeffrey Chen
1 year ago

Thank you all!

Boyi Hu
Boyi Hu (@guest_5990)
1 year ago

Nice work!

Haolan Zheng
Haolan Zheng (@guest_7790)
1 year ago

Hi Nika, you did a really good job! Do you have any ideas right now about the task design in future experiments? What kind of specific task would you want to assign to your participants?

Haolan Zheng
Haolan Zheng (@guest_7798)
1 year ago

Hi Nika, great job! Do you have any ideas right now on what kind of task you may assign to the participants in future eye-tracking experiments?

Nika Banaei
Nika Banaei (@guest_8472)
1 year ago

Hi Haolan,

Thank you! We are currently working with some ER doctors at Shands regarding diagnosis waste. We hope to use the diagnosis waste scenario as the estimation task in the visualization study. For example, we would ask participants to estimate the chance of a patient having a certain diagnosis.