Facilitating conditional probability problems with visuals

Authors:
Vince Kellen;Susy Chan;Xiaowen Fang
Affiliations:
DePaul University, School of Computer Science, Telecommunications and Information Systems, Chicago, IL;DePaul University, School of Computer Science, Telecommunications and Information Systems, Chicago, IL;DePaul University, School of Computer Science, Telecommunications and Information Systems, Chicago, IL
Venue:
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: interaction platforms and techniques
Year:
2007

Citing 7
Cited 1

Understanding Bayesian reasoning via graphical displays

CHI '89 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Visual explanations: images and quantities, evidence and narrative

Visual explanations: images and quantities, evidence and narrative
Readings in information visualization: using vision to think

Readings in information visualization: using vision to think
Business information visualization

Communications of the AIS
The challenge of information visualization evaluation

Proceedings of the working conference on Advanced visual interfaces
Painting pictures to augment advice

Proceedings of the working conference on Advanced visual interfaces
Reasoning, Models, and Images: Behavioral Measures and Cortical Activity

Journal of Cognitive Neuroscience

Effects of spatial ability and richness of motion cue on learning in mechanically complex domain

Computers in Human Behavior

Quantified Score

Hi-index	0.00

Visualization

Abstract

In tasks such as disease diagnosis, interpretation of evidence in criminal trials and management of security and risk data, people need to process conditional probabilities to make critical judgments and decisions. As dual-coding theory and the cognitive theory of multimedia learning (CTML) would predict, visual representations (VRs) should aid in these tasks. Conditional probability problems are difficult and require subjects to build a mental model of set inclusion relationships to solve them. Evidence from neurological research confirms that mental model construction relies on visual spatial processing. Prior research has shown conflicting accounts of whether visuals aid in these problems. Prior research has also revealed that individuals differ in their ability to perform spatial processing tasks. Do visuals help solve these problems? Do visualization interface designers need to take into account the nuances of spatial processing and individual differences? This study uses a 3×2 factorial design to determine the relationship between subject's spatial abilities (high or low) and visual and text representations on user performance and satisfaction.