TY  - JOUR
PY  - 2015//
TI  - Improving Bayesian reasoning: the effects of phrasing, visualization, and spatial ability
JO  - IEEE transactions on visualization and computer graphics
A1  - Ottley, Alvitta
A1  - Peck, Evan
A1  - Harrison, Lane
A1  - Afergan, Daniel
A1  - Ziemkiewicz, Caroline
A1  - Taylor, Holly
A1  - Han, Paul
A1  - Chang, Remco
SP  - 529
EP  - 538
VL  - 22
IS  - 1
N2  - Decades of research have repeatedly shown that people perform poorly at estimating and understanding conditional probabilities that are inherent in Bayesian reasoning problems. Yet in the medical domain, both physicians and patients make daily, life-critical judgments based on conditional probability. Although there have been a number of attempts to develop more effective ways to facilitate Bayesian reasoning, reports of these findings tend to be inconsistent and sometimes even contradictory. For instance, the reported accuracies for individuals being able to correctly estimate conditional probability range from 6% to 62%. In this work, we show that problem representation can significantly affect accuracies. By controlling the amount of information presented to the user, we demonstrate how text and visualization designs can increase overall accuracies to as high as 77%. Additionally, we found that for users with high spatial ability, our designs can further improve their accuracies to as high as 100%. By and large, our findings provide explanations for the inconsistent reports on accuracy in Bayesian reasoning tasks and show a significant improvement over existing methods. We believe that these findings can have immediate impact on risk communication in health-related fields.<p />  <p>Language: en</p>
LA  - en
SN  - 1077-2626
UR  - http://dx.doi.org/10.1109/TVCG.2015.2467758
ID  - ref1
ER  -