Friday, February 26th, 2016

Evan M. Peck – Peck, Evan M.; Carlin, Emily; and Jacob, Robert. “Designing Brain-Computer Interfaces for Attention-Aware Systems.” Computer 48, no. 10 (2015) : 34.

Evan M. Peck, Assistant Professor of Computer Science

Brain-computer interfaces (BCIs) hold great promise for improving information delivery and preserving user attention, but this promise has not yet translated to practical use. A prototype BCI that optimizes email notifications in noisy, complex environments, CARSON combines multiple measures from the brain to predict both cognitive workload and message relevancy to determine the optimum time to interrupt the user.

Peck, Evan M.; Carlin, Emily; and Jacob, Robert. “Designing Brain-Computer Interfaces for Attention-Aware Systems.” Computer 48, no. 10 (2015) : 34.

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Friday, February 26th, 2016

Evan Peck – Ottley, Alvitta; Peck, Evan M.; Harrison, Lane T.; Afergan, Daniel; Ziemkiewicz, Caroline; Taylor, Holly A.; Han, Paul K.J.; and Chang, Remco. “Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability.” IEEE Transactions on Visualization and Computer Graphics 22, no. 1 (2016) : 529-538.

Evan Peck, Assistant Professor of Computer Science

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.

Ottley, Alvitta; Peck, Evan M.; Harrison, Lane T.; Afergan, Daniel; Ziemkiewicz, Caroline; Taylor, Holly A.; Han, Paul K.J.; and Chang, Remco. “Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability.” IEEE Transactions on Visualization and Computer Graphics 22, no. 1 (2016) : 529-538.

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