Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diameter change for both right and left eye were the most prominent features, blinking rate also made a moderately important contribution to this highly accurate prediction of low, medium and high cognitive load. The existing results on accuracy considerably outperform prior approaches and demonstrate the applicability of our framework to detect cognitive load.

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Cognitive styles theories suggest that we divide into visual and verbal thinkers. In this paper we describe a method designed to encourage visual communication between designers and their audiences. This new visual feedback method is based on enabling fast intuitive selections by the crowd from image banks when responding to an idea. Visual summarization reduces the massed image choices to a small number of representative images. These summaries are then consumed at a glance by designers receiving the feedback leading to thoughtful reflection on their designs. We report an evaluation using two types of imagery for feedback. Twelve designers took part, receiving visual feedback in response to their designs. In semi-structured interviews they described their interpretation of the feedback, how it inspired them to change their designs and contrasted it with text feedback. Eleven of the twelve designers revealed that they would be enthusiastic users of a service providing this new mode of feedback.

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