Whether it be through viewing the performance of our stocks or through poll charts on the news or perhaps even through health/life charts in video games, we are constantly bombarded with tons of information via graphs and visualizations throughout the day. We tend to make decisions and form opinions based of these visuals, and hence, it is very important that the information is represented in a way that we can easily consume, interpret and perceive. This is what makes the study of graphical perception so important. If the creators of the visualizations understand how the consumers will perceive their creations, they will be able to provide more efficient visualizations, not only saving every one time and money, but also enabling the consumers to make better decisions and form better opinions.
Cleveland and McGill (1984), through their study on Graphical Perception, set crucial ground work in the theory of graphic perception by identifying key human elementary perceptual tasks that are the most basic form of human perceptual activities when extracting quantitative information from graphics. They pointed out how the same amount of quantitative information represented in certain visual forms can be more effective in delivering the true message than others due to various psychological and cognitive reasons. They also proposed different methods for reducing visual bias and observation errors. A more recent paper by Heer and Bostock (2010) made further progress by assessing the efficacy of crowdsourcing in graphic perception of visualization design via Amazon’s Mechanical Turk and helped gain new insights into visualization design through various new experiments.
Traditional data presentation methods require conscious thinking for almost all of the work. Data visualization shifts the balance toward greater use of visual perception, taking advantage of our powerful eyes whenever possible. New insights into visual perception and cognition are arising from work in various disciplines besides information visualization, such as human factors and human-computer interaction, but none are more ground-breaking than those arising from the cognitive sciences. Studies focusing on Preattentive visual processing, on how data visualizations can take advantage of certain processing of visuals than occur prior to conscious awareness.
All in all, I think graphical perception is extremely important to make data visualization efficient. I do appreciate all the research that has been done in this area, but I feel there is still a lot of work that needs to be done to figure out more efficient ways that allow for visualizations to be interpreted by faster parts of the brain that require less energy, which results in more efficient cognition.