# The Correlation between Users' Cognitive Characteristics and Visualization Literacy
## 1 Introduction  
Visualization literacy is \"the ability and skill to read and interpret visually represented data in and to extract information from data visualizations\" ([1] p. 552). In recent years, visualization literacy has become more and more important because many people have started using data visualizations to support their communication, problem-solving, and decision-making in various areas. However, a recent study shows that ordinary people have a low level of visualization literacy and limitations in understanding and interpreting data visualizations [2]. To get a better understanding of how people understand data visualizations, many researchers in the information visualization community have conducted empirical studies. Some researchers conducted a qualitative study to characterize users' underlying cognitive behaviors with data visualizations [3]. Other researchers introduced new methods to quantitatively measure users' ability to read and interpret data visualizations [1, 4]. To systematically teach data visualizations and improve users' visualization literacy, we should understand users better by characterizing their states and abilities more clearly. Traditionally, researchers in the area of educational studies have investigated various individual characteristics (e.g.,cognitive characteristics, personality, and previous knowledge of learners) and their relationships to learners' aptitude for more systematic and efficient learning and instruction. Researchers have suggested various forms of learning and instruction methods depending on individual differences and proven their effectiveness based on learning outcomes [5; 6]. In such a manner, we can certainly gain valuable insights by investigating more closely users' cognitive characteristics related to visualization literacy. Furthermore, that understanding will be the basis for designing efficient and tailored education and training systems to improve visualization literacy. For instance, if we know users' cognitive abilities related to visualization literacy, we can pay special attention to their abilities in terms of visualization literacy. If we know users' cognitive characteristics associated with visualization literacy, we can design instructions that meet users' needs and aptitudes. Thus, in this study, we investigate the correlation between cognitive characteristics and visualization literacy as a first step. Prior research has investigated the relationship between graph comprehension and various types of cognitive abilities, such as numeracy, need for cognition, and visualizer-verbalizer style e.g., [7; 8; 9]. Many studies have introduced mixed results about the relationship between the individual differences in the users' cognitive abilities and confidence and accuracy of comprehending graphic representations of data. Despite such continuing efforts, previous studies have not tested wide breadth of visualization types and analytic task types because they conducted studies with a small selection of charts (usually, bar chart, line chart, and pie chart) being used in a limited context, such as in health risk assessment, news reading, and education. Furthermore, prior research has tended to focus on graph comprehension/literacy, which is a different concept from visualization literacy, which captures users' abilities to perform analytic tasks (e.g., derive trends and compare) using a variety of data visualizations. Therefore, we believe that we need to test the findings in the graph comprehension/literacy fields in an information visualization context. To fill the research gap, our study aims to extend previous research efforts in the visual data analysis context by conducting a within-subject experiment with a wider breadth of 12 types of visualizations and analytic task types.