# The Correlation between Users' Cognitive Characteristics and Visualization Literacy
## 2 Background
### Visualization Literacy  
Over the last few years, several researchers have investigated visualization literacy in various directions. [PERSON] et al. [4] define it as \"the ability to confidently use a given data visualization to translate questions specified in the data domain into visual queries in the visual domain, as well as interpreting visual patterns in the visual domain as properties in the data domain\" and [PERSON] et al. [1] refer it as \"the ability and skill to read and interpret visually represented data in and to extract16 information from data visualizations.\" There are also related concepts such as visual literacy [10], in which is defined as the \"ability to understand, interpret, and evaluate visual messages.\" However, this is rooted in more to semiotics, which distinguishes it from visualization literacy. [PERSON] et al. [2] surveyed the ordinary users' level of visualization literacy and their familiarity using a questionnaire and qualitatively explored users' cognitive activities when they tried to make sense of data visualizations [3]. More importantly, some researchers attempted to develop assessment tests to quantitatively measure the visualization literacy of users [1; 4]. [PERSON] et al. [1] proposed the Visualization Literacy Assessment Test (VLAT) in which followed the test construction procedure in Psychological and Educational Measurement. The VLAT consisted of 12 data visualization types, which were the most frequently exposed to the users, and 53 test items that covered eight eight types of tasks. The wide use of a range of visualization tasks helps ensure that it can cover a broad spectrum of participants' abilities with visualization.  
Even though the developed assessment tests might be too limited to measure an all-inclusive ability to read and interpret data visualizations, researchers, designers, and educators can acquire valuable information from test results. From the test results, they could identify users' the strengths and weaknesses in reading and interpreting data visualizations of users as well as the current level of visualization literacy.  
One of the ultimate goals of visualization literacy research is to promote and improve the visualization literacy of users through education and training [1]. In response to this, a couple of interesting education approaches have been proposed. [PERSON] and [PERSON] [11] tried to teach an unfamiliar data visualization (e.g., treemap) by linking it to a familiar data visualization (e.g., pie chart); and [PERSON] and [PERSON] [12] adapted the learning-by-doing approach to teach an unfamiliar data visualization (i.e., parallel-coordinates plot). Most recently, [PERSON] et al. [13] investigated the current practices and challenges in teaching and learning data visualizations in early education settings and identified design goals to improve the visualization literacy of elementary students. However, researchers still lack an understanding of the role of user cognitive characteristics in visualization literacy even though they affect individuals' learning abilities and pace in response to education materials and training programs, as copious literature on differential psychology has indicated [5]. Understanding the correlation between visualization literacy and cognitive characteristics can provide insightful implications on tailored education for individuals with various levels in of ability.