Cognitive processes and traits related to graphic comprehension

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© 2014 by IGI Global. All rights reserved. The subject of how visualizations and graphics in general can be understood by their viewers draws on theories from many fields of research. Such theories might address the formal structure of the visualization, the style and graphic design skills of the creator, the task driving the viewer's interaction with the visualization, the type of data being represented, or the skills and experiences of viewer. This chapter focuses on this last question and presents a set of interrelated constructs and viewer traits that contribute to (or interfere with) a viewer's ability to analyze a particular data visualization. The review covers spatial thinking skills, cognitive styles, mental models, and cognitive load in its discussion of theoretical constructs related to graphic comprehension. The review also addresses how these cognitive processes vary by age, sex, and disciplinary background-the most common demographic characteristics studied in relation to graphic comprehension. Together, the constructs and traits contribute to a diverse and nuanced understanding of the viewers of data visualizations.






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Angela Zoss

Senior Manager, IT

Since December 2021, Angela has served as the Interim Head and now Head of the Assessment and User Experience Strategy (AUXS) Department. She provides leadership and direction for the Duke University Libraries’ strategy and practice of Assessment and User Experience, including developing, maintaining, and integrating user-focused web content, data, and discovery platforms.

From 2018 to 2021, Angela worked as the Assessment and Data Visualization Analyst in AUXS, offering support for special data projects and providing leadership and project management for a variety of teams related to library reporting, gathering user data, and making improvements to library spaces and services.

From 2012 to 2018, Angela worked as the Data Visualization Coordinator for Data and Visualization Services (now the Center for Data and Visualization Sciences). In that role she created many library workshops and short courses on visualization; consulted with students, researchers, and faculty members on research projects; and helped to introduce visualization concepts and tools into a variety of undergraduate and graduate courses.

Ph.D. in Information Science, Indiana University (2018)
M.S. in Communication, Cornell University (2008)
B.A. in Cognitive Science and Communication & Culture, Indiana University (2003)

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