Peter Cheng
University of Sussex, Brighton, United Kingdom
Providing an approach to model the memory structures that humans build as they use visualizations could be useful for researchers, designers and educators in the field of information visualization. Cheng and colleagues formulated Representation Interpretive Structure Theory (RIST) for that purpose. RIST adopts a human information processing perspective in order to address the immediate, short timescale, cognitive load likely to be experienced by visualization users. RIST is operationalized in a graphical modeling notation and browser-based editor. This paper demonstrates the utility of RIST by showing that (a): RIST models are compatible with established empirical and computational cognitive findings about differences in human performance on alternative representations; (b) they can encompass existing explanations from the literature; and, (c) they provide new explanations about causes of those performance differences.
doi.org/10.1145/3613904.3642276
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)