Karen B. Schloss
Associate Professor
Department of Psychology, and Wisconsin Institute for Discovery
University of Wisconsin-Madison

 

 

 

 

 

 

 

Building a framework of assignment inference to understand expectations about the meaning of visual features in visual communication

Abstract: Visual communication is fundamental to how humans share information, from weather patterns, to disease prevalence, to their latest scientific discoveries. When people attempt to interpret information visualizations, such as graphs, maps, diagrams, and signage, they are faced with the task of mapping perceptual features onto meanings.  Sometimes, visualization designs include legends, labels, or accompanying verbal descriptions to help determine which visual features mean what. However, people have expectations about how visual features will map to concepts (called inferred mappings), and they find it more difficult to interpret visualizations that violate those expectations. Traditionally, studies on inferred mappings distinguished factors relevant for visualizations of categorical vs. continuous information. In this talk, I will discuss recent work that unites these two domains within a single framework of assignment inference. Assignment inference is the process by which people infer mappings between perceptual features and concepts represented in encoding systems. I will begin by presenting evidence that observers infer globally optimal assignments by maximizing the “merit,” or “goodness,” of each possible assignment. I will then discuss factors that contribute to merit in assignment inference and explain how we can model the combination of multiple (sometimes competing) sources of merit to predict human judgments. This work has increased our understanding of people’s expectations about the meanings of visual features, which can be used to make visual communication more effective and efficient.

ZOOM: https://arizona.zoom.us/j/83147532015
Passcode: Cogs595

Lab site: Visual Reasoning Lab