Determining patterns in data is an important and often difficult task for scientists and students. Unfortunately, graphing and analysis software typically is largely inaccessible to users with vision impairment. Using sound to represent data (i.e., sonification or auditory graphs) can make data analysis more accessible; however, there are few guidelines for designing such displays for maximum effectiveness. One crucial yet understudied design issue is exactly how changes in data (e.g., temperature) are mapped onto changes in sound (e.g., pitch), and how this may depend on the specific user. In this study, magnitude estimation was used to determine preferred data-to-display mappings, polarities, and psychophysical scaling functions relating data values to underlying acoustic parameters (frequency, tempo, or modulation index) for blind and visually impaired listeners. The resulting polarities and scaling functions are compared to previous results with sighted participants. There was general agreement about polarities obtained with the two listener populations, with some notable exceptions. There was also evidence for strong similarities regarding the magnitudes of the slopes of the scaling functions, again with some notable differences. For maximum effectiveness, sonification software designers will need to consider carefully their intended users’ vision abilities. Practical implications and limitations are discussed.
Universal Design of Auditory Graphs: A Comparison of Sonification Mappings for Visually Impaired and Sighted Listeners
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