Abstract:
Auditory graphsdisplays that represent graphical, quantitative information with soundhave the potential to make graphical representations of data more accessible to blind students and researchers as well as sighted people. No research to date, however, has systematically addressed the attributes of data that contribute to the complexity (the ease or difficulty of comprehension) of auditory graphs. A pair of studies examined the role of both data density (i.e., the number of discrete data points presented per second) and the number of trend reversals for both point estimation and trend identification tasks with auditory graphs. For the point estimation task, results showed main effects of both variables, with a larger effect attributable to performance decrements for graphs with more trend reversals. For the trend identification task, a large main effect was again observed for trend reversals, but an interaction suggested that the effect of the number of trend reversals was different across lower data densities (i.e., as density increased from 1 to 2 data points per second). Results are discussed in terms of data sonification applications and rhythmic theories of auditory pattern perception.