{'fraction_non_alphanumeric': 0.02883799830364716, 'fraction_numerical': 0.0016963528413910093, 'mean_word_length': 5.344086021505376, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Data density and trend reversals in auditory graphs: Effects on point-estimation and trend-identification tasks

Auditory graphs—displays that represent quantitative information with sound—have the potential to make data (and therefore science) more accessible for diverse user populations. 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 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, more trend reversals led to performance decrements. 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.