{'fraction_non_alphanumeric': 0.02467343976777939, 'fraction_numerical': 0.01741654571843251, 'mean_word_length': 5.0, '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}}
Towards visualizing eye movement data from interactive stimuli

Recording of eye movement data can help to understand where and at what participants look. However, analyzing eye movement data is a time consuming task. Using visualization techniques in the analysis process can help to uncover concealed relationships within the data and can therefore be seen as one means in the analysis of eye movement data. The most well known visualization techniques in eye tracking are heat maps or scanpaths. In recent years more visualization techniques have been developed, as for example scaled traces [Goldberg and Helfman 2010], eyePatterns [West et al. 2006], or eSeeTrack [Tsang et al. 2010].