SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies

Abstract

Our eye movements are driven by a continuous trade-off between the need for detailed examination of objects of interest and the necessity to keep an overview of our surrounding. In consequence, behavioral patterns that are characteristic for our actions and their planning are typically manifested in the way we move our eyes to interact with our environment. Identifying such patterns from individual eye movement measurements is however highly challenging. In this work, we tackle the challenge of quantifying the influence of experimental factors on eye movement sequences. We introduce an algorithm for extracting sequence-sensitive features from eye movements and for the classification of eye movements based on the frequencies of small subsequences. Our approach is evaluated against the state-of-the art on a novel and a very rich collection of eye movements data derived from four experimental settings, from static viewing tasks to highly dynamic outdoor settings. Our results show that the proposed method is able to classify eye movement sequences over a variety of experimental designs. The choice of parameters is discussed in detail with special focus on highlighting different aspects of general scanpath shape. Algorithms and evaluation data are available at: www.ti.uni-tuebingen.de/scanpathcomparison.html.

How different visualizations affect human reasoning about uncertainty: An analysis of visual behaviour

“…R-Rl is the transition from total rainy days to rainy days with low pressure). An analysis of longer subsequences was not performed as the possible number of unique transition patterns increases dramatically with the pattern length, and each tends to occur with a much lower frequency, leading to sparse data that is difficult to interpret (Kübler et al 2017;. To identify significant differences in the distribution of transitions between the Correct and Incorrect groups, two permutation tests, one for each condition, with 10,000 permutations each were performed using the Hellinger distance as defined by equation 2.…”

Predicting artificial visual field losses: A gaze-based inference study

“…Plotting a receiver operating characteristic curve, the authors reported a 76% sensitivity at 90% specificity. Kübler, Rothe, Schiefer, Rosenstiel, and Kasneci (2017) obtained above-chance-level results when separating patients (glaucoma and hemianopia) who failed at a driving task from patients who succeeded at the same task, as well as a healthy control group.…”

Tang Dynasty Chair Feature Design Based on Kansei Evaluation and Eye Tracking System

“…Previous research (Nodine et al 1993) has indicated that feature importance is correlated with a variety of gaze response data variables such as fixation time (Isham and Geng 2013), fixation points, and first-located time (Kubler et al 2017). This gaze data provided insight into how people evaluate the features while making preference decisions (Du and Macdonald 2014, Ho and Lu 2014, Husic-Mehmedovic et al 2017).…”