Task Classification Model for Visual Fixation, Exploration, and Search
This provides evidence for a long-debated hypothesis in vision science, though it is incremental as it applies existing methods to a specific dataset.
The paper tackled the problem of decoding an observer's task from eye movements, achieving 95.4% accuracy in task classification using filtered eye movement data.
Yarbus' claim to decode the observer's task from eye movements has received mixed reactions. In this paper, we have supported the hypothesis that it is possible to decode the task. We conducted an exploratory analysis on the dataset by projecting features and data points into a scatter plot to visualize the nuance properties for each task. Following this analysis, we eliminated highly correlated features before training an SVM and Ada Boosting classifier to predict the tasks from this filtered eye movements data. We achieve an accuracy of 95.4% on this task classification problem and hence, support the hypothesis that task classification is possible from a user's eye movement data.