Inferring attention through cursor trajectories

AbstractThe present research infers aspects of spatial attention from movement to targets (and preferably not to foils) of a mouse-controlled cursor on a computer monitor. The long-term goal is a data-rich and rapid assessment technique that can be used to diagnose individual and clinical deficits of attention. The aim of this present research is validating the approach using a college population of subjects. In the experiment, participants attempt to move a cursor toward three spatial positions at which targets appear rapidly but at irregular times, and attempt to inhibit movements toward foils appearing at those positions. We assume that cursor movements toward a position indicates attention has been directed toward that position. A modified Hidden Markov Model (HMM) uses five sources of evidence, all based on parameters to be estimated, to predict the time varying movement of attention: four aspects of cursor movement and a probability that attention will move from one time interval to the next. Five minutes of data are used to estimate parameters for each subject that produce a predicted attention trajectory which best matches what the subject is instructed to do. These parameters are used to predict the attention trajectory for the remainder of the hour of testing. The predictions of attention movements are then matched to the appearance of targets and foils to infer such components of attention as ability to respond to targets vs foils, times to do so, and changes in these components over time. The results illustrate a promising approach to assessment of attention that could likely be employed for both adults and children in clinical settings requiring short testing periods.


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