In human vision, acuity and color sensitivity are greatest at the center of fixation and fall off rapidly as visual eccentricity increases. Humans exploit the high resolution of central vision by actively moving their eyes three to four times each second. Here we demonstrate that it is possible to classify the task that a person is engaged in from their eye movements using multivariate pattern classification. The results have important theoretical implications for computational and neural models of eye movement control. They also have important practical implications for using passively recorded eye movements to infer the cognitive state of a viewer, information that can be used as input for intelligent human-computer interfaces and related applications.
Henderson et al. REF demonstrated that it is possible to classify the task that a person is engaged in, i.e. cognitive states from their eye movements by multivariate pattern classification specifically naive Bayes.
Predicting Cognitive State from Eye Movements
PLoS ONE
PLoS ONE
Computer Science Medicine