Predicting Cognitive State from Eye Movements

TLDR

Human vision has highest acuity at the center of fixation, and people move their eyes several times per second to exploit this. The study aims to classify the task a person is engaged in from their eye movements using multivariate pattern classification. They apply multivariate pattern classification to eye‑movement recordings to infer task identity. The approach yields theoretical insights into eye‑movement control models and practical utility for inferring cognitive state to inform intelligent human‑computer interfaces.

Abstract

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.