Predicting an observer's task using multi-fixation pattern analysis

Since Yarbus's seminal work in 1965, vision scientists have argued that people's eye movement patterns differ depending upon their task. This suggests that we may be able to infer a person's task (or mental state) from their eye movements alone. Recently, this was attempted by Greene et al. [2012] in a Yarbus-like replication study; however, they were unable to successfully predict the task given to their observer. We reanalyze their data, and show that by using more powerful algorithms it is possible to predict the observer's task. We also used our algorithms to infer the image being viewed by an observer and their identity. More generally, we show how off-the-shelf algorithms from machine learning can be used to make inferences from an observer's eye movements, using an approach we call Multi-Fixation Pattern Analysis (MFPA).