Eye-movement sequence statistics and hypothesis-testing with classical recurrence analysis

Dynamical systems analysis tools, like Recurrence Plotting (RP), allow for concise mathematical representations of complex systems with relatively simple descriptive metrics. These methods are invariant for phase-space trajectories of a time series from a dynamical system, allowing analyses on simplified data sets which preserve the system model's dynamics. In the past decade, recurrence methods have been applied to eye-tracking, but those analyses avoided Time-Delay Embedding (TDE). Without TDE, we lose the assumption that phase-space trajectories are being preserved in the recurrence plot. Thus, analysis has been typically limited to clustering fixation locations in the image space, instead of clustering data sequences in the phase space. We will show how classical recurrence analysis methods can be extended to allow for multi-modal data visualization and quantification, by presenting an open-source python implementation for analyzing eye movements.