Per-Subject Oculomotor Plant Mathematical Models and the Reliability of Their Parameters

The practical value of oculomotor plant mathematical models (OPMMs) has been demonstrated across various domains, including biometrics and eye movement prediction. To further enhance their utilization, new optimization approaches are commonly developed and introduced within the research community. In this study, we demonstrate a new integration of a previously developed per-subject optimization procedure for an Enderle OPMM and introduce methods to evaluate the reliability of OPMM parameters using the intraclass correlation coefficient (ICC) and Kendall's Coefficient of Concordance (KCC). We evaluated two per-subject OPMM models, Bahill and Enderle, using the 'GazeBase' eye movement dataset. The models differed in accuracy, expressed as the per-sample error in degrees of visual angle, based on the differences between the actual and predicted saccade trajectories. We found that some of the parameter estimates for both models were quite unreliable. We discuss the importance of addressing low-reliability issues and suggest methods to modify the models to enhance reliability and performance.