title: Bayesian selection of Markov models for symbol sequences: application to microsaccadic eye movements.
abstract: Complex biological dynamics often generate sequences of discrete events which can be described as a Markov process. The order of the underlying Markovian stochastic process is fundamental for characterizing statistical dependencies within sequences. As an example for this class of biological systems, we investigate the Markov order of sequences of microsaccadic eye movements from human observers. We calculate the integrated likelihood of a given sequence for various orders of the Markov process and use this in a Bayesian framework for statistical inference on the Markov order. Our analysis shows that data from most participants are best explained by a first-order Markov process. This is compatible with recent findings of a statistical coupling of subsequent microsaccade orientations. Our method might prove to be useful for a broad class of biological systems.
authors.affiliation: Institute of Mathematics, Focus Area for Dynamics of Complex Systems, University of Potsdam, Potsdam, Germany. mario.bettenbuehl@uni-potsdam.de
authors.forename: Mario
authors.initials: M
authors.lastname: Bettenbühl
authors.forename: Marco
authors.initials: M
authors.lastname: Rusconi
authors.forename: Ralf
authors.initials: R
authors.lastname: Engbert
authors.forename: Matthias
authors.initials: M
authors.lastname: Holschneider
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D005403:Fixation, Ocular / Q000502:physiology; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D012438:Saccades / Q000502:physiology*; D013647:Task Performance and Analysis; D055815:Young Adult