title: Cognitive state prediction using an EM algorithm applied to Gamma distributed data.
abstract: Behavioral tests are widely used to quantify features of cognitive processing. For a large class of behavioral signals, the observed variables are non-Gaussian and dynamic; classical estimation algorithms are ill-suited to modeling such data. In this research, we propose a mathematical framework to predict a cognitive state variable related to behavioral signals, which are best modeled using a Gamma distribution. The proposed algorithm combines a Gamma Smoother and EM algorithm in the prediction process. The algorithm is applied to reaction time recorded from subjects performing a Multi-Source Interference Task (MSIT) to dynamically quantify their cognitive flexibility through the course of the experiment.
authors.forename: Ali
authors.initials: A
authors.lastname: Yousefi
authors.forename: Angelique C
authors.initials: AC
authors.lastname: Paulk
authors.forename: Thilo
authors.initials: T
authors.lastname: Deckersbach
authors.forename: Darin D
authors.initials: DD
authors.lastname: Dougherty
authors.forename: Emad N
authors.initials: EN
authors.lastname: Eskandar
authors.forename: Alik S
authors.initials: AS
authors.lastname: Widge
authors.forename: Uri T
authors.initials: UT
authors.lastname: Eden
mesh_terms: D000465:Algorithms; D003071:Cognition / Q000502:physiology*; D006801:Humans; D008962:Models, Theoretical; D011601:Psychophysics / Q000706:statistics & numerical data; D011930:Reaction Time; D016008:Statistical Distributions