Cognitive state prediction using an EM algorithm applied to Gamma distributed data
Ali Yousefi, Angelique C. Paulk, Thilo Deckersbach, Darin Dean Dougherty, Emad N. Eskandar, Alik S. Widge, Uri T. Eden · 2015
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.