A1079
Title: Some modeling considerations involving the exponentially-modified Gaussian (EMG) distribution
Authors: Yanxi Li - Metropolitan State University of Denver (United States) [presenting]
Abstract: Fitts' law is often employed as a predictive model for human movement, especially in the field of human-computer interaction. Models with an assumed Gaussian error structure are usually adequate when applied to data collected from controlled studies. However, observational data (often referred to as data gathered "in the wild") typically display noticeable positive skewness relative to a mean trend as users do not routinely try to minimize their task completion time. As such, the exponentially modified Gaussian (EMG) regression model has been applied to aimed movement data. However, it is also of interest to reasonably characterize those regions where a user likely was not trying to minimize their task completion time. A novel model is proposed with a two-component mixture structure - one Gaussian and one exponential - on the errors to identify such a region. An expectation-conditional-maximization (ECM) algorithm is developed to estimate such a model, and some of the algorithm's properties are established. The efficacy of the proposed model, as well as its ability to inform model-based clustering, are addressed through extensive simulations and an insightful analysis of a human-aiming performance study.
