Bayesian Approach to Multimodal Data in Human Factors Engineering

Abstract

Human behavior is complex, especially when engaging with large or ill-defined systems, and traditional statistical approaches typically cannot directly analyze and interpret human behavioral data. There are several statistical techniques that could be applied to modeling human behavior; however, because of the parallels between Bayesian thinking and how humans interact with complex systems, Bayesian analytical methods may be the most appropriate for modeling and predicting human behavior. This chapter reviews human factors research and experimentation, Bayesian thinking, and demonstrates an example of how to perform a Bayesian statistical analysis of human behavioral data in a human–computer interaction task. The data used in the numerical example comes from an experimental study on 3D gestural human–computer interaction for anesthetic tasks [1]. Anesthesia providers were asked to perform hand gestures to interact with a computer system and perform specific anesthetic tasks, and a Bayesian statistical approach was used to predict intuitive gesture choice based on system factors (e.g., contextual task) and individual factors (e.g., expertise). The data of interest for the Bayesian modeling is the intuitive gesture choice made by the participants which is categorical with many different levels. Thus, the numerical example outlined in this chapter analyzes data that is distributed multinomially.