Computational Human Performance Modeling using Queuing Network in an Open-Source Platform

Abstract

Traditional usability testing requires using human subjects to perform designed tasks and measuring their performance (e.g., task completion time, workload). Although human-subject tests have great values, they are often time consuming and expensive, which prevents User Interface (UI) designers from exploring a larger design space, especially in the early design stages. A computational human model is promising to serve as a supplementary tool for designers to test a large number of design concepts and prototypes using simulations. The cognitive architecture of QN-MHP (Queuing Network-Model Human Processor) simulates human cognition as a queuing network of information processing servers based on the findings from experimental psychology and neuroscience, and it has been shown as a promising tool for UI testing. Previously, QN-MHP was implemented using several commercial software. In this project, a basic version of QN-MHP is implemented in an open-source platform with Python and a discrete event simulation library SimPy. This allows the model to be used and co-developed with the broader research community of computational human modeling. The implemented model was verified and validated with simple visual-manual tasks by comparing the model outputs with the empirical data from the literature.