An Affordance-Based Model of Human Action Selection in a Human-Machine Interaction System with Cognitive Interpretations

Abstract

Current technology is not sufficient to automate all desired tasks. Human-machine interaction (HMI) has thus become a key control and design factor for tasks requiring human-level decision-making or information synthesis. Such processes require a formal representation of human actions (including decision-making) when modeling HMI systems; however, successful prescriptive approaches to this end have still been elusive. This article extends the affordance-based finite state automata model, conditioning human prior experience and natural memory decay of task knowledge (or skill decay). The new model draws upon both reinforcement learning and natural memory decay for decision-making on action choice. An empirical study is carried out to specify how action choice is affected or updated by reinforcement learning based on past experience, and Wickelgren's decay function is jointly employed to predict human decision-making behavior.