Imitating Human Responses via a Dual-Process Model Approach

Human-autonomous system teaming is becoming more prevalent in the Air Force and in society. Often, the concept of a shared mental model is discussed as a means to enhance collaborative work arrangements between a human and an autonomous system. The idea being that when the models are aligned, the team is more productive due to an increase in trust, predictability, and apparent understanding. This research presents the Dual-Process Model using multivariate normal probability density functions (DPM-MN), which is a cognitive architecture algorithm based on the psychological dual-process theory. The dual-process theory proposes a bipartite decision-making process in people. It labels the intuitive mode as “System 1” and the reflective mode as “System 2”. The current research suggests by leveraging an agent which forms decisions based on a dual-process model, an agent in a human-machine team can maintain a better shared mental model with the user. Evaluation of DPM-MN in a game called Space Navigator shows that DPM-MN presents a successful dual-process theory motivated model

A Cognitive Architecture Based on Dual Process Theory

This paper proposes a cognitive architecture based on Kahneman’s dual process theory [1]. The long-term memory is modeled as a transparent neural network that develops autonomously by interacting with the environment. The working memory is modeled as a buffer containing nodes of the long-term memory. Computations are defined as processes in which working memory content is transformed according to rules that are stored in the long-term memory. In this architecture, symbolic and subsymbolic reasoning steps can be combined and resource-bounded computations can be defined ranging from formal proofs to association chains. Key words: cognitive architecture, dual process theory, computation, transparent neural network