A computational model of human trust in supervisory control of robotic swarms

Trust is an important factor in the interaction between humans and automation to mediate the reliance action of human operators. In this work, we study human factors in supervisory control of robotic swarms and develop a computational model of human trust on swarm systems with varied levels of autonomy (LOA). We extend the classic trust theory by adding an intermediate feedback loop to the trust model, which formulates the human trust evolution as a combination of both open-loop trust anticipation and closed-loop trust feedback. A Kalman filter model is implemented to apply the above structure. We conducted a human experiment to collect user data of supervisory control of robotic swarms. Participants were requested to direct the swarm in a simulated environment to finish a foraging task using control systems with varied LOA. We implement three LOAs: manual, mixed-initiative (MI), and fully autonomous LOA. In the manual and autonomous LOA, swarms are controlled by a human or a search algorithm exclusively, while in the MI LOA, the human operator and algorithm collaboratively control the swarm. We train a personalized model for each participant and evaluate the model performance on a separate data set. Evaluation results show that our Kalman model outperforms existing models including inverse reinforcement learning and dynamic Bayesian network methods. In summary, the proposed work is novel in the following aspects: 1) This Kalman estimator is the first to model the complete trust evolution process with both closed-loop feedback and open-loop trust anticipation. 2) The proposed model analyzes time-series data to reveal the influence of events that occur during the course of an interaction; namely, a user’s intervention and report of levels of trust. 3) The proposed model considers the operator’s cognitive time lag between perceiving and processing the system display. 4) The proposed model uses the Kalman filter structure to fuse information from different sources to estimate a human operator's mental states. 5) The proposed model provides a personalized model for each individual

Fundamental Principles of Neural Organization of Cognition

The manuscript advances a hypothesis that there are few fundamental principles of neural organization of cognition, which explain several wide areas of the cognitive functioning. We summarize the fundamental principles, experimental, theoretical, and modeling evidence for these principles, relate them to hypothetical neural mechanisms, and made a number of predictions. We consider cognitive functioning including concepts, emotions, drives-instincts, learning, “higher” cognitive functions of language, interaction of language and cognition, role of emotions in this interaction, the beautiful, sublime, and music. Among mechanisms of behavior we concentrate on internal actions in the brain, learning and decision making. A number of predictions are made, some of which have been previously formulated and experimentally confirmed, and a number of new predictions are made that can be experimentally tested. Is it possible to explain a significant part of workings of the mind from a few basic principles, similar to how Newton explained motions of planets? This manuscript summarizes a part of contemporary knowledge toward this goal