Experimental Investigation and Queuing Network (QN) Modeling of Speed-Accuracy Tradeoff (SAT) and Speed-Confidence Tradeoff (SCT) in Multi-Task Human-Robot Collaboration (HRC)

Abstract

In Human-Robot Collaboration (HRC) environments, people are often required to perform multiple tasks under time pressure while monitoring or interacting with robotic systems. Time pressure may make people decide faster, but with lower accuracy and confidence, showing Speed-Accuracy Tradeoff (SAT) and Speed-Confidence Tradeoff (SCT). Understanding how humans make decisions under multitasking conditions with time pressure is important for enhancing safety and productivity. To investigate these effects, we conducted experiments in which participants viewed video clips of a robot arm reaching for one of two possible objects and predicted the robot’s final target with four levels of time pressure. In the multitasking condition, participants also performed a concurrent tracking task to simulate a continuous robot control task. Experimental results revealed clear evidence of SAT and SCT, along with significant negative effects of multitasking on prediction accuracy, confidence, and response time. To explain and account for these effects, we developed a computational model, by using the departure processes of the Queuing Network–Model Human Processor (QN-MHP) as a diffusion decision model. The model accurately replicated experimental results, highlighting its potential for predicting human behavior in multitasking human-robot collaboration scenarios.