A Heuristic Strategy for Cognitive State-based Feedback Control to Accelerate Human Learning. Issue 41 (2022)

• Record Type: Journal Article
• Authors: Yuh, Madeleine S.; Byeon, Sooyung; Hwang, Inseok; Jain, Neera
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 41(2022)
• Page Start: 107
• Page End: 112
• Publication Date: 2022
• Subjects: Co-Learning -- self-learning -- Human centered automation -- Human self–confidence -- Shared control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2023.01.111
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Autonomous systems are increasingly being used for the purpose of training humans to attain new skills or perform new tasks. In these contexts, autonomous systems should be responsive to, and guide, human behavior such that skill or task performance is maximized. These systems generally rely on human performance to determine if assistance is needed. However, it is recognized that these systems should also respond to human cognitive factors, such as self–confidence, that are relevant for human learning. We propose and experimentally validate a heuristic control strategy, based on both a user's performance and self-reported self-confidence as they, that determines whether or not they receive automated assistance in learning how to land a quadrotor in a simulated environment. Through a human subject study involving a benchmark strategy that is solely performance-based, we show that the proposed strategy not only successfully calibrates the self-confidence of the participants, but also leads to statistically significant improvements in participants' task performance and consistency after 20 trials relative to outcomes for participants who experience the benchmark strategy.