Cognitive Modeling of Automation Adaptation in a Time Critical Task

Frontiers in Psychology

article

Junya Morita¹  Kazuhisa Miwa²  Akihiro Maehigashi³  Hitoshi Terai⁴  Kazuaki Kojima⁵  Frank E. Ritter⁶ 

[1] Department of Behavior Informatics, Faculty of Informatics, Shizuoka University;Department of Cognitive and Psychological Sciences, Graduate School of Informatics, Nagoya University;Center for Research and Development in Admissions, Shizuoka University;Department of Information and Computer Sciences, Faculty of Humanity-Oriented Science and Engineering, Kinki University;Learning Technology Laboratory, Teikyo University;College of Information Sciences and Technology, Pennsylvania State University, University Park, United States

关键词: automated operation;    reinforcement learning;    ACT-R;    Semi-Markov Decision Process;    trust calibration;   

DOI  :  10.3389/fpsyg.2020.02149

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

This paper presents a cognitive model that simulates an adaptation process to automation in a time-critical task. The paper uses a simple tracking task (which represents vehicle operation) to reveal how the reliance on automation changes as the success probabilities of the automatic and manual mode vary. The model was developed by using a cognitive architecture, ACT-R (Adaptive Control of Thought-Rational). We also introduce two methods of reinforcement learning: the summation of rewards over time and a gating mechanism. The model performs this task through productions that manage perception and motor control. The utility values of these productions are updated based on rewards in every perception-action cycle. A run of this model simulated the overall trends of the behavioral data such as the performance (tracking accuracy), the auto use ratio, and the number of switches between the two modes, suggesting some validity of the assumptions made in our model. This work shows how combining different paradigms of cognitive modeling can lead to practical representations and solutions to automation and trust in automation.

【 授权许可】

CC BY