A Control-Systems Approach to Understanding Human Learning

理解人类学习的控制系统方法

• 批准号: 1405257
• 负责人: Jesse Hoagg
• 金额: $ 24.95万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1405257&HistoricalAwards=false
• 关键词: Control; Systems; Approach; Understanding; Human

The objective of this project is to understand how humans learn to control unknown dynamic systems. Humans possess exceptional learning capabilities that help them control complex systems with virtually no prior information. For example, humans ride bicycles, fly kites, and play with hula hoops. No existing control technique can match a human's ability to learn to interact with a wide variety of uncertain dynamic systems. This project addresses fundamental questions of human learning and control: What control strategies do humans learn? How do humans learn to control unknown dynamic systems? Understanding human learning has a high potential to transform a wide variety of technologies, such as human-interface devices and robotic-assist systems. For example, neurological injuries often lead to impaired motor control. Robotic-therapy devices have demonstrated some success in rehabilitation; however, improved understanding of human learning is necessary to unlock the potential of these technologies. As another example, learning to interact with complex systems, such as orthotic devices and haptic interfaces, can require significant training. Improved understanding of human learning will lead to interactive methods that accelerate learning. The control strategies that humans learn and the processes used to learn them are currently unknown. The predominant human-learning theory in neuroscience is the "internal model" hypothesis, which proposes that humans construct and use models for control. However, evidence in support of the internal model hypothesis is inconclusive. This project offers a new approach to human-learning research that applies principles from control systems to address fundamental questions of human learning and control. Specifically, a series of human-subject-based experiments will be performed to study human learning. First, this project seeks to identify the strategies that humans employ to control dynamic systems. These experiments focus on identifying the strategies that humans use for systems with challenging characteristics, such as nonlinearities, instabilities, nonminimum-phase-zero dynamics, and high relative degree. A novel subsystem identification method will be used to mathematically model the controllers employed by the human subjects. Next, this project aims to identify the learning mechanisms that allow humans to adapt to and control unknown dynamic systems. Human learning mechanisms will be studied by examining how humans learn at different frequencies, by comparing human learning to adaptive control, and by exploring how humans use persistently exciting signals to learn.

该项目的目标是了解人类如何学习控制未知的动态系统。人类拥有卓越的学习能力，可以帮助他们在几乎没有先验信息的情况下控制复杂的系统。例如，人类骑自行车、放风筝、玩呼啦圈。现有的控制技术无法与人类学习与各种不确定动态系统交互的能力相媲美。该项目解决了人类学习和控制的基本问题：人类学习什么控制策略？人类如何学习控制未知的动态系统？了解人类学习具有很大的潜力来改变各种技术，例如人机界面设备和机器人辅助系统。 例如，神经损伤通常会导致运动控制受损。机器人治疗设备在康复方面已经取得了一些成功；然而，要释放这些技术的潜力，必须加深对人类学习的理解。另一个例子，学习与复杂系统（例如矫形设备和触觉界面）交互可能需要大量培训。对人类学习的理解的提高将带来加速学习的交互式方法。人类学习的控制策略以及用于学习这些策略的过程目前尚不清楚。神经科学中占主导地位的人类学习理论是“内部模型”假说，该假说提出人类构建和使用模型进行控制。然而，支持内部模型假设的证据尚无定论。该项目为人类学习研究提供了一种新方法，该方法应用控制系统的原理来解决人类学习和控制的基本问题。具体来说，将进行一系列基于人类受试者的实验来研究人类学习。首先，该项目旨在确定人类用来控制动态系统的策略。这些实验的重点是确定人类用于具有挑战性特征的系统的策略，例如非线性、不稳定性、非最小相位零动力学和高相对度。一种新颖的子系统识别方法将用于对人类受试者使用的控制器进行数学建模。接下来，该项目旨在确定使人类能够适应和控制未知动态系统的学习机制。将通过研究人类如何以不同频率学习、将人类学习与适应性控制进行比较以及探索人类如何使用持续令人兴奋的信号来学习来研究人类学习机制。