A Control-Systems Approach to Understanding Human Learning

该项目的目标是了解人类如何学习控制未知的动态系统。人类拥有特殊的学习能力，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，玩呼拉圈。没有现有的控制技术可以匹配人类的能力，学习与各种各样的不确定动态系统进行交互。这个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？人类如何学会控制未知的动态系统？理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。机器人治疗设备已经在康复方面取得了一些成功;然而，要释放这些技术的潜力，必须提高对人类学习的理解。作为另一示例，学习与复杂系统（诸如矫正设备和触觉接口）交互可能需要大量的训练。对人类学习的更好理解将导致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它们的过程目前尚不清楚。在神经科学中占主导地位的人类学习理论是“内部模型”假说，它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是不确定的。该项目为人类学习研究提供了一种新的方法，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，非最小相位零动态和高相对度。一种新的子系统识别方法将被用来数学建模的控制器所采用的人类受试者。接下来，该项目旨在确定允许人类适应和控制未知动态系统的学习机制。人类学习机制将通过研究人类如何在不同频率下学习，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兴奋的信号来学习来研究。

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.