EAGER/Collaborative Research: Challenging the Cognitive-Control Divide

这个早期概念探索性研究(AGUGER)合作研究项目是一位机器人和控制理论专家与一位实验和计算运动神经科学专家之间的合作研究。它在认知科学、实验心理学和控制工程之间架起了桥梁。这项工作的智力前提是，人类认知的量化理论可能建立在人类运动功能的极限情况之上。这一前提为控制相关认知的全面量化理论的发展奠定了基础。其结果将成为复杂运动控制系统的人性化设计的无价工具。基于这些基本对象的控制策略将更直观地被人类操作员理解，包括对即将发生的故障的预测。将结果扩展到运动控制之外，提供了一种新的知识处理系统，能够与人类进行更自然的交互。这个项目的目标是阐明和测试人类认知的量化的、与控制相关的理论，以解决认知科学和控制理论之间日益扩大的分歧。核心假设是，认知功能源于用于运动控制的神经结构，并受到其制约。复杂的运动动作由定义为吸引子(例如，固定点、极限环等)的动态原语的有限“库”组成。该项目假设，认知过程中存在类似的动态基元成分，通过重新调整运动行为中的动态基元的用途，可以获得定量的细节，特别是在操纵复杂物体时，如工具，其中运动和认知功能之间的联系可能最强。该项目基于一系列新颖的实验：数据序列是由不同的人类参与者物理操作复杂的动态对象产生的。替代数据集是通过计算机模拟相同目标的运动来生成的，这些运动最小化了均方作用力。由低通滤波的零均值高斯白噪声产生的、与人类表现的波动相当的随机波动被添加到模拟力和运动时间序列中。在没有被告知来源的情况下，第二组受试者被呈现为演变的抽象时间序列的结果，并被要求预测他们的结果。随后，他们被要求根据他们的经验为抽象系统生成控制输入，以完成指定的任务。根据这一假设，受试者将比合成系统更成功地预测人类控制系统的结果，并将产生与人类控制系统输入更接近的控制输入。

This EArly-concept Grant for Exploratory Research (EAGER) collaborative research project is between an expert in robotics and control theory and an expert in experimental and computational motor neuroscience. It bridges cognitive science, experimental psychology and control engineering. The intellectual premise of the work is that a quantitative theory of human cognition may be built on top of limiting cases of human motor function. This premise lays the foundation for the development of a comprehensive quantitative theory of control-relevant cognition. The result will be an invaluable tool for the human-friendly design of complex motion control systems. Control strategies based on these fundamental objects would be more intuitively understandable by human operators, including prediction of impending failure. Extension of the results beyond motion control provide a new class of knowledge-processing systems capable of more natural interactions with humans. The objective of this project is to articulate and test a quantitative, control-relevant theory of human cognition, to address a growing divide between cognitive science and control theory. The core hypothesis is that cognitive functions emerged from and are constrained by neural structures used for motor control. Complex motor actions are composed from a limited "library" of dynamic primitives, defined as attractors (e.g. fixed points, limit cycles, etc.). The project postulates that a similar composition of dynamic primitives underlies cognitive processes and that quantitative details may be obtained by re-purposing dynamic primitives found in motor behavior, especially in the manipulation of complex objects such as tools where the link between motor and cognitive function may be strongest. The project is based on a novel series of experiments: A data series is generated by various human participants physically manipulating a complex dynamic object. Alternative data sets are generated by computer simulation of movements to the same targets that minimize mean-squared applied force. Random fluctuations generated by low-pass filtered zero-mean Gaussian white noise and of magnitude comparable to the fluctuations in human performance are added to the simulated force and motion time-series. Without being told the origin, a second set of subjects are presented with the results as evolving abstract time-series and asked to predict their outcome. Subsequently, they are asked to generate a control input for the abstract system, based on their experience, to accomplish a specified task. According to the hypothesis, the subjects will more successfully predict the outcome of human-controlled systems than the synthetic systems, and will generate control inputs that more closely match the human-controlled system inputs.