Adaptive Sensory-Motor Planning by Humans and Machines

这是i -9045939授权的续期。该项目旨在开发神经网络架构，以解释行为个体如何成功地实时适应不断变化的环境所施加的约束。这些体系结构能够以这样一种方式自动调整，即尽管环境在变化，高层目标和计划仍能继续实现。项目包括分析三维空间的表征是如何实时自主学习的。它将解决如何在一个更大的系统中使用这种空间表征，该系统能够学习执行熟练的手臂运动序列，例如手写和视觉引导的对象操作。该项目将研究如何在没有任何进一步学习的情况下，使用可变长度和质量的工具（如笔或钩子）执行计划好的动作序列。它还将研究工作记忆如何完成可变速率、实时短期记忆存储的时间顺序代码，如快速语音、打字和眼动序列的代码。这个项目将研究时间规划网络的自组织层次如何压缩复杂动作序列的控制。这将导致神经网络架构如何学习控制方法来实现有价值的目标。最后，该研究将讨论如何将感觉-认知学习系统和认知-运动学习系统的互补设计结合到一个神经结构中，从而实现每种设计的优点，而不受其单独的限制。所有的项目都将通过涉及大型行为和神经数据库的计算机模拟来测试其神经结构的生物学相关性。他们还将提供这些架构的数学和计算分析，以表征具有新的实时处理能力的自适应智能机器的电路设计。这个项目将提供在人机交互过程中对用户的不同层次建模的基本见解。

This is a renewal of grant IRI-9045939. This project aims at the development of neural network architectures to explain how behaving individuals adapt successfully in real-time to constraints imposed by changing environments. These architectures are capable of automatically adjusting in such a way that high level goals and plans continue to be realized despite changing circumstances. Projects include an analysis of how representations of 3-D space are autonomously learned in real-time. It will address how such spatial representations can be used in a larger system capable of learning to perform skilled arm movement sequences, such as handwriting and visually-guided object manipulation. The project will examine how planned action sequences can be performed with a tool of variable length and mass, such as a pen or hook, without any further learning. It will also study how a working memory can accomplish variable- rate, real-time short term memory storage of temporal order codes, such as codes for rapid speech, typewriting, and eye movement sequences. This project will examine how a self- organizing hierarchy of temporal planning networks can compress the control of complex action sequences. This will lead into how a neural network architecture can learn to control approach toward a valued goal. Finally, the research will address how complementary designs for sensory-cognitive learning systems and cognitive-motor learning systems can be combined into a neural architecture that achieves the benefits of each type of design, without their individual limitations. All of the projects will test the biological relevance of their neural architectures by computer simulations involving large behavioral and neural data bases. They will also provide mathematical and computational analyses of these architectures to characterize circuit designs for adaptive, intelligent machines with new real-time processing capabilities. This project will provide basic insights into various levels of modeling of a user during human-computer interactions.