Adaptive Sensory-Motor Planning by Humans and Machines

点击翻译按钮获取中文摘要

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.