This project is being funded through the Learning and Intelligent Systems (LIS) initiative. It is concerned with learning that is incremental in nature, resulting in skills that improve over time. Such learning is responsible for the development of perceptual discriminations, stimulus associations, and motor skills, and is seen in both humans and animals. The goal of the research is a systems-level neural theory of incremental learning. The project combines computer simulations with neurophysiological recording from the brains of behaving rats and monkeys, functional magnetic resonance imaging in humans, and robotic implementation. It is known that many parts of the mammalian brain contribute to incremental learning. Although the cortex may play a central role, other brain areas known to make vital contributions include the basal ganglia, hippocampus, amygdala, and cerebellum. It is important to understand the roles of these various areas and their interactions with each other during learning. Experimental work to be performed includes recording from various areas of the brains of rats and monkeys during learning and imaging the brains of humans performing analogous tasks. Models of neural function will be developed based on the monkey experiments and will be implemented on a mobile robot. These implemented models will allow the training of the robot, and a comparison of the learning that takes place with that observed in humans and animals. The development of theories of incremental learning will provide a better understanding of how the process occurs and may result in improved approaches to the development of skills in both humans and machines.
