Sensory prediction errors play a key role in theories of sensorimotor control. I will give an overview of the two complementary roles of sensory prediction errors; their use in learning a forward model of the of the body and environment, and their use in state and context estimation for online control. I will then present ongoing work that examines how sensory prediction errors can be used to partition control and learning across multiple context-specific motor memories and determine when to develop a new memory rather than modify existing memories.
McNamee, D., & Wolpert, D. M. (2019). Internal models in biological control. Annual review of control, robotics, and autonomous systems, 2, 339-364.
Wolpert, D. M., & Flanagan, J. R. (2001). Motor prediction. Current biology, 11(18), R729-R732.
Speaker: Nathaniel Sawtell
Sensory prediction errors and the related concept of a forward model have an important role in theories of sensory processing and motor control and are well-supported by human behavioral studies. I will discuss examples in which putative neural correlates for sensory prediction errors have been identified along with what is known about the mechanisms for generating such signals. Recent work in the cerebellum questioning the traditional dichotomy between sensory and reward prediction errors will also be discussed.