Learning from errors
Learning through trial and error is required for daily living (“Experience is simply the name we give our mistakes” – Oscar Wilde), whether it be for a child learning to write or a stroke patient learning to walk again. Behavioral computational models provide a framework for understanding the impact of error on human learning. They pose that the brain continuously updates an error-sensitivity signal controlling how much is learned from past errors. Using machine-learning decoding of brain activity during learning tasks, we will identify the neural representation of error-sensitivity. This project will provide crucially missing information about the brain computations during learning.