 According to this hypothesis of Wolfram Schultz, rewards that are expected do not produce a second phasic dopamine response in certain dopaminergic cells, but rewards that are unexpected, or greater than expected, produce a short-lasting increase in synaptic dopamine, whereas the omission of an expected reward actually causes dopamine release to drop below its background level.The "prediction error" hypothesis has drawn particular interest from computational neuroscientists, because an influential computational-learning method known as temporal difference learning makes heavy use of a signal that encodes prediction error.This confluence of theory and data has led to a fertile interaction between neuroscientists and computer scientists interested in machine learning.