Actor-Critis algorithms from Reinforcement Learning (in machine learning) have two artificial neural netwroks (ANNs).
The actor network is responsible for selecting an action given a state of the environment.The critic network is responsible for evaluating the action selected by the actor network by learning the value of environment states.Our cortex and numerous subcortical nuclei (e.g. basal ganglia) are responsible for learning and optimizing rewards.
In particular these areas of the brain learn to criticize others by predicting the rewards or punishments for our actions.These are areas which push us to act or remain silent.We also have metacogntion - the ability to predict what will happen if we act in different ways.