Abstract

Current computational models predict reward based solely on learning. Real motivation involves that but also more. Brain reward systems can dynamically generate incentive salience, by integrating prior learned values with even novel physiological states (e.g., natural appetites; drug-induced mesolimbic sensitization) to cause intense desires that were themselves never learned. We hope future computational models may capture this too