The effects of motivation on habitual instrumental behavior


Yael Niv


This thesis provides a normative computational analysis of how motivation affects decision making. More specifically, we provide a reinforcement learning model of optimal self-paced (free-operant) learning and behavior, and use it to address three broad classes of questions: (1) Why do animals work harder in some instrumental tasks than in others? (2) How do motivational states affect responding in such tasks, particularly in those cases in which behavior is habitual, that is, when responding is insensitive to changes in the specific worth of its goals, such as a higher value of food when hungry rather than sated? and (3) Why do dopaminergic manipulations cause global changes in the vigor of responding, and how is this related to prominent accounts of the role of dopamine in providing basal ganglia and frontal cortical areas with a reward prediction error signal that can be used for learning to choose between actions?