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?
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