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, particu- larly 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? A fundamental question in behavioral neuroscience concerns the decision-making processes by which an- imals and humans select actions in the face of reward and punishment. In Chapter 1 we provide a brief overview of the current status of this research, focused on three themes: behavior, computation and neural substrates. In behavioral psychology, this question has been investigated through the paradigms of Pavlo- vian (classical) and instrumental (operant) conditioning, and much evidence has accumulated regarding the associations that control different aspects of learned behavior. The computational field of reinforcement learning has provided a normative framework within which conditioned behavior can be understood. In this, optimal action selection is based on predictions of long-run future consequences, such that decision making is aimed at maximizing rewards and minimizing punishment. Neuroscientific evidence from lesion studies, pharmacological manipulations and electrophysiological recordings in behaving animals have further pro- vided tentative links to neural structures underlying key computational constructs in these models. Most notably, much evidence suggests that the neuromodulator dopamine provides basal ganglia target structures with a reward prediction error that can influence learning and action selection, particularly in stimulus-driven habitual instrumental behavior. However, although reinforcement learning models have long promised to unify computational, psycholog- ical and neural accounts of appetitively conditioned behavior, we claim here that they suffer from a large theoretical oversight. While a bulk of data on animal conditioning comes from free-operant experiments measuring how fast animals will work for reinforcement, existing reinforcement learning models lack any notion of vigor or response rate, focusing instead only on competition between different responses, and so they are silent about these tasks. In Chapter 2 we first review the basic characteristics of free-operant behavior, illustrating the effects of reinforcement schedules on rates of responding. We then develop a rein- forcement learning model in which vigor selection is optimized together with response selection. The model suggests that subjects choose how vigorously to perform selected actions by optimally balancing the costs and benefits of different speeds of responding. Importantly, we show that this model accounts normatively for effects of reinforcement schedules on response rates, such as the fact that responding on ratio schedules is faster than responding on interval schedules that yield the same rate of reinforcement. Finally, the model highlights the importance of the net rate of rewards in quantifying the opportunity cost of time, and thus in determining response vigor. In Chapter 3 we flesh out the implications of this model for the motivational control of habitual behavior. In general, understanding the effects of motivation on instrumental action selection is fundamental to the study of decision making. Recent work has shown that motivational control can be used to divide instrumental behavior into two classes: ‘goal-directed' behavior is immediately sensitive to motivation-induced changes in the values of its specific consequences, while ‘habitual' behavior is not. Because habitual behavior constitutes a large proportion of our daily activities, it is thus important to ask how does motivation affect habitual behavior? That is, how can habitual behavior be performed such as to achieve motivationally relevant goals? In Chapter 3 we flesh out the implications of this model for the motivational control of habitual behavior. In general, understanding the effects of motivation on instrumental action selection is fundamental to the study of decision making. Recent work has shown that motivational control can be used to divide instrumental behavior into two classes: ‘goal-directed' behavior is immediately sensitive to motivation-induced changes in the values of its specific consequences, while ‘habitual' behavior is not. Because habitual behavior constitutes a large proportion of our daily activities, it is thus important to ask how does motivation affect habitual behavior? That is, how can habitual behavior be performed such as to achieve motivationally relevant goals? We start by defining motivation as a mapping from outcomes to utilities. Incorporating this into the com- putational framework of optimal response rates, we show that in general, the optimal effects of motivation on behavior should be two-fold: On the one hand, motivation should affect the choice between actions such that actions leading to those outcomes that are more highly valued are more probable. This corresponds to the traditional directing effect of motivation. On the other hand, by influencing the opportunity cost of time, motivation should affect the response rates of all chosen actions, irrespective of their specific outcomes, as in the decades-old (but controversial) idea that motivation energizes behavior. This global effect of moti- vation explains not only why hungry rats work harder for food, but also sheds light on the counterintuitive observation that they will sometimes work harder for water. Based on the computational view of habitual behavior as arising from cached values summarizing long-run reward predictions, we suggest that habitual action selection can direct responding properly only in those motivational states which pertained during behavioral training. However, this does not imply insensitivity to novel motivational states. In these, we propose that the outcome-independent, global effects of motivational can ‘energize' habitual actions, as a well-founded approximation to the optimal solution in a trained situation. That is, habitual response rates can be adjusted to the current motivational state, in a way that is optimal given the computational limitations of the habitual system. Our computational framework suggests that the energizing function of motivation is mediated by the ex- pected net rate of rewards. In Chapter 4, we put forth the hypothesis that this important quantity is reported by tonic levels of dopamine. Dopamine neurotransmission has long been known to exert a powerful influ- ence over the vigor, strength or rate of responding. However, there exists no clear understanding of the computational foundation for this effect. Previous reinforcement learning models of habitual behavior have indeed suggested an interpretation of the function of dopaminergic signals in the brain. However, these have concentrated only on the role of precisely timed phasic dopaminergic signals in learning the predictive value of different actions, and have ignored both tonic dopamine transmission and response vigor. Our tonic dopamine hypothesis focuses on the involvement of dopamine in the control of vigor, explaining why higher levels of dopamine are associated with globally higher response rates, ie, why, like motivation, dopamine ‘energizes' behavior. In this way, through the computational framework of optimal choice of response rates, we suggest an explanation of the motivational control of habitual behavior, on both the behavioral and the neural levels. Reinforcement learning models of animal learning are appealing not only because they provide a normative basis for decision-making, but also because they show that optimal action selection can be learned through online incremental experience with the environment, using only locally available information. To complete the picture of how dopamine influences free-operant learning and behavior, in Chapter 5 we describe an online algorithm of the type usually associated with instrumental learning and decision-making, which is suitable for learning to select actions and latencies according to our new framework. There are two major differences between learning in our model and previous online reinforcement learning algorithms: First, most prior applications have dealt with discounted reinforcement learning while we use average reward reinforcement learning. Second, unlike previous models that have focused on discrete action selection, the action space in our model is inherently continuous, as it includes a choice of response latency. We thus propose a new online learning algorithm that is specifically suitable for our needs. In this, building on the experimental characteristics of response latencies, we suggest a functional parameterization of the action space that drastically reduces the complexity of learning. Moreover, we suggest a formulation of online action selection in which response rates are directly affected by the net reward rate. We show that our algorithm learns to respond appropriately, and with nearly optimal latencies, and discuss its implications for the differences between the learning of interval and ratio schedules. In Chapter 6, the last of the main chapters, we deviate from the theoretical analysis of behavior, to describe two instrumental condi