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Sign-tracking (ST) rats show enhanced cue sensitivity before drug experience that predicts greater discrete cue-induced drug seeking compared with goal-tracking or intermediate rats. Cue-evoked dopamine in the nucleus accumbens (NAc) is a neurobiological signa)ture of sign-tracking behaviors. Here, we examine a critical regulator of the dopamine system, endocannabinoids, which bind the cannabinoid receptor-1 (CB1R) in the ventral tegmental area (VTA) to control cue-evoked striatal dopamine levels. We use cell type)specific optogenetics, intra-VTA pharmacology, and fiber photometry to test the hypothesis that VTA CB1R receptor signaling regu)lates NAc dopamine levels to control sign tracking. We trained male and female rats in a Pavlovian lever autoshaping (PLA) task to determine their tracking groups before testing the effect of VTA fi NAc dopamine inhibition. We found that this circuit is critical for mediating the vigor of the ST response. Upstream of this circuit, intra-VTA infusions of rimonabant, a CB1R inverse agonist, dur)ing PLA decrease lever and increase food cup approach in sign-trackers. Using fiber photometry to measure fluorescent signals from a dopamine sensor, GRABDA (AAV9-hSyn-DA2m), we tested the effects of intra-VTA rimonabant on NAc dopamine dynamics during autoshaping in female rats. We found that intra-VTA rimonabant decreased sign-tracking behaviors, which was associated with increases in NAc shell, but not core, dopamine levels during reward delivery [unconditioned stimulus (US)]. Our results suggest that CB1R signaling in the VTA influences the balance between the conditioned stimulus-evoked and US-evoked dopamine responses in the NAc shell and biases behavioral responding to cues in sign-tracking rats. [ABSTRACT FROM AUTHOR]

Model-free and model-based computations are argued to distinctly update action values that guide decision-making processes. It is not known, however, if these model-free and model-based reinforcement learning mechanisms recruited in operationally based instrumental tasks parallel those engaged by pavlovian-based behavioral procedures. Recently, computational work has suggested that individual differences in the attribution of incentive salience to reward predictive cues, that is, sign- and goaltracking behaviors, are also governed by variations in model-free and model-based value representations that guide behavior. Moreover, it is not appreciated if these systems that are characterized computationally using model-free and model-based algorithms are conserved across tasks for individual animals. In the current study, we used a within-subject design to assess sign-tracking and goal-tracking behaviors using a pavlovian conditioned approach task and then characterized behavior using an instrumental multistage decision-making (MSDM) task in male rats. We hypothesized that both pavlovian and instrumental learning processes may be driven by common reinforcement-learning mechanisms. Our data confirm that sign-tracking behavior was associated with greater reward-mediated, model-free reinforcement learning and that it was also linked to model-free reinforcement learning in the MSDM task. Computational analyses revealed that pavlovian model-free updating was correlated with model-free reinforcement learning in the MSDM task. These data provide key insights into the computational mechanisms mediating associative learning that could have important implications for normal and abnormal states. [ABSTRACT FROM AUTHOR]

Phasic activity of midbrain dopamine neurons is currently thought to encapsulate the prediction-error signal described in Sutton and Barto's (1981) model-free reinforcement learning algorithm. This phasic signal is thought to contain information about the quantitative value of reward, which transfers to the reward-predictive cue after learning. This is argued to endow the reward-predictive cue with the value inherent in the reward, motivating behavior toward cues signaling the presence of reward. Yet theoretical and empirical research has implicated prediction-error signaling in learning that extends far beyond a transfer of quantitative value to a reward-predictive cue. Here, we review the research which demonstrates the complexity of how dopaminergic prediction errors facilitate learning. After briefly discussing the literature demonstrating that phasic dopaminergic signals can act in the manner described by Sutton and Barto (1981), we consider how these signals may also influence attentional processing across multiple attentional systems in distinct brain circuits. Then, we discuss how prediction errors encode and promote the development of context-specific associations between cues and rewards. Finally, we consider recent evidence that shows dopaminergic activity contains information about causal relationships between cues and rewards that reflect information garnered from rich associative models of the world that can be adapted in the absence of direct experience. In discussing this research we hope to support the expansion of how dopaminergic prediction errors are thought to contribute to the learning process beyond the traditional concept of transferring quantitative value. [ABSTRACT FROM AUTHOR]

The dopamine system is thought to be involved in making decisions about reward. Here we recorded from the ventral tegmental area in rats learning to choose between differently delayed and sized rewards. As expected, the activity of many putative dopamine neurons reflected reward prediction errors, changing when the value of the reward increased or decreased unexpectedly. During learning, neural responses to reward in these neurons waned and responses to cues that predicted reward emerged. Notably, this cue-evoked activity varied with size and delay. Moreover, when rats were given a choice between two differently valued outcomes, the activity of the neurons initially reflected the more valuable option, even when it was not subsequently selected. [ABSTRACT FROM AUTHOR]

Animals prefer a small, immediate reward over a larger delayed reward (time discounting). Lesions of the orbitofrontal cortex (OFC) can either increase or decrease the breakpoint at which animals abandon the large delayed reward for the more immediate reward as the delay becomes longer. Here we argue that the varied effects of OFC lesions on delayed discounting reflect two different patterns of activity in OFC; one that bridges the gap between a response and an outcome and another that discounts delayed reward. These signals appear to reflect the spatial location of the reward and/or the action taken to obtain it, and are encoded independently from representations of absolute value. We suggest a dual role for output from OFC in both discounting delayed reward, while at the same time supporting new learning for them. [ABSTRACT FROM AUTHOR]

To survive in a complex environment, individuals form associations between environmental stimuli and rewards to organize and optimize reward seeking behaviors. The basolateral amygdala (BLA) uses these learned associations to inform decision-making processes. In this review, we describe functional projections between BLA and its cortical and striatal targets that promote learning and motivational processes central to decision-making. Specifically, we compare and contrast divergent projections from the BLA to the orbitofrontal (OFC) and to the nucleus accumbens (NAc) and examine the roles of these pathways in associative learning, value-guided decision-making, choice behaviors, as well as cue and context-driven drug seeking. Finally, we consider how these projections are involved in disorders of motivation, with a focus on Substance Use Disorder. [ABSTRACT FROM AUTHOR]