Adaptation of learning and decision-making might depend on the regulation of activity in the prefrontal cortex. Here we examined how volatility of reward probabilities influences learning and neural activity in the primate prefrontal cortex. We found that animals selected recently rewarded targets more often when reward probabilities of different options fluctuated across trials than when they were fixed. Additionally, neurons in the orbitofrontal cortex displayed more sustained activity related to the outcomes of their previous choices when reward probabilities changed over time. Such volatility also enhanced signals in the dorsolateral prefrontal cortex related to the current but not the previous location of the previously rewarded target. These results suggest that prefrontal activity related to choice and reward is dynamically regulated by the volatility of the environment and underscore the role of the prefrontal cortex in identifying aspects of the environment that are responsible for previous outcomes and should be learned.

Background: Disruptions in the decision-making processes that guide action selection are a core feature of many psychiatric disorders, including addiction. Decision-making is influenced by the goal directed and habitual systems that can be computationally characterized using model-based and model-free reinforcement learning algorithms, respectively. Recent evidence suggests an imbalance in the influence of these reinforcement-learning systems on behavior in substancedependent individuals, but it is unknown if these disruptions are a manifestation of chronic drug use and/or are a preexisting risk factor for addiction. Methods:We trained adult, male rats on a multi-stage decision-making task to quantifying model-free and model-based processes before and after self-administration of methamphetamine or saline.Results: Individual differences in model-free, but not model-based, learning prior to any drug use predicted subsequent methamphetamine self-administration: rats with lower model-free behavior took more methamphetamine than rats with higher model-free behavior. This relationship was selective to model-free updating following a rewarded, but not unrewarded, choice. Modelfree and model-based learning were both reduced in rats following methamphetamine selfadministration, which was due to a decrement in the ability of rats to use unrewarded outcomes appropriately. Moreover, the magnitude of drug-induced disruptions in model-free learning were not correlated with disruptions in model-based behavior indicating that drug self-administration independently altered both reinforcement-learning strategies.

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Flexible decision-making in dynamic environments requires both retrospective appraisal of reinforced actions and prospective reasoning about the consequences of actions. These complementary reinforcement-learning systems can be characterized computationally with model-free and model-based algorithms, but how these processes interact at a neurobehavioral level in normal and pathological states is unknown. Here, we developed a translationally analogous multistage decision-making (MSDM) task to independently quantify modelfree and model-based behavioral mechanisms in rats. We provide the first direct evidence that male rats, similar to humans, use both model-free and model-based learning when making value-based choices in the MSDM task and provide novel analytic approaches for independently quantifying these reinforcement-learning strategies. Furthermore, we report that ex vivo dopamine tone in the ventral striatum and orbitofrontal cortex correlate with model-based, but not model-free, strategies, indicating that the biological mechanisms mediating decision-making in the multistage task are conserved in rats and humans. This new multistage task provides a unique behavioral platform for conducting systems-level analyses of decision-making in normal and pathological states. Significance StatementDecision-making is influenced by both a retrospective "model-free" system and a prospective "model-based" system in humans, but the biobehavioral mechanisms mediating these learning systems in normal and disease states are unknown. Here, we describe a translationally analogous multistage decision-making task to provide a behavioral platform for conducting neuroscience studies of decision-making in rats. We provide the first evidence that choice behavior in rats is influenced by model-free and model-based systems and demonstrate that model-based, but not model-free, learning is associated with corticostriatal dopamine tone. This novel behavioral paradigm has the potential to yield critical insights into the mechanisms mediating decision-making alterations in mental disorders.

Many humans exhibit a strong preference for fairness during decision-making. Although there is evidence that social factors influence reward-related and affective neural processing, it is unclear if this effect is mediated by compulsory outcome evaluation processes or results from slower deliberate cognition. Here we show that the feedback-related negativity (FRN) and late positive potential (LPP), two signatures of early hedonic processing, are modulated by the fairness of rewards during a passive rating task. We find that unfair payouts elicit larger FRNs than fair payouts, whereas fair payouts elicit larger LPPs than unfair payouts. This is true both in the time-domain, where the FRN and LPP are related, and in the time-frequency domain, where the two signals are largely independent. Ultimately, this work demonstrates that fairness affects the early stages of reward and affective processing, suggesting a common biological mechanism for social and personal reward evaluation.