Dopamine neurons adapt their responses to reward value based on the information provided by reward-predicting stimuli. This adaptation allows them to maintain sensitivity across a wide range of reward values by adjusting their gain according to the variance of the reward. The study shows that dopamine neurons encode both the magnitude and probability of rewards, and their responses are influenced by the likelihood of different reward outcomes. When presented with unpredictable liquid volumes, dopamine neurons showed increased activity for larger volumes, indicating that their responses depend on the difference between actual and expected reward values. However, the magnitude of activation or suppression was similar across a wide range of reward differences, suggesting that the sensitivity of dopamine neurons adapts based on the discrepancy between potential outcomes rather than the absolute difference. The study also found that dopamine neurons' responses to reward-predicting stimuli carry information about expected reward value, while their sustained activity reflects reward uncertainty, such as variance. These findings suggest that dopamine neurons adapt their sensitivity to reward value based on the context and predictability of the reward, allowing for efficient and accurate representation of reward information. This adaptation is crucial for decision-making and reinforcement learning, as it enables the brain to allocate limited resources effectively to discriminate between likely and unlikely reward outcomes.Dopamine neurons adapt their responses to reward value based on the information provided by reward-predicting stimuli. This adaptation allows them to maintain sensitivity across a wide range of reward values by adjusting their gain according to the variance of the reward. The study shows that dopamine neurons encode both the magnitude and probability of rewards, and their responses are influenced by the likelihood of different reward outcomes. When presented with unpredictable liquid volumes, dopamine neurons showed increased activity for larger volumes, indicating that their responses depend on the difference between actual and expected reward values. However, the magnitude of activation or suppression was similar across a wide range of reward differences, suggesting that the sensitivity of dopamine neurons adapts based on the discrepancy between potential outcomes rather than the absolute difference. The study also found that dopamine neurons' responses to reward-predicting stimuli carry information about expected reward value, while their sustained activity reflects reward uncertainty, such as variance. These findings suggest that dopamine neurons adapt their sensitivity to reward value based on the context and predictability of the reward, allowing for efficient and accurate representation of reward information. This adaptation is crucial for decision-making and reinforcement learning, as it enables the brain to allocate limited resources effectively to discriminate between likely and unlikely reward outcomes.