Bayesian models successfully account for several of dopamine (DA)’s effects on contextual calibration in interval timing and reward estimation. In these models, tonic levels of DA control the precision of stimulus encoding, which is weighed against contextual information when making decisions. When DA levels are high, the animal relies more heavily on the (highly precise) stimulus encoding, whereas when DA levels are low, the context affects decisions more strongly. Here, we extend this idea to intertemporal choice and probability discounting tasks. In intertemporal choice tasks, agents must choose between a small reward delivered soon and a large reward delivered later, whereas in probability discounting tasks, agents must choose between a small reward that is always delivered and a large reward that may be omitted with some probability. Beginning with the principle that animals will seek to maximize their reward rates, we show that the Bayesian model predicts a number of curious empirical findings in both tasks. First, the model predicts that higher DA levels should normally promote selection of the larger/later option, which is often taken to imply that DA decreases ‘impulsivity,’ and promote selection of the large/risky option, often taken to imply that DA increases ‘risk-seeking.’ However, if the temporal precision is sufficiently decreased, higher DA levels should have the opposite effect—promoting selection of the smaller/sooner option (higher impulsivity) and the small/safe option (lower risk-seeking). Second, high enough levels of DA can result in preference reversals. Third, selectively decreasing the temporal precision, without manipulating DA, should promote selection of the larger/later and large/risky options. Fourth, when a different post-reward delay is associated with each option, animals will not learn the option-delay contingencies, but this learning can be salvaged when the post-reward delays are made more salient. Finally, the Bayesian model predicts correlations among behavioral phenotypes: Animals that are better timers will also appear less impulsive.

贝叶斯模型成功地解释了多巴胺 (DA) 在间隔时间和奖励估计中对上下文校准的影响。在这些模型中，DA 的强直水平控制刺激编码的精度，在做出决策时会根据上下文信息进行权衡。当 DA 水平高时，动物更依赖于（高度精确的）刺激编码，而当 DA 水平低时，环境对决策的影响更大。在这里，我们将这个想法扩展到跨期选择和概率贴现任务。在跨期选择任务中，代理人必须在很快提供的小奖励和稍后提供的大奖励之间做出选择，而在概率贴现任务中，代理人必须在始终提供的小奖励和可能会以一定概率省略的大奖励之间做出选择。从动物将寻求最大化奖励率的原则开始，我们表明贝叶斯模型预测了两项任务中的许多奇怪的实证发现。首先，该模型预测较高的 DA 水平通常会促进选择较大/较晚的选项，这通常被认为暗示 DA 会降低“冲动性”，并促进选择较大/有风险的选项，通常被认为暗示 DA 会增加“寻求风险。” 然而，如果时间精度充分降低，更高的 DA 水平应该会产生相反的效果——促进选择更小/更早的选项（更高的冲动性）和更小/安全的选项（更低的风险寻求）。其次，足够高水平的 DA 会导致偏好逆转。第三，在不操纵 DA 的情况下有选择地降低时间精度，应该促进选择更大/更晚和更大/有风险的选项。第四，当不同的奖励后延迟与每个选项相关联时，动物将不会学习选项延迟意外事件，但当奖励后延迟变得更加突出时，可以挽救这种学习。最后，贝叶斯模型预测了行为表型之间的相关性：更善于计时的动物也会显得不那么冲动。