The real world is uncertain, and while ever changing, it constantly presents itself in terms of new sets of behavioral options. To attain the flexibility required to tackle these challenges successfully, most mammalian brains are equipped with certain computational abilities that rely on the prefrontal cortex (PFC). By examining learning in terms of internal models associating stimuli, actions, and outcomes, we argue here that adaptive behavior relies on specific interactions between multiple systems including: (1) selective models learning stimulus–action associations through rewards; (2) predictive models learning stimulus- and/or action–outcome associations through statistical inferences anticipating behavioral outcomes; and (3) contextual models learning external cues associated with latent states of the environment. Critically, the PFC combines these internal models by forming task sets to drive behavior and, moreover, constantly evaluates the reliability of actor task sets in predicting external contingencies to switch between task sets or create new ones. We review different models of adaptive behavior to demonstrate how their components map onto this unifying framework and specific PFC regions. Finally, we discuss how our framework may help to better understand the neural computations and the cognitive architecture of PFC regions guiding adaptive behavior.

现实世界是不确定的，虽然不断变化，但它不断地以新的行为选择的形式呈现出来。为了获得成功应对这些挑战所需的灵活性，大多数哺乳动物大脑都配备了依赖前额皮质 (PFC) 的某些计算能力。通过根据关联刺激、行动和结果的内部模型来检验学习，我们认为适应性行为依赖于多个系统之间的特定相互作用，包括：（1）通过奖励学习刺激-行动关联的选择性模型； （2）预测模型通过预测行为结果的统计推断来学习刺激和/或行动结果关联； (3) 情境模型学习与环境潜在状态相关的外部线索。重要的是，PFC 通过形成任务集来组合这些内部模型来驱动行为，此外，不断评估参与者任务集在预测外部突发事件以在任务集之间切换或创建新任务集时的可靠性。我们回顾了不同的适应性行为模型，以展示它们的组成部分如何映射到这个统一框架和特定的 PFC 区域。最后，我们讨论我们的框架如何帮助更好地理解引导适应性行为的 PFC 区域的神经计算和认知架构。