People balance the benefits of cognitive work against the costs of cognitive effort. Models that incorporate prospective estimates of the costs of cognitive effort into decision making require a mechanism by which these costs are learned. However, it remains an open question what brain systems are important for this learning, particularly when learning is not tied explicitly to a decision about what task to perform. In this fMRI experiment, we parametrically manipulated the level of effort a task requires by increasing task switching frequency across six task contexts. In a scanned learning phase, participants implicitly learned about the task switching frequency in each context. In a subsequent test phase, participants made selections between pairs of these task contexts. We modeled learning within a reinforcement learning framework, and found that effort expectations that derived from task-switching probability and response time (RT) during learning were the best predictors of later choice behavior. Prediction errors (PE) from these two models were associated with FPN during distinct learning epochs. Specifically, PE derived from expected RT was most correlated with the fronto-parietal network early in learning, whereas PE derived from expected task switching frequency was correlated with the fronto-parietal network late in learning. These results suggest that multiple task-related factors are tracked by the brain while performing a task that can drive subsequent estimates of effort costs.

人们在认知工作的好处与认知努力的成本之间取得平衡。将认知努力成本的前瞻性估计纳入决策制定的模型需要一种学习这些成本的机制。然而，什么大脑系统对这种学习很重要仍然是一个悬而未决的问题，特别是当学习与执行什么任务的决定没有明确联系时。在这个 fMRI 实验中，我们通过增加六个任务上下文中的任务切换频率来参数化地操纵任务所需的努力水平。在扫描学习阶段，参与者隐含地了解了每个上下文中的任务切换频率。在随后的测试阶段，参与者在这些任务上下文对之间进行选择。我们在强化学习框架内对学习进行建模，并发现从学习期间的任务切换概率和响应时间 (RT) 得出的努力期望是后期选择行为的最佳预测指标。在不同的学习时期，这两个模型的预测误差 (PE) 与 FPN 相关。具体来说，来自预期 RT 的 PE 与学习早期的额顶网络最相关，而来自预期任务切换频率的 PE 与学习后期的额顶网络相关。这些结果表明，在执行一项可以推动后续工作成本估算的任务时，大脑会跟踪多个与任务相关的因素。在不同的学习时期，这两个模型的预测误差 (PE) 与 FPN 相关。具体来说，来自预期 RT 的 PE 与学习早期的额顶网络最相关，而来自预期任务切换频率的 PE 与学习后期的额顶网络相关。这些结果表明，在执行一项可以推动后续工作成本估算的任务时，大脑会跟踪多个与任务相关的因素。在不同的学习时期，这两个模型的预测误差 (PE) 与 FPN 相关。具体而言，来自预期 RT 的 PE 与学习早期的额顶网络最相关，而来自预期任务切换频率的 PE 与学习后期的额顶网络相关。这些结果表明，在执行一项可以推动后续工作成本估算的任务时，大脑会跟踪多个与任务相关的因素。