Neural systems underlying the learning of cognitive effort costs
AbstractPeople 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 open 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 outside the scanner, participants made selections between pairs of these task contexts. Notably, during learning, participants were not aware of this later choice phase. Nonetheless, participants avoided task contexts requiring more task switching. 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. Interestingly, prediction errors (PE) from these two models were differentially associated with separate brain networks during distinct learning epochs. Specifically, PE derived from expected RT was most correlated with the cingulo-opercular network early in learning, whereas PE derived from expected task switching frequency was correlated with the fronto-parietal network late in learning. These observations are discussed in relation to the contribution of cognitive control systems to new task learning and how this may bear on effort-based decisions.Significance StatementOn a daily basis, we make decisions about cognitive effort expenditure. It has been argued that we avoid cognitively effortful tasks to the degree subjective costs outweigh the benefits of the task. Here, we investigate the brain systems that learn about task demands for use in later effort-based decisions. Using reinforcement learning models, we find that learning about both expected response time and task switching frequency affect later effort-based decisions and these are differentially tracked by distinct brain networks during different epochs of learning. The results indicate that more than one signal is used by the brain to associate effort costs with a given task.