Cognitive effort is typically aversive, evident in people's tendency to avoid cognitively demanding tasks. The 'cost of control' hypothesis suggests that engagement of cognitive control systems of the brain makes a task costly and the currency of that cost is a reduction in anticipated rewards. However, prior studies have relied on binary hard versus easy task subtractions to manipulate cognitive effort and so have not tested this hypothesis in "dose-response" fashion. In a sample of 50 participants, we parametrically manipulated the level of effort during fMRI scanning by systematically increasing cognitive control demands during a demand-selection paradigm over six levels. As expected, frontoparietal control network (FPN) activity increased, and reward network activity decreased, as control demands increased across tasks. However, avoidance behavior was not attributable to the change in FPN activity, lending only partial support to the cost of control hypothesis. By contrast, we unexpectedly observed that the de-activation of a task-negative brain network corresponding to the Default Mode Network (DMN) across levels of the cognitive control manipulation predicted the change in avoidance. In summary, we find partial support for the cost of control hypothesis, while highlighting the role of task-negative brain networks in modulating effort avoidance behavior.
Timing is an integral part of physical activities. Walking as a routine form of physical activity might affect interval timing primarily in two different ways within the pacemaker–accumulator timing-theoretic framework: (1) by increasing the speed of the pacemaker due to its physiological effects; (2) by decreasing attention to time and consequently slowing the rate of temporal integration by serving as a secondary task. In order to elucidate the effect of movement on subjective time, in two different experiments we employed a temporal reproduction task conducted on the treadmill under four different encoding–decoding conditions: (1) encoding and reproducing (decoding) the duration while standing (rest); (2) encoding the duration at rest and reproducing it while moving: (3) both encoding and reproducing the duration while moving; and (4) encoding the duration while moving and reproducing it at rest. In the first experiment, participants were tested either in the 4 or the 8 km/h movement condition, whereas in the second experiment a larger sample was tested only in the 4 km/h movement condition. Data were de-trended to control for long-term performance drifts. In Experiment 1, overall durations encoded at rest and reproduced during motion were under-reproduced whereas durations encoded during motion and reproduced at rest were over-reproduced only in the 8 km/h condition. In Experiment 2, the same results were observed in the 4 km/h condition with a larger sample size. These effects on timing behavior provide support for the clock speed-driven effect of movement and contradicts the predictions of attention-based mediation.
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 taskrelated factors are tracked by the brain while performing a task that can drive subsequent estimates of effort costs.
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