Hierarchical systems can adapt by adjusting the strengths of their components in response to environmental feedback. Regimens for propagating adjustments through a hierarchy are either cascading or distributional, depending on whether the sum of the adjustments is variable or fixed. Both types of regimens can be dampened, amplified or sustained, depending on whether nodes higher in the hierarchy are adjusted less, more or with the same amount as lower nodes. We show that a cascading regimen learns most efficiently with amplified propagation, while a distributional regimen learns most efficiently with sustained propagation. Cognitive scientists ought to explore a wider range of propagation regimens.