Studies modelling learning curves of an overall task and subtasks
I've been interested in this issue from another direction, namely, in how to model the acquisition of hierarchically decomposeable behaviors of the type you describe; and how these behaviors, once acquired, can be used as 'high order primitives' to bootstrap other learning.  This is an important issue for artificial intelligence and machine learning, in particular, as a way to address the combinatorial explosion involved with learning complex tasks in realistic domains.

Barto et al. (2004) provide a nice overview that amends the standard reinforcement learning paradigm to take these hierarchical ideas into account, and includes an algorithmic implementation for a simple but non-trivial domain.  Oudeyer et al. (2007) expand on how hierarchicality can make behavior acquisition more efficient, and implement their algorithm in a robot in a developmental setting.  Botvinick et al. (2009) provide a more recent review of the state of this work, as well as an overview of possible neural correlates of some of the processes.

Taken together, these references may possibly be useful to you as dispatches from domains other than the one you're exactly interested in.  I've found such disparate formulations to be fertile sources of inspiration and synthesis.