Studies modelling learning curves of an overall task and subtasks Background

Lee and Anderson (2001) published an article in which they argued  that learning a complex skill can be understood in terms of learning various component skills. They did this by performing a hierarchical task analysis on the Kanfer-Ackerman Air Traffic Control Task.
They then re-analysed a dataset where participants performed the task over repeated trials. By examining key logs, they were able to extract out the speed with which overall goals were achieved (e.g., landing planes) as well as subtasks (e.g., moving planes through the hold patterns, etc.). They then analysed the relationship between practice and performance (i.e., the learning curve) for both the overall task and the subtasks. Specifically, they fit three parameter power functions.

I'm currently doing some research exploring similarities and differences between overall and subtask learning curves, and I'm keen to identify other papers that have modelled subtask and overall task performance. However, the learning and performance literature is pretty broad and crosses multiple disciplines, and thus, somewhat challenging to search. I've also tried the cited by link for the article on Google Scholar.

Question:


What, if any, other studies have been published that have modelled the relationship between both practice and subtask performance, and practice and overall task performance?


My preference is for studies where the dependent variable is task/subtask completion time, but any dependent variable representing performance would be interesting.

References


Lee, F. J. and Anderson, J. R. (2001). Does learning a complex task have
to be complex?: A study in learning decomposition. Cognitive Psychology,
42(3):267-316. FREE PDF
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.