ABSTRACT
For many problems, there are multiple solution paths that lead from the initial problem
state to the goal state. Different paths may require different amount of time and effort.
With experience, the problem-solver learns to choose solution paths that requires less
time and effort, and as a result, performance improves. For example, if the problem is to
drive to a particular destination in a city, numerous solution paths may exist. If the person
is new to the city, very little knowledge is available to decide which paths to take. In this
case, decisions on which paths to take may solely rely on simple heuristics, such as
hill-climbing. Unless the city is extremely complex, simple heuristics are usually
sufficient to lead the person to the destination. However, although simple heuristics are
usually sufficient to provide a solution to the problem, there is no guarantee that the
solution is good (or fast, in the current example). Fortunately, with experience, the person
may be able to acquire information about the speeds for various routes in the city. With
this kind of information, the person may be able to choose faster paths that lead to the
destination. Although many cognitive mechanisms have been proposed to account for this
kind of learning in problem solving (e.g. Anzai and Simon, 1979, Agre & Shrager, 1994,
Lovett and Anderson, 1996), not many studies have directly addressed the effects of
experienced effort in problem-solving, and how people learn to choose less effortful
solution paths with experience. In my dissertation, I am going to design several
experiments to understand how people acquire problem-specific information and how
they use the information to improve their performance. Specifically, I will focus on how
people learn the amount of effort involved in different solution paths, and how they
improve performance by choosing the less effortful paths. I am planning to build
cognitive models using ACT-R (Anderson & Lebiere, 1998) to understand the
mechanisms behind this kind of learning. In this extended abstract, I will focus on
describing the task and the model that I am planning to build.