The strategy underlying most computational models of word reading is to specify the organization of the reading system--its architecture and the processes and representations it employs--and to demonstrate that this organization would give rise to the behavior observed in word reading tasks. This approach fails to adequately address the variation in reading behavior observed across and within linguistic communities. Only computational models that incorporate learning can fully account for variation in organization. However, even extant learning models (e.g., the triangle model) must be extended if they are to fully account for variation in organization. The challenges associated with extending theories in this way are discussed.