Cognitive architectures, like programming languages, make commitments only at the implementation level and have limited explanatory power. Their universality implies that it is hard, if not impossible, to justify them in detail from finite quantities of data. It is more fruitful to focus on particular tasks such as language understanding and propose testable theories at the computational and algorithmic levels.