Proceedings of the Annual Meeting of the Cognitive Science Society

In this paper we present a theory of how machines can learn from attention focusing failures. Our method requires that learning mechanisms have available a detailed model of decision-making mechanisms they are to modify; it is therefore central to this research to develop and present such a model. The portions of our developing model presented below concern those parts of a decision-making apparatus that should be approximately the same & somone agent to another. Though learning mechanisms would have to be sensitive to both the idiosyncratic and agent-invariant elements of aji cidaptable decision architecture, w e have concentrated on the invariant elements, which provide the most general constraints on learning.