Abstract
The data learning problem is a phenomenon that arises when an agent employing a cognitive architecture faces the task of acquiring declarative information from an external source, such as the “answer” to a “question”. Because the agent has to pay attention to both question and answer in order to learn the association between them, it is problematic for the agent to learn to produce the answer in response to the question alone. This observation helps shape the basic characteristics of human memory. The problem was first reported with the Soar architecture, but it arises also in ACT-R, and this paper argues that it will occur in any cognitive architecture, connectionist as well as symbolic, which is specified in a sufficiently explicit manner to avoid having the theorist act as an implicit homunculus for the agent.
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