Topologies of learning and development

How systems can represent and how systems can learn are two central problems in the study of cognition. Conventional contemporary approaches to these problems are vitiated by a shared error in their presuppositions about representation Consequently, such approaches share further errors about the sorts of architectures that are required to support either representation or learning. We argue that the architectural requirements for genuine representing systems lead to architectural characteristics that are necessary (though not sufficient) for heuristic learning and development These architectural constraints, in turn, explain properties of the functioning of the central nervous system that remain inexplicable for standard approaches. Copyright (C) 1996 Elsevier Science Ltd