Abstract

This article describes how an abstract framework for perception and cognition may be realised in terms of neural mechanisms and neural processing. This framework—called information compression by multiple alignment, unification and search (ICMAUS)—has been developed in previous research as a generalized model of any system for processing information, either natural or artificial. Applications of the framework include the representation and integration of diverse kinds of knowledge, analysis and production of natural language, unsupervised inductive learning, fuzzy pattern recognition, recognition through multiple levels of abstraction, probabilistic ‘deduction ’ and abduction, chains of reasoning, nonmonotonic reasoning, ‘explaining away’, solving geometric analogy problems, and others. A key idea in the ICMAUS framework is that information compression is important both as a means of economising on the transmission or storage of information and also as the basis of probabilistic reasoning. The proposals in this article may be seen as an extension and development of Hebb’s

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