Distributed Data Mining (DDM) performs partial analysis of data at distributed locations and sends a summarized version to the peer sites or a central location for further analysis. Meta-learning is a technique that generates local classifiers (concepts or models) from distributed data sets to use in producing a global classifier. This inherently distributed nature of metalearning provides much advantage in implementing practical DDM systems. Currently machine learning techniques such as supervised neural networks, decision trees, rules and genetic algorithms are used in the metalearning process. Inspired by the cognitive representation of human memory, this paper presents a novel mechanism known as Concept-Episodic Associative Memory with a Neighborhood Effect (C-EAMwNE) to compute metaclassifiers. C-EAMwNE is an enhanced version of EAMwNE model previously developed by the authors which overcomes practical limitations of other existing cognitive representations. C-EAMwNE is applied to a multi-agent DDM system with learning agents and a central administrator agent. Learning agents use CEAMwNE to generate meta-classifiers at distributed data sites and communicate them to the central administrator agent (CAA). CAA produces a final concept description from the distributed classifiers to be used in classification tasks.