A Dynamical Model for Information Retrieval and Emergence of Scale-Free Clusters in a Long Term Memory Network

TL;DR

This paper introduces a dynamic model of long-term memory networks for information retrieval, demonstrating the emergence of scale-free structures and power-law distributions that enhance understanding of cognitive and semantic systems.

Contribution

It presents a novel dynamical model based on the Kintsch-Ericsson scheme, showing how scale-free clusters emerge in a memory network during information retrieval.

Findings

Power-law distributions emerge in the network structure

The network exhibits scale-free graph properties

Information retrieval acts as an amplifier for cognitive structures

Abstract

The classical forms of knowledge representation fail when a strong dynamical interconnection between system and environment comes into play. We propose here a model of information retrieval derived from the Kintsch-Ericsson scheme, based upon a long term memory (LTM) associative net whose structure changes in time according to the textual content of the analyzed documents. Both the theoretical analysis carried out by using simple statistical tools and the tests show the appearing of typical power-laws and the net configuration as a scale-free graph. The information retrieval from LTM shows that the entire system can be considered to be an information amplifier which leads to the emergence of new cognitive structures. It has to be underlined that the expanding of the semantic domain regards the user-network as a whole system.