The Hyper-Cortex of Human Collective-Intelligence Systems

TL;DR

This paper extends the hierarchical cortical model to collective intelligence systems, proposing a hyper-cortical network for representing abstract collective concepts and demonstrating its application in solving scientific problems using digital-library data.

Contribution

It introduces the concept of a hyper-cortical network for collective intelligence, expanding neurological models to engineered systems for problem abstraction and solution generation.

Findings

Hyper-cortical network effectively models collective concepts.

Application to digital-library metadata solves scientific problems.

Demonstrates scalability of the hyper-cortical approach.

Abstract

Individual-intelligence research, from a neurological perspective, discusses the hierarchical layers of the cortex as a structure that performs conceptual abstraction and specification. This theory has been used to explain how motor-cortex regions responsible for different behavioral modalities such as writing and speaking can be utilized to express the same general concept represented higher in the cortical hierarchy. For example, the concept of a dog, represented across a region of high-level cortical-neurons, can either be written or spoken about depending on the individual's context. The higher-layer cortical areas project down the hierarchy, sending abstract information to specific regions of the motor-cortex for contextual implementation. In this paper, this idea is expanded to incorporate collective-intelligence within a hyper-cortical construct. This hyper-cortex is a multi-layered network used to represent abstract collective concepts. These ideas play an important role in understanding how collective-intelligence systems can be engineered to handle problem abstraction and solution specification. Finally, a collection of common problems in the scientific community are solved using an artificial hyper-cortex generated from digital-library metadata.