A complex network framework to model cognition: unveiling correlation structures from connectivity

TL;DR

This paper introduces a complex network framework to model cognition, revealing how different network topologies influence correlation structures among cognitive processes and connecting dynamical systems with statistical models.

Contribution

It demonstrates the role of network topology in shaping cognitive correlation structures and links dynamical systems with factor analysis models.

Findings

Different network topologies produce varying correlation models.

A non-trivial attractor relates to a known network centrality.

Correlation structures can range from single to multiple factors.

Abstract

Several approaches to cognition and intelligence research rely on statistics-based models testing, namely factor analysis. In the present work we exploit the emerging dynamical systems perspective putting the focus on the role of the network topology underlying the relationships between cognitive processes. We go through a couple of models of distinct cognitive phenomena and yet find the conditions for them to be mathematically equivalent. We find a non-trivial attractor of the system that corresponds to the exact definition of a well-known network centrality and hence stress the interplay between the dynamics and the underlying network connectivity, showing that both of the two are relevant. The connectivity structure between cognitive processes is not known but yet it is not any. Regardless of the network considered, it is always possible to recover a positive manifold of correlations. However, we show that different network topologies lead to different plausible statistical models concerning correlations structure, ranging from one to multiple factors models and richer correlation structures.