Por favor, use este identificador para citar o enlazar este documento: https://hdl.handle.net/2445/124564
Título: A complex network framework to model cognition: unveiling correlation structures from connectivity
Autor: Rosell-Tarragó, Gemma
Cozzo, Emanuele
Díaz Guilera, Albert
Materia: Cognició
Estadística matemàtica
Cognition
Mathematical statistics
Fecha de publicación: 12-jul-2018
Publicado por: Wiley
Resumen: Several approaches to cognition and intelligence research rely on statistics-based model testing, namely, factor analysis. In the present work, we exploit the emerging dynamical system 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 nontrivial attractor of the system that corresponds to the exact definition of a well-known network centrality and hence stresses the interplay between the dynamics and the underlying network connectivity, showing that both of the two are relevant. Correlation matrices evince there must be a meaningful structure underlying real data. Nevertheless, the true architecture regarding the connectivity between cognitive processes is still a burning issue of research. Regardless of the network considered, it is always possible to recover a positive manifold of correlations. Furthermore, we show that different network topologies lead to different plausible statistical models concerning the correlation structure, ranging from one to multiple factor models and richer correlation structures.
Nota: Reproducció del document publicat a: https://doi.org/10.1155/2018/1918753
Es parte de: Complexity, 2018, vol. 2018, p. 1918753
URI: https://hdl.handle.net/2445/124564
Recurso relacionado: https://doi.org/10.1155/2018/1918753
ISSN: 1076-2787
Aparece en las colecciones:Articles publicats en revistes (Física de la Matèria Condensada)
Articles publicats en revistes (Institut de Recerca en Sistemes Complexos (UBICS))

Archivos de este documento:
Archivo Descripción DimensionesFormato 
681799.pdf2.67 MBAdobe PDFMostrar/Abrir


Este documento está sujeto a una Licencia Creative Commons Creative Commons