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oai:recercat.cat:2445/1245642025-12-04T20:46:33Zcom_2072_1057col_2072_478822col_2072_478914col_2072_478917

00925njm 22002777a 4500 dc Rosell-Tarragó, Gemma author Cozzo, Emanuele author Díaz Guilera, Albert author 2018-09-14T10:39:22Z 2019-07-12T05:10:17Z 2018-07-12 2018-09-14T10:39:23Z 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. Cognició Estadística matemàtica Cognition Mathematical statistics A complex network framework to model cognition: unveiling correlation structures from connectivity