systems theory
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2022 ◽  
Vol 418 ◽  
pp. 126822
Boris Furtula ◽  
Slavko Radenković ◽  
Izudin Redžepović ◽  
Niko Tratnik ◽  
Petra Žigert Pleteršek

2022 ◽  
Vol 176 ◽  
pp. 121361
Christopher Münch ◽  
Emanuel Marx ◽  
Lukas Benz ◽  
Evi Hartmann ◽  
Martin Matzner

2022 ◽  
Vol 12 ◽  
Inês Hipólito

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DST) mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science (EECS).

Athenea ◽  
2022 ◽  
Vol 2 (6) ◽  
pp. 22-27
Luis Jose Gonzalez lugo

Scientific essay. References [1]G. Guerrero Pino, «Determinismo, modelos y modalidades,» Revista de Filosofía, vol. XIII, nº 24, pp. 191-216, 2000. [2]V. S. Pugachev and I. N. Sinitsyn, Stochastic Systems, Theory and Applications, 2002. [3]V. G. Kulkarni, Introduction to Modeling and Analysis of Stochastic Systems, Springer, 2011. [4]R. D. Snee, «Statistical Thinking and Its Contribution to Total Quality,» The American Statistian, pp. 116-121, 1990. [5]M. Pfannkuch and C. J. Wild, «Statistical Thinking in Empirical Enquiry,» International Statistical Review, vol. 67, nº 3, pp. 223-265, 1999. [6]E. Morin, Introducción al Pensamiento Complejo, Gedisa, 1998. [7]R. Corcho, Galileo y el método científico, NATGEO CIENCIAS, 2018. [8]A. L. Arango Arias, «Aporte de Galileo a la Ciencia Moderna,» Revista Académica e Institucional de la U.C.P.R., nº 75, pp. 57-65, 2006. [9]E. Morin, El Método, Ediciones Cátedra, 2017.

Matthew Jay Lyons ◽  
Senaida Fernandez Poole ◽  
Ross C. Brownson ◽  
Rodney Lyn

Racial disparities in breast cancer present a vexing and complex challenge for public health. A diverse array of factors contributes to disparities in breast cancer incidence and outcomes, and, thus far, efforts to improve racial equity have yielded mixed results. Systems theory offers a model that is well-suited to addressing complex issues. In particular, the concept of a systemic leverage point offers a clue that may assist researchers, policymakers, and interventionists in formulating innovative and comprehensive approaches to eliminating racial disparities in breast cancer. Naming systemic racism as a fundamental cause of disparities, we use systems theory to identify residential segregation as a key leverage point and a driver of racial inequities across the social, economic, and environmental determinants of health. We call on researchers, policymakers, and interventionists to use a systems-informed, community-based participatory approach, aimed at harnessing the power of place, to engage directly with community stakeholders in coordinating efforts to prevent breast cancer, and work toward eliminating disparities in communities of color.

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