Beliefs, Epistemic Regress and Doxastic Justification

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Título: Beliefs, Epistemic Regress and Doxastic Justification
Autor/es: Nescolarde-Selva, Josué Antonio | Usó i Domènech, Josep Lluís | Segura, Lorena | Gash, Hugh
Grupo/s de investigación o GITE: Sistémica y Cibernética (SYC)
Centro, Departamento o Servicio: Universidad de Alicante. Departamento de Matemática Aplicada
Palabras clave: Basic propositions | Belief system | Derived beliefs | Derived propositions | Epistemic justification | Infinite regress | Knowledge | Perceptual beliefs | Substantive beliefs
Fecha de publicación: 10-oct-2023
Editor: Springer Nature
Cita bibliográfica: Foundations of Science. 2023. https://doi.org/10.1007/s10699-023-09927-8
Resumen: By justification we understand what makes a belief epistemologically viable: generally this is considered knowledge that is true. The problem is defining this with a higher degree of precision because this is where different conflicting conceptions appear. On the one hand, we can understand justification as what makes it reasonable to acquire or maintain a belief; on the other, it is what increases the probability that the belief is true. This work tries to prove that beliefs depend on other beliefs that are epistemically justified and that such beliefs are the result of (i.e., they arise from) our privileged intuition of reality. For this, we examine the concept of epistemic regress. Epistemic reasons authorize a proposition P to be the conclusion of an argument in which such reasons function as premises and are vulnerable to epistemic regress. The three most important approaches to epistemic regress are Infinitism, Coherentism and Foundationalism.
Patrocinador/es: Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research work has been partially funded by the Generalitat Valenciana through the project CIBEST, postdoctoral stay grants at Northwestern Polytechnical University titled: Optimization of the Smarta application in collaboration with NPU (CIBEST/2022/205). This research work has been partially funded by the Spanish Government and Fondo Europeo de Desarrollo Regional (FEDER) through the project TRIVIAL: Technological Resources for Intelligent VIral AnaLysis through NLP (PID2021-122263OB-C22).
URI: http://hdl.handle.net/10045/137843
ISSN: 1233-1821 (Print) | 1572-8471 (Online)
DOI: 10.1007/s10699-023-09927-8
Idioma: eng
Tipo: info:eu-repo/semantics/article
Derechos: © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Revisión científica: si
Versión del editor: https://doi.org/10.1007/s10699-023-09927-8
Aparece en las colecciones:INV - SYC - Artículos de Revistas

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