Truth and validity of knowledge from the Decoy theory
79
REFERENCIAS BIBLIOGRÁFICAS
Blouw, P.; Buckwalter, W. & Turri, J. 2018. Gettier cases:
A taxonomy. In R. Borges, C. de Almeida, & P.
Klein (Eds.). Explaining knowledge: New essays
on the Gettier problem (pp. 242-252). Oxford
University Press.
Buckwalter, W. & Turri, J. 2020. Knowledge and truth:
A skeptical challenge. Pacic Philosophical
Quarterly, 101: 93-101
Calio, F.; Nadarevic, L. & Musch, J. 2020.How explicit
warnings reduce the truth eect: A multinomial
modeling approach. Acta Psychologica, 211:
1-11.
Cerón, M.A.U. 2016. Cuatro niveles de conocimiento en
relación a la ciencia. Una propuesta taxonómica.
Ensayo, 24: 83-90.
Diaconu, M.A. 2014. Truth and knowledge in
postmodernism. Procedia - Social and Behavioral
Sciences, 137: 165-169.
Fortunato, S.; Bergstrom, C.T.; Borner, K.; Evans, J.A.;
Helbing, D.; Milojevi´c, S. & Vespignani, A.
2018. Science of science. Science, 359: 1-9.
Greco, J. 2010. Achieving knowledge: A virtue-theoretic
account of epistemic normativity. Cambridge
University Press.
Johnson, L.P.N.; Khemlani, S.S. & Goodwin, G.P.
2015.Logic, probability, and human reasoning.
Trends in Cognitive Sciences, 19: 201-214.
Konstantinos, V.K. 2019. When a research prob-
lem is solved, others are (re)intro-
duced.Schurz,G.,Hume’s problem solved: e
optimality of meta-induction, MIT. Journal of
Mathematical Psychology, 98: 1-3.
Kuhn, T.S. 1962. e Structure of Scientic Revolutions.
University of Chicago Press, Chicago, IL.
Li, J.; Yin, Y.; Fortunato, S. & Wang, D. 2020.
Scientic elite revisited: patterns of productivity,
collaboration, authorship and impact. Journal of
the RoyalSociety Interface, 17: 1-10.
Martínez, M.M. 2006. Conocimiento cientíco general
y conocimiento ordinario. Cinta de Moebio, 27:
1-10.
Mittal, S. 2016. A survey of techniques for approximate
computing. Association for Computing
Machinery Computing Surveys, 48: 1-33.
Pennycook, G.; Cannon, T.D. & Rand, D.G. 2018.
Prior exposure increases perceived accuracy of
fake news. Journal of Experimental Psychology:
General, 147: 1865-1880.
Porter, A.L.; Chiavetta, D. & Newman, N.C. 2020.
Measuring tech emergence: a contest.
Technological Forecasting and Social Change,
159: 1-10.
Sandoval, W.A.; Greene, J.A. & Bråten, I. 2016.
Understanding and promoting thinking about
knowledge: Origins, issues, and future directions
of research on epistemic cognition. Review of
Research in Education, 40: 457-496.
Schneider, J.W. & Costas, R. 2017. Identifying potential
“breakthrough” publications using rened citation
analyses: three related explorative approaches.
Journal of the Association for Information
ScienceandTechnology, 68: 709-723.
Schünemann, B.; Schidelko, L.P.; Proft, M. & Rakoczy,
H. 2022. Children understand subjective
(undesirable) desires before they understand
subjective (false) beliefs. Journal of Experimental
Child Psychology, 213: 1-16.
Suominen, A.; Peng, H. & Ranaei, S. 2019. Examining
the dynamics of an emerging research network
using the case of triboelectric nanogenerators.
Technological Forecasting and Social Change,
146: 820-830.
Turri, J. 2017. Knowledge attributions and behavioral
predictions. Cognitive Science, 41: 2253-2261.
Unkelbach, C.; Koch, A.; Silva, R.R. & Garcia, M.T.
2019. Truth by repetition: Explanations,and
implications. Current Directions in Psychological
Science, 28: 247-253.
Velázquez, H. 2016. Realidad, conocimiento y verdad
en el pensamiento de Samuel Achkolnik. Nuevo
Pensamiento, 6: 1-27.
Wolf, L.A. 2015.Research as problem solving: eoretical
frameworks as tools. Journal of Emergency
Nursing, 41: 83-85.