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This paper proposes a new conceptual framework called Collective Predictive Coding as a Model of Science (CPC-MS) to formalize and understand scientific activities.
Novelty and curiosity as determinants of exploratory behavior
Daniel Berlyne · 1950
Earlier work this paper cites.
Logical foundations of probability
Rudolf Carnap · 1950
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The Logic of Scientific Discovery
Karl Raimund Popper · 1959
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The Structure of Scientific Revolutions
Thomas S Kuhn · 1962
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The matthew effect in science: The reward and communication systems of science are considered
Robert K. Merton · 1968
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The methodology of scientific research programmes
Imre Lakatos · 1978
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Laboratory life: The construction of scientific facts
Bruno Latour and Steve Woolgar · 1979
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A confutation of convergent realism
Larry Laudan · 1981
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Mechanics: Volume 1
L.D. Landau and E.M. Lifshitz · 1982
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Scientific discovery: Computational explorations of the creative processes
Pat Langley · 1987
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Science as social knowledge: Values and objectivity in scientific inquiry
Helen E Longino · 1990
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The Advancement of Science: Science Without Legend, Objectivity Without Illusions
Philip Kitcher · 1993
Earlier work this paper cites.
Dendral: a case study of the first expert system for scientific hypothesis formation
Robert K Lindsay, Bruce G Buchanan, Edward A Feigenbaum, and Joshua Lederberg · 1993
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Ross D King, Kenneth E Whelan, Ffion M Jones, Philip GK Reiser, Christopher H Bryant, Stephen H Muggleton, Douglas B Kell, and Stephen G Oliver · 2004
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Min Jeong Kang, Ming Hsu, Ian M Krajbich, George Loewenstein, Samuel M McClure, Joseph Tao-yi Wang, and Colin F Camerer · 2009
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Ross D King, Jem Rowland, Stephen G Oliver, Michael Young, Wayne Aubrey, Emma Byrne, Maria Liakata, Magdalena Markham, Pinar Pir, Larisa N Soldatova, et al · 2009
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