Foundations of Science 14 (4):249-271 (2008)

Abstract
Error is protean, ubiquitous and crucial in scientific process. In this paper it is argued that understanding scientific process requires what is currently absent: an adaptable, context-sensitive functional role for error in science that naturally harnesses error identification and avoidance to positive, success-driven, science. This paper develops a new account of scientific process of this sort, error and success driving Self-Directed Anticipative Learning (SDAL) cycling, using a recent re-analysis of ape-language research as test example. The example shows the limitations of other accounts of error, in particular Mayo’s (Error and the growth of experimental knowledge, 1996) error-statistical approach, and SDAL cycling shows how they can be fruitfully contextualised.
Keywords Error in science  Scientific method  Adaptive learning
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Reprint years 2009
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DOI 10.1007/s10699-008-9155-6
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References found in this work BETA

Error and the Growth of Experimental Knowledge.Deborah Mayo - 1996 - International Studies in the Philosophy of Science 15 (1):455-459.
Representational Content in Humans and Machines.Mark H. Bickhard - 1993 - Journal of Experimental and Theoretical Artificial Intelligence 5:285-33.
Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction.Deborah G. Mayo & Aris Spanos - 2006 - British Journal for the Philosophy of Science 57 (2):323-357.

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Citations of this work BETA

On the Import of Constraints in Complex Dynamical Systems.Cliff Hooker - 2013 - Foundations of Science 18 (4):757-780.
A Theory of Scientific Study.Robert Luk - 2017 - Foundations of Science 22 (1):11-38.
Re-Modelling Scientific Change: Complex Systems Frames Innovative Problem Solving.Cliff Hooker - 2018 - Lato Sensu, Revue de la Société de Philosophie des Sciences 5 (1):4-12.
A New Problem-Solving Paradigm for Philosophy of Science.Cliff Hooker - 2018 - Perspectives on Science 26 (2):266-291.

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