Error, error-statistics and self-directed anticipative learning

Foundations of Science 14 (4):249-271 (2009)
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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References found in this work BETA
Mark H. Bickhard (1993). Representational Content in Humans and Machines. Journal of Experimental and Theoretical Artificial Intelligence 5:285-33.
Kevin Elliott (2004). Error as Means to Discovery. Philosophy of Science 71 (2):174-197.

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Citations of this work BETA
Cliff Hooker (2013). Georg Simmel and Naturalist Interactivist Epistemology of Science. Studies in History and Philosophy of Science Part A 44 (3):311-317.
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