Error, error-statistics and self-directed anticipative learning

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

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References found in this work

Error and the growth of experimental knowledge.Deborah Mayo - 1996 - International Studies in the Philosophy of Science 15 (1):455-459.
Error and the Growth of Experimental Knowledge.Deborah Mayo - 1997 - British Journal for the Philosophy of Science 48 (3):455-459.
Representational content in humans and machines.Mark H. Bickhard - 1993 - Journal of Experimental and Theoretical Artificial Intelligence 5:285-33.

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