To transform the phenomena: Feyerabend, proliferation, and recurrent neural networks

Philosophy of Science 64 (4):420 (1997)
Paul Feyerabend recommended the methodological policy of proliferating competing theories as a means to uncovering new empirical data, and thus as a means to increase the empirical constraints that all theories must confront. Feyerabend's policy is here defended as a clear consequence of connectionist models of explanatory understanding and learning. An earlier connectionist "vindication" is criticized, and a more realistic and penetrating account is offered in terms of the computationally plastic cognitive profile displayed by neural networks with a recurrent architecture.
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DOI 10.1086/392618
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Christopher J. Preston (2005). Restoring Misplaced Epistemology. Ethics, Place and Environment 8 (3):373 – 384.

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