Theories, models, and equations in biology: The heuristic search for emergent simplifications in neurobiology
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Philosophy of Science 75 (5):1008-1021 (2008)
This article considers claims that biology should seek general theories similar to those found in physics but argues for an alternative framework for biological theories as collections of prototypical interlevel models that can be extrapolated by analogy to different organisms. This position is exemplified in the development of the Hodgkin‐Huxley giant squid model for action potentials, which uses equations in specialized ways. This model is viewed as an “emergent unifier.” Such unifiers, which require various simplifications, involve the types of heuristics discussed in Wimsatt’s writings on reduction, but with a twist. Here, the heuristics are used to generate emergent rather than reductive explanations. †To contact the author, please write to: Department of History and Philosophy of Science, University of Pittsburgh, 1017 Cathedral of Learning, Pittsburgh, PA 15260; e‐mail: firstname.lastname@example.org.
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Worth Boone & Gualtiero Piccinini (2016). The Cognitive Neuroscience Revolution. Synthese 193 (5):1509-1534.
David Michael Kaplan (2011). Explanation and Description in Computational Neuroscience. Synthese 183 (3):339-373.
Spencer Phillips Hey (2016). Heuristics and Meta-Heuristics in Scientific Judgement. British Journal for the Philosophy of Science 67 (2):471-495.
M. Chirimuuta (2014). Minimal Models and Canonical Neural Computations: The Distinctness of Computational Explanation in Neuroscience. Synthese 191 (2):127-153.
Andrew Wayne (2012). Emergence and Singular Limits. Synthese 184 (3):341-356.
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