David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Synthese 183 (3):313-338 (2011)
Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of techniques for abstracting the functional properties of the system, which may not coincide with its mechanistic organization. I describe these techniques and show that despite being non-mechanistic, these cognitive models can satisfy the normative constraints on good explanations
|Keywords||models mechanisms psychological explanation cognition|
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Citations of this work BETA
M. Chirimuuta (2014). Minimal Models and Canonical Neural Computations: The Distinctness of Computational Explanation in Neuroscience. Synthese 191 (2):127-153.
Nir Fresco (2012). The Explanatory Role of Computation in Cognitive Science. Minds and Machines 22 (4):353-380.
Worth Boone & Gualtiero Piccinini (forthcoming). The Cognitive Neuroscience Revolution. Synthese:1-26.
David Michael Kaplan (2015). Moving Parts: The Natural Alliance Between Dynamical and Mechanistic Modeling Approaches. Biology and Philosophy 30 (6):757-786.
Daniel A. Wilkenfeld, Dillon Plunkett & Tania Lombrozo (forthcoming). Depth and Deference: When and Why We Attribute Understanding. Philosophical Studies:1-21.
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