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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References found in this work BETA
William Bechtel (2005). Explanation: A Mechanist Alternative. Studies in History and Philosophy of Biol and Biomed Sci 36 (2):421--441.
William Bechtel (2009). Looking Down, Around, and Up: Mechanistic Explanation in Psychology. Philosophical Psychology 22 (5):543-564.
Alisa Bokulich (2011). How Scientific Models Can Explain. Synthese 180 (1):33 - 45.
Citations of this work BETA
Nir Fresco (2012). The Explanatory Role of Computation in Cognitive Science. Minds and Machines 22 (4):353-380.
Justin Garson (2012). Function, Selection, and Construction in the Brain. Synthese 189 (3):451-481.
M. Chirimuuta (2014). Minimal Models and Canonical Neural Computations: The Distinctness of Computational Explanation in Neuroscience. Synthese 191 (2):127-153.
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