Hochstein, Eric (2023) Integration without Integrated Models or Theories. [Preprint]
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Abstract
It is traditionally thought that integration in cognitive science requires combining different perspectives, elements, and insights into an integrated model or theory of the target phenomenon. In this paper I argue that this type of integration is frequently not possible in cognitive science due to our reliance on using different idealizing and simplifying assumptions in our models and theories. Despite this, I argue that we can still have integration in cognitive science and attain all the benefits that integrated models would provide, without the need for their construction. Models which make incompatible idealizing assumptions about the target phenomenon can still be integrated by understanding how to draw coherent and compatible inferences across them. I discuss how this is possible, and demonstrate how this supports a different kind of integration. This sense of integration allows us to use collections of contradictory models to develop a consistent, comprehensive and non-contradictory understanding of a single unified phenomenon without the need for a single integrated model or theory.
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Item Type: | Preprint | ||||||
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Additional Information: | e | ||||||
Keywords: | idealization, integration, unification, abstraction, ontic commitments, metaphysical commitments, implicit commitments, models, theories | ||||||
Subjects: | Specific Sciences > Cognitive Science General Issues > Models and Idealization Specific Sciences > Neuroscience General Issues > Philosophers of Science Specific Sciences > Psychology |
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Depositing User: | Dr. Eric Hochstein | ||||||
Date Deposited: | 29 Jul 2023 20:26 | ||||||
Last Modified: | 29 Jul 2023 20:26 | ||||||
Item ID: | 22351 | ||||||
Subjects: | Specific Sciences > Cognitive Science General Issues > Models and Idealization Specific Sciences > Neuroscience General Issues > Philosophers of Science Specific Sciences > Psychology |
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Date: | 29 July 2023 | ||||||
URI: | https://philsci-archive.pitt.edu/id/eprint/22351 |
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