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Minds and Machines

, Volume 28, Issue 2, pp 311–330 | Cite as

Reduction Without Elimination: Mental Disorders as Causally Efficacious Properties

Article
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Abstract

We argue that any account of mental disorders that meets the desideratum of assigning causal efficacy to mental disorders faces the so-called “causal exclusion problem”. We argue that fully reductive accounts solve this problem but run into the problem of multiple realizability. Recently advocated symptom-network approaches avoid the problem of multiple realizability, but they also run into the causal exclusion problem. Based on a critical analysis of these accounts, we will present our own account according to which mental disorders are dispositional properties that are token-identical to physical properties. More specifically, they are analyzed as dispositions to cause the specific set of symptoms. We argue that our account is not only able to account for multiple realizability without running into the causal exclusion problem, but that it also allows for the integration of very different factors into the description and explanation of mental disorders, such as neurological and neurochemical factors on the one side and social and cultural factors on the other. It thereby gives the psychiatric level of causal explanation autonomy while securing the causal efficacy of mental disorders in a causally closed physical world.

Keywords

Causal explanation Psychiatry Causal exclusion problem Token-identity Dispositional analysis 

Notes

Acknowledgements

This paper originated mainly in the subproject B06 of CRC991 kindely financed by the German Research Foundation (DFG). The authors would like to thank the many supporting people which contributed to the quality of the paper, including two anonymous reviewers, Jürgen Zielasek, Alexandros Tillas and Markus Schrenk.

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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Department of Philosophy, Institut für PhilosophieHeinrich-Heine-UniversityDüsseldorfGermany

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