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URN to cite this document: urn:nbn:de:bvb:703-epub-6981-5
Title data
Kästner, Lena:
Modeling psychopathology : 4D multiplexes to the rescue.
In: Synthese.
Vol. 201
(2022)
Issue 1
.
- No. 9.
ISSN 1573-0964
DOI der Verlagsversion: https://doi.org/10.1007/s11229-022-04008-y
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Project information
| Project financing: |
Deutsche Forschungsgemeinschaft VolkswagenStiftung |
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Abstract
Accounts of mental disorders focusing either on the brain as neurophysiological substrate or on systematic connections between symptoms are insufficient to account for the multifactorial nature of mental illnesses. Recently, multiplexes have been suggested to provide a holistic view of psychopathology that integrates data from different factors, at different scales, or across time. Intuitively, these multi-layered network structures present quite appealing models of mental disorders that can be constructed by powerful computational machinery based on increasing amounts of real-world data. In this paper, I systematically examine what challenges psychopathology models face and to what extent different species of psychopathology models can address them. My analysis highlights that while multiplexes, as they are usually conceived, appear promising, they suffer from the same problems as other approaches. To remedy this, I suggest, we must go a step further and combine different kinds of multiplexes into 4D models. Once we embrace 4D multiplexes and identify appropriate ways to constrain them, we might unlock the true potential of multiplexes for making headway in psychopathology research.
Further data
| Item Type: | Article in a journal |
|---|---|
| Keywords: | Mental illness; Mental disorder; Multiplex; Symptom network model; Connectivity; Temporal dynamics; Multifactorial model |
| DDC Subjects: | 100 Philosophy and psychology |
| Institutions of the University: | Faculties > Faculty of Cultural Studies Faculties |
| Language: | English |
| Originates at UBT: | Yes |
| URN: | urn:nbn:de:bvb:703-epub-6981-5 |
| Date Deposited: | 09 May 2023 08:52 |
| Last Modified: | 09 May 2023 08:53 |
| URI: | https://epub.uni-bayreuth.de/id/eprint/6981 |
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