Causal reductionism is the widespread assumption that there is no room for additional causes once we have accounted for all elementary mechanisms within a system. Due to its intuitive appeal, causal reductionism is prevalent in neuroscience: once all neurons have been caused to fire or not to fire, it seems that causally there is nothing left to be accounted for. Here, we argue that these reductionist intuitions are based on an implicit, unexamined notion of causation that conflates causation with prediction. By means of a simple model organism, we demonstrate that causal reductionism cannot provide a complete and coherent account of ‘what caused what’. To that end, we outline an explicit, operational approach to analyzing causal structures.
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Erkenntnis Open Access 14 October 2022
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The code used for the simulations can be accessed freely at https://github.com/wmayner/pyphi/blob/develop/pyphi/examples.py/.
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We thank M. Boly, A. Cattani, F. Ellia, G. Findlay, B. Juel, W. Marshall, W. Mayner, G. Mindt and R. Verhagen, and especially J. Hendren, for their comments on the manuscript. This project was made possible through support from Templeton World Charity Foundation (nos. TWCF0216 and TWCF0526) and by The Tiny Blue Dot Foundation (UW 133AAG3451). The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of Templeton World Charity Foundation and The Tiny Blue Dot Foundation.
The authors declare no competing interests.
Peer review information Nature Neuroscience thanks Viktor Jirsa and Klaas Stephan for their contribution to the peer review of this work.
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Grasso, M., Albantakis, L., Lang, J.P. et al. Causal reductionism and causal structures. Nat Neurosci 24, 1348–1355 (2021). https://doi.org/10.1038/s41593-021-00911-8