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Get Free AccessThis paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. We call this approach computational phenomenology because it applies methods originally developed in computational modelling to phenomenology. The first section presents a brief review of the project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project, and situates our project with respect to these projects. The third section reviews the generative modelling framework. The following section presents our new approach to neurophenomenology based on generative modelling. We then discuss how this application of generative modelling differs from previous attempts to use it to explain consciousness. In summary, generative modelling allows us to construct a computational model of the inferential or interpretive process that best explain this or that kind of lived experience.
Michael Lifshitz, Giuseppe Pagnoni, Ryan Smith, Guillaume Dumas, Antoine Lutz, Karl Friston, Axel Constant, Maxwell J. D. Ramstead, Anil K. Seth, Casper Hesp, Lars Sandved-Smith, Jonas Mago (2021). From generative models to generative passages: A computational approach to (neuro)phenomenology. , DOI: https://doi.org/10.31234/osf.io/k9pbn.
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Type
Preprint
Year
2021
Authors
12
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.31234/osf.io/k9pbn
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