From Generative Models to Generative Passages: A Computational Approach to (Neuro) Phenomenology.
Maxwell J D Ramstead, Anil K Seth, Casper Hesp, Lars Sandved-smith, Jonas Mago, Michael Lifshitz, Giuseppe Pagnoni, Ryan Smith, Guillaume Dumas, Antoine Lutz, Karl Friston, Axel Constant
Review of philosophy and psychology January 1, 2022 Peer reviewed DOI: 10.1007/s13164-021-00604-y via PubMed
Summary
A version of computational phenomenology is presented, utilizing generative modelling techniques from computational neuroscience to formalize descriptions of lived experiences. The paper reviews the project to naturalize phenomenology, addresses philosophical objections, and details the generative modelling framework. It concludes by highlighting how this approach differs from prior attempts to use generative modelling for understanding consciousness, ultimately constructing a model that explains various kinds of lived experience.
Study at a glance
| Key finding | The approach constructs a computational model of the inferential processes that explain different kinds of lived experience. |
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Abstract
This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as computational phenomenology because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience.