From generative models to generative passages: A computational approach to (neuro)phenomenology

A new approach called computational phenomenology uses generative modeling techniques from computational neuroscience to study conscious experience. The paper reviews efforts to naturalize phenomenology, addresses philosophical objections, and explains how generative models can simulate the inferential processes underlying specific types of lived experience. This differs from prior uses of generative modeling for consciousness by focusing on modeling the interpretive process that best accounts for particular phenomenal experiences.

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Abstract

This 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.