The Computational Approach to Neuro-Phenomenology

The intention of this workshop is to explore the possibilities to naturalize phenomenology using modeling techniques developed in computational neuroscience and biology. Specifically, we intend to develop a new version of neurophenomenology as ‘generative passages’ (Lutz, 2002; Roy et al., 1999; Varela, 1997), based on methodological innovations that have emerged recently under the rubric of generative modeling (Friston, 2019; Hesp et al., 2021; Sandved-Smith et al., 2021). This approach represents a distinctive formulation of computational phenomenology because it applies methods from computational modeling directly to phenomenology. The aim of this workshop is to present a specific application of computational phenomenology to what phenomenological philosophers have called ‘constitution’, see Sokolowski (1964). The aim of such model is to map phenomenological constitution onto the notion of inference, as it is used in generative modeling to infer the (causal) structure of the process generating some data or, more simply, making sense of data.

During this workshop we will come together with 25 researchers in the fields of phenomenology, neuroscience, philosophy, and computational modeling to discuss how the recent advancements in computational modeling–specifically bayesian mechanics and the active inference framework–may inform the study of lived experience and vice versa. The program during these days will consist of one or two keynote lectures per day, discussion rounds, and working groups that are aimed to translate our discussions into a special issue for the neuroscience of consciousness journal.