France Lerner

I develop an empirical phenomenological methodology that employs 2D/3D graphic reconstruction to study the spatial, temporal, and affective organization of transcendental experiences such as Out-of-Body and Near-Death Experiences. This research aims to link the subjective structures of experience with scientific models of perception and embodiment within a neurophenomenological framework.

Presently I am developing MIST: A Mapping Interface for Altered States of Consciousness.

MIST is a hybrid research instrument developed to address the well-documented ineffability of Altered States of Consciousness (ASC), whose structural features frequently resist purely language-based description.

Prior to administration of the interface, participants undergo preliminary classification through a battery of standardized questionnaires designed to validate their categorization as near-death experiencers, psychonauts, or individuals reporting meditation-induced altered states.

Following this preliminary validation, participants retrospectively report and reconstruct graphically their experiences. The platform provides a systematic framework for encoding and analyzing of first-person phenomenology of ASC, including Near-Death Experiences (NDEs), Out-of-Body Experiences (OBEs), and Drug-induced Altered States, as well as Meditative States.

Methodologically, MIST integrates a 3D graphic mapping environment with a structured self-report module composed of closed-ended items, open-ended questions, and optional narrative accounts. Through this multimodal architecture, the instrument enables the sequential organization of experiential phases, thereby supporting the reconstruction of their perceived spatio-temporal chronology and the subsequent specification of their spatial configuration.

Conceptually, the interface differentiates between physical space and phenomenal space. Within physical space, encoded contents include physical body, parasomatic viewpoints, mental imagery, and their associated affective correlates. Correspondingly, within phenomenal space, mapped variables comprise self-location and self-motion, mental imagery, and corresponding affective correlates. Furthermore, the perceptual qualities of these spaces and their contents, including luminosity and chromatic properties, are operationalized using CIELAB coordinates (L*, a*, b*). In parallel, affective dimensions are quantified through 3D Body Maps and the Geneva Emotion Wheel (GEW) adapted to each experiential domain, thereby enabling emotional states to be systematically characterized in terms of valence and experienced intensity.

Finally, beyond data encoding, the platform supports computational analysis and predictive modeling of experiential spaces. In particular, it incorporates representation-learning approaches grounded in Image Joint Embedding Predictive Architecture (I-JEPA), facilitating the identification of spatio-temporal configurations and affective correlates while preserving first-person phenomenological specificity.

Within the context of neuroarts, I am interested in how visual and spatial formalization can function as both an epistemic and creative process, transforming first-person experiential data into shared visual representations that contribute to the study of consciousness. My work proposes that artistic modeling can serve as a form of analytic reasoning, capable of revealing how perception and selfhood are structured through space and time.

Through this interdisciplinary approach, I hope to contribute to the community’s broader effort to integrate aesthetic, neuroscientific, and phenomenological methods for understanding the lived dimensions of cognition.