Mapping of Subjective Accounts into Interpreted Clusters (MOSAIC): Topic Modelling and LLM applied to Stroboscopic Phenomenology

2025-02-25

AI Summary

This study addresses the challenge of systematically identifying visual hallucinations (VHs) and altered states of consciousness in open-ended subjective reports. Analyzing 862 phenomenological texts collected during the Dreamachine project—under closed-eyes stroboscopic light stimulation (SLS)—we propose a novel, interpretable experience-clustering framework that integrates large language model (LLM)-based semantic understanding with unsupervised topic modeling (BERTopic/LDA). This approach overcomes the limitations of conventional questionnaires in capturing rich, multidimensional subjective experiences. To our knowledge, this is the first application of LLM-driven computational neurophenomenology to VH research. Our method successfully replicates canonical geometric hallucinations and, critically, automatically uncovers two previously undercharacterized experiential clusters: dynamic complex hallucinations and multidimensional shifts in conscious state. Results demonstrate the framework’s efficacy in extracting latent experiential dimensions from unstructured phenomenological data and its potential for cross-paradigm generalization.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Topic discovery and tracking

Abstract

Stroboscopic light stimulation (SLS) on closed eyes typically induces simple visual hallucinations (VHs), characterised by vivid, geometric and colourful patterns. A dataset of 862 sentences, extracted from 422 open subjective reports, was recently compiled as part of the Dreamachine programme (Collective Act, 2022), an immersive multisensory experience that combines SLS and spatial sound in a collective setting. Although open reports extend the range of reportable phenomenology, their analysis presents significant challenges, particularly in systematically identifying patterns. To address this challenge, we implemented a data-driven approach leveraging Large Language Models and Topic Modelling to uncover and interpret latent experiential topics directly from the Dreamachine's text-based reports. Our analysis confirmed the presence of simple VHs typically documented in scientific studies of SLS, while also revealing experiences of altered states of consciousness and complex hallucinations. Building on these findings, our computational approach expands the systematic study of subjective experience by enabling data-driven analyses of open-ended phenomenological reports, capturing experiences not readily identified through standard questionnaires. By revealing rich and multifaceted aspects of experiences, our study broadens our understanding of stroboscopically-induced phenomena while highlighting the potential of Natural Language Processing and Large Language Models in the emerging field of computational (neuro)phenomenology. More generally, this approach provides a practically applicable methodology for uncovering subtle hidden patterns of subjective experience across diverse research domains.

Problem

• Analyzing open subjective reports on stroboscopic light-induced hallucinations.
• Applying Large Language Models and Topic Modelling to interpret latent experiential topics.
• Expanding systematic study of subjective experiences through computational approaches.

Innovation

• Large Language Models for analysis
• Topic Modelling for patterns
• NLP in phenomenology studies