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A dataset of 898 sentences, extracted from 407 open subjective reports, was recently compiled as part of the Dreamachine programme (https:\/\/dreamachine.world\/) (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\u2019s text-based reports. Our analysis confirmed the presence of simple visual hallucinations 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 field of computational phenomenology. More generally, this approach provides a practically applicable methodology for uncovering subtle hidden patterns of subjective experience across diverse research domains. Open-source implementation and an interactive web application are provided to facilitate application of this methodology.<\/jats:p>","DOI":"10.1093\/nc\/niag008","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T12:52:36Z","timestamp":1771505556000},"source":"Crossref","is-referenced-by-count":4,"title":["Mapping of Subjective Accounts into Interpreted Clusters (MOSAIC): Topic Modelling and LLM applied to Stroboscopic Phenomenology"],"prefix":"10.1093","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4548-5349","authenticated-orcid":false,"given":"Romy","family":"Beaut\u00e9","sequence":"first","affiliation":[{"name":"School of Engineering and Informatics, Sussex Centre for Consciousness Science, University of Sussex , Falmer, Brighton, BN1 9QT,","place":["United Kingdom"]},{"name":"School of Engineering and Informatics, Sussex AI, University of Sussex , Falmer, Brighton, BN1 9QT,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David J","family":"Schwartzman","sequence":"additional","affiliation":[{"name":"School of Engineering and Informatics, Sussex Centre for Consciousness Science, University of Sussex , Falmer, Brighton, BN1 9QT,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guillaume","family":"Dumas","sequence":"additional","affiliation":[{"name":"CHUSJ Azrieli Research Centre\/Mila-Quebec AI Institute, University of Montreal , 3175 Chemin de la C\u00f4te-Sainte-Catherine, Montr\u00e9al, QC, H3T 1C5,","place":["Canada"]},{"name":"CHUSJ Azrieli Research Centre\/Mila-Quebec AI Institute, University of Montreal , 6666 Rue Saint-Urbain, Montr\u00e9al, QC, H2S 3H1,","place":["Canada"]},{"name":"Canadian Institute for Advanced Research, Toronto, MaRS Centre , West Tower, 661 University Avenue, Suite 505, Toronto, ON, M5G 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