Mapping Altered Temporality: Human-Validated LLM Analysis of Naturalistic Trip Reports
Year: 2026
Type: preprint
Abstract: Psychedelic substances can profoundly alter the experience of time, yet we lack a detailed account of which distinct temporal alterations occur, how prevalent they are, and how they are interrelated. A key obstacle is that the methods best suited to capturing the phenomenological repertoire of altered temporality are difficult to scale and may therefore fail to reliably capture rare phenomena, whereas scalable psychometric tools lack the categorical resolution to tease apart distinct types of experience. Here we combine resolution with scale, applying a theorist-constructed phenomenological codebook to naturalistic psychedelic trip reports through large language model (LLM)-based, corpus-scale content analysis. First, we establish that altered temporality resolves into seven textually distinguishable categories, recovered with similar reliability by human and LLM coders. Second, we apply LLM-based qualitative analysis to 3,291 excerpts from a corpus of 7,303 psychedelic trip reports, confirming that this categorical structure holds at corpus scale. Third, we estimate category prevalence and identify a stable associative structure comprising a complex of rare, interconnected alterations centered on timelessness and metaphysical appraisals, alongside an independent, more prevalent alteration in the subjective rate and duration of temporal flow. Fourth, we show that a similar relative prevalence profile and broad associative pattern, derived from the English-language Shroomery corpus, replicate in the independent Polish-language Neurogroove repository. Our study provides a high-resolution map of psychedelic temporality, arrived at by a method that may generalize beyond it, toward recovering the taxonomic structure of altered states of consciousness.
Source: PsyArXiv (OSF Preprints)
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225319766