Sean Li Ming Noah ^1* Miranda Shen ¹ Earth Erowid ² Fire Erowid ² Michael A Silver ¹

• ¹ University of California, Berkeley, Berkeley, United States
• ² Erowid Center, Grass Valley, United States

Psychedelic compounds such as LSD, psilocybin, mescaline, and DMT can dramatically alter visual perception. However, the extent to which visual effects of psychedelics consistently vary for different substances is an open question. The visual effects of a given psychedelic compound can range widely both across and within individuals, so datasets with large numbers of participants and descriptions of qualitative effects are required to adequately address this question with the necessary sensitivity. Here we present an observational study with narrative self-report texts, leveraging the massive scale of the Erowid experience report dataset. We analyzed reports associated with 103 different psychoactive substances, with a median of 217 reports per substance. Thirty of these substances are standardly characterized as psychedelics, while 73 substances served as comparison substances. To quantitatively analyze these semantic data, we associated each sentence in the self-report dataset with a vector representation using an embedding model from OpenAI, and then we trained a classifier to identify which sentences described visual effects, based on the sentences' embedding vectors. We observed that the proportion of sentences describing visual effects varies significantly and consistently across substances, even within the group of psychedelics. We then analyzed the distributions of psychedelics' visual effect sentences across different categories of effects (for example, motion, color, or form), again finding significant and consistent variation. Overall, our findings indicate reliable variation across psychedelic substances' propensities to affect vision and in their qualitative effects on visual perception.