Towards a latent space cartography of subjective experience in mental health

The way that individuals subjectively experience the world greatly influences their own mental well-being. However, it remains a considerable challenge to precisely characterize the breadth and depth of such experiences. One persistent problem is the lack of objective tools for directly quantifying and comparing narrative reports of subjective experiences. Here, we develop a new approach to map and compare reports of experience using the latent space of artificial neural networks.

Using a series of 31 prompts, including 30 images and one open-ended question, we quantified how the verbal reports provided by participants (n = 210, 50% female) deviate from one another and how these variations are linked to subjective experience and mental health.

We found that latent space embeddings of experience can accurately predict subjective judgments of valence and arousal in a series of emotional pictures. Furthermore, we show that narrative reports to ambiguous images can accurately predict transdiagnostic factors of mental health. While distortions in the latent space of artificial neural networks are notoriously difficult to interpret, we propose a new approach to synthesize visual stimuli with generative artificial intelligence that can be used to explore semantic distortions in reported experiences.

In sum, latent space cartography could offer a promising avenue for objectively quantifying distortions of subjective experience in mental health and could ultimately help identify new therapeutic targets for clinical interventions.