ORIGINAL RESEARCH article

Front. Sleep

Sec. Sleep, Behavior and Mental Health

Volume 4 - 2025 | doi: 10.3389/frsle.2025.1625185

This article is part of the Research TopicUnderstanding conscious experiences during sleepView all articles

Dreams are more "predictable" than you think

Provisionally accepted
  • 1European Commission, Joint Research Centre (JRC), Ispra, Italy
  • 2University of Sussex, Brighton, United Kingdom

The final, formatted version of the article will be published soon.

A growing body of work has used machine learning and AI tools to analyse dream reports, and compare them to other textual content. Since these tools are usually trained on text from the web, researchers have speculated they might not be suited to model dreams reports, often labelled as "unusual" and "bizarre" content.We used a set of large language models (LLMs) to encode dream reports from DreamBank and Wikipedia. To estimate the ability of LLMs to model and predict textual reports we adopted perplexity, a measure based on entropy, formally, the exponentiated log-likelihood of a sequence. Intuitively, perplexity indicates how "surprising" a sequence of words is to a model.In most models, perplexity scores for dream reports were significantly lower than those for Wikipedia articles. Moreover, we found that perplexity scores were significantly different in reports produced by male vs female participants, and between blind and normally sighted individuals. In one case, we found this difference to be significant between clinical and healthy subjects.Discussion: Dream reports were found to be generally easier to model and predict than Wikipedia articles. LLMs were also found to implicitly encode group differences previously observed in the literature based on gender, visual impairment, and clinical population.

Keywords: dreams reports analysis, dream reports modelling, Gender difference, dreaming in blind participants, machine learning, Large language models, Natural Language Processing

Received: 08 May 2025; Accepted: 30 Jun 2025.

Copyright: © 2025 Bertolini, Consoli and Weeds. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Lorenzo Bertolini, European Commission, Joint Research Centre (JRC), Ispra, Italy

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