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
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.