Dreams Are More "Predictable'' Than You Think

Dream reports are often thought to be semantically unique compared to other types of text, which could pose problems for natural language processing tools trained on non-dream corpora. This study used state-of-the-art large language models to compare dream reports from DreamBank with Wikipedia articles. Overall, DreamBank as a whole did not deviate from Wikipedia in terms of predictability. However, individual dream reports were significantly more predictable than Wikipedia articles. Preliminary evidence indicates that word count, gender, and visual impairment can significantly influence how predictable a dream report appears to the model.

Study at a glance

Abstract

A consistent body of evidence suggests that dream reports significantly vary from other types of textual transcripts with respect to semantic content. Furthermore, it appears to be a widespread belief in the dream/sleep research community that dream reports constitute rather ``unique'' strings of text. This might be a notable issue for the growing amount of approaches using natural language processing (NLP) tools to automatically analyse dream reports, as they largely rely on neural models trained on non-dream corpora scraped from the web. In this work, I will adopt state-of-the-art (SotA) large language models (LLMs), to study if and how dream reports deviate from other human-generated text strings, such as Wikipedia. Results show that, taken as a whole, DreamBank does not deviate from Wikipedia. Moreover, on average, single dream reports are significantly more predictable than Wikipedia articles. Preliminary evidence suggests that word count, gender, and visual impairment can significantly shape how predictable a dream report can appear to the model.