Structural differences between REM and non-REM dream reports assessed by graph analysis

Dream reports collected after rapid eye movement (REM) sleep are typically longer, more vivid, and more story-like than those from non-REM sleep, but traditional measures may be confounded by report length. By analyzing 133 dream reports from 20 participants as non-semantic directed word graphs, researchers found that REM dream reports have greater connectedness—words recur with longer range—compared to N2 sleep reports. Graph measures predicted dream complexity, with higher connectedness and lower randomness linked to more complex reports. The largest connected component improved models using report length alone for predicting sleep stage and complexity. Graph analysis offers an automated method to complement traditional dream report analysis.

Study at a glance

Abstract

Dream reports collected after rapid eye movement sleep (REM) awakenings are, on average, longer, more vivid, bizarre, emotional and story-like compared to those collected after non-REM. However, a comparison of the word-to-word structural organization of dream reports is lacking, and traditional measures that distinguish REM and non-REM dreaming may be confounded by report length. This problem is amenable to the analysis of dream reports as non-semantic directed word graphs, which provide a structural assessment of oral reports, while controlling for individual differences in verbosity. Against this background, the present study had two main aims: Firstly, to investigate differences in graph structure between REM and non-REM dream reports, and secondly, to evaluate how non-semantic directed word graph analysis compares to the widely used measure of report length in dream analysis. To do this, we analyzed a set of 133 dream reports obtained from 20 participants in controlled laboratory awakenings from REM and N2 sleep. We found that: (1) graphs from REM sleep possess a larger connectedness compared to those from N2; (2) measures of graph structure can predict ratings of dream complexity, where increases in connectedness and decreases in randomness are observed in relation to increasing dream report complexity; and (3) measures of the Largest Connected Component of a graph can improve a model containing report length in predicting sleep stage and dream report complexity. These results indicate that dream reports sampled after REM awakening have on average a larger connectedness compared to those sampled after N2 (i.e. words recur with a longer range), a difference which appears to be related to underlying differences in dream complexity. Altogether, graph analysis represents a promising method for dream research, due to its automated nature and potential to complement report length in dream analysis.