Visual and emotional aspects of waking memories follow different paths as people fall asleep. Healthy adults viewed an emotional image, then were awakened seconds to minutes later during wakefulness, N1, or N2 sleep stages. Semantic similarity between the image and subsequent hypnagogic imagery—the 'image residue'—persisted and even increased during N1 sleep, lasting longer with more time in that stage. In contrast, the emotional tone of the image—the 'affect residue'—gradually faded as sleep progressed, becoming neutralized and reaching its lowest point during N2. Brain theta power (4.5–6.5 Hz) during N1 was inversely related to image residue. These findings suggest that visual content and strong negative emotions may decouple at sleep onset, possibly aiding emotional processing.
Word-embedding techniques, which quantify word associations in text, can identify patterns in dream reports even when the dataset is small. Latent Semantic Analysis (LSA) outperformed Skip-gram in extracting semantic associations from small corpora in two tests. LSA captured relevant word associations in dream collections, including cases with low-frequency words or few dreams. This approach offers a complementary method to traditional frequency-based dream content analysis and opens new avenues for dream interpretation and decoding.
Word embeddings are typically studied in large text datasets, but few studies examine small corpora like single-person text production. This paper compares Skip-gram and LSA for extracting semantic patterns from small dream report series. LSA outperformed Skip-gram on small training corpora in two semantic tests. As a case study, LSA captured relevant word associations in dream reports even with few dreams or low-frequency words. The authors propose LSA can explore word associations in dream reports, offering new insights for psychology research.