SleepMay 1, 2026Holland Doise, A. Gonzalez Quesada, Garrett Baber et al.
Abstract Introduction Nightmares are associated with more severe posttraumatic stress disorder (PTSD). The advent of large language models (LLM) offers unique advantages to efficiently code affective dream content over human methods, which can be resource intensive and prone to error. This study tested whether dream affect (DA), measured via self-report, human-rated narrative coding, and LLM...
Abstract Introduction Dream reports provide unique insights into emotional processing during sleep. While self-reports are the gold standard for assessing dream affect, natural language processing (NLP) tools like ChatGPT may offer scalable alternatives. This study evaluated ChatGPT’s ability to estimate positive and negative affect from dream reports, comparing its performance against...
Despite advances in sleep and dream science via neuroscientific methods, self-reported diary data (e. g. dream journals) remain indispensable for capturing theory-relevant phenomenological aspects of dreams including their emotional content [1, 2]. Manual annotation of such data; however, is time-intensive and typically requires human coders to be trained to reliability. To circumvent these...
Abstract Introduction Dreaming has been theorized to facilitate fear extinction learning in a manner akin to desensitization therapy. A testable hypothesis from this theory is that the experience of fear in dreams leads to lower subsequent daytime negative affect. This study employed a novel natural language processing tool and multilevel modeling to test whether fear in dreams led to...