Future dreams of electric sheep: Case study of a possibly precognitive lucid dreamer with AI scoring

A precognitive dream is a dream about seemingly unpredictable future events that nonetheless seem to be predicted by the dream. It has been most convincingly replicated in two case studies using a single skilled precognitive dreamer (Maimonides studies by Krippner et al., 1971, 1972). Instead of repeating these original studies with another skilled precognitive dreamer, here we set out to determine whether an individual with another unusual dreaming skill – that of entering a lucid dream state almost at will and sketching images seen in that state upon awakening – could become a precognitive dreamer with practice. We pre-registered a formal experiment with two sets of 5 trials. In each trial: 1) the dreamer recorded the contents of his lucid dream in a transcript, 2) the dreamer emailed the transcript to a skeptical target-selector, 3) the target-selector used a random number generator to select a target, 4) the target-selector sent the URL for the target to the dreamer, 5) the target-selector sent the target URL and the dream transcript to the analyst, 6) the analyst stored the date, dream transcript, and target together in a database. We used three methods of judging – 1) a pre-registered but flawed judging method using two skilled human judges [producing 3 hits out of 10], 2) an exploratory method drawing on unskilled human judges [producing 1 hit out of 10], and 3) an exploratory method comparing judging performance across five different text embedding models within large language AI models [producing 5 hits out of 10]. AI-judged methods offered clear evidence for precognition, including a dream-target match conservatively calculated to be highly unlikely to be obtained by chance (p < 1.2x10-5), but a confirmatory experiment is required before drawing firm conclusions. Further, several of the accurate transcript/target pair matches made by the top-performing text embedding models matched those of the skilled human judges, suggesting that the AI method captured human sensibilities and expanded on them. The differences in accuracy among the embedding models have implications for the selection of AI models for future free-response experiments and can begin to give shape to a future of AI participation in screening, training, performance, and analysis in multiple free-response contexts.