ChatGPT-3.5-generated narratives of mystical, supernatural, or anomalous entity encounters approximate but do not fully match the phenomenology of real-life accounts. The AI descriptions covered each encounter type, mapped to a Rasch hierarchy of anomalous perceptions, showed below-average scores on the Survey of Strange Events, and referenced at least one recognition pattern of Haunted People Syndrome. Inter-rater reliability was fair, and correlations among narratives were low but generally positive. The findings suggest that prototypical depictions based on popular source material can mimic core features of these experiences, yet they lack the full depth of spontaneous reports.
A reanalysis of a case study by Auerbach et al. (2023) on a poltergeist-like disturbance investigated with virtual technology during the COVID-19 pandemic finds that the case strongly aligns with the Haunted People Syndrome (HP-S) model. HP-S conceptualizes ghostly episodes as an interactionist phenomenon arising from individuals with heightened somatic-sensory sensitivities, stirred by dis-ease states, contextualized with paranormal belief, and reinforced via perceptual contagion and threat-agency detection. Content analysis by an independent researcher showed the case had below-average 'haunt intensity' and a pattern resembling embellished or false testimony, yet it displayed most HP-S recognition patterns. The findings imply that ghostly episodes are best understood through a biopsychosocial lens, regardless of potential psi contributions.