Revisiting the Unusual: Investigating the Prevalence and Validity of Nonordinary Experiences as a Window into Consciousness

Revisiting the Unusual: Investigating the Prevalence and Validity of Nonordinary Experiences as a Window into Consciousness | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Revisiting the Unusual: Investigating the Prevalence and Validity of Nonordinary Experiences as a Window into Consciousness Ronald Fischer, Giovanna Bortolini, Tiago Bortolini, Everton Maraldi, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6907742/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Consciousness research has largely overlooked systematic examination of nonordinary experiences (NOEs) in the general population. Such experiences are subjectively marked as unusual or special compared to mundane ones, are challenging to quantify and disciplinary biases complicate accurate assessments of population-wide prevalence rates. Using descriptive, non-judgmental phrasing of items, we were able to validate 31 experiences (N = 1,284). Across a series of pre-registered experiments with general population samples (total N = 11,629), relative lifetime prevalence rates were estimated consistently, but absolute point estimates were sensitive to measurement and study conditions. Overall, we observe much higher prevalence rates of nonordinary experiences than indicated by associated clinical diagnosis rates (e g., schizophrenia, dissociative disorders, depression). Our study demonstrates that several types of nonordinary experiences—often dismissed as rare or pathological—are in fact widespread in general populations, opening novel perspectives for integrating these experiences into a broader empirical science of consciousness. Biological sciences/Neuroscience/Cognitive neuroscience/Consciousness Social science/Psychology/Human behaviour Health sciences/Diseases/Psychiatric disorders/Psychosis Social science/Anthropology/Biological anthropology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Researchers have long pondered the nature of consciousness, particularly how we experience and reflect on our subjective states 1–3 . Yet, despite decades of consciousness research, the nature and variability of subjective experiences themselves as the core component of consciousness remain relatively unexplored territory. One major challenge is that subjective experiences are not objective recordings of internal or external events, but are the result of sensory input and top-down neurocognitive processes 4,5 . Particularly intriguing are those experiences that diverge sharply from ordinary sensory perception–what is considered to be “out there” in the world and thus expand the ‘experiential repertoire’ of individuals, such as hearing voices, seeing visions, sensing unseen presences, or experiencing unexpectedly overwhelming emotions 6 . Documented across cultures and history, these unusual phenomena feature prominently in humanity’s earliest recorded narratives 7,8 , underscoring their persistent importance to human thought. Yet, research remains fragmented, often filtered through disciplinary lenses (psychiatric, spiritual, cultural) that yield conflicting interpretations and widely varying prevalence estimates 9–11 . This inconsistency creates a scientific paradox: how can seemingly similar populations report dramatically differing rates of these experiences? Addressing this paradox is crucial for progress in consciousness science, which requires reliable data on the prevalence of these specific subjective experiences and their validity. Here we report on a large-scale, cross-contextual validation of a culturally adapted inventory of nonordinary experiences, as an attempt to minimize disciplinary bias and response artifacts. Specifically, our study further aims to offer a more neutral and objective approach to measuring subjective experiences and explain the paradox of diverging estimates of lifetime experiences within the same populations. Beyond addressing inconsistencies across scientific disciplines, systematically exploring nonordinary experiences (NOEs) 12–14 can offer novel insights into the fundamental workings of the human mind and consciousness 6,15 . Here, we follow Taves and Barlev’ terminology of NOEs, describing experiences marked as special or standing out to participants relative to what they may consider everyday or ordinary experiences 12 . Current exploration of such conscious experiences has reached an impasse by focusing on a narrow set of experimental settings 2,16 , limiting our understanding of experiences that appear rare at the individual level but are surprisingly common when surveyed broadly in populations 9,11,15,17–20 . To date, the experiences that may be relevant for exploring features of these conscious states are variably framed as religious, indicative of extraordinary gifts or abilities, symptoms of clinical or pathological problems, or evidence of paranormal forces. Those perspectives affect the methods used and the disciplinary framing of the inquiry, which in turn can influence participants’ willingness to acknowledge and report such experiences 12,14 . One paradox is the widely varying prevalence rates for some of these experiences. For example, in the context of a clinical survey, 13.3% of the respondents reported hallucinatory experiences 9 , whereas in a study on religious experiences in the same population, up to 41% of participants reported seeing spirits or hearing voices of dead people 11 . Although hearing voices is one of the classical symptoms of psychosis, which is rare in the general population, non-clinically relevant voice hearing estimates can vary between practically zero and above 80% 21 . Progress can be made along several trajectories. At the most basic level, learning and development takes place via the accumulation and integration of diverse experiences – an understanding of the distribution of nonordinary experiences in the general population can provide unique insights into the dynamic interplay between culture and the mind during socialization 15,22 . Examining the prevalence of vastly different experiences that straddle the boundaries of consciousness experience will provide important observational data for further targeted explorations of consciousness, given the range of sensory inputs involved (visual, auditory, tactile, etc.), the involvement of a sense of a coherent self and possible alterations in the states of consciousness 6,23,24 On a more practical level, accurately assessing the prevalence experiences of potential interest to clinicians is essential, because a better understanding of these phenomena aids in differential diagnoses, identifies unmet needs in the population, and highlights early signs that could prompt timely interventions before more serious clinical symptoms develop 25 . Current models suggest a continuum between non-clinical and clinically significant experiences, with non-clinical manifestations offering opportunities for early intervention 26 . Conversely, such improved understanding may also help avoid over-diagnosis of psychiatric syndromes, by more accurately informing the prevalence of experience phenotypes in otherwise healthy and normally functioning individuals. Ultimately, this can improve health service planning and enable the more efficient allocation of scarce resources 27 informing timely responses and deepening our understanding of the interactions between biological and sociocultural factors that give rise to such non-ordinary conscious experiences. To accurately estimate prevalence, it is crucial to consider factors that influence whether individuals report such experiences, including comprehension of the question, retrieval of relevant experiences, calibrating, and reporting answers 28,29 . Comprehension and recall of relevant experiences may be facilitated or hindered by the phrasing of survey items and study context to match the local meaning of the experience. For example, an experience being phrased and included in a clinical study context may not be mapped to relevant experiences if they occur in non-clinical, culturally-salient contexts. Once retrieved, willingness to report can be hindered by perceived stigma or self-stigma associated with the experience 30,31 or biases due to the question format 28,32,33 . To overcome these issues, measures must capture the core phenomenological aspects of experiences and express them in a clear, neutral, and non-judgmental form. To make progress on tackling questions around consciousness, a broader set of experiences spanning a range of sensory and cognitive features is desirable 1,3,34 . Our study adopts Taves and Barlev’s participant-centered approach, shifting the focus from the researcher’s disciplinary perspective to the participant’s, enabling a bottom-up, subject-driven reporting process on the phenomenological features. We validated a Brazilian Portuguese version of their carefully constructed inventory of subjective experiences 13 encompassing a broad spectrum of emotional, cognitive, sensory, and self-related phenomena that have been of interest across the social, behavioral and medical sciences 12,35–38 . It is arguably the inventory with the broadest list of both subjectively positive and negative experiences of interest to scholars in medicine, neuroscience, psychology, sociology, religious studies, management studies, and anthropology (Table S1). Our first step was to use qualitative methods that probe and improve item phrasings to facilitate participants’ understanding of survey content 13,29,39,40 . Qualitative methods are valuable for exploring sensitive experiences because they: a) ensure participants interpret items correctly, and b) enable iterative refinement for better clarity and validity. Engaging participants from the target population highlights the importance of community involvement and lived experience in mental health research 41,42 . Using this robustly validated inventory, we systematically investigated how methodological variables affect prevalence reporting, including survey framing, implied normativity, and uncertainty signaling. Participants often use subtle cues to infer information about the implied nature of the experience and the severity and frequency of those experiences 28,43 . To test potential influences on reporting, we experimentally manipulated: a) the context in which survey questions were presented, b) the implied normativity of the experiences, and c) the inclusion or exclusion of hedges signaling uncertainty. Specifically, we test whether presenting the items in the context of a mental health survey influences lifetime prevalence ratings, whether presenting the items with a frequency scale that implies high prevalence rates augments lifetime prevalence ratings and whether the presence of an ‘I don’t know’ option changes prevalence ratings because it may allow individuals to avoid providing an answer. We also examined whether experience items that are more ambiguous or difficult to understand are less likely to be endorsed than items that are clearer or more easily understood. This is a crucial advance because validity is often treated as a precondition for estimating prevalence, but we argue that the precision of validity information is crucial for accurately estimating prevalence rates. All studies were administered online using nationally representative samples, and analyses were preregistered (unless otherwise noted). Finally, pooling data from diverse national samples allowed us to provide more robust and nuanced estimates of the lifetime prevalence of these experiences, accounting for factors that might otherwise skew or obscure the findings. In summary, our study provides a pathway for high-quality estimates of the lifetime prevalence of human experiences that are difficult to measure empirically but have fascinated scholars of the human mind since the dawn of human civilization. Results Validation of experience items A first challenge for estimating the lifetime prevalence of nonordinary experiences is achieving a neutral and non-judgmental way of measuring the phenotypical core characteristics of the experiences, in a way that is understandable by general population samples. We opted for an iterative qualitative design with pseudorandom presentation of items (preregistration link). Participants from the general population (N = 1,284 Brazilian citizens, 57% female, mean age = 42 years; SD ± 13) were asked to provide examples of experiences in response to test items. Four trained coders independently coded the open-ended responses in terms of whether the respondent had understood the question 13 , 39 . Items with unclear responses were checked and rephrased if necessary, before further validity probing was performed. Using a criterion of 80% of responses being classified as understood in a sample of at least 20 responses 13 , we found sufficient evidence of validity for 31 items from the original list of 38 items in our population. Ten items had to be revised to be understood by participants. Among the non-validated items, experiences asking about events that explicitly involve meaning making processes (e.g., receiving messages, deep insights) were more difficult to validate (Table S2). Estimating prevalence rates involves calculating a validation score (VS), which includes the risks of false positives (a 'Yes' for those without the experience) and false negatives (a 'No' for those with the experience) 44 . In addition to the overall VS, we calculated two complementary indices: Positive Proportion Understood (PPU) and Negative Proportion Understood (NPU) scores 13 . PPU refers to the proportion of participants who endorsed an item and also clearly understood it, providing evidence that affirmative responses likely reflect genuine experiences. This is particularly important for rarer experiences, where misinterpretation could lead to inflated prevalence. NPU refers to the proportion of participants who denied the experience and also demonstrated clear understanding, which is especially relevant for common experiences—helping to ensure that non-endorsement reflects a true absence of the experience rather than confusion or misunderstanding. The results show that seven items (déjà vu, love, places (special), pleasure, animated places, meaning in life, faces) passed the VS but not the NPU (Fig. 1 ). This suggests 13 that individuals who did not report the experience during validation might have misunderstood the items, indicating they possibly had the experience but could not identify the relevant categories. This could lead to underestimating such experiences in the general population samples. Two items (devotion to objects & people), previously studied in highly select populations 3 , passed the VS but showed a PPU < .80, suggesting that prevalence rates may be overestimated and should be considered with caution. Framing effects on lifetime prevalence estimates One central challenge for estimating prevalence rates is the study context, which may heighten perceptions of stigma or self-stigma (internalized negative evaluations of oneself when having specific experiences), which may reduce the willingness to affirm having had specific experiences 30 , 45 . We tested whether mental health framing affects participants' willingness to report these experiences. A second feature that may influence response behavior is the presence of hedging response options, such as ‘I don’t know’ responses. Hedging might occur for several reasons. Affirming that one has had the experience may result in stigmatizing responses or may involve self-stigmatization (due to internalized negative stereotypes) 30 . Hedging may also be a result of subjective uncertainty about the nature of the experience or uncertain memories about events, which may increase doubts about whether a personal experience qualifies as relevant for providing an affirmative answer 28 . To test the importance of the study context and the availability of hedging on lifetime prevalence responses, participants from the general population (N = 1,652, Mean age = 47, SD age ± 16, 29.8% female, preregistration link, data link) were randomly assigned to either of two conditions: a) a survey which started either with a battery of mental health screening tools (GAD-7 and PHQ-9) and mental health questions (life satisfaction, loneliness) or the nonordinary experiences survey and b) the response options either included a hedging (I don’t know) option or not. Running a multilevel logistic regression on the yes-responses to each of the experiences, we only found a main effect of presentation condition, but no other effects (see Table S4). The implied mental health context by presenting mental health screening scales first significantly decreased the average lifetime prevalence rate of nonordinary experiences compared to presenting experience items first (OR = 0.67, SE ± .056, 95% CI 0.578–0.797, p < .001; Fig. 2 A). One alternative explanation for this effect might be fatigue or general order effects. To address this possibility, we added a third condition in which participants first responded to a non-clinical personality inventory. Comparing these two conditions (mental health screening first, experiences items first) from the previous study with the added personality first condition (N = 1,242, Mean age = 49, SD age ± 15, 36% female, preregistration link, data link) we did not observe a significant difference for the personality first vs experiences first condition (Fig. 2 B; Table S5). Implied norm effects and response hedging Our previous studies suggested that subtle cues, such as the presentation order of surveys or information embedded in the question or response formats, may influence response behavior 28 , 46 . Here, we probed this further. The original inventory has a yes vs no response format. When confronted with this response choice, there is little information available to individuals to detect whether such experiences may be frequent. On the other hand, using a frequency response scale immediately sets expectations for their prevalence 43 , 47 . We conducted an experimental study with a general population sample (N = 7,518, Mean age = 44, SD age ± 15, 49% female, data link) and asked participants to either respond to the experience items with 1) a binary yes-no response (i.e., have had the experience or not), 2) a binary response with the presence of a hedging (“I don’t know”) option or 3) with a five point frequency response scale, that varied from ‘I never had this experience’ to ‘I have had this experience 10 times or more’. This study was not preregistered. We estimated the implied lifetime prevalence rates (calculated as the percentage of participants that have had the experience at least once) across conditions. A multilevel ordinal regression analysis with a random effect for participants was employed to compare the hedging and frequency conditions against the binary response condition, while controlling for possible experience-specific effects by including interactions with the individual experience items. Compared to the binary response options, individuals in the frequency response condition (OR = 4.68, SE ± .44, 95% CI 3.883–5.641, p < .001) reported overall higher yes responses, implying greater lifetime prevalence. The comparison of the binary response with hedging option did not show any significant result (OR = 1.24, SE ± .10, 95% CI = 1.045–1.468, p = .013). Overall, the frequency effect compared to the binary response option with the availability of a hedging option was substantively larger (z = 11.123, p < .001, Fig. 3 ). We also observed 29 significant interactions between specific items and response conditions (see Table S6). These interactions suggest subtle shifts across experiences, which raises an interesting question about absolute vs relative lifetime frequency effects. Absolute prevalence rates indicate the absolute number of individuals reporting a given experience, whereas relative prevalence rates focus on the ordering of prevalences, that is, whether certain experiences are relatively more common than others in a sample. The two estimates may not coincide. For example, while absolute rates may vary dramatically, the relative rank order might be well preserved, suggesting an overall response shift across all experiences. Alternatively, absolute rates variability may be relatively minor, but relative rates may vary more widely due to factors such as differential stigma associated with specific experiences. One option to explore these possibilities is to examine the relative consistency of prevalence rates via the rank-order correlations across conditions. The results of such analysis suggested that the relative ordering of the lifetime prevalence rates was well preserved, with all r > .90 (Table S7). To ensure that this correlation was not masking differences in rank positions for individual items, we computed the variability of the ordinal positions across the conditions (Table S8). The average mean rank varied by 1.74 points (SD ± 1.31), suggesting that most items maintained a consistent position across formats. We identified one outlier (Misfortune item; SD ± 5.77). Overall, these findings indicate that absolute prevalence rates are sensitive to implied frequency or normativity, but the relative ordering of experiences in the population is preserved. Given the importance of absolute prevalence rates for clinical purposes of determining diagnosis rates and estimating unmet needs, our findings suggest that binary scales may lead to lower absolute estimates. In contrast, researchers interested in the mechanisms underlying specific experiences are more likely to be interested in the relative prevalence rates. The results suggest that items that elicit higher endorsement in one format show similar rank positions in the other formats, despite differences in absolute response rates. Sensitivity of lifetime prevalence rates to implied frequency effects The previous study suggested that individuals use information about the implied frequency when responding to whether they had a specific experience in their lives or not. This information use may be strategic 28 , 33 and might be influenced by the implied frequency of the response scales. For example, relatively rare experiences may be too uncommon to be influenced by frequency information, whereas moderately common experiences may be more sensitive to frequency information. People may have first or second-hand information on these experiences, which raises the plausibility of higher prevalence effects, which in turn may be malleable by higher implied prevalence via the manipulated frequency response scale. We ran an experimental study in which people from the general population (N = 2,035; Mean age = 42; SD ± 15; 56% female, preregistration link, data link) were randomly assigned to one of three conditions of information bias induced via different response scales: 1) Binary yes/no responses, 2) Low implied prevalence (response scale: up to 10 times or more in one’s life time), or 3) High implied prevalence (response scale: up to 100 times or more in one’s life time). Examining implied lifetime prevalence rates in a multilevel logistic regression, we found significant increases for both Low: OR = 1.46, SE ± .11, 95% CI = 1.262–1.690, p < .001 and High implied prevalence rates: OR = 1.57, SE ± .12, 95% CI = 1.358–1.818, p < .001, compared to binary response scales. The relative difference between the two OR’s was not significant from each other (z = − .677; p = .499. (Fig. 4 A, Table S9). Explicitly testing the information value of the population level prevalence rates on participant responses (see Fig. 4 B), we replicated the condition effect: OR = 1.468; SE ± .072, 95% CI = 1.33–1.617, p < .001 and identified a population-level prevalence effect: OR = 346.969; SE ± 146.644, 95% CI = 151.543–794.412, p .95; Table S11) and low rank-order changes (rank SD ± 1.38, SD ± .0.97; Table S12) again supported the robustness of relative frequency rates. This suggests that participants use information about implied prevalence rates available via response scale formats as a general heuristic across experiences, but participants were not sensitive to the relative implied prevalence rates. The relevance of validation information on prevalence rates Validity is essential for research. Although we collected data using items that passed the 80% validity threshold, we observed variation in validity scores during the validation process which may indicate that some individuals interpret items differently (data link).We meta-analytically summarized the associations between the different validity scores and prevalence rates across samples and observed reliable associations across all studies (Figure S13). Specifically, items with a higher number of ambivalent responses that could not be classified, lower PPU, and higher NPU had statistically lower endorsement rates. To evaluate the relative impact of these significant validity factors compared to measurement effects described in the previous studies (framing, hedging, and implied prevalence via frequency response scale format), we conducted a multilevel logistic regression analysis using all previously reported data (total N = 11,628; random participant effects, see Table S14). All predictors were significant ( p < .05), except for the availability of hedging options. Higher NPU values (OR = .402, SE ± .009, 95% CI .385-.419, p < .001) and higher proportions of unclear responses during the validation process (OR = .011, SE ± .000, 95% CI .010 − .011, p < .001) were associated with lower prevalence rates. In contrast, higher PPU values were associated with increased prevalence rates (OR = 20.399, SE ± 1.062, 95% CI 18.419–22.591, p < .001). Variability in validity parameters is therefore associated with prevalence estimates. Estimating absolute prevalence of nonordinary experiences To estimate the overall prevalence of these nonordinary experiences, we pooled the estimates across our reported studies here using sample-size weighted effects (Fig. 5 ; Table S15; data link). We found substantial heterogeneity in prevalence estimates (mean I² = 93.5; minimum I² = 71.10 for "seeing animated objects"). All values were above the .70 threshold 48 , 49 for I 2 , indicating that estimates are highly heterogeneous and caution is needed in interpreting absolute prevalence rates. Despite this heterogeneity, interesting relative prevalence patterns emerged. Roughly a third of the experiences are reported by more than half of the population, with the lower confidence intervals above the 50% mark. In this group, emotional and cognitively driven experiences predominate, while clinically relevant experiences suggesting altered sensory states are clustered at the lower end. Notably, even the rarest experience ("experiencing animated objects") occurred in approximately 18% of participants (range 13–24%). Other clinically relevant experiences such as Out-of-body experience, perceiving lights or reporting memories of past life were reported by about one in five participants. The prevalence rate of these experiences is substantially higher in these general population samples than would be expected based on the associated clinical conditions (e.g., psychosis, schizophrenia, and dissociative disorders, which tend to have a lifetime prevalence 47 – 49 of around 1% in the general population). Our findings imply that such experiences, despite appearing unusual, are relatively frequent in the general population and may not be pathological in themselves. Extending these observations to other experiences that involve alterations in the sensory system or a sense of agency and which are often considered clinically relevant, we observed high rates of feeling touched (M = 39.3%; range: 30% – 48%), feeling guided by a force ( M = 40.1%%; range: 27% – 55%), feeling the presence of a force ( M = 41.6%; range: 30% – 57%), or extrasensory perception ( M = 47%; range: 41% – 55%). These parameters suggest that such experiences are common in the general population, and without additional information on an individual's health and well-being, pathological explanations are not immediately justified. Discussion Our study shows that it is possible to obtain consistent relative lifetime prevalence estimates using locally validated items focusing on phenomenological features 13 of experiences that have fascinated scholars for millennia. Advancing on discrepant prevalence estimates reported in different literatures, the results strongly suggest that many seemingly unusual experiences are common to very common, and even experiences that may serve as markers of psychopathology are experienced by 30–50% of the population at least once in their lifetime. At the same time, the absolute lifetime prevalence rates are sensitive to measurement conditions, which can explain the wide range of estimates in the previous literature. We highlight three key take-home messages for estimating lifetime prevalence rates in future studies, before addressing substantive questions about these experiences. First, it is important to carefully test item understanding in target populations 39 . Researcher-designed questions may not align with participants' interpretations, leading to inaccurate estimates. Our results demonstrate that validity information was associated with lifetime prevalence estimates across more than 10,000 participants, which can have wide-ranging implications. For example, feeling extremely devoted to other people has been primarily studied in ritualistic and religious settings, but this experience is also relevant for politics (strong attachment to political leaders or groups 50 , 51 ), management 52 , 53 or clinical work (strong attachment in relationships 54 ). Our NPU data suggests misinterpretations of such items could significantly underestimate true prevalence rates. Second, the response scale substantially influences prevalence estimates. Clinical research has favored binary lifetime prevalence rates, whereas social science prefers a frequency response scale. The two formats lead to diverging estimates on lifetime prevalence. Consistent with cognitive theories, participants likely interpret response formats as implicit cues about researchers’ assumptions 28 , 33 , 43 , 46 . The discrepancy between binary vs frequency scales might account for a substantive portion of the gap observed between previous studies 9 , 11 (see the supplement for relevant calculations). Third, framing and study context affect prevalence rates. When experiences were presented within a mental health context, affirmative responses declined, which may explain the lower prevalence rates observed in clinical studies 9 , 17 – 20 . Considering that clinically focused surveys are framed in a mental health context, often use binary response scales and may use pathological framing of experiences, this could lead to unexamined interactive effects which may further suppress responses. Estimates of lifetime prevalence are certainly not independent of the research context and this needs more attention 29 , 55 . The preservation of relative prevalence, on the other hand, allows novel insights into the underlying dynamics of mind-culture interactions. Experiences that are more common in a population show higher prevalence rates than rarer experiences, relatively independently of the research context. A precondition for such future studies is that a diverse set of experiences is studied. Focusing on specific substantive experience prevalence rates, positive emotional experiences, relatively innocuous cognitive lapses (e.g., experiences of déjà vu and absorption), and lucid dreaming were found to be highly prevalent. Déjà vu was the most common experience, supporting previous work implying that familiarity-based recognition errors may be frequent and relatively harmless 56 , 57 . Given its lower NPU score, actual prevalence rates of dejà vu might be even higher. Lucid dreams were equally common, in line with some previous estimates 58 . Absorption, defined here cognitively as losing track of time during task engagement, was also highly prevalent. Though interpretations of absorption are diverse, ranging from conditions of flow or creativity to fantasy proneness and disposition to experience altered states of consciousness 59 , 60 , our measure focused on its cognitive aspect, without implying altered states of consciousness. These three most common experiences centrally involve cognitive functions (attention and memory in particular), suggesting that objectively faulty cognitive processes are salient features of human cognition. Given their high prevalence, such experiences constitute important elements of people’s experiential repertoire. Their ubiquity suggests that alterations of awareness are a central feature of human consciousness, which has interesting implications for the proposition that consciousness and awareness are intertwined 1 . Other experiences frequently reported (prevalence around 70%) included emotional experiences that ‘stand out’, such as compassion, love, joy, and pleasure. This raises questions about individuals who do not report these emotions. What is it like not having had an outstanding or highly remarkable experience of love, joy or pleasure that clearly stands out? The prevalence of experiences such as compassion also aligns with arguments that positive emotions that connect individuals are likely a fundamental feature of human sociality, and without widespread other-focused emotional experiences human societies would not function 61 . The high prevalence of hopelessness is equally noteworthy, given its status as a depression marker 62 , 63 . These high levels point to unmet needs in the community given the overall rates of depression 64 – 66 , but may also imply the importance of resilience and post-traumatic growth 67 , which allows individuals to overcome and learn from these experiences. All humans will suffer major setbacks at some point, which shifts the focus to effective mechanisms to overcome challenges. Focusing on relatively rare experiences, even the least frequent experiences were reported by about one in five individuals. These include clinically relevant experiences such as out-of-body experiences, seeing unexplained lights, or reporting memories of past lives. Some experiences involving alterations of sensory perception or self-awareness (e.g., touch, guidance, presence, extrasensory perceptions, hearing voices) were even more frequent, reported by 30% or more of the population. These rates are substantially higher than associated clinical conditions (e.g., psychosis, schizophrenia, dissociative disorders) 68 – 70 , suggesting these sensory alterations are not necessarily pathological in the general population. These findings raise interesting questions about the interaction between human perception, the environment, and human development. Frequent misalignments between the sensory system and the physical world require explanation, and supernatural or spiritual explanatory frameworks might offer intuitive and easier interpretations. In other words, the high prevalence of perceptual anomalies could provide the fertile ground for the ‘kindling’ and spread of nonphysical, supernatural, and spiritual explanations, potentially contributing to the universal presence of religious systems across human cultures 15 , 22 . At the same time, more systematic research is needed on what differentiates clinically relevant nonordinary experiences from relatively innocuous experiences. In summary, we propose that extracting common phenomenological features of subjective experiences and estimating their relative prevalence in a general population is possible. By providing validity and prevalence evidence of subjective first-person accounts, we take a significant step toward establishing a rigorous science of human experiences that can be further queried using neuroscientific methods. To understand human consciousness, especially in the context of unique and nonordinary subjective experiences, we need to be able to question personal experiences in a scientific way – moving from subjective and idiographic noncomparable impressions to sample-level perspectives that allow generalizations about shared features. Our report offers one pathway in this larger scientific quest. Method Study 1 – Validation of the Inventory for Nonordinary Experiences Sample Participants were members of the general population and native Portuguese speakers. We recruited participants via an online panel ( www.netquest.com ). Participants were compensated with points, which they could exchange for various gifts available on the Netquest platform. Inclusion criteria required participants to provide informed consent and be at least 18. Data collection occurred from December 2023 to January 2025 (data link). Instrument We used the Inventory for Nonordinary Experiences 13 with 38 items validated in the US and India. The inventory comprises items related to the experiences and a separate appraisal section. In the current study, we only focus on the validation and prevalence of the experience items included in the original inventory. As reported above, 31 items were validated in our sample and we report prevalence data on those 31 items in the subsequent studies. Translation The Brazilian Portuguese translation of the initial 38-item version followed a parallel committee approach 71 , 72 : two independent translations were obtained from two autonomously working groups of native Portuguese speakers. Each group used the intended interpretations of the items 13 as a reference to resolve translation problems. These translations were subsequently compared by representatives from both translation groups. Any discrepancies in the translations were thoroughly discussed until a consensus was reached on the translation. In case of disagreements, a survey was conducted among judges, who were provided with the original English version and the Portuguese translations. The item version that received the most votes was subsequently chosen as the final version for the validation stage. Validation Study Procedure Given the challenges of validating single items, we used the Response Process Evaluation 39 , which involves a series of probing questions to query whether participants had understood an item as intended. We conducted a series of pilot studies (total N = 68) to refine the response validation process and adjust the response probes for our local context. In the first pilot, we observed that participants often provided very brief answers, making it challenging for the coders to determine whether they understood the item. Consequently, in the second pilot, we changed the "example probe" to encourage participants to elaborate and describe their experiences in detail, including their feelings and perceptions (instead of "describing succinctly" as used in the original probe). Based on the collected responses on the response probes in the first and second pilots, we also recognized that an "I don't know" response option may be beneficial, as a number of individuals expressed doubt whether they had indeed had the experience described or not 73 . Therefore, in the third pilot, we incorporated this third "I don't know" option in addition to "Yes" and "No". The validation probes used in the main study are shown in Table 1 . We also experimented with the number of items shown per participant. Analyzing time and response quality (number of words written, amount of detail provided, etc.), responses to only 2 items per participant showed the best quality vs. cost ratio (considering the time spent on each item). We then used iterative online meta-surveys with these probing questions. Participants were presented with two INOE experience items and asked for item understanding via the final set of probes. Table 1 Final version of the adapted Brazilian response probes Type of probe and Questions Response options & branching logic Response probe Have you ever had this experience? Yes No I don't know Example probe If "Yes": Describe in detail the situation or context in which you… [the item is repeated] If "No": Even if you haven't experienced this yourself, try to give an example of such an experience someone else might have had. This will help us to understand whether the situation we are presenting can be clearly understood. If "I don't know": Even if you're not sure or don't remember a specific situation, try to describe an experience in which you… [the item is repeated]. Fitting probe Why do you think this experience that you reported fits in the context of the sentence that we showed to you? Only shown when answering "Yes" or "I don't know" to the Response probe Paraphrase probe When asking someone if they have had an experience like the one below, how would you phrase that question in your own words? Comprehension probe Do you think you understand what kind of experience we are talking about? Yes No Open probe Is there any other comment or suggestions that you may want to make? Anything that hindered your comprehension? Data pre-processing One researcher assessed data quality. Any responses that met the criteria for the following categories were excluded from the analysis: a) nonsensical responses (e.g., "fhasdjfasd"), b) indications of protocol non-compliance (e.g., copying text from websites, providing irrelevant information), c) demonstrating a lack of understanding or adherence to the research protocol, and d) empty responses. Validation Score Calculation A team of four trained Brazilian coders independently evaluated whether the participants understood each item as intended. The intended interpretations 13 were used as a guidance in this classification process. Responses were scored on a 5-point scale (1 = Understood, 2 = Probably Understood, 3 = Not Enough Information, 4 = Probably Not Understood, 5 = Not Understood). All coders were encouraged to provide comments and explanations of their classification. To evaluate whether an item was understood as intended, we used the Validation Score (VS) 13 defined as the proportion of responses classified as understood to the number of both understood and not understood responses \(\:\left(\frac{U}{U+NU}\right)\) . Ambivalent responses (coded as 3) were not included in the calculation. One problem of this overall VS is that false positive (a 'Yes' response when they have not had the experience) or false negative (a 'No' response when they have had the experience) responses may occur 44 . To determine the prevalence of potential false positives and false negatives, we followed Taves et al. and computed the proportion of 'Yes' responses that are rated as 'Understood' (Positive Proportion Understood/PPU; \(\:PPU=\frac{{U}_{Yes}}{{U}_{Yes}+N{U}_{Yes}}\) ), and the proportion of 'No' responses that are rated as 'Understood' (Negative Proportion Understood/NPU: \(\:NPU=\frac{{U}_{No}}{{U}_{No}+N{U}_{No}}\) ). Validity Criteria and Rater Agreement The original RPE method uses group discussion of conflicting coder evaluations to reach consensus on the classification of each response 39 , with responses receiving unanimous ratings of 1 or 2 classified as 'Understood' (U), while those receiving unanimous ratings of 4 or 5 were classified as 'Not Understood' (NU). Responses with unanimous ratings of 3 retained their original score as the overall rating, indicating insufficient information. According to the original guidelines, disagreement between raters was discussed until a consensus was reached, and the VS scores were calculated based on these consensus scores. However, this approach can be time-consuming, difficult to document transparently, and susceptible to groupthink and criteria drift. To increase efficiency and transparency, we revised the response classification process and introduced a rule-based classification scheme. First, we converted the five-point quality codes from each rater into three-point categorizations used for calculating the VS score: Understood/ Not Understood/ Unclear. We then formulated the following three explicit rules that had started to emerge organically in our discussions: 75% Majority Rule If there is a 75% majority decision (3 out of 4 coders agree on the classification of a response), the majority choice prevails. In these cases, the three coders often had good and converging arguments for their decision. The single coder with a different opinion typically focused on specific details of the responses, missed specific nuances, and agreed with the majority following the discussion. To provide a practical example, if Coder 1 = understood (U), Coder 2 = understood (U), Coder 3 = understood (U), and Coder 4 = not enough information (3), then the final decision will be that the response is categorized as "understood." 50:50 Rule : If there was an even split between the coders team and there was no majority for either understood or not understood, the response was classified as 3 (not enough information available). For example, if Coder 1 = understood (U), Coder 2 = understood (U), Coder 3 = not understood (NU) and Coder 4 = not understood (NU), then the final decision was coded as "not enough information" (3). The important point here is that the classifications had to be evenly split for different classification outcomes (half of the raters classified the response as Understood, the other half as Not Understood). 2-1-1 Rule If there is general disagreement around options, e.g., two of the coders agreed with each other and the other two provided divergent classifications, an adjudicator made a final decision on the evaluation of the response. For example, if Coder 1 = understood (U), Coder 2 = understood (U), Coder 3 = not understood (NU), and Coder 4 = not enough information (3), then the adjudicator would examine all the comments by the individual coders, the overall responses by the participant in relation to this item (and sometimes also the responses to the second item to consider possible response tendencies of the individual). If raters were split with half of the raters advocating for ‘not enough information’ (coded as 3), the adjudicator was also used to make a final decision. The adjudicator had the final responsibility for deciding a classification of the item responses. Criteria for Item Revisions To judge an item as preliminary validated, the overall percentage for the VS had to be 80% or higher in a sample of at least 20 individuals who provided valid responses. If an item did not meet this criterion, the original protocol 13 , 39 suggested adapting the item to increase clarity or provide specific examples to help understand the item. There were no objective criteria specified that could help in deciding when to adjust or adapt an item or whether to collect more data before making adjustments. We extensively discussed at what moment it would make sense to test an item again and at what moment it would be better to rewrite the item to increase clarity. We adopted some preliminary criteria to guide our adaptation process. Specifically, if an item reached an understood percentage of at least 60% in a sample of at least 10 individuals, the item was not modified and re-evaluated in a new batch of 5 to 10 respondents. This 60% criteria was informed by observations of high variability in small samples. Adding more participants was deemed important to provide more precision to help identify if the item needed adjustment or might be understandable in a broader sample. If the level of understanding was below 60% in a sample of at least 10 respondents, the item wording was re-evaluated and adapted to improve clarity by taking into consideration the available item responses. The revised version was then tested again. Due to time and financial constraints, we decided to test a maximum of 5 different wordings. In some cases, we decided to test less than 5 wordings if the Validation Scores were below 60% and no clear alternatives to rephrase an item to increase clarity were evident based on the available item responses. Study 2 – Framing effects on lifetime prevalence estimates We conducted an experimental study. Participants were randomly assigned to either one of two conditions: a) mental health context vs experience context and b) response options with hedging (I don’t know option) available or not. The mental health context was manipulated by presenting a screening tool for Generalized Anxiety 74 and Depression severity 75 , 76 as well as mental health questions (life satisfaction, loneliness) before experience items. The control condition was to present the INOE first and the mental health questions second. For the manipulation of hedging options, participants were randomly assigned to either a yes vs no response option condition or to a condition which included a hedging option (I don’t know). The data was analyzed with a multilevel logistic regression on the yes-responses for each of the experiences with a random intercept for participant using the lme4 (version 1.1–35.5) and lmerTest (version 3.1-3) packages in R. After completing the data collection, we added a further condition in which individuals were first presented with a short version of non-clinical personality measure 77 before responding to the experience items. In this condition, individuals were given the yes vs no response scale only. This condition was compared with the yes vs no branch of the experiment in which individuals either answered the mental health questions or the experience questions first. The analysis was conducted with a multilevel logistic regression with random intercepts for participants, focusing on the yes-responses only. Study 3– Implied norm effects and response hedging In this experimental study, individuals from the general population responded to a survey that presented nonordinary experience items with 1) a binary yes-no (have had the experience or not), 2) a binary yes vs no response with the presence of a hedging (I don’t know) option or 3) a six point frequency response scale, that varied from ‘Never’, ‘Once’, ‘2–3 times’, ‘4–5 times’, ‘6–10 times’ or ‘More than 10 times’. We coded the lifetime prevalence rates as the number of yes responses for the first two conditions and any response indicating that the individual has had the experience once or more in the frequency response condition. We performed a multilevel ordinal regression analysis on the yes-response with a random effect for participants using the lme4 (version 1.1–35.5) and lmerTest (version 3.1-3) packages in R. Condition and experience item were treated as categorical variables and we included interaction effects between condition and experience item to explore possible experience-specific effects, setting the highest lifetime prevalence item as the intercept. Given the number of interactions, we controlled family-wise error rates with a Bonferroni correction. Follow-up tests on the relative and absolute stability of lifetime prevalence rates were conducted at the population level. We examined the degree to which the rank order of item lifetime prevalence rates was preserved across conditions by computing pairwise Spearman rank-order correlations and assessing the consistency of individual items ordinal positions. Confidence intervals for these correlations were obtained via boostrap procedure to ensure robust estimates. In addition, to evaluate the variability of individual item ranking across conditions, we derived the standard deviation of ordinal ranks for each item and applied a nonparametric outlier detection approach, informed by a Shapiro-Wilk test for normality and interquartile range criteria. Study 4 – sensitivity of lifetime prevalence rates to implied relative frequency We conducted an experiment in which individuals were assigned to one of three conditions: 1) a binary response condition with yes vs no responses, 2) a low-frequency response condition with responses varying in six steps between ‘never’ and up to ‘10 times or more’ in one’s life time, or 3) a high-frequency response condition varying in six steps from ‘never’ to up to ‘100 times or more’ in one’s life time. We coded the lifetime prevalence for each experience as the response to yes or any response that indicated that the individual has had the experience at least once. Lifetime prevalence was coded as 1 and never had the experience as 0. To test the effects of response format on reported prevalence, we fitted a multilevel logistic regression model with lifetime prevalence as the dependent variable, response format as a fixed effect (with the binary condition as the reference category), and random intercepts for both participants and experience items using the lme4 (version 1.1–35.5) and lmerTest (version 3.1-3) packages in R. To explore linear and nonlinear base rate effects, we rescaled the prevalence rates in the yes vs no binary response condition so that the minimum lifetime prevalence for the lowest prevalence experience was set to zero. Both the linear and quadratic effect of this rescaled base rate were included as a predictor in the multilevel logistic regression model. The experimental condition was entered as a categorical predictor and the interaction between response condition and the linear and quadratic base rates were included. As before, random intercepts were included for both participants and items. We controlled family-wise error rates with a Bonferroni correction. To further assess whether the absence of interaction effects masked condition-specific shifts in item-level prevalence, we calculated rank order correlations between the three response conditions using item-level prevalence rates. We computed Spearman rank-order correlations for each pair of conditions and derived the mean correlation across comparisons. Additionally, to evaluate potential deviations in rank consistency at the item level, we calculated the standard deviation of each item’s rank across conditions. To assess the distributional properties of these deviations, we conducted Shapiro Wilk tests for normality. Because the data was normally distributed, we used a parametric cutoff, defined as two standard deviations above the mean rank standard deviation, to flag potential outlier items. Study 5 – The relevance of validation information on prevalence rates To estimate the association between the overall validations core, the positive proportion understood (PPU), negative proportion understood (NPU) and proportion of unclear answers (which could not be clearly coded without further follow-up questioning during validation stage) with the relative prevalence rates in each study and condition, we computed Spearman correlations at the study level for each experimental condition. These correlations were then r-to-z transformed and we computed a random effects meta-analysis model with sample size as weights with the metafor (version 4.8-0) package in R. To compare the relative importance of the significant validity parameters using the random effects meta-analysis, we then computed a multilevel logistic regression model using the lme4 (version 1.1–35.5) and lmerTest (version 3.1-3) packages in R. We included the significant parameters from the meta-analysis as well as the main effects of the mental health vs personality framing, availability of hedging and general effects of normative effects (combining the low and high frequency conditions) using the total sample size from all studies. We included a random effect for participants. We controlled family-wise error rates with a Bonferroni correction. Study 6 - Estimating absolute prevalence of nonordinary intense experiences To obtain an overall estimate of the population level lifetime prevalence of each experience, we conducted a random effects meta-analysis using REML within the metafor package (version 4.8-0) in R. We pooled the percentage of yes responses (e.g., individuals report having had the experience at least once in their life) for each study weighted by sample size. We computed average effect sizes as well as heterogeneity effects. To estimate the overall prevalence, we pooled all the estimates across all the individual studies and conditions reported above and estimated the average as well as the variability using sample size weighted effects. Declarations The research was approved by the D’Or Institute for Research and Education Research Ethics Committee in Brazil, registration (CAAE) numbers of the approved applications: 65573322.6.0000.5249 and 79141724.9.0000.5249 References Chalmers, D. J. Facing Up to the Problem of Consciousness. in The Character of Consciousness (ed. Chalmers, D. J.) 0 (Oxford University Press, 2010). doi: 10.1093/acprof:oso/9780195311105.003.0001 . Seth, A. K. & Bayne, T. Theories of consciousness. Nat. Rev. Neurosci. 23, 439–452 (2022). Cogitate Consortium et al. An adversarial collaboration to critically evaluate theories of consciousness. Preprint at https://doi.org/10.1101/2023.06.23.546249 (2023). Clark, A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36, 181–204 (2013). Friston, K. & Kiebel, S. Predictive coding under the free-energy principle. Philos. Trans. R. Soc. B Biol. Sci. (2009) doi: 10.1098/rstb.2008.0300 . Timmermann, C. et al. A neurophenomenological approach to non-ordinary states of consciousness: hypnosis, meditation, and psychedelics. Trends Cogn. Sci. 27, 139–159 (2023). Van de Mieroop, M. Philosophy before the Greeks: The Pursuit of Truth in Ancient Babylonia . (Princeton University Press, Princeton Oxford, 2017). Wood, M. The Story of China: A portrait of a civilisation and its people . (Simon & Schuster Ltd, London, 2021). McGrath, J. J. et al. Psychotic Experiences in the General Population: A Cross-National Analysis Based on 31 261 Respondents From 18 Countries. JAMA Psychiatry 72, 697–705 (2015). Maraldi, E. de O. & Krippner, S. Cross-cultural research on anomalous experiences: Theoretical issues and methodological challenges. Psychol. Conscious. Theory Res. Pract. 6, 306–319 (2019). Monteiro de Barros, M. C. et al. Prevalence of spiritual and religious experiences in the general population: A Brazilian nationwide study. Transcult. Psychiatry 13634615221088701 (2022) doi: 10.1177/13634615221088701 . Taves, A. & Barlev, M. A feature-based approach to the comparative study of “nonordinary” experiences. Am. Psychol. 78, 50–61 (2023). Taves, A. et al. The Inventory of Nonordinary Experiences (INOE): Evidence of Validity in the United States and India. PLOS ONE (2023) doi: http://dx.doi.org/10.1371/journal.pone.0287780 . Maraldi, E. O. et al. Nonordinary Experiences, Well-being and Mental Health: A Systematic Review of the Evidence and Recommendations for Future Research. J. Relig. Health 63, 410–444 (2024). Luhrmann, T. M. et al. Sensing the presence of gods and spirits across cultures and faiths. Proc. Natl. Acad. Sci. 118, e2016649118 (2021). Gomez-Marin, A. & Seth, A. K. A science of consciousness beyond pseudo-science and pseudo-consciousness. Nat. Neurosci. 28, 703–706 (2025). Akram, U. et al. Prevalence of anxiety, depression, mania, insomnia, stress, suicidal ideation, psychotic experiences, & loneliness in UK university students. Sci. Data 10, 621 (2023). Pechey, R. & Halligan, P. Prevalence and correlates of anomalous experiences in a large non-clinical sample. Psychol. Psychother. Theory Res. Pract. 85, 150–162 (2012). Acunzo, D., Cardeña, E. & Terhune, D. B. Anomalous experiences are more prevalent among highly suggestible individuals who are also highly dissociative. Cognit. Neuropsychiatry 25, 179–189 (2020). Nuevo, R. et al. The continuum of psychotic symptoms in the general population: a cross-national study. Schizophr. Bull. 38, 475–485 (2012). Beavan, V., Read, J. & Cartwright, C. The prevalence of voice-hearers in the general population: A literature review. J Ment Health 20, 281–292 (2011). Weisman, K. & Luhrmann, T. M. Shifting Between Models of Mind: New Insights Into How Human Minds Give Rise to Experiences of Spiritual Presence and Alternative Realities. Top. Cogn. Sci. tops.70002 (2025) doi: 10.1111/tops.70002 . Gosseries, O. et al. Exploration of trance states: phenomenology, brain correlates, and clinical applications. Curr. Opin. Behav. Sci. 58, 101400 (2024). Rauf, B. et al. Associations between sleep variables and ostensibly paranormal experiences and paranormal beliefs: A scoping review. Br. J. Psychol. 114, 797–818 (2023). Hansen, L. et al. Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning. JAMA Psychiatry (2025) doi: 10.1001/jamapsychiatry.2024.4702 . DeRosse, P. & Karlsgodt, K. H. Examining the Psychosis Continuum. Curr. Behav. Neurosci. Rep. 2, 80–89 (2015). Savill, M., D’Ambrosio, J., Cannon, T. D. & Loewy, R. L. Psychosis risk screening in different populations using the Prodromal Questionnaire: A systematic review. Early Interv. Psychiatry 12, 3–14 (2018). Tourangeau, R. The survey response process from a cognitive viewpoint. Qual. Assur. Educ. 26, 169–181 (2018). Fischer, R. & Rudnev, M. From MIsgivings to MIse-en-scène: The role of invariance in personality science. Eur. J. Personal. 08902070241283081 (2024) doi: 10.1177/08902070241283081 . Corrigan, P. W. & Rao, D. On the Self-Stigma of Mental Illness: Stages, Disclosure, and Strategies for Change. Can. J. Psychiatry Rev. Can. Psychiatr. 57, 464–469 (2012). Link, B. G. & Phelan, J. C. Conceptualizing Stigma. Annu. Rev. Sociol. 27, 363–385 (2001). Krosnick, J. A. Response strategies for coping with the cognitive demands of attitude measures in surveys. Appl. Cogn. Psychol. 5, 213–236 (1991). Schaeffer, N. C. & Presser, S. The Science of Asking Questions. Annu. Rev. Sociol. 29, 65–88 (2003). Tononi, G. et al. Consciousness or pseudo-consciousness? A clash of two paradigms. Nat. Neurosci. 28, 694–702 (2025). Kelly, T. M. Classification and Statistical Manual of Extrasensory Experiences, 1st Edition: CSM-EE . (lulu.com, 2014). WHO | International Classification of Diseases, 11th Revision (ICD-11). WHO http://www.who.int/classifications/icd/en/ (2020). Diagnostic and Statistical Manual of Mental Disorders: DSM-5-TR . (American Psychiatric Association Publishing, Washington, DC, 2022). Taves, A. Mystical and Other Alterations in Sense of Self: An Expanded Framework for Studying Nonordinary Experiences. Perspect. Psychol. Sci. J. Assoc. Psychol. Sci. 15, 669–690 (2020). Wolf, M. G., Ihm, E., Maul, A. & Taves, A. The Response Process Evaluation Method. Adv. Methods Pract. Psychol. Sci. (2025) doi: 10.31234/osf.io/rbd2x . Fischer, R., Karl, J. A., Luczak-Roesch, M. & Hartle, L. Why We Need to Rethink Measurement Invariance: The Role of Measurement Invariance for Cross-Cultural Research. Cross-Cult. Res. 10693971241312459 (2025) doi: 10.1177/10693971241312459 . Lived experience: shifting focus. Nat. Ment. Health 1, 145–145 (2023). Fischer, R. et al. Cultural challenges for adapting behavioral intervention frameworks: A critical examination from a cultural psychology perspective. Clin. Psychol. Rev. 102425 (2024). Schwarz, N. & Oyserman, D. Asking Questions About Behavior: Cognition, Communication, and Questionnaire Construction. Am. J. Eval. 22, 127–160 (2001). Akobeng, A. K. Understanding diagnostic tests 1: sensitivity, specificity and predictive values. Acta Paediatr. 96, 338–341 (2007). Gerlinger, G. et al. Personal stigma in schizophrenia spectrum disorders: a systematic review of prevalence rates, correlates, impact and interventions. World Psychiatry 12, 155–164 (2013). Rivera-Garrido, N., Ramos-Sosa, M. P., Accerenzi, M. & Brañas-Garza, P. Continuous and binary sets of responses differ in the field. Sci. Rep. 12, 14376 (2022). Schwarz, N., Hippler, H.-J., Deutsch, B. & Strack, F. Response scales: Effects of category range on reported behavior and comparative judgments. Public Opin. Q. 49, 388–395 (1985). Migliavaca, C. B. et al. Meta-analysis of prevalence: I2 statistic and how to deal with heterogeneity. Res. Synth. Methods 13, 363–367 (2022). Higgins, J. P. T. & Thompson, S. G. Quantifying heterogeneity in a meta-analysis. Stat. Med. 21, 1539–1558 (2002). Kakkar, H. & Sivanathan, N. When the appeal of a dominant leader is greater than a prestige leader. Proc. Natl. Acad. Sci. 114, 6734–6739 (2017). van Kleef, G. A. et al. No guts, no glory? How risk-taking shapes dominance, prestige, and leadership endorsement. J. Appl. Psychol. 106, 1673–1694 (2021). Warnock, K. N., Ju, C. S. & Katz, I. M. A Meta-analysis of Attachment at Work. J. Bus. Psychol. 39, 1239–1257 (2024). Schyns, B. & and Day, D. Critique and review of leader–member exchange theory: Issues of agreement, consensus, and excellence. Eur. J. Work Organ. Psychol. 19, 1–29 (2010). Sibley, C. G., Fischer, R. & Liu, J. H. Reliability and validity of the revised experiences in close relationships (ECR-R) self-report measure of adult romantic attachment. Pers. Soc. Psychol. Bull. 31, 1524–1536 (2005). de Oliveira Maraldi, E. Response Bias in Research on Religion, Spirituality and Mental Health: A Critical Review of the Literature and Methodological Recommendations. J. Relig. Health 59, 772–783 (2020). Cleary, A. M. & Claxton, A. B. Déjà Vu: An Illusion of Prediction. Psychol. Sci. 29, 635–644 (2018). Brown, A. S. A review of the déjà vu experience. Psychol. Bull. 129, 394–413 (2003). Saunders, D. T., Roe, C. A., Smith, G. & Clegg, H. Lucid dreaming incidence: A quality effects meta-analysis of 50 years of research. Conscious. Cogn. 43, 197–215 (2016). Jamieson, G. A. The modified tellegen absorption scale: A clearer window on the structure and meaning of absorption. Aust. J. Clin. Exp. Hypn. 33, 119–139 (2005). Terhune, D. B. & Jamieson, G. A. Hallucinations and the meaning and structure of absorption. Proc. Natl. Acad. Sci. 118, e2108467118 (2021). Waal, F. B. M. de. Putting the Altruism Back into Altruism: The Evolution of Empathy. Annu. Rev. Psychol. 59, 279–300 (2008). Liu, R. T., Kleiman, E. M., Nestor, B. A. & Cheek, S. M. The Hopelessness Theory of Depression: A Quarter Century in Review. Clin. Psychol. Publ. Div. Clin. Psychol. Am. Psychol. Assoc. 22, 345–365 (2015). Abramson, L. Y., Metalsky, G. I. & Alloy, L. B. Hopelessness depression: A theory-based subtype of depression. Psychol. Rev. 96, 358–372 (1989). Yang, J., Zhang, L., Yang, C., Li, X. & Li, Z. Global, Regional, and National Epidemiology of Depression in Working-Age Individuals, 1990–2019. Depress. Anxiety 2024, 4747449 (2024). Zhang, Y. et al. Change in the global burden of depression from 1990–2019 and its prediction for 2030. J. Psychiatr. Res. 178, 16–22 (2024). Shorey, S., Ng, E. D. & Wong, C. H. J. Global prevalence of depression and elevated depressive symptoms among adolescents: A systematic review and meta-analysis. Br. J. Clin. Psychol. 61, 287–305 (2022). Jayawickreme, E. et al. Post-traumatic growth as positive personality change: Challenges, opportunities, and recommendations. J. Pers. 89, 145–165 (2021). Simeone, J. C., Ward, A. J., Rotella, P., Collins, J. & Windisch, R. An evaluation of variation in published estimates of schizophrenia prevalence from 19902013: a systematic literature review. BMC Psychiatry 15, 193 (2015). Castillejos, M. C., Martín-Pérez, C. & Moreno-Küstner, B. A systematic review and meta-analysis of the incidence of psychotic disorders: the distribution of rates and the influence of gender, urbanicity, immigration and socio-economic level. Psychol. Med. 48, 2101–2115 (2018). Sar, V. Epidemiology of Dissociative Disorders: An Overview. Epidemiol. Res. Int. 2011, 404538 (2011). Hambleton, R. K. & Zenisky, A. L. Translating and Adapting Tests for Cross-Cultural Assessments. in Cross-Cultural Research Methods in Psychology (eds. Matsumoto, D. & van de Vijver, F. J. R.) 46–70 (Cambridge University Press, Cambridge, 2010). doi: 10.1017/CBO9780511779381.004 . Cross-Cultural Survey Methods . (J. Wiley, Hoboken, N.J, 2003). Ip, E. H., Magee, M. F., Youssef, G. A. & Chen, S.-H. Gleaning Information for Cognitive Operations from Don’t Know Responses in Cognitive and Noncognitive Assessments. Multivar. Behav. Res. 54, 159–172 (2019). Spitzer, R. L., Kroenke, K., Williams, J. B. W. & Löwe, B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch. Intern. Med. 166, 1092–1097 (2006). Kroenke, K., Spitzer, R. L. & Williams, J. B. W. The PHQ-9. J. Gen. Intern. Med. 16, 606–613 (2001). Kroenke, K., Spitzer, R. L., Williams, J. B. W. & Löwe, B. The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review. Gen. Hosp. Psychiatry 32, 345–359 (2010). Soto, C. J. & John, O. P. Short and extra-short forms of the Big Five Inventory–2: The BFI-2-S and BFI-2-XS. J. Res. Personal. 68, 69–81 (2017). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryFileFullJune12.docx Supplementary information: Revisiting the Unusual: Investigating the Prevalence and Validity of Nonordinary Experiences as a Window into Consciousness Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6907742","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":473207152,"identity":"5d1b2aaf-57a8-4de9-9dee-b03ef3b710d3","order_by":0,"name":"Ronald Fischer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie3RsWrCUBTG8U8Kx+WSrCdE6CukCNFB2ldJETI5O6l0ShfFNQ/jcCRglhBXwSVT5rg5dGhUHByumq3Q++cuZ/hxORzAZPqrFQSbgPphoCDPkIDgRBcSNiBfF5LgIel9L9ZVMAZbdlIWh9W2Y6WLAofVVEs6WT7kIAcTh723uNwrJ0u9VlymWsI88vgzwoxYkatkr7xdiBclm3uke6wJk52R+yP5U8TnM8GIXIhcyURPVOb3g5xPu/jOXIb1LhusYxE9ac+7u2o84NdlUvJR3j+sNGoVlcy05CpvRzkfqGkPfzGZTKb/0y/fT08ewmL7UAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3055-3955","institution":"Cognitive Neuroscience Unit, D'Or Institute for Research and Education","correspondingAuthor":true,"prefix":"","firstName":"Ronald","middleName":"","lastName":"Fischer","suffix":""},{"id":473207153,"identity":"75eab1a1-11ac-4ed5-af78-18a5e4d80b2b","order_by":1,"name":"Giovanna Bortolini","email":"","orcid":"","institution":"Institute D'OR for Research and Education","correspondingAuthor":false,"prefix":"","firstName":"Giovanna","middleName":"","lastName":"Bortolini","suffix":""},{"id":473207154,"identity":"ed808649-5d42-4fe2-be0b-eeaaf3c79227","order_by":2,"name":"Tiago Bortolini","email":"","orcid":"","institution":"Institute D'OR for Research and Education","correspondingAuthor":false,"prefix":"","firstName":"Tiago","middleName":"","lastName":"Bortolini","suffix":""},{"id":473207155,"identity":"133f4ada-270a-4e88-9981-f24355d954a6","order_by":3,"name":"Everton Maraldi","email":"","orcid":"","institution":"Institute D'OR for Research and Education","correspondingAuthor":false,"prefix":"","firstName":"Everton","middleName":"","lastName":"Maraldi","suffix":""},{"id":473207156,"identity":"96d4f0d2-071a-4efe-88c7-d24813848664","order_by":4,"name":"Larissa Hartle","email":"","orcid":"","institution":"Institute D'OR for Research and Education","correspondingAuthor":false,"prefix":"","firstName":"Larissa","middleName":"","lastName":"Hartle","suffix":""},{"id":473207157,"identity":"cb462ea2-2950-4a58-aa15-081f6ecd4add","order_by":5,"name":"Maria Vitoria de Lima Varejao","email":"","orcid":"","institution":"Institute D'OR for Research and Education","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Vitoria de Lima","lastName":"Varejao","suffix":""},{"id":473207158,"identity":"582822fd-b28f-4303-8521-31ec5539f7fe","order_by":6,"name":"Maria Clara Laport","email":"","orcid":"https://orcid.org/0009-0002-0060-1730","institution":"Institute D'OR for Research and Education","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Clara","lastName":"Laport","suffix":""},{"id":473207159,"identity":"f65a8c96-0753-45be-9165-31db7182fad7","order_by":7,"name":"Gustavo Granjeiro","email":"","orcid":"","institution":"Institute D'OR for Research and Education","correspondingAuthor":false,"prefix":"","firstName":"Gustavo","middleName":"","lastName":"Granjeiro","suffix":""},{"id":473207160,"identity":"67b05169-9c9a-4ead-8011-2341b2e60f3d","order_by":8,"name":"Maria Oliveira","email":"","orcid":"","institution":"Federal University of Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Oliveira","suffix":""},{"id":473207161,"identity":"617efc50-a04d-49f0-99cf-a43ff90133d7","order_by":9,"name":"Elliott Ihm","email":"","orcid":"","institution":"University of California at Santa Barbara","correspondingAuthor":false,"prefix":"","firstName":"Elliott","middleName":"","lastName":"Ihm","suffix":""},{"id":473207162,"identity":"475cb191-56ba-40f5-8cb0-763855e22594","order_by":10,"name":"Ann Taves","email":"","orcid":"","institution":"University of California at Santa Barbara","correspondingAuthor":false,"prefix":"","firstName":"Ann","middleName":"","lastName":"Taves","suffix":""},{"id":473207163,"identity":"038c0278-1811-4ebb-b9d7-157b325f996a","order_by":11,"name":"Jorge Moll","email":"","orcid":"","institution":"IDOR","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"","lastName":"Moll","suffix":""}],"badges":[],"createdAt":"2025-06-16 17:35:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6907742/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6907742/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85615559,"identity":"1586bded-d604-45b7-b503-74e15b9fefab","added_by":"auto","created_at":"2025-06-29 14:31:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":146614,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Validation Sore (VS), Positive Proportion Understood (PPU), and Negative Proportion Understood (NPU) scores by tested item.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. The bar plot displays the VS (Validation Score), PPU (Positive Proportion Understood), and NPU (Negative Proportion Understood) for each item, ordered by the VS value. Item wordings and item groupings are shown in Table S1. Colors show the thematic item grouping from the original study\u003c/em\u003e\u003csup\u003e\u003cem\u003e13\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e. Number of iterations for each item during the adaptation process are shown in brackets next to the item label. The dashed black line is shown at VS=80 to highlight the threshold for validation scores. Of the 31 validated items, 21 required no further adjustments, whereas 10 had to be revised at least once to pass the validity threshold.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6907742/v1/71f5294740ea895c7e5e05ea.png"},{"id":85615553,"identity":"171c7df4-3655-4e81-a630-04629c91bda1","added_by":"auto","created_at":"2025-06-29 14:31:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27392,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of study context and response hedging on lifetime prevalence rates.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: Panel A: Effect of study framing and hedging options on prevalence responses. Using a multilevel logistic regression with a random effect for participants, a significant effect of study framing (OR = 0.67, SE \u003c/em\u003e±\u003cem\u003e .056, 95% CI 0.578 – 0.797, p \u0026lt; .001) emerged, but no effect of response hedging (OR = 1.015, SE \u003c/em\u003e±\u003cem\u003e.085, 95% CI 0.862 – 1.194, \u0026nbsp;p = .86) and no interaction between the framing and response hedging (OR = 1.085, SE \u003c/em\u003e±\u003cem\u003e .128, 95% CI 0.863 – 1.363, p = .492). Panel B: Comparing a personality framing condition with the mental health and neutral condition (INOE first), the different was not statistically reliable in a multilevel logistic regression with random effects for participants (OR = 0.89, SE \u003c/em\u003e± \u003cem\u003e.077\u003c/em\u003e, \u003cem\u003e95% CI 0.76 – 1.06, p = .190). \u0026nbsp;The mental health framing effect was replicated (OR = 0.74, SE \u003c/em\u003e± \u003cem\u003e.064\u003c/em\u003e, \u003cem\u003e95% CI 0.62 – 0.88, p \u0026lt; .001).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6907742/v1/756c9f455d643f38fdc58064.png"},{"id":85615554,"identity":"7dfea798-9400-4b36-ae36-d4cc7c5e6332","added_by":"auto","created_at":"2025-06-29 14:31:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17984,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated prevalence rates by response format, comparing a Binary response format with a Binary plus response hedging and Frequency scale.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: Average predicted lifetime prevalence (%) derived from multilevel ordinal regression comparing response formats: Binary response (yes/no), Binary with hedging (\"I don’t know\") option, and Frequency scale. \u0026nbsp;Item by condition effects suggested significant effects for 24 items in the Frequency scale vs Binary response condition and five items in the Binary + Hedging vs Binary response condition (see Table S6). Error bars represent 95% confidence intervals.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6907742/v1/66a3e343234e58bce0715eb1.png"},{"id":85616888,"identity":"f8fc5a6c-7602-473e-a1fd-688348f05d38","added_by":"auto","created_at":"2025-06-29 14:39:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":82094,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of response format and baseline prevalence on implied lifetime prevalence rates.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: (A) Predicted probabilities of lifetime prevalence (\"yes\") responses comparing binary and Frequency response scales (Low frequency: \"never to \u0026gt;10 times\"; High frequency: \"never to \u0026gt;100 times\"). Error bars represent 95% confidence intervals. (B) Predicted probabilities illustrating interactions between response scales and baseline prevalence rates (rescaled to the lowest prevalence rate in the binary condition to show the relative information bias by response scale). Shaded areas and error bars represent 95% confidence intervals derived from multilevel logistic regression models.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6907742/v1/a1f9e1886e89dbfa9a147cd6.png"},{"id":85617838,"identity":"66ff762b-9bb4-4a4d-8eaf-e98bf88cacb5","added_by":"auto","created_at":"2025-06-29 14:47:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":104846,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated Prevalence per Experience\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: Estimated prevalence of responses for each item (N = 11,629), arranged in descending order of prevalence. The bars represent the estimated prevalence, while the black error bars indicate the 95% confidence intervals based on the random-effects meta-analysis.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6907742/v1/5bed92822d618d11366d48c2.png"},{"id":85615555,"identity":"21cfd079-073a-40e6-869b-a001f0a65ff3","added_by":"auto","created_at":"2025-06-29 14:31:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":70329,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of Translation, Adaptation and Validation Process\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: VS = Validation score, k = number of items generated, N = number of responses per item version\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6907742/v1/d5f23a11263e603aac418b1a.png"},{"id":85619266,"identity":"bdba2598-1c5c-4199-b580-003fe8326534","added_by":"auto","created_at":"2025-06-29 14:55:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1457714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6907742/v1/d3bdbb0c-cfd1-4d68-85b8-7eb73e1c9ab2.pdf"},{"id":85615557,"identity":"f2601f48-7219-41ad-8617-ec3fa9b4ad07","added_by":"auto","created_at":"2025-06-29 14:31:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":521549,"visible":true,"origin":"","legend":"Supplementary information: Revisiting the Unusual: Investigating the Prevalence and Validity of Nonordinary Experiences as a Window into Consciousness","description":"","filename":"SupplementaryFileFullJune12.docx","url":"https://assets-eu.researchsquare.com/files/rs-6907742/v1/4f1a72584039d4166cf8fd60.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Revisiting the Unusual: Investigating the Prevalence and Validity of Nonordinary Experiences as a Window into Consciousness","fulltext":[{"header":"Introduction","content":"\u003cp\u003eResearchers have long pondered the nature of consciousness, particularly how we experience and reflect on our subjective states\u003csup\u003e1–3\u003c/sup\u003e. Yet, despite decades of consciousness research, the nature and variability of subjective experiences themselves as the core component of consciousness remain relatively unexplored territory. One major challenge is that subjective experiences are not objective recordings of internal or external events, but are the result of sensory input and top-down neurocognitive processes\u003csup\u003e4,5\u003c/sup\u003e. Particularly intriguing are those experiences that diverge sharply from ordinary sensory perception–what is considered to be “out there” in the world and thus expand the ‘experiential repertoire’ of individuals, such as hearing voices, seeing visions, sensing unseen presences, or experiencing unexpectedly overwhelming emotions\u003csup\u003e6\u003c/sup\u003e. Documented across cultures and history, these unusual phenomena feature prominently in humanity’s earliest recorded narratives\u003csup\u003e7,8\u003c/sup\u003e, underscoring their persistent importance to human thought. Yet, research remains fragmented, often filtered through disciplinary lenses (psychiatric, spiritual, cultural) that yield conflicting interpretations and widely varying prevalence estimates\u003csup\u003e9–11\u003c/sup\u003e. This inconsistency creates a scientific paradox: how can seemingly similar populations report dramatically differing rates of these experiences? Addressing this paradox is crucial for progress in consciousness science, which requires reliable data on the prevalence of these specific subjective experiences and their validity. Here we report on a large-scale, cross-contextual validation of a culturally adapted inventory of nonordinary experiences, as an attempt to minimize disciplinary bias and response artifacts. \u0026nbsp;Specifically, our study further aims to offer a more neutral and objective approach to measuring subjective experiences and explain the paradox of diverging estimates of lifetime experiences within the same populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeyond addressing inconsistencies across scientific disciplines, systematically exploring nonordinary experiences (NOEs)\u003csup\u003e12–14\u003c/sup\u003e can offer novel insights into the fundamental workings of the human mind and consciousness\u003csup\u003e6,15\u003c/sup\u003e. Here, we follow Taves and Barlev’ terminology of NOEs, describing experiences marked as special or standing out to participants relative to what they may consider everyday or ordinary experiences\u003csup\u003e12\u003c/sup\u003e. Current exploration of such conscious experiences has reached an impasse by focusing on a narrow set of experimental settings\u003csup\u003e2,16\u003c/sup\u003e, limiting our understanding of experiences that appear rare at the individual level but are surprisingly common when surveyed broadly in populations\u0026nbsp;\u003csup\u003e9,11,15,17–20\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo date, the experiences that may be relevant for exploring features of these conscious states are variably framed as religious, indicative of extraordinary gifts or abilities, symptoms of clinical or pathological problems, or evidence of paranormal forces. Those perspectives affect the methods used and the disciplinary framing of the inquiry, which in turn can influence participants’ willingness to acknowledge and report such experiences\u0026nbsp;\u003csup\u003e12,14\u003c/sup\u003e. One paradox is the widely varying prevalence rates for some of these experiences. For example, in the context of a clinical survey, 13.3% of the respondents reported hallucinatory experiences\u003csup\u003e9\u003c/sup\u003e, whereas in a study on religious experiences in the same population, up to 41% of participants reported seeing spirits or hearing voices of dead people\u0026nbsp;\u003csup\u003e11\u003c/sup\u003e. Although hearing voices is one of the classical symptoms of psychosis, which is rare in the general population, non-clinically relevant voice hearing estimates can vary between practically zero and above 80%\u003csup\u003e21\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProgress can be made along several trajectories. At the most basic level, learning and development takes place via the accumulation and integration of diverse experiences – an understanding of the distribution of nonordinary experiences in the general population can provide unique insights into the dynamic interplay between culture and the mind during socialization\u003csup\u003e15,22\u003c/sup\u003e. Examining the prevalence of vastly different experiences that straddle the boundaries of consciousness experience will provide important observational data for further targeted explorations of consciousness, given the range of sensory inputs involved (visual, auditory, tactile, etc.), the involvement of a sense of a coherent self and possible alterations in the states of consciousness\u0026nbsp;\u003csup\u003e6,23,24\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn a more practical level, accurately assessing the prevalence experiences of potential interest to clinicians is essential, because a better understanding of these phenomena aids in differential diagnoses, identifies unmet needs in the population, and highlights early signs that could prompt timely interventions before more serious clinical symptoms develop\u003csup\u003e25\u003c/sup\u003e. Current models suggest a continuum between non-clinical and clinically significant experiences, with non-clinical manifestations offering opportunities for early intervention\u003csup\u003e26\u003c/sup\u003e. \u0026nbsp;Conversely, such improved\u0026nbsp;understanding may also help avoid over-diagnosis of psychiatric syndromes, by more accurately informing the prevalence of experience phenotypes in otherwise healthy and normally functioning individuals.\u0026nbsp;Ultimately, this can improve health service planning and enable the more efficient allocation of scarce resources\u003csup\u003e27\u003c/sup\u003e informing timely responses and deepening our understanding of the interactions between biological and sociocultural factors that give rise to such non-ordinary conscious experiences.\u003c/p\u003e\n\u003cp\u003eTo accurately estimate prevalence, it is crucial to consider factors that influence whether individuals report such experiences, including comprehension of the question, retrieval of relevant experiences, calibrating, and reporting answers\u003csup\u003e28,29\u003c/sup\u003e. Comprehension and recall of relevant experiences may be facilitated or hindered by the phrasing of survey items and study context to match the local meaning of the experience. For example, an experience being phrased and included in a clinical study context may not be mapped to relevant experiences if they occur in non-clinical, culturally-salient contexts. Once retrieved, willingness to report can be hindered by perceived stigma or self-stigma associated with the experience\u003csup\u003e30,31\u003c/sup\u003e or biases due to the question format\u0026nbsp;\u003csup\u003e28,32,33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo overcome these issues, measures must capture the core phenomenological aspects of experiences and express them in a clear, neutral, and non-judgmental form. To make progress on tackling questions around consciousness, a broader set of experiences spanning a range of sensory and cognitive features is desirable\u003csup\u003e1,3,34\u003c/sup\u003e. Our study adopts Taves and Barlev’s participant-centered approach, shifting the focus from the researcher’s disciplinary perspective to the participant’s, enabling a bottom-up, subject-driven reporting process on the phenomenological features. We validated a Brazilian Portuguese version of their carefully constructed inventory of subjective experiences\u003csup\u003e13\u003c/sup\u003e encompassing a broad spectrum of emotional, cognitive, sensory, and self-related phenomena that have been of interest across the social, behavioral and medical sciences\u003csup\u003e12,35–38\u003c/sup\u003e. It is arguably the inventory with the broadest list of both subjectively positive and negative experiences of interest to scholars in medicine, neuroscience, psychology, sociology, religious studies, management studies, and anthropology (Table S1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur first step was to use qualitative methods that probe and improve item phrasings to facilitate participants’ understanding of survey content\u003csup\u003e13,29,39,40\u003c/sup\u003e. Qualitative methods are valuable for exploring sensitive experiences because they: a) ensure participants interpret items correctly, and b) enable iterative refinement for better clarity and validity. Engaging participants from the target population highlights the importance of community involvement and lived experience in mental health research\u0026nbsp;\u003csup\u003e41,42\u003c/sup\u003e. Using this robustly validated inventory, we systematically investigated how methodological variables affect prevalence reporting, including survey framing, implied normativity, and uncertainty signaling. Participants often use subtle cues to infer information about the implied nature of the experience and the severity and frequency of those experiences\u003csup\u003e28,43\u003c/sup\u003e. To test potential influences on reporting, we experimentally manipulated: a) the context in which survey questions were presented, b) the implied normativity of the experiences, and c) the inclusion or exclusion of hedges signaling uncertainty.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecifically, we test whether presenting the items in the context of a mental health survey influences lifetime prevalence ratings, whether presenting the items with a frequency scale that implies high prevalence rates augments lifetime prevalence ratings and whether the presence of an ‘I don’t know’ option changes prevalence ratings because it may allow individuals to avoid providing an answer. We also examined whether experience items that are more ambiguous or difficult to understand are less likely to be endorsed than items that are clearer or more easily understood. This is a crucial advance because validity is often treated as a precondition for estimating prevalence, but we argue that the precision of validity information is crucial for accurately estimating prevalence rates. All studies were administered online using nationally representative samples, and analyses were preregistered (unless otherwise noted).\u003c/p\u003e\n\u003cp\u003eFinally, pooling data from diverse national samples allowed us to provide more robust and nuanced estimates of the lifetime prevalence of these experiences, accounting for factors that might otherwise skew or obscure the findings. In summary, our study provides a pathway for high-quality estimates of the lifetime prevalence of human experiences that are difficult to measure empirically but have fascinated scholars of the human mind since the dawn of human civilization.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n\u003ch2\u003eValidation of experience items\u003c/h2\u003e\n\u003cp\u003eA first challenge for estimating the lifetime prevalence of nonordinary experiences is achieving a neutral and non-judgmental way of measuring the phenotypical core characteristics of the experiences, in a way that is understandable by general population samples. We opted for an iterative qualitative design with pseudorandom presentation of items (preregistration link). Participants from the general population (N\u0026thinsp;=\u0026thinsp;1,284 Brazilian citizens, 57% female, mean age\u0026thinsp;=\u0026thinsp;42 years; SD\u0026thinsp;\u0026plusmn;\u0026thinsp;13) were asked to provide examples of experiences in response to test items. Four trained coders independently coded the open-ended responses in terms of whether the respondent had understood the question\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Items with unclear responses were checked and rephrased if necessary, before further validity probing was performed. Using a criterion of 80% of responses being classified as understood in a sample of at least 20 responses\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, we found sufficient evidence of validity for 31 items from the original list of 38 items in our population. Ten items had to be revised to be understood by participants. Among the non-validated items, experiences asking about events that explicitly involve meaning making processes (e.g., receiving messages, deep insights) were more difficult to validate (Table S2).\u003c/p\u003e\n\u003cp\u003eEstimating prevalence rates involves calculating a validation score (VS), which includes the risks of false positives (a 'Yes' for those without the experience) and false negatives (a 'No' for those with the experience)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In addition to the overall VS, we calculated two complementary indices: Positive Proportion Understood (PPU) and Negative Proportion Understood (NPU) scores\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. PPU refers to the proportion of participants who endorsed an item and also clearly understood it, providing evidence that affirmative responses likely reflect genuine experiences. This is particularly important for rarer experiences, where misinterpretation could lead to inflated prevalence. NPU refers to the proportion of participants who denied the experience and also demonstrated clear understanding, which is especially relevant for common experiences\u0026mdash;helping to ensure that non-endorsement reflects a true absence of the experience rather than confusion or misunderstanding. The results show that seven items (d\u0026eacute;j\u0026agrave; vu, love, places (special), pleasure, animated places, meaning in life, faces) passed the VS but not the NPU (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). This suggests\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e that individuals who did not report the experience during validation might have misunderstood the items, indicating they possibly had the experience but could not identify the relevant categories. This could lead to underestimating such experiences in the general population samples. Two items (devotion to objects \u0026amp; people), previously studied in highly select populations\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, passed the VS but showed a PPU\u0026thinsp;\u0026lt;\u0026thinsp;.80, suggesting that prevalence rates may be overestimated and should be considered with caution.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eFraming effects on lifetime prevalence estimates\u003c/h2\u003e\n\u003cp\u003eOne central challenge for estimating prevalence rates is the study context, which may heighten perceptions of stigma or self-stigma (internalized negative evaluations of oneself when having specific experiences), which may reduce the willingness to affirm having had specific experiences \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. We tested whether mental health framing affects participants' willingness to report these experiences.\u003c/p\u003e\n\u003cp\u003eA second feature that may influence response behavior is the presence of hedging response options, such as \u0026lsquo;I don\u0026rsquo;t know\u0026rsquo; responses. Hedging might occur for several reasons. Affirming that one has had the experience may result in stigmatizing responses or may involve self-stigmatization (due to internalized negative stereotypes)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Hedging may also be a result of subjective uncertainty about the nature of the experience or uncertain memories about events, which may increase doubts about whether a personal experience qualifies as relevant for providing an affirmative answer\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo test the importance of the study context and the availability of hedging on lifetime prevalence responses, participants from the general population (N\u0026thinsp;=\u0026thinsp;1,652, Mean age\u0026thinsp;=\u0026thinsp;47, SD age\u0026thinsp;\u0026plusmn;\u0026thinsp;16, 29.8% female, preregistration link, data link) were randomly assigned to either of two conditions: a) a survey which started either with a battery of mental health screening tools (GAD-7 and PHQ-9) and mental health questions (life satisfaction, loneliness) or the nonordinary experiences survey and b) the response options either included a hedging (I don\u0026rsquo;t know) option or not. Running a multilevel logistic regression on the yes-responses to each of the experiences, we only found a main effect of presentation condition, but no other effects (see Table S4). The implied mental health context by presenting mental health screening scales first significantly decreased the average lifetime prevalence rate of nonordinary experiences compared to presenting experience items first (OR\u0026thinsp;=\u0026thinsp;0.67, SE\u0026thinsp;\u0026plusmn;\u0026thinsp;.056, 95% CI 0.578\u0026ndash;0.797, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eOne alternative explanation for this effect might be fatigue or general order effects. To address this possibility, we added a third condition in which participants first responded to a non-clinical personality inventory. Comparing these two conditions (mental health screening first, experiences items first) from the previous study with the added personality first condition (N\u0026thinsp;=\u0026thinsp;1,242, Mean age\u0026thinsp;=\u0026thinsp;49, SD age\u0026thinsp;\u0026plusmn;\u0026thinsp;15, 36% female, preregistration link, data link) we did not observe a significant difference for the personality first vs experiences first condition (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB; Table S5).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eImplied norm effects and response hedging\u003c/h3\u003e\n\u003cp\u003eOur previous studies suggested that subtle cues, such as the presentation order of surveys or information embedded in the question or response formats, may influence response behavior\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Here, we probed this further. The original inventory has a yes vs no response format. When confronted with this response choice, there is little information available to individuals to detect whether such experiences may be frequent. On the other hand, using a frequency response scale immediately sets expectations for their prevalence\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. We conducted an experimental study with a general population sample (N\u0026thinsp;=\u0026thinsp;7,518, Mean age\u0026thinsp;=\u0026thinsp;44, SD age\u0026thinsp;\u0026plusmn;\u0026thinsp;15, 49% female, data link) and asked participants to either respond to the experience items with 1) a binary yes-no response (i.e., have had the experience or not), 2) a binary response with the presence of a hedging (\u0026ldquo;I don\u0026rsquo;t know\u0026rdquo;) option or 3) with a five point frequency response scale, that varied from \u0026lsquo;I never had this experience\u0026rsquo; to \u0026lsquo;I have had this experience 10 times or more\u0026rsquo;. This study was not preregistered. We estimated the implied lifetime prevalence rates (calculated as the percentage of participants that have had the experience at least once) across conditions. A multilevel ordinal regression analysis with a random effect for participants was employed to compare the hedging and frequency conditions against the binary response condition, while controlling for possible experience-specific effects by including interactions with the individual experience items. Compared to the binary response options, individuals in the frequency response condition (OR\u0026thinsp;=\u0026thinsp;4.68, SE\u0026thinsp;\u0026plusmn;\u0026thinsp;.44, 95% CI 3.883\u0026ndash;5.641, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) reported overall higher yes responses, implying greater lifetime prevalence. The comparison of the binary response with hedging option did not show any significant result (OR\u0026thinsp;=\u0026thinsp;1.24, SE\u0026thinsp;\u0026plusmn;\u0026thinsp;.10, 95% CI\u0026thinsp;=\u0026thinsp;1.045\u0026ndash;1.468, p\u0026thinsp;=\u0026thinsp;.013). Overall, the frequency effect compared to the binary response option with the availability of a hedging option was substantively larger (z\u0026thinsp;=\u0026thinsp;11.123, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWe also observed 29 significant interactions between specific items and response conditions (see Table S6). These interactions suggest subtle shifts across experiences, which raises an interesting question about absolute vs relative lifetime frequency effects. Absolute prevalence rates indicate the absolute number of individuals reporting a given experience, whereas relative prevalence rates focus on the ordering of prevalences, that is, whether certain experiences are relatively more common than others in a sample. The two estimates may not coincide. For example, while absolute rates may vary dramatically, the relative rank order might be well preserved, suggesting an overall response shift across all experiences. Alternatively, absolute rates variability may be relatively minor, but relative rates may vary more widely due to factors such as differential stigma associated with specific experiences.\u003c/p\u003e\n\u003cp\u003eOne option to explore these possibilities is to examine the relative consistency of prevalence rates via the rank-order correlations across conditions. The results of such analysis suggested that the relative ordering of the lifetime prevalence rates was well preserved, with all r\u0026thinsp;\u0026gt;\u0026thinsp;.90 (Table S7). To ensure that this correlation was not masking differences in rank positions for individual items, we computed the variability of the ordinal positions across the conditions (Table S8). The average mean rank varied by 1.74 points (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31), suggesting that most items maintained a consistent position across formats. We identified one outlier (Misfortune item; SD\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77).\u003c/p\u003e\n\u003cp\u003eOverall, these findings indicate that absolute prevalence rates are sensitive to implied frequency or normativity, but the relative ordering of experiences in the population is preserved. Given the importance of absolute prevalence rates for clinical purposes of determining diagnosis rates and estimating unmet needs, our findings suggest that binary scales may lead to lower absolute estimates. In contrast, researchers interested in the mechanisms underlying specific experiences are more likely to be interested in the relative prevalence rates. The results suggest that items that elicit higher endorsement in one format show similar rank positions in the other formats, despite differences in absolute response rates.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eSensitivity of lifetime prevalence rates to implied frequency effects\u003c/h3\u003e\n\u003cp\u003eThe previous study suggested that individuals use information about the implied frequency when responding to whether they had a specific experience in their lives or not. This information use may be strategic \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and might be influenced by the implied frequency of the response scales. For example, relatively rare experiences may be too uncommon to be influenced by frequency information, whereas moderately common experiences may be more sensitive to frequency information. People may have first or second-hand information on these experiences, which raises the plausibility of higher prevalence effects, which in turn may be malleable by higher implied prevalence via the manipulated frequency response scale. We ran an experimental study in which people from the general population (N\u0026thinsp;=\u0026thinsp;2,035; Mean age\u0026thinsp;=\u0026thinsp;42; SD\u0026thinsp;\u0026plusmn;\u0026thinsp;15; 56% female, preregistration link, data link) were randomly assigned to one of three conditions of information bias induced via different response scales: 1) Binary yes/no responses, 2) Low implied prevalence (response scale: up to 10 times or more in one\u0026rsquo;s life time), or 3) High implied prevalence (response scale: up to 100 times or more in one\u0026rsquo;s life time). Examining implied lifetime prevalence rates in a multilevel logistic regression, we found significant increases for both Low: OR\u0026thinsp;=\u0026thinsp;1.46, SE\u0026thinsp;\u0026plusmn;\u0026thinsp;.11, 95% CI\u0026thinsp;=\u0026thinsp;1.262\u0026ndash;1.690, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001 and High implied prevalence rates: OR\u0026thinsp;=\u0026thinsp;1.57, SE\u0026thinsp;\u0026plusmn;\u0026thinsp;.12, 95% CI\u0026thinsp;=\u0026thinsp;1.358\u0026ndash;1.818, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, compared to binary response scales. The relative difference between the two OR\u0026rsquo;s was not significant from each other (z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.677; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.499. (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA, Table S9).\u003c/p\u003e\n\u003cp\u003eExplicitly testing the information value of the population level prevalence rates on participant responses (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB), we replicated the condition effect: OR\u0026thinsp;=\u0026thinsp;1.468; SE\u0026thinsp;\u0026plusmn;\u0026thinsp;.072, 95% CI\u0026thinsp;=\u0026thinsp;1.33\u0026ndash;1.617, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001 and identified a population-level prevalence effect: OR\u0026thinsp;=\u0026thinsp;346.969; SE\u0026thinsp;\u0026plusmn;\u0026thinsp;146.644, 95% CI\u0026thinsp;=\u0026thinsp;151.543\u0026ndash;794.412, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001 (Table S10). Experiences with higher implied population-level prevalence were more likely to be endorsed across conditions. The absence of interaction effects, high rank-order correlations (r\u0026thinsp;\u0026gt;\u0026thinsp;.95; Table S11) and low rank-order changes (rank SD\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38, SD\u0026thinsp;\u0026plusmn;\u0026thinsp;.0.97; Table S12) again supported the robustness of relative frequency rates. This suggests that participants use information about implied prevalence rates available via response scale formats as a general heuristic across experiences, but participants were not sensitive to the relative implied prevalence rates.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eThe relevance of validation information on prevalence rates\u003c/h3\u003e\n\u003cp\u003eValidity is essential for research. Although we collected data using items that passed the 80% validity threshold, we observed variation in validity scores during the validation process which may indicate that some individuals interpret items differently (data link).We meta-analytically summarized the associations between the different validity scores and prevalence rates across samples and observed reliable associations across all studies (Figure S13). Specifically, items with a higher number of ambivalent responses that could not be classified, lower PPU, and higher NPU had statistically lower endorsement rates. To evaluate the relative impact of these significant validity factors compared to measurement effects described in the previous studies (framing, hedging, and implied prevalence via frequency response scale format), we conducted a multilevel logistic regression analysis using all previously reported data (total N\u0026thinsp;=\u0026thinsp;11,628; random participant effects, see Table S14). All predictors were significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05), except for the availability of hedging options. Higher NPU values (OR\u0026thinsp;=\u0026thinsp;.402, SE\u0026thinsp;\u0026plusmn;\u0026thinsp;.009, 95% CI .385-.419, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and higher proportions of unclear responses during the validation process (OR\u0026thinsp;=\u0026thinsp;.011, SE\u0026thinsp;\u0026plusmn;\u0026thinsp;.000, 95% CI .010 \u0026minus;\u0026thinsp;.011, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) were associated with lower prevalence rates. In contrast, higher PPU values were associated with increased prevalence rates (OR\u0026thinsp;=\u0026thinsp;20.399, SE\u0026thinsp;\u0026plusmn;\u0026thinsp;1.062, 95% CI 18.419\u0026ndash;22.591, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Variability in validity parameters is therefore associated with prevalence estimates.\u003c/p\u003e\n\u003ch3\u003eEstimating absolute prevalence of nonordinary experiences\u003c/h3\u003e\n\u003cp\u003eTo estimate the overall prevalence of these nonordinary experiences, we pooled the estimates across our reported studies here using sample-size weighted effects (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; Table S15; data link). We found substantial heterogeneity in prevalence estimates (mean \u003cem\u003eI\u0026sup2;\u003c/em\u003e = 93.5; minimum \u003cem\u003eI\u0026sup2;\u003c/em\u003e = 71.10 for \"seeing animated objects\"). All values were above the .70 threshold\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e for \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e, indicating that estimates are highly heterogeneous and caution is needed in interpreting absolute prevalence rates. Despite this heterogeneity, interesting relative prevalence patterns emerged. Roughly a third of the experiences are reported by more than half of the population, with the lower confidence intervals above the 50% mark. In this group, emotional and cognitively driven experiences predominate, while clinically relevant experiences suggesting altered sensory states are clustered at the lower end. Notably, even the rarest experience (\"experiencing animated objects\") occurred in approximately 18% of participants (range 13\u0026ndash;24%).\u003c/p\u003e\n\u003cp\u003eOther clinically relevant experiences such as Out-of-body experience, perceiving lights or reporting memories of past life were reported by about one in five participants. The prevalence rate of these experiences is substantially higher in these general population samples than would be expected based on the associated clinical conditions (e.g., psychosis, schizophrenia, and dissociative disorders, which tend to have a lifetime prevalence\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e of around 1% in the general population). Our findings imply that such experiences, despite appearing unusual, are relatively frequent in the general population and may not be pathological in themselves. Extending these observations to other experiences that involve alterations in the sensory system or a sense of agency and which are often considered clinically relevant, we observed high rates of feeling touched (M\u0026thinsp;=\u0026thinsp;39.3%; range: 30% \u0026ndash; 48%), feeling guided by a force (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40.1%%; range: 27% \u0026ndash; 55%), feeling the presence of a force (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;41.6%; range: 30% \u0026ndash; 57%), or extrasensory perception (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;47%; range: 41% \u0026ndash; 55%). These parameters suggest that such experiences are common in the general population, and without additional information on an individual's health and well-being, pathological explanations are not immediately justified.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study shows that it is possible to obtain consistent relative lifetime prevalence estimates using locally validated items focusing on phenomenological features\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e of experiences that have fascinated scholars for millennia. Advancing on discrepant prevalence estimates reported in different literatures, the results strongly suggest that many seemingly unusual experiences are common to very common, and even experiences that may serve as markers of psychopathology are experienced by 30\u0026ndash;50% of the population at least once in their lifetime. At the same time, the absolute lifetime prevalence rates are sensitive to measurement conditions, which can explain the wide range of estimates in the previous literature. We highlight three key take-home messages for estimating lifetime prevalence rates in future studies, before addressing substantive questions about these experiences.\u003c/p\u003e \u003cp\u003eFirst, it is important to carefully test item understanding in target populations\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Researcher-designed questions may not align with participants' interpretations, leading to inaccurate estimates. Our results demonstrate that validity information was associated with lifetime prevalence estimates across more than 10,000 participants, which can have wide-ranging implications. For example, feeling extremely devoted to other people has been primarily studied in ritualistic and religious settings, but this experience is also relevant for politics (strong attachment to political leaders or groups\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e), management\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e or clinical work (strong attachment in relationships\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e). Our NPU data suggests misinterpretations of such items could significantly underestimate true prevalence rates.\u003c/p\u003e \u003cp\u003eSecond, the response scale substantially influences prevalence estimates. Clinical research has favored binary lifetime prevalence rates, whereas social science prefers a frequency response scale. The two formats lead to diverging estimates on lifetime prevalence. Consistent with cognitive theories, participants likely interpret response formats as implicit cues about researchers\u0026rsquo; assumptions\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The discrepancy between binary vs frequency scales might account for a substantive portion of the gap observed between previous studies\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e (see the supplement for relevant calculations).\u003c/p\u003e \u003cp\u003eThird, framing and study context affect prevalence rates. When experiences were presented within a mental health context, affirmative responses declined, which may explain the lower prevalence rates observed in clinical studies\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Considering that clinically focused surveys are framed in a mental health context, often use binary response scales and may use pathological framing of experiences, this could lead to unexamined interactive effects which may further suppress responses. Estimates of lifetime prevalence are certainly not independent of the research context and this needs more attention\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe preservation of relative prevalence, on the other hand, allows novel insights into the underlying dynamics of mind-culture interactions. Experiences that are more common in a population show higher prevalence rates than rarer experiences, relatively independently of the research context. A precondition for such future studies is that a diverse set of experiences is studied.\u003c/p\u003e \u003cp\u003eFocusing on specific substantive experience prevalence rates, positive emotional experiences, relatively innocuous cognitive lapses (e.g., experiences of d\u0026eacute;j\u0026agrave; vu and absorption), and lucid dreaming were found to be highly prevalent. D\u0026eacute;j\u0026agrave; vu was the most common experience, supporting previous work implying that familiarity-based recognition errors may be frequent and relatively harmless\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Given its lower NPU score, actual prevalence rates of dej\u0026agrave; vu might be even higher. Lucid dreams were equally common, in line with some previous estimates \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Absorption, defined here cognitively as losing track of time during task engagement, was also highly prevalent. Though interpretations of absorption are diverse, ranging from conditions of flow or creativity to fantasy proneness and disposition to experience altered states of consciousness\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, our measure focused on its cognitive aspect, without implying altered states of consciousness. These three most common experiences centrally involve cognitive functions (attention and memory in particular), suggesting that objectively faulty cognitive processes are salient features of human cognition. Given their high prevalence, such experiences constitute important elements of people\u0026rsquo;s experiential repertoire. Their ubiquity suggests that alterations of awareness are a central feature of human consciousness, which has interesting implications for the proposition that consciousness and awareness are intertwined\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOther experiences frequently reported (prevalence around 70%) included emotional experiences that \u0026lsquo;stand out\u0026rsquo;, such as compassion, love, joy, and pleasure. This raises questions about individuals who do not report these emotions. What is it like \u003cem\u003enot\u003c/em\u003e having had an outstanding or highly remarkable experience of love, joy or pleasure that clearly stands out? The prevalence of experiences such as compassion also aligns with arguments that positive emotions that connect individuals are likely a fundamental feature of human sociality, and without widespread other-focused emotional experiences human societies would not function\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe high prevalence of hopelessness is equally noteworthy, given its status as a depression marker\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. These high levels point to unmet needs in the community given the overall rates of depression\u003csup\u003e\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, but may also imply the importance of resilience and post-traumatic growth\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, which allows individuals to overcome and learn from these experiences. All humans will suffer major setbacks at some point, which shifts the focus to effective mechanisms to overcome challenges.\u003c/p\u003e \u003cp\u003eFocusing on relatively rare experiences, even the least frequent experiences were reported by about one in five individuals. These include clinically relevant experiences such as out-of-body experiences, seeing unexplained lights, or reporting memories of past lives. Some experiences involving alterations of sensory perception or self-awareness (e.g., touch, guidance, presence, extrasensory perceptions, hearing voices) were even more frequent, reported by 30% or more of the population. These rates are substantially higher than associated clinical conditions (e.g., psychosis, schizophrenia, dissociative disorders)\u003csup\u003e\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, suggesting these sensory alterations are not necessarily pathological in the general population.\u003c/p\u003e \u003cp\u003eThese findings raise interesting questions about the interaction between human perception, the environment, and human development. Frequent misalignments between the sensory system and the physical world require explanation, and supernatural or spiritual explanatory frameworks might offer intuitive and easier interpretations. In other words, the high prevalence of perceptual anomalies could provide the fertile ground for the \u0026lsquo;kindling\u0026rsquo; and spread of nonphysical, supernatural, and spiritual explanations, potentially contributing to the universal presence of religious systems across human cultures\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. At the same time, more systematic research is needed on what differentiates clinically relevant nonordinary experiences from relatively innocuous experiences.\u003c/p\u003e \u003cp\u003eIn summary, we propose that extracting common phenomenological features of subjective experiences and estimating their relative prevalence in a general population is possible. By providing validity and prevalence evidence of subjective first-person accounts, we take a significant step toward establishing a rigorous science of human experiences that can be further queried using neuroscientific methods. To understand human consciousness, especially in the context of unique and nonordinary subjective experiences, we need to be able to question personal experiences in a scientific way \u0026ndash; moving from subjective and idiographic noncomparable impressions to sample-level perspectives that allow generalizations about shared features. Our report offers one pathway in this larger scientific quest.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy 1 \u0026ndash; Validation of the Inventory for Nonordinary Experiences\u003c/h2\u003e\n\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n\u003ch2\u003eSample\u003c/h2\u003e\n\u003cp\u003eParticipants were members of the general population and native Portuguese speakers. We recruited participants via an online panel (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.netquest.com\u003c/span\u003e\u003c/span\u003e). Participants were compensated with points, which they could exchange for various gifts available on the Netquest platform. Inclusion criteria required participants to provide informed consent and be at least 18. Data collection occurred from December 2023 to January 2025 (data link).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eInstrument\u003c/h2\u003e\n\u003cp\u003eWe used the Inventory for Nonordinary Experiences\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e with 38 items validated in the US and India. The inventory comprises items related to the experiences and a separate appraisal section. In the current study, we only focus on the validation and prevalence of the experience items included in the original inventory. As reported above, 31 items were validated in our sample and we report prevalence data on those 31 items in the subsequent studies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eTranslation\u003c/h2\u003e\n\u003cp\u003eThe Brazilian Portuguese translation of the initial 38-item version followed a parallel committee approach\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e: two independent translations were obtained from two autonomously working groups of native Portuguese speakers. Each group used the intended interpretations of the items\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e as a reference to resolve translation problems. These translations were subsequently compared by representatives from both translation groups. Any discrepancies in the translations were thoroughly discussed until a consensus was reached on the translation. In case of disagreements, a survey was conducted among judges, who were provided with the original English version and the Portuguese translations. The item version that received the most votes was subsequently chosen as the final version for the validation stage.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eValidation Study Procedure\u003c/h2\u003e\n\u003cp\u003eGiven the challenges of validating single items, we used the Response Process Evaluation\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, which involves a series of probing questions to query whether participants had understood an item as intended. We conducted a series of pilot studies (total N\u0026thinsp;=\u0026thinsp;68) to refine the response validation process and adjust the response probes for our local context. In the first pilot, we observed that participants often provided very brief answers, making it challenging for the coders to determine whether they understood the item. Consequently, in the second pilot, we changed the \"example probe\" to encourage participants to elaborate and describe their experiences in detail, including their feelings and perceptions (instead of \"describing succinctly\" as used in the original probe). Based on the collected responses on the response probes in the first and second pilots, we also recognized that an \"I don't know\" response option may be beneficial, as a number of individuals expressed doubt whether they had indeed had the experience described or not\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Therefore, in the third pilot, we incorporated this third \"I don't know\" option in addition to \"Yes\" and \"No\". The validation probes used in the main study are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. We also experimented with the number of items shown per participant. Analyzing time and response quality (number of words written, amount of detail provided, etc.), responses to only 2 items per participant showed the best quality vs. cost ratio (considering the time spent on each item).\u003c/p\u003e\n\u003cp\u003eWe then used iterative online meta-surveys with these probing questions. Participants were presented with two INOE experience items and asked for item understanding via the final set of probes.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eFinal version of the adapted Brazilian response probes\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eType of probe and\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQuestions\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eResponse options \u0026amp; branching logic\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponse probe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHave you ever had this experience?\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003cp\u003eI don't know\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExample probe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIf \"Yes\":\u003c/p\u003e\n\u003cp\u003eDescribe in detail the situation or context in which you\u0026hellip; [the item is repeated]\u003c/p\u003e\n\u003cp\u003eIf \"No\":\u003c/p\u003e\n\u003cp\u003eEven if you haven't experienced this yourself, try to give an example of such an experience someone else might have had. This will help us to understand whether the situation we are presenting can be clearly understood.\u003c/p\u003e\n\u003cp\u003eIf \"I don't know\":\u003c/p\u003e\n\u003cp\u003eEven if you're not sure or don't remember a specific situation, try to describe an experience in which you\u0026hellip; [the item is repeated].\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFitting probe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhy do you think this experience that you reported fits in the context of the sentence that we showed to you?\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOnly shown when answering \"Yes\" or \"I don't know\" to the Response probe\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParaphrase probe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhen asking someone if they have had an experience like the one below, how would you phrase that question in your own words?\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComprehension probe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDo you think you understand what kind of experience we are talking about?\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOpen probe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIs there any other comment or suggestions that you may want to make? Anything that hindered your comprehension?\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eData pre-processing\u003c/h2\u003e\n\u003cp\u003eOne researcher assessed data quality. Any responses that met the criteria for the following categories were excluded from the analysis: a) nonsensical responses (e.g., \"fhasdjfasd\"), b) indications of protocol non-compliance (e.g., copying text from websites, providing irrelevant information), c) demonstrating a lack of understanding or adherence to the research protocol, and d) empty responses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003eValidation Score Calculation\u003c/h2\u003e\n\u003cp\u003eA team of four trained Brazilian coders independently evaluated whether the participants understood each item as intended. The intended interpretations\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e were used as a guidance in this classification process. Responses were scored on a 5-point scale (1\u0026thinsp;=\u0026thinsp;Understood, 2\u0026thinsp;=\u0026thinsp;Probably Understood, 3\u0026thinsp;=\u0026thinsp;Not Enough Information, 4\u0026thinsp;=\u0026thinsp;Probably Not Understood, 5\u0026thinsp;=\u0026thinsp;Not Understood). All coders were encouraged to provide comments and explanations of their classification.\u003c/p\u003e\n\u003cp\u003eTo evaluate whether an item was understood as intended, we used the Validation Score (VS)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e defined as the proportion of responses classified as understood to the number of both understood and not understood responses \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\frac{U}{U+NU}\\right)\\)\u003c/span\u003e\u003c/span\u003e. Ambivalent responses (coded as 3) were not included in the calculation.\u003c/p\u003e\n\u003cp\u003eOne problem of this overall VS is that false positive (a 'Yes' response when they have not had the experience) or false negative (a 'No' response when they have had the experience) responses may occur\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. To determine the prevalence of potential false positives and false negatives, we followed Taves et al. and computed the proportion of 'Yes' responses that are rated as 'Understood' (Positive Proportion Understood/PPU; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:PPU=\\frac{{U}_{Yes}}{{U}_{Yes}+N{U}_{Yes}}\\)\u003c/span\u003e\u003c/span\u003e), and the proportion of 'No' responses that are rated as 'Understood' (Negative Proportion Understood/NPU: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NPU=\\frac{{U}_{No}}{{U}_{No}+N{U}_{No}}\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003eValidity Criteria and Rater Agreement\u003c/h2\u003e\n\u003cp\u003eThe original RPE method uses group discussion of conflicting coder evaluations to reach consensus on the classification of each response\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, with responses receiving unanimous ratings of 1 or 2 classified as 'Understood' (U), while those receiving unanimous ratings of 4 or 5 were classified as 'Not Understood' (NU). Responses with unanimous ratings of 3 retained their original score as the overall rating, indicating insufficient information. According to the original guidelines, disagreement between raters was discussed until a consensus was reached, and the VS scores were calculated based on these consensus scores.\u003c/p\u003e\n\u003cp\u003eHowever, this approach can be time-consuming, difficult to document transparently, and susceptible to groupthink and criteria drift. To increase efficiency and transparency, we revised the response classification process and introduced a rule-based classification scheme.\u003c/p\u003e\n\u003cp\u003eFirst, we converted the five-point quality codes from each rater into three-point categorizations used for calculating the VS score: Understood/ Not Understood/ Unclear. We then formulated the following three explicit rules that had started to emerge organically in our discussions:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e75% Majority Rule\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf there is a 75% majority decision (3 out of 4 coders agree on the classification of a response), the majority choice prevails. In these cases, the three coders often had good and converging arguments for their decision. The single coder with a different opinion typically focused on specific details of the responses, missed specific nuances, and agreed with the majority following the discussion. To provide a practical example, if Coder 1\u0026thinsp;=\u0026thinsp;understood (U), Coder 2\u0026thinsp;=\u0026thinsp;understood (U), Coder 3\u0026thinsp;=\u0026thinsp;understood (U), and Coder 4\u0026thinsp;=\u0026thinsp;not enough information (3), then the final decision will be that the response is categorized as \"understood.\"\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e50:50 Rule\u003c/em\u003e: If there was an even split between the coders team and there was no majority for either understood or not understood, the response was classified as 3 (not enough information available). For example, if Coder 1\u0026thinsp;=\u0026thinsp;understood (U), Coder 2\u0026thinsp;=\u0026thinsp;understood (U), Coder 3\u0026thinsp;=\u0026thinsp;not understood (NU) and Coder 4\u0026thinsp;=\u0026thinsp;not understood (NU), then the final decision was coded as \"not enough information\" (3). The important point here is that the classifications had to be evenly split for different classification outcomes (half of the raters classified the response as Understood, the other half as Not Understood).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2-1-1 Rule\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf there is general disagreement around options, e.g., two of the coders agreed with each other and the other two provided divergent classifications, an adjudicator made a final decision on the evaluation of the response. For example, if Coder 1\u0026thinsp;=\u0026thinsp;understood (U), Coder 2\u0026thinsp;=\u0026thinsp;understood (U), Coder 3\u0026thinsp;=\u0026thinsp;not understood (NU), and Coder 4\u0026thinsp;=\u0026thinsp;not enough information (3), then the adjudicator would examine all the comments by the individual coders, the overall responses by the participant in relation to this item (and sometimes also the responses to the second item to consider possible response tendencies of the individual). If raters were split with half of the raters advocating for \u0026lsquo;not enough information\u0026rsquo; (coded as 3), the adjudicator was also used to make a final decision. The adjudicator had the final responsibility for deciding a classification of the item responses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003eCriteria for Item Revisions\u003c/h2\u003e\n\u003cp\u003eTo judge an item as preliminary validated, the overall percentage for the VS had to be 80% or higher in a sample of at least 20 individuals who provided valid responses. If an item did not meet this criterion, the original protocol\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e suggested adapting the item to increase clarity or provide specific examples to help understand the item. There were no objective criteria specified that could help in deciding when to adjust or adapt an item or whether to collect more data before making adjustments.\u003c/p\u003e\n\u003cp\u003eWe extensively discussed at what moment it would make sense to test an item again and at what moment it would be better to rewrite the item to increase clarity. We adopted some preliminary criteria to guide our adaptation process. Specifically, if an item reached an understood percentage of at least 60% in a sample of at least 10 individuals, the item was not modified and re-evaluated in a new batch of 5 to 10 respondents. This 60% criteria was informed by observations of high variability in small samples. Adding more participants was deemed important to provide more precision to help identify if the item needed adjustment or might be understandable in a broader sample. If the level of understanding was below 60% in a sample of at least 10 respondents, the item wording was re-evaluated and adapted to improve clarity by taking into consideration the available item responses. The revised version was then tested again.\u003c/p\u003e\n\u003cp\u003eDue to time and financial constraints, we decided to test a maximum of 5 different wordings. In some cases, we decided to test less than 5 wordings if the Validation Scores were below 60% and no clear alternatives to rephrase an item to increase clarity were evident based on the available item responses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy 2 \u0026ndash; Framing effects on lifetime prevalence estimates\u003c/h2\u003e\n\u003cp\u003eWe conducted an experimental study. Participants were randomly assigned to either one of two conditions: a) mental health context vs experience context and b) response options with hedging (I don\u0026rsquo;t know option) available or not. The mental health context was manipulated by presenting a screening tool for Generalized Anxiety\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e and Depression severity\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e as well as mental health questions (life satisfaction, loneliness) before experience items. The control condition was to present the INOE first and the mental health questions second. For the manipulation of hedging options, participants were randomly assigned to either a yes vs no response option condition or to a condition which included a hedging option (I don\u0026rsquo;t know). The data was analyzed with a multilevel logistic regression on the yes-responses for each of the experiences with a random intercept for participant using the lme4 (version 1.1\u0026ndash;35.5) and lmerTest (version 3.1-3) packages in R.\u003c/p\u003e\n\u003cp\u003eAfter completing the data collection, we added a further condition in which individuals were first presented with a short version of non-clinical personality measure \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e before responding to the experience items. In this condition, individuals were given the yes vs no response scale only. This condition was compared with the yes vs no branch of the experiment in which individuals either answered the mental health questions or the experience questions first. The analysis was conducted with a multilevel logistic regression with random intercepts for participants, focusing on the yes-responses only.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy 3\u0026ndash; Implied norm effects and response hedging\u003c/h2\u003e\n\u003cp\u003eIn this experimental study, individuals from the general population responded to a survey that presented nonordinary experience items with 1) a binary yes-no (have had the experience or not), 2) a binary yes vs no response with the presence of a hedging (I don\u0026rsquo;t know) option or 3) a six point frequency response scale, that varied from \u0026lsquo;Never\u0026rsquo;, \u0026lsquo;Once\u0026rsquo;, \u0026lsquo;2\u0026ndash;3 times\u0026rsquo;, \u0026lsquo;4\u0026ndash;5 times\u0026rsquo;, \u0026lsquo;6\u0026ndash;10 times\u0026rsquo; or \u0026lsquo;More than 10 times\u0026rsquo;. We coded the lifetime prevalence rates as the number of yes responses for the first two conditions and any response indicating that the individual has had the experience once or more in the frequency response condition. We performed a multilevel ordinal regression analysis on the yes-response with a random effect for participants using the lme4 (version 1.1\u0026ndash;35.5) and lmerTest (version 3.1-3) packages in R. Condition and experience item were treated as categorical variables and we included interaction effects between condition and experience item to explore possible experience-specific effects, setting the highest lifetime prevalence item as the intercept. Given the number of interactions, we controlled family-wise error rates with a Bonferroni correction.\u003c/p\u003e\n\u003cp\u003eFollow-up tests on the relative and absolute stability of lifetime prevalence rates were conducted at the population level. We examined the degree to which the rank order of item lifetime prevalence rates was preserved across conditions by computing pairwise Spearman rank-order correlations and assessing the consistency of individual items ordinal positions. Confidence intervals for these correlations were obtained via boostrap procedure to ensure robust estimates. In addition, to evaluate the variability of individual item ranking across conditions, we derived the standard deviation of ordinal ranks for each item and applied a nonparametric outlier detection approach, informed by a Shapiro-Wilk test for normality and interquartile range criteria.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy 4 \u0026ndash; sensitivity of lifetime prevalence rates to implied relative frequency\u003c/h2\u003e\n\u003cp\u003eWe conducted an experiment in which individuals were assigned to one of three conditions: 1) a binary response condition with yes vs no responses, 2) a low-frequency response condition with responses varying in six steps between \u0026lsquo;never\u0026rsquo; and up to \u0026lsquo;10 times or more\u0026rsquo; in one\u0026rsquo;s life time, or 3) a high-frequency response condition varying in six steps from \u0026lsquo;never\u0026rsquo; to up to \u0026lsquo;100 times or more\u0026rsquo; in one\u0026rsquo;s life time. We coded the lifetime prevalence for each experience as the response to yes or any response that indicated that the individual has had the experience at least once. Lifetime prevalence was coded as 1 and never had the experience as 0. To test the effects of response format on reported prevalence, we fitted a multilevel logistic regression model with lifetime prevalence as the dependent variable, response format as a fixed effect (with the binary condition as the reference category), and random intercepts for both participants and experience items using the lme4 (version 1.1\u0026ndash;35.5) and lmerTest (version 3.1-3) packages in R.\u003c/p\u003e\n\u003cp\u003eTo explore linear and nonlinear base rate effects, we rescaled the prevalence rates in the yes vs no binary response condition so that the minimum lifetime prevalence for the lowest prevalence experience was set to zero. Both the linear and quadratic effect of this rescaled base rate were included as a predictor in the multilevel logistic regression model. The experimental condition was entered as a categorical predictor and the interaction between response condition and the linear and quadratic base rates were included. As before, random intercepts were included for both participants and items. We controlled family-wise error rates with a Bonferroni correction. To further assess whether the absence of interaction effects masked condition-specific shifts in item-level prevalence, we calculated rank order correlations between the three response conditions using item-level prevalence rates. We computed Spearman rank-order correlations for each pair of conditions and derived the mean correlation across comparisons. Additionally, to evaluate potential deviations in rank consistency at the item level, we calculated the standard deviation of each item\u0026rsquo;s rank across conditions. To assess the distributional properties of these deviations, we conducted Shapiro Wilk tests for normality. Because the data was normally distributed, we used a parametric cutoff, defined as two standard deviations above the mean rank standard deviation, to flag potential outlier items.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy 5 \u0026ndash; The relevance of validation information on prevalence rates\u003c/h2\u003e\n\u003cp\u003eTo estimate the association between the overall validations core, the positive proportion understood (PPU), negative proportion understood (NPU) and proportion of unclear answers (which could not be clearly coded without further follow-up questioning during validation stage) with the relative prevalence rates in each study and condition, we computed Spearman correlations at the study level for each experimental condition. These correlations were then r-to-z transformed and we computed a random effects meta-analysis model with sample size as weights with the metafor (version 4.8-0) package in R.\u003c/p\u003e\n\u003cp\u003eTo compare the relative importance of the significant validity parameters using the random effects meta-analysis, we then computed a multilevel logistic regression model using the lme4 (version 1.1\u0026ndash;35.5) and lmerTest (version 3.1-3) packages in R. We included the significant parameters from the meta-analysis as well as the main effects of the mental health vs personality framing, availability of hedging and general effects of normative effects (combining the low and high frequency conditions) using the total sample size from all studies. We included a random effect for participants. We controlled family-wise error rates with a Bonferroni correction.\u003c/p\u003e\n\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n\u003ch2\u003eStudy 6 - Estimating absolute prevalence of nonordinary intense experiences\u003c/h2\u003e\n\u003cp\u003eTo obtain an overall estimate of the population level lifetime prevalence of each experience, we conducted a random effects meta-analysis using REML within the metafor package (version 4.8-0) in R. We pooled the percentage of yes responses (e.g., individuals report having had the experience at least once in their life) for each study weighted by sample size. We computed average effect sizes as well as heterogeneity effects. To estimate the overall prevalence, we pooled all the estimates across all the individual studies and conditions reported above and estimated the average as well as the variability using sample size weighted effects.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe research was approved by the D’Or Institute for Research and Education Research Ethics Committee in Brazil, registration (CAAE) numbers of the approved applications: 65573322.6.0000.5249 and 79141724.9.0000.5249\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChalmers, D. J. Facing Up to the Problem of Consciousness. in \u003cem\u003eThe Character of Consciousness\u003c/em\u003e (ed. Chalmers, D. J.) 0 (Oxford University Press, 2010). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/acprof:oso/9780195311105.003.0001\u003c/span\u003e\u003cspan address=\"10.1093/acprof:oso/9780195311105.003.0001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeth, A. K. \u0026amp; Bayne, T. Theories of consciousness. \u003cem\u003eNat. Rev. Neurosci.\u003c/em\u003e 23, 439\u0026ndash;452 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCogitate Consortium \u003cem\u003eet al.\u003c/em\u003e An adversarial collaboration to critically evaluate theories of consciousness. Preprint at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2023.06.23.546249\u003c/span\u003e\u003cspan address=\"10.1101/2023.06.23.546249\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClark, A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. \u003cem\u003eBehav. Brain Sci.\u003c/em\u003e 36, 181\u0026ndash;204 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriston, K. \u0026amp; Kiebel, S. Predictive coding under the free-energy principle. \u003cem\u003ePhilos. Trans. R. Soc. B Biol. Sci.\u003c/em\u003e (2009) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1098/rstb.2008.0300\u003c/span\u003e\u003cspan address=\"10.1098/rstb.2008.0300\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimmermann, C. \u003cem\u003eet al.\u003c/em\u003e A neurophenomenological approach to non-ordinary states of consciousness: hypnosis, meditation, and psychedelics. \u003cem\u003eTrends Cogn. Sci.\u003c/em\u003e 27, 139\u0026ndash;159 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan de Mieroop, M. \u003cem\u003ePhilosophy before the Greeks: The Pursuit of Truth in Ancient Babylonia\u003c/em\u003e. (Princeton University Press, Princeton Oxford, 2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWood, M. \u003cem\u003eThe Story of China: A portrait of a civilisation and its people\u003c/em\u003e. (Simon \u0026amp; Schuster Ltd, London, 2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGrath, J. J. \u003cem\u003eet al.\u003c/em\u003e Psychotic Experiences in the General Population: A Cross-National Analysis Based on 31 261 Respondents From 18 Countries. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e 72, 697\u0026ndash;705 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaraldi, E. de O. \u0026amp; Krippner, S. Cross-cultural research on anomalous experiences: Theoretical issues and methodological challenges. \u003cem\u003ePsychol. Conscious. Theory Res. Pract.\u003c/em\u003e 6, 306\u0026ndash;319 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro de Barros, M. C. \u003cem\u003eet al.\u003c/em\u003e Prevalence of spiritual and religious experiences in the general population: A Brazilian nationwide study. \u003cem\u003eTranscult. Psychiatry\u003c/em\u003e 13634615221088701 (2022) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/13634615221088701\u003c/span\u003e\u003cspan address=\"10.1177/13634615221088701\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaves, A. \u0026amp; Barlev, M. A feature-based approach to the comparative study of \u0026ldquo;nonordinary\u0026rdquo; experiences. \u003cem\u003eAm. Psychol.\u003c/em\u003e 78, 50\u0026ndash;61 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaves, A. \u003cem\u003eet al.\u003c/em\u003e The Inventory of Nonordinary Experiences (INOE): Evidence of Validity in the United States and India. \u003cem\u003ePLOS ONE\u003c/em\u003e (2023) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1371/journal.pone.0287780\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0287780\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaraldi, E. O. \u003cem\u003eet al.\u003c/em\u003e Nonordinary Experiences, Well-being and Mental Health: A Systematic Review of the Evidence and Recommendations for Future Research. \u003cem\u003eJ. Relig. Health\u003c/em\u003e 63, 410\u0026ndash;444 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuhrmann, T. M. \u003cem\u003eet al.\u003c/em\u003e Sensing the presence of gods and spirits across cultures and faiths. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e 118, e2016649118 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGomez-Marin, A. \u0026amp; Seth, A. K. A science of consciousness beyond pseudo-science and pseudo-consciousness. \u003cem\u003eNat. Neurosci.\u003c/em\u003e 28, 703\u0026ndash;706 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkram, U. \u003cem\u003eet al.\u003c/em\u003e Prevalence of anxiety, depression, mania, insomnia, stress, suicidal ideation, psychotic experiences, \u0026amp; loneliness in UK university students. \u003cem\u003eSci. Data\u003c/em\u003e 10, 621 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePechey, R. \u0026amp; Halligan, P. Prevalence and correlates of anomalous experiences in a large non-clinical sample. \u003cem\u003ePsychol. Psychother. Theory Res. Pract.\u003c/em\u003e 85, 150\u0026ndash;162 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcunzo, D., Carde\u0026ntilde;a, E. \u0026amp; Terhune, D. B. Anomalous experiences are more prevalent among highly suggestible individuals who are also highly dissociative. \u003cem\u003eCognit. Neuropsychiatry\u003c/em\u003e 25, 179\u0026ndash;189 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNuevo, R. \u003cem\u003eet al.\u003c/em\u003e The continuum of psychotic symptoms in the general population: a cross-national study. \u003cem\u003eSchizophr. Bull.\u003c/em\u003e 38, 475\u0026ndash;485 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeavan, V., Read, J. \u0026amp; Cartwright, C. The prevalence of voice-hearers in the general population: A literature review. \u003cem\u003eJ Ment Health\u003c/em\u003e 20, 281\u0026ndash;292 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeisman, K. \u0026amp; Luhrmann, T. M. Shifting Between Models of Mind: New Insights Into How Human Minds Give Rise to Experiences of Spiritual Presence and Alternative Realities. \u003cem\u003eTop. Cogn. Sci.\u003c/em\u003e tops.70002 (2025) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/tops.70002\u003c/span\u003e\u003cspan address=\"10.1111/tops.70002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGosseries, O. \u003cem\u003eet al.\u003c/em\u003e Exploration of trance states: phenomenology, brain correlates, and clinical applications. \u003cem\u003eCurr. Opin. Behav. Sci.\u003c/em\u003e 58, 101400 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRauf, B. \u003cem\u003eet al.\u003c/em\u003e Associations between sleep variables and ostensibly paranormal experiences and paranormal beliefs: A scoping review. \u003cem\u003eBr. J. Psychol.\u003c/em\u003e 114, 797\u0026ndash;818 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansen, L. \u003cem\u003eet al.\u003c/em\u003e Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e (2025) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamapsychiatry.2024.4702\u003c/span\u003e\u003cspan address=\"10.1001/jamapsychiatry.2024.4702\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeRosse, P. \u0026amp; Karlsgodt, K. H. Examining the Psychosis Continuum. \u003cem\u003eCurr. Behav. Neurosci. Rep.\u003c/em\u003e 2, 80\u0026ndash;89 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSavill, M., D\u0026rsquo;Ambrosio, J., Cannon, T. D. \u0026amp; Loewy, R. L. Psychosis risk screening in different populations using the Prodromal Questionnaire: A systematic review. \u003cem\u003eEarly Interv. Psychiatry\u003c/em\u003e 12, 3\u0026ndash;14 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTourangeau, R. The survey response process from a cognitive viewpoint. \u003cem\u003eQual. Assur. Educ.\u003c/em\u003e 26, 169\u0026ndash;181 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischer, R. \u0026amp; Rudnev, M. From MIsgivings to MIse-en-sc\u0026egrave;ne: The role of invariance in personality science. \u003cem\u003eEur. J. Personal.\u003c/em\u003e 08902070241283081 (2024) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/08902070241283081\u003c/span\u003e\u003cspan address=\"10.1177/08902070241283081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorrigan, P. W. \u0026amp; Rao, D. On the Self-Stigma of Mental Illness: Stages, Disclosure, and Strategies for Change. \u003cem\u003eCan. J. Psychiatry Rev. Can. Psychiatr.\u003c/em\u003e 57, 464\u0026ndash;469 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLink, B. G. \u0026amp; Phelan, J. C. Conceptualizing Stigma. \u003cem\u003eAnnu. Rev. Sociol.\u003c/em\u003e 27, 363\u0026ndash;385 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrosnick, J. A. Response strategies for coping with the cognitive demands of attitude measures in surveys. \u003cem\u003eAppl. Cogn. Psychol.\u003c/em\u003e 5, 213\u0026ndash;236 (1991).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchaeffer, N. C. \u0026amp; Presser, S. The Science of Asking Questions. \u003cem\u003eAnnu. Rev. Sociol.\u003c/em\u003e 29, 65\u0026ndash;88 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTononi, G. \u003cem\u003eet al.\u003c/em\u003e Consciousness or pseudo-consciousness? A clash of two paradigms. \u003cem\u003eNat. Neurosci.\u003c/em\u003e 28, 694\u0026ndash;702 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly, T. M. \u003cem\u003eClassification and Statistical Manual of Extrasensory Experiences, 1st Edition: CSM-EE\u003c/em\u003e. (lulu.com, 2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO | International Classification of Diseases, 11th Revision (ICD-11). \u003cem\u003eWHO\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.who.int/classifications/icd/en/\u003c/span\u003e\u003cspan address=\"http://www.who.int/classifications/icd/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eDiagnostic and Statistical Manual of Mental Disorders: DSM-5-TR\u003c/em\u003e. (American Psychiatric Association Publishing, Washington, DC, 2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaves, A. Mystical and Other Alterations in Sense of Self: An Expanded Framework for Studying Nonordinary Experiences. \u003cem\u003ePerspect. Psychol. Sci. J. Assoc. Psychol. Sci.\u003c/em\u003e 15, 669\u0026ndash;690 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolf, M. G., Ihm, E., Maul, A. \u0026amp; Taves, A. The Response Process Evaluation Method. \u003cem\u003eAdv. Methods Pract. Psychol. Sci.\u003c/em\u003e (2025) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31234/osf.io/rbd2x\u003c/span\u003e\u003cspan address=\"10.31234/osf.io/rbd2x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischer, R., Karl, J. A., Luczak-Roesch, M. \u0026amp; Hartle, L. Why We Need to Rethink Measurement Invariance: The Role of Measurement Invariance for Cross-Cultural Research. \u003cem\u003eCross-Cult. Res.\u003c/em\u003e 10693971241312459 (2025) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/10693971241312459\u003c/span\u003e\u003cspan address=\"10.1177/10693971241312459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLived experience: shifting focus. \u003cem\u003eNat. Ment. Health\u003c/em\u003e 1, 145\u0026ndash;145 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischer, R. \u003cem\u003eet al.\u003c/em\u003e Cultural challenges for adapting behavioral intervention frameworks: A critical examination from a cultural psychology perspective. \u003cem\u003eClin. Psychol. Rev.\u003c/em\u003e 102425 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwarz, N. \u0026amp; Oyserman, D. Asking Questions About Behavior: Cognition, Communication, and Questionnaire Construction. \u003cem\u003eAm. J. Eval.\u003c/em\u003e 22, 127\u0026ndash;160 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkobeng, A. K. Understanding diagnostic tests 1: sensitivity, specificity and predictive values. \u003cem\u003eActa Paediatr.\u003c/em\u003e 96, 338\u0026ndash;341 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerlinger, G. \u003cem\u003eet al.\u003c/em\u003e Personal stigma in schizophrenia spectrum disorders: a systematic review of prevalence rates, correlates, impact and interventions. \u003cem\u003eWorld Psychiatry\u003c/em\u003e 12, 155\u0026ndash;164 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRivera-Garrido, N., Ramos-Sosa, M. P., Accerenzi, M. \u0026amp; Bra\u0026ntilde;as-Garza, P. Continuous and binary sets of responses differ in the field. \u003cem\u003eSci. Rep.\u003c/em\u003e 12, 14376 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwarz, N., Hippler, H.-J., Deutsch, B. \u0026amp; Strack, F. Response scales: Effects of category range on reported behavior and comparative judgments. \u003cem\u003ePublic Opin. Q.\u003c/em\u003e 49, 388\u0026ndash;395 (1985).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMigliavaca, C. B. \u003cem\u003eet al.\u003c/em\u003e Meta-analysis of prevalence: I2 statistic and how to deal with heterogeneity. \u003cem\u003eRes. Synth. Methods\u003c/em\u003e 13, 363\u0026ndash;367 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiggins, J. P. T. \u0026amp; Thompson, S. G. Quantifying heterogeneity in a meta-analysis. \u003cem\u003eStat. Med.\u003c/em\u003e 21, 1539\u0026ndash;1558 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKakkar, H. \u0026amp; Sivanathan, N. When the appeal of a dominant leader is greater than a prestige leader. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e 114, 6734\u0026ndash;6739 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Kleef, G. A. \u003cem\u003eet al.\u003c/em\u003e No guts, no glory? How risk-taking shapes dominance, prestige, and leadership endorsement. \u003cem\u003eJ. Appl. Psychol.\u003c/em\u003e 106, 1673\u0026ndash;1694 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarnock, K. N., Ju, C. S. \u0026amp; Katz, I. M. A Meta-analysis of Attachment at Work. \u003cem\u003eJ. Bus. Psychol.\u003c/em\u003e 39, 1239\u0026ndash;1257 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchyns, B. \u0026amp; and Day, D. Critique and review of leader\u0026ndash;member exchange theory: Issues of agreement, consensus, and excellence. \u003cem\u003eEur. J. Work Organ. Psychol.\u003c/em\u003e 19, 1\u0026ndash;29 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSibley, C. G., Fischer, R. \u0026amp; Liu, J. H. Reliability and validity of the revised experiences in close relationships (ECR-R) self-report measure of adult romantic attachment. \u003cem\u003ePers. Soc. Psychol. Bull.\u003c/em\u003e 31, 1524\u0026ndash;1536 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Oliveira Maraldi, E. Response Bias in Research on Religion, Spirituality and Mental Health: A Critical Review of the Literature and Methodological Recommendations. \u003cem\u003eJ. Relig. Health\u003c/em\u003e 59, 772\u0026ndash;783 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCleary, A. M. \u0026amp; Claxton, A. B. D\u0026eacute;j\u0026agrave; Vu: An Illusion of Prediction. \u003cem\u003ePsychol. Sci.\u003c/em\u003e 29, 635\u0026ndash;644 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown, A. S. A review of the d\u0026eacute;j\u0026agrave; vu experience. \u003cem\u003ePsychol. Bull.\u003c/em\u003e 129, 394\u0026ndash;413 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaunders, D. T., Roe, C. A., Smith, G. \u0026amp; Clegg, H. Lucid dreaming incidence: A quality effects meta-analysis of 50 years of research. \u003cem\u003eConscious. Cogn.\u003c/em\u003e 43, 197\u0026ndash;215 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJamieson, G. A. The modified tellegen absorption scale: A clearer window on the structure and meaning of absorption. \u003cem\u003eAust. J. Clin. Exp. Hypn.\u003c/em\u003e 33, 119\u0026ndash;139 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerhune, D. B. \u0026amp; Jamieson, G. A. Hallucinations and the meaning and structure of absorption. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e 118, e2108467118 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaal, F. B. M. de. Putting the Altruism Back into Altruism: The Evolution of Empathy. \u003cem\u003eAnnu. Rev. Psychol.\u003c/em\u003e 59, 279\u0026ndash;300 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, R. T., Kleiman, E. M., Nestor, B. A. \u0026amp; Cheek, S. M. The Hopelessness Theory of Depression: A Quarter Century in Review. \u003cem\u003eClin. Psychol. Publ. Div. Clin. Psychol. Am. Psychol. Assoc.\u003c/em\u003e 22, 345\u0026ndash;365 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbramson, L. Y., Metalsky, G. I. \u0026amp; Alloy, L. B. Hopelessness depression: A theory-based subtype of depression. \u003cem\u003ePsychol. Rev.\u003c/em\u003e 96, 358\u0026ndash;372 (1989).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, J., Zhang, L., Yang, C., Li, X. \u0026amp; Li, Z. Global, Regional, and National Epidemiology of Depression in Working-Age Individuals, 1990\u0026ndash;2019. \u003cem\u003eDepress. Anxiety\u003c/em\u003e 2024, 4747449 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. \u003cem\u003eet al.\u003c/em\u003e Change in the global burden of depression from 1990\u0026ndash;2019 and its prediction for 2030. \u003cem\u003eJ. Psychiatr. Res.\u003c/em\u003e 178, 16\u0026ndash;22 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShorey, S., Ng, E. D. \u0026amp; Wong, C. H. J. Global prevalence of depression and elevated depressive symptoms among adolescents: A systematic review and meta-analysis. \u003cem\u003eBr. J. Clin. Psychol.\u003c/em\u003e 61, 287\u0026ndash;305 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJayawickreme, E. \u003cem\u003eet al.\u003c/em\u003e Post-traumatic growth as positive personality change: Challenges, opportunities, and recommendations. \u003cem\u003eJ. Pers.\u003c/em\u003e 89, 145\u0026ndash;165 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimeone, J. C., Ward, A. J., Rotella, P., Collins, J. \u0026amp; Windisch, R. An evaluation of variation in published estimates of schizophrenia prevalence from 19902013: a systematic literature review. \u003cem\u003eBMC Psychiatry\u003c/em\u003e 15, 193 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastillejos, M. C., Mart\u0026iacute;n-P\u0026eacute;rez, C. \u0026amp; Moreno-K\u0026uuml;stner, B. A systematic review and meta-analysis of the incidence of psychotic disorders: the distribution of rates and the influence of gender, urbanicity, immigration and socio-economic level. \u003cem\u003ePsychol. Med.\u003c/em\u003e 48, 2101\u0026ndash;2115 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSar, V. Epidemiology of Dissociative Disorders: An Overview. \u003cem\u003eEpidemiol. Res. Int.\u003c/em\u003e 2011, 404538 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHambleton, R. K. \u0026amp; Zenisky, A. L. Translating and Adapting Tests for Cross-Cultural Assessments. in \u003cem\u003eCross-Cultural Research Methods in Psychology\u003c/em\u003e (eds. Matsumoto, D. \u0026amp; van de Vijver, F. J. R.) 46\u0026ndash;70 (Cambridge University Press, Cambridge, 2010). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/CBO9780511779381.004\u003c/span\u003e\u003cspan address=\"10.1017/CBO9780511779381.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eCross-Cultural Survey Methods\u003c/em\u003e. (J. Wiley, Hoboken, N.J, 2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIp, E. H., Magee, M. F., Youssef, G. A. \u0026amp; Chen, S.-H. Gleaning Information for Cognitive Operations from Don\u0026rsquo;t Know Responses in Cognitive and Noncognitive Assessments. \u003cem\u003eMultivar. Behav. Res.\u003c/em\u003e 54, 159\u0026ndash;172 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpitzer, R. L., Kroenke, K., Williams, J. B. W. \u0026amp; L\u0026ouml;we, B. A brief measure for assessing generalized anxiety disorder: the GAD-7. \u003cem\u003eArch. Intern. Med.\u003c/em\u003e 166, 1092\u0026ndash;1097 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroenke, K., Spitzer, R. L. \u0026amp; Williams, J. B. W. The PHQ-9. \u003cem\u003eJ. Gen. Intern. Med.\u003c/em\u003e 16, 606\u0026ndash;613 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroenke, K., Spitzer, R. L., Williams, J. B. W. \u0026amp; L\u0026ouml;we, B. The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review. \u003cem\u003eGen. Hosp. Psychiatry\u003c/em\u003e 32, 345\u0026ndash;359 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoto, C. J. \u0026amp; John, O. P. Short and extra-short forms of the Big Five Inventory\u0026ndash;2: The BFI-2-S and BFI-2-XS. \u003cem\u003eJ. Res. Personal.\u003c/em\u003e 68, 69\u0026ndash;81 (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6907742/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6907742/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConsciousness research has largely overlooked systematic examination of nonordinary experiences (NOEs) in the general population. Such experiences are subjectively marked as unusual or special compared to mundane ones, are challenging to quantify and disciplinary biases complicate accurate assessments of population-wide prevalence rates. Using descriptive, non-judgmental phrasing of items, we were able to validate 31 experiences (N = 1,284). Across a series of pre-registered experiments with general population samples (total N = 11,629), relative lifetime prevalence rates were estimated consistently, but absolute point estimates were sensitive to measurement and study conditions. Overall, we observe much higher prevalence rates of nonordinary experiences than indicated by associated clinical diagnosis rates (e g., schizophrenia, dissociative disorders, depression). Our study demonstrates that several types of nonordinary experiences—often dismissed as rare or pathological—are in fact widespread in general populations, opening novel perspectives for integrating these experiences into a broader empirical science of consciousness.\u003c/p\u003e","manuscriptTitle":"Revisiting the Unusual: Investigating the Prevalence and Validity of Nonordinary Experiences as a Window into Consciousness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-29 14:31:47","doi":"10.21203/rs.3.rs-6907742/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-psychology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commspsychol","sideBox":"Learn more about [Communications Psychology](http://www.nature.com/commspsychol/)","snPcode":"44271","submissionUrl":"https://mts-commspsychol.nature.com/cgi-bin/main.plex","title":"Communications Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d181a58-469e-4f46-b178-b4a82f4c6144","owner":[],"postedDate":"June 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":50255595,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Consciousness"},{"id":50255596,"name":"Social science/Psychology/Human behaviour"},{"id":50255597,"name":"Health sciences/Diseases/Psychiatric disorders/Psychosis"},{"id":50255598,"name":"Social science/Anthropology/Biological anthropology"}],"tags":[],"updatedAt":"2025-07-01T15:10:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-29 14:31:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6907742","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6907742","identity":"rs-6907742","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}