Extraordinary Claims Require Extraordinary Evidence: The Case of Non-Local Perception, a Classical and Bayesian Review of Evidences

Starting from the famous phrase “extraordinary claims require extraordinary evidence,” we will present the evidence supporting the concept that human visual perception may have non-local properties, in other words, that it may operate beyond the space and time constraints of sensory organs, in order to discuss which criteria can be used to define evidence as extraordinary. This evidence has been obtained from seven databases which are related to six different protocols used to test the reality and the functioning of non-local perception, analyzed using both a frequentist and a new Bayesian meta-analysis statistical procedure. According to a frequentist meta-analysis, the null hypothesis can be rejected for all six protocols even if the effect sizes range from 0.007 to 0.28. According to Bayesian meta-analysis, the Bayes factors provides strong evidence to support the alternative hypothesis (H1) over the null hypothesis (H0), but only for three out of the six protocols. We will discuss whether quantitative psychology can contribute to defining the criteria for the acceptance of new scientific ideas in order to avoid the inconclusive controversies between supporters and opponents

On the primacy and irreducible nature of first-person versus third-person information

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Stage 1 Registered Report: Anomalous perception in a Ganzfeld condition - A meta-analysis of more than 40 years investigation

This meta-analysis is an investigation into anomalous perception (i.e., conscious identification of information without any conventional sensorial means). The technique used for eliciting an effect is the ganzfeld condition (a form of sensory homogenization that eliminates distracting peripheral noise). The database consists of peer-reviewed studies published between January 1974 and June 2020 inclusive. The overall effect size will be estimated using a frequentist model and a Bayesian random model. Moderator analysis will be used to examine the influence of level of experience of participants and the type of task. Publication bias will be estimated by using three different tests. Trend analysis will be conducted on the cumulative database

Stage 1 Registered Report: Testing mediumship accuracy with a triple-blind protocol

Even if mediumship is a practice that probably originated at the outset of human history and is embedded in many cultural and religious traditions, scientific investigations into mediumship are quite recent. In this study, we aim to investigate if self-defined mediums can retrieve information about specific deceased persons in an unconventional way, that is, without retrieving the information from the parents or friends of the deceased person or by other conventional means. To our knowledge, this is the first registered report related to this phenomenon. Our experimental design will be triple-blinded: the mediums and their interviewer will only know the first name of the deceased person and of the requesting person. The parents or the close friends of the deceased person will not interact in any way with the medium. Furthermore, they will be blind about which of two lists of information (readings) paired with deceased persons of the same gender is related to their parents or friend. Accuracy of information will be analysed by three criteria: (1) percentage of correct identification of the readings related to the requested person; (2) comparison of the global scores (on a scale 1 to 6) assigned to the intended readings with that of control readings; and (3) comparison of the difference between the percentage of correct minus incorrect information identified in the intended readings with that of the control readings

Dead Reckoning: A Multiteam System Approach to Commentaries on the Drake-S Equation for Survival

We used a multiteam system approach (MTS) to map the critical and constructive feedback from four invited Commentaries on Rock et al.’s (2023) probabilistic analysis of purported evidence for postmortem survival. The goal was to mine actionable insights to guide future research with the potential for important learnings or breakthroughs about the nature or limits of human consciousness and their relation to transpersonal psychology. The commentators’ input identified only a few measurable variables or empirical tactics that conceivably challenge or refine our latest Drake-S Equation for survival. However, a review of these suggestions using logical and statistical criteria revealed that none immediately upend our previous conclusion that the published effect sizes for various Known Confounds (including hypothetical living agent psi ) do not fully account for the published prevalence rates of Anomalous Experiences traditionally interpretated as survival. However, the commentators proposed several good recommendations for new studies that could eventually alter this calculus. Accordingly, we outline the architecture of a proposed cross-disciplinary research program that extends the present MTS approach and its collected insights and focuses strictly on empiricism over rhetoric in this domain. The results of this coordinated effort should likewise help to clarify a range of psychological and biomedical phenomena that speak to the nature and limits of human consciousness

Is Biological Death Final? Recomputing the Drake-S Equation for Postmortem Survival of Consciousness

This participatory team science project extended Laythe and Houran’s (2022) prior application of a famous probabilistic argument known as the ‘Drake equation’ to the question of postmortem survival. Specifically, we evaluated effect sizes from peer-reviewed, empirical studies to determine the maximum average percentage effect that ostensibly supports (i.e., anomalous effects ) or refutes (i.e., known confounds ) the survival hypothesis. But unlike the earlier application, this research included a study-specific estimate of the hypothesized variable of ‘living agent psi’ via a new meta-analysis of empirical studies (N = 17) with exceptional subjects vs participants from the general population. Our updated analysis found that putative psi was a meaningful variable, although it along with other known confounds still did not account for 30.3% of survival-related phenomena that appear to attest directly to human consciousness continuing after physical (biological) death. Thus, the popular conventional variables that we measured here are seemingly insufficient to account for a sizable portion of the purported empirical data that has been interpreted as evidence of survival. Our conclusion is nonetheless tempered by several assumptions and limitations of our speculative exercise, which ultimately does not affirm the existence of an ‘afterlife’ but rather highlights the need for measurements with greater precision and/ or a more comprehensive set of quantifiable variables. Therefore, we discuss how our probabilistic approach provides important heuristics to guide future research in this highly controversial domain that touches both parapsychology and transpersonal psychology

Is Biological Death Final? Recomputing the Drake-S Equation for Postmortem Survival of Consciousness

This participatory team science project extended Laythe and Houran’s (2022) prior application of a famous probabilistic argument known as the Drake equation to the question of postmortem survival. Specifically, we evaluated effect sizes from peer-reviewed, empirical studies to determine the maximum average percentage effect that ostensibly supports (i.e., anomalous effects ) or refutes (i.e., known confounds ) the survival hypothesis. But unlike the earlier application, this research included a study-specific estimate of the hypothesized variable of living agent psi via a new meta-analysis of empirical studies (N = 17) with exceptional subjects vs participants from the general population. Our updated analysis found that putative psi was a meaningful variable, although it along with other known confounds still did not account for 30.3% of survival-related phenomena that appear to attest directly to human consciousness continuing after physical (biological) death. Thus, the popular conventional variables that we measured here are seemingly insufficient to account for a sizable portion of the purported empirical data that has been interpreted as evidence of survival. Our conclusion is nonetheless tempered by several assumptions and limitations of our speculative exercise, which ultimately does not affirm the existence of an ‘afterlife’ but rather highlights the need for measurements with greater precision and/ or a more comprehensive set of quantifiable variables. Therefore, we discuss how our probabilistic approach provides important heuristics to guide future research in this highly controversial domain that touches both parapsychology and transpersonal psychology

A macroscopic violation of no-signaling in time inequalities? How to test temporal entanglement with behavioral observables

In this paper we applied for the first time the no-signaling in time (NSIT) formalism discussed by Kofler and Brukner (2013) to investigate temporal entanglement between binary human behavioral unconscious choices at t1 with binary random outcomes at t2. NSIT consists of a set of inequalities and represents mathematical conditions for macro-realism which require only two measurements in time. The analyses of three independent experiments show a strong violation of NSIT in two out of three of them, suggesting the hypothesis of a quantum-like temporal entanglement between human choices at t1 with binary random outcomes at t2. We discuss the potentialities of using NSIT to test temporal entanglement with behavioral measures

Applying machine learning EEG signal classification to emotion related brain anticipatory activity

Machine learning approaches have been fruitfully applied to several neurophysiological signal classification problems. Considering the relevance of emotion in human cognition and behaviour, an important application of machine learning has been found in the field of emotion identification based on neurophysiological activity. Nonetheless, there is high variability in results in the literature depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight into machine learning applied to emotion identification based on electrophysiological brain activity. For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features. Furthermore, we also contrasted the performance of static and dynamic (time evolving) approaches. The best static feature-classifier combination was the SVM with spectral features (51.8%), followed by LDA with spectral features (51.4%) and kNN with temporal features (51%). The best dynamic feature‑classifier combination was the SVM with temporal features (63.8%), followed by kNN with temporal features (63.70%) and LDA with temporal features (63.68%). The results show a clear increase in classification accuracy with temporal dynamic features