Quantum-based psychophysical correlation models offer an attractive framework for predicting mind-matter interactions. We report a test of such interactions in the form of observer effects on quantum-based random number generator (QRNG) outcomes. Specifically, we tested the influence of certain motive states on related stimulus presentations chosen by the QRNG. Deviations from randomness were expected among participants exhibiting high incongruence (HI) characteristics but not among those who exhibited low incongruence (LI). Our first experiment, testing the effects of three psychological needs—attachment, self-esteem protection, and control— with a Bayesian analysis yielded anecdotal evidence for H1 only for self-esteem-related stimuli within the HI group. The second experiment was a selective continuation of the promising self-esteem protection condition, exploring the further sequential course of evidence for H1 and its oscillation over time. Our criterion for confirming H1 was initially reached in the target group. Shortly thereafter, a decline to a final result of anecdotal evidence for H1 occurred. To test the systematic trend in the data against chance fluctuations, further post hoc analyses comparing the maximum Bayes Factor, the curve’s energy, and frequency spectrum analysis between both groups to 10,000 simulations were performed. These analyses indicated that the HI subsample’s data differed significantly from chance fluctuations, whereas the LI subsample’s data did not. In sum, the results suggest that core affectively laden subconscious beliefs can manifest through volatile yet statistically detectable deviations from quantum randomness when precisely triggered by an adequate task.
Quantum-based psychophysical correlation models offer an attractive framework for predicting mind-matter interactions. We report a test of such interactions in the form of observer effects on quantum-based random number generator (QRNG) outcomes. Specifically, we tested the influence of certain motive states on related stimulus presentations chosen by the QRNG. Deviations from randomness were expected among participants exhibiting high incongruence (HI) characteristics but not among those who exhibited low incongruence (LI). Our first experiment, testing the effects of three psychological needs—attachment, self-esteem protection, and control— with a Bayesian analysis yielded anecdotal evidence for H1 only for self-esteem-related stimuli within the HI group. The second experiment was a selective continuation of the promising self-esteem protection condition, exploring the further sequential course of evidence for H1 and its oscillation over time. Our criterion for confirming H1 was initially reached in the target group. Shortly thereafter, a decline to a final result of anecdotal evidence for H1 occurred. To test the systematic trend in the data against chance fluctuations, further post hoc analyses comparing the maximum Bayes Factor, the curve’s energy, and frequency spectrum analysis between both groups to 10,000 simulations were performed. These analyses indicated that the HI subsample’s data differed significantly from chance fluctuations, whereas the LI subsample’s data did not. In sum, the results suggest that core affectively laden subconscious beliefs can manifest through volatile yet statistically detectable deviations from quantum randomness when precisely triggered by an adequate task.
A vivid discussion revolves around the role of the human mind in the quantum measurement process. While some authors argue that conscious observation is a necessary element to achieve the transition from quantum to classical states during measurement (Wigner, 1963), some go even further and propose a more active influence of the human mind on the probabilities of quantum measurement outcomes (e.g. Atmanspacher, Römer, & Walach, 2002; Penrose & Hameroff, 2011). This proposition was tested in micro-psychokinesis (micro-Pk) research were intentional observer effects on quantum random number generators (RNGs) were investigated. In the studies presented here we extended this line of research and tested the impact unconscious goals on micro-Pk. Our focus was cigarette addiction as an unconscious drive and hypothesized that regular cigarette smokers would influence the outcome of a quantum RNG that determined whether the participant was going to see a smoking-related or a neutral picture. Study 1 revealed strong evidence for micro-Pk (BF = 66.06) supporting the H1. As expected, no deviation from chance was found with non-smokers. In Study 2, a pre-registered highly powered replication attempt, failed to reproduce this result and showed strong evidence for H0 (BF = 11.07). When the data from both studies are combined a remarkable change in effect across time (resembling a combination of appearance followed by decline) can be seen only in the smoker’s subsample. Appearance-and-decline effects were absent in the non-smokers sample and in a simulation. Based on von Lucadou’s Model of Pragmatic Information we suggest that (micro-)Pk effects follow a systematic pattern comparable to a dampened harmonic oscillation. This concept may shed new light on past and future Pk research.
Objective. Testing specific mind-related effects on reality formation by investigating deviations from quantum randomness during the process of intentional observation, also known as micro-psychokinesis (micro-PK), is one empirical approach to address the mind-matter problem. While meta-analytical evidence over decades of research suggests that observers’ conscious or unconscious motivational states could produce significant micro-PK effects on quantum number generator (QRNG) outcomes, direct replication attempts often failed or the initially strong and apparently stable effects declined over time. Therefore, in addition to a direct replication strategy, alternative confirmatory approaches have been proposed that apply Higher-Level Analytical Strategies (HLASs) to test these effects existence. The present study reports a preregistered replication attempt of a micro-PK effect on QRNG outcomes documented by a specific HLAS variant, the Change of Evidence (CoE) measures, which were performed on a post-hoc basis in an original study.Method. To assess the observers’ unconscious intentional states, in a re-analysis of an existing data set we determined participants’ personality traits (PTs) on Cluster C and analyzed the relation between Cluster C outcome and trait-related sentences selected via QRNG outputs mirroring the respective typical concerns associated with the PTs vs. neutral stimuli. We predicted the presentation of more PT-related stimuli than chance expectation among participants exhibiting high PT assessment scores (PT-high) but not among individuals who exhibited low levels of these characteristics (PT-low). Although in line with this prediction initially strong Bayesian evidence for H1 was found within the PT-high group, this effect unexpectedly declined upon further data collection to indecisive evidence. Three post-hoc CoE tests were then performed comparing the empirical data to a randomly generated set of 10,000 simulations to distinguish a false positive finding i.e., an unsystematic, random Bayesian evidence variation, from a true positive effect that later declined i.e., from a systematic one. These exploratory post-hoc re-analyses of an existing data set rejected the null hypotheses in all CoE tests performed. These findings were then tested for replicability in a preregistered replication study which will be reported here.Results. The replication attempt failed. No Bayesian evidence for micro-PK was found and none of the CoE analyses could reject the null hypothesis in the PT-high group. As expected, no effects were found in the PT-low group either.Conclusion. The findings of the herein presented analyses go along with the results of earlier studies reporting similar experimental approaches which initially revealed strong and supposedly robust evidence for the postulated effects in non-preregistered original experiments but failed in a direct replication. Most central, the present findings indicate, that even CoE measures following the idea of HLASs to circumvent the non-replicability problem cannot provide replicable higher order micro-PK effects. The results obtained are discussed with regard to the Model of Pragmatic Information (MPI).
Intentional effects of human observerson the output of quantum-based random number generators (tRNG) have been studied for decades now. This research has been known as micro-psychokinesis (micro-PK) and many studies in the field reported evidence for mentally induced non-random deviations from chance. A most recent meta-analysis from Bösch, et al. (2006) revealed a very small and heterogeneous overall effect size that indicated a significant deviation from chance across studies. There remains doubt among the scientific community on the existence of micro-PK given: (i) the small and heterogenous effect; and (ii) the fact that several independent replication attempts of prominent studies failed to confirm the original results. The study presented here was intended to provide decisive evidence for or against the existence of micro-PK. An online experiment with 12,571 participants was conducted. The Bayesian analysis revealed strong evidence for H0 (BF01= 10.07). Thus, micro-PK did not exist in the data. A closer inspection of the temporal change of the effect seemed to suggest a non-random oscillative structure with a higher frequency than observed in simulated data. The possible role of entropy and the relation to the model of pragmatic information from von Lucadou (2015) is discussed.
According to standard quantum theory, the occurrence of a specific outcome during a quantum measurement is completely random (see Bell 1964). However, some authors refer to revised versions of quantum mechanics (e.g., Penrose & Hameroff 2011, Mensky 2013, Stapp 2017), and propose that the human mind can actually influence the probability of such outcomes. Empirical support for this idea has been provided by micro-psychokinesis (micro-PK) research (see Bösch, Steinkamp, & Boller 2006). However, attempts to replicate these findings have often failed (e.g. Jahn et al. 2000). In an attempt to explain these failures, von Lucadou, Römer, and Walach (2007) established a theoretical model for predicting unsystematic variations of such an influencing effect across replications, resulting in a decline of the effect in micro-PK data over time. Maier, Dechamps, and Pflitsch (2018) slightly expanded this theory by proposing that the temporal variation of such an effect follows a systematic pattern, which can be tested and used for prediction making. In this research we generated such a prediction using data from two previous studies that initially demonstrated a strong micro-PK followed by a subsequent decline in the effect over the course of 297 participants (Maier & Dechamps 2018), we then put it to test with an additionally preregistered set of recollected data from 203 subjects. We compared these results with 10,000 simulated data sets (each set with an N = 203) each comprising random data. Three tests were applied to the experimental data: an area under the curve analysis, a local maximum fit test, and an endpoint fit test. These tests revealed no significant fit of the real data regarding the predicted data pattern. Further analyses explored additional techniques, including an analysis of the highest reached Bayes Factor (BF) over the course of the experiments, the overall orientation of the BF curve, and its transformation into oscillatory components via a Fourier analysis. All these methods allowed for statistically significant differentiations between experimental data on the one side, and the control group and simulation data on the other. We conclude that the analyses of the temporal development of an effect along these lines constitute a fruitful approach toward testing non-random and volatile time trends within micro-PK data.
According to standard quantum theory, the occurrence of a specific outcome during a quantum measurement is completely random (see Bell 1964). However, some authors refer to revised versions of quantum mechanics (e.g., Penrose & Hameroff 2011, Mensky 2013, Stapp 2017), and propose that the human mind can actually influence the probability of such outcomes. Empirical support for this idea has been provided by micro-psychokinesis (micro-PK) research (see Bösch, Steinkamp, & Boller 2006). However, attempts to replicate these findings have often failed (e.g. Jahn et al. 2000). In an attempt to explain these failures, von Lucadou, Römer, and Walach (2007) established a theoretical model for predicting unsystematic variations of such an influencing effect across replications, resulting in a decline of the effect in micro-PK data over time. Maier, Dechamps, and Pflitsch (2018) slightly expanded this theory by proposing that the temporal variation of such an effect follows a systematic pattern, which can be tested and used for prediction making. In this research we generated such a prediction using data from two previous studies that initially demonstrated a strong micro-PK followed by a subsequent decline in the effect over the course of 297 participants (Maier & Dechamps 2018), we then put it to test with an additionally preregistered set of recollected data from 203 subjects. We compared these results with 10,000 simulated data sets (each set with an N = 203) each comprising random data. Three tests were applied to the experimental data: an area under the curve analysis, a local maximum fit test, and an endpoint fit test. These tests revealed no significant fit of the real data regarding the predicted data pattern. Further analyses explored additional techniques, including an analysis of the highest reached Bayes Factor (BF) over the course of the experiments, the overall orientation of the BF curve, and its transformation into oscillatory components via a Fourier analysis. All these methods allowed for statistically significant differentiations between experimental data on the one side, and the control group and simulation data on the other. We conclude that the analyses of the temporal development of an effect along these lines constitute a fruitful approach toward testing non-random and volatile time trends within micro-PK data.
Quantum mechanics (QM) proposes that a quantum system measurement does not register a pre-existing reality but rather establishes reality from the superposition of potential states. Measurement reduces the quantum state according to a probability function, the Born rule, realizing one of the potential states. Consequently, a classical reality is observed. The strict randomness of the measurement outcome is well-documented (and theoretically predicted) and implies a strict indeterminacy in the physical world’s fundamental constituents. Wolfgang Pauli, with Carl Gustav Jung, extended the QM framework to measurement outcomes that are meaningfully related to human observers, providing a psychophysical theory of quantum state reductions. The Pauli-Jung model (PJM) proposes the existence of observer influences on quantum measurement outcomes rooted in the observer’s unconscious mind. The correlations between quantum state reductions and (un)conscious states of observers derived from the PJM and its mathematical reformulation within the model of pragmatic information (MPI) were empirically tested. In all studies, a subliminal priming paradigm was used to induce a biased likelihood for specific quantum measurement outcomes (i.e., a higher probability of positive picture presentations; Studies 1 and 2) or more pronounced oscillations of the evidence than expected by chance for such an effect (Studies 3 and 4). The replicability of these effects was also tested. Although Study 1 found strong initial evidence for such effects, later replications (Studies 2 to 4) showed no deviations from the Born rule. The results thus align with standard QM, arguing against the incompleteness of standard QM in psychophysical settings like those established in the studies. However, although no positive evidence exists for the PJM and the MPI, the data do not entirely falsify the model’s validity.