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Intentional Observer Effects on Quantum Randomness: A Bayesian Analysis Reveals Evidence Against Micro-Psychokinesis

Markus a maier, moritz c dechamps, markus pflitsch.

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Edited by: Mark Nieuwenstein, University of Groningen, Netherlands

Reviewed by: Jacob Jolij, University of Groningen, Netherlands; Fei Luo, Institute of Psychology (CAS), China

*Correspondence: Markus A. Maier, [email protected]

This article was submitted to Cognition, a section of the journal Frontiers in Psychology

Received 2017 Nov 22; Accepted 2018 Mar 8; Collection date 2018.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Intentional effects of human observation on 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 H 0 (BF 01 = 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.

Keywords: quantum observation, micro-psychokinesis, random number generator, RNG, model of pragmatic information

Introduction

From time immemorial, humans have been fascinated by the relationship between the spiritual and physical worlds. Speculation focuses on both the mind and body/brain connection and the relationship between the mind and the outside physical world. Specifically, the idea that the mind can materialize ideas/desires or modify aspects of physical reality has been widely addressed in religion, mythology, and philosophy. Descartes, the great philosopher, mathematician, and devoted gambler, wrote about his personal experience with mind and reality during the 1640s. He observed that an otherwise random gambling outcome could be positively influenced by a gambler with a happy and optimistic mood (cited in Davidenko, 1990 , p. 306). It was another 300 years before empirical examination of mind-matter interactions occurred. In the 1940s Rhine (1944) explored mind effects on dice tosses. Since then, there have been a large number of studies that examined mental influence or mentally induced statistical variations on inanimate probabilistic systems such as tumbling dice, tossing coins, or random number generators (RNGs). This research is now incorporated in work that is termed “micro-psychokinesis” (micro-PK) research ( Varvoglis and Bancel, 2015 ).

The use of quantum-based RNGs, so-called true random number generators (tRNGs), as an optimal source of randomness has become the standard in micro-PK research (e.g., Jahn et al., 1980 ). In meta-analyses of 100s of studies performed using tRNGs, a small but significant effect of the human mind on non-random deviations from chance was found ( Bösch et al., 2006 ; see also Radin and Nelson, 1989 , 2003 ). Despite this, micro-PK is not a generally accepted phenomenon in science. This is because meta-analysis has several flaws, including the ability to be biased by the inclusion of successful studies. In addition, high-powered attempts to replicate positive micro-PK tests have not been successful (e.g., Jahn et al., 2000 ; Maier and Dechamps, in press ). The goal of this study was to conduct a decisive scientific test for micro-PK. A large-scale assessment was performed that used Bayesian techniques to consolidate data until clear evidence for or against the existence of micro-PK was found.

Micro-PK research using tRNGs began in the 1960s with researchers using quantum states as a source of true randomness. Over the following decades, the body of research data increased (e.g., Schmidt, 1970 ; Jahn et al., 1980 , 1987 ). A meta-analysis by Radin and Nelson (1989) , including 597 studies conducted up until 1987, found a strong effect supporting micro-PK. This result was confirmed 15 years later in a meta-analysis with additional 176 new studies ( Radin and Nelson, 2003 ). However, these meta-analyses included studies using both tRNGs and poorer-quality algorithmically-based RNGs. A more recent meta-analysis by Bösch et al. (2006) only included studies using tRNGs. This analysis of 380 studies undertaken between 1961 and 2004 identified a very small and heterogeneous effect that indicated a significant deviation from chance ( Bösch et al., 2006 ). A significant negative correlation between sample size and effect size was also found ( Bösch et al., 2006 ). Given the small, heterogeneous effect and this correlation, the authors concluded that the observed effect might have been caused by publication bias ( Bösch et al., 2006 ); other researchers have questioned this interpretation ( Radin et al., 2006 ) and a deeper inspection of the Radin and Nelson (1989 , 2003 ) meta-analyses confirms that these aspects do not apply to their data. Nevertheless many scientists agree that evidence derived from meta-analyses alone does not provide a convincing argument for the existence of micro-PK effects. In addition, meta-analysis methods have recently been criticized, especially with regard to the impact of heterogeneity (e.g., Ioannidis, 2016 ). This has led to the suggestion that “ a single high-quality, well-reported study can be recommended instead of a statistical synthesis of heterogeneous studies ” ( Brugha et al., 2012 , p. 450). A similar suggestion was made by van Elk et al. (2015) .

However, high-quality studies aimed at replicating existing results are scarce in micro-PK research. One example is the Jahn et al. (2000) study that utilized research teams from the PEARlab at Princeton University, the Grenzgebiete der Psychologie und Psychohygiene at Freiburg, and the Center for Behavioral Medicine at the Justus Liebig University Giessen. They attempted to replicate the Jahn et al. (1987) benchmark study involving 97 subjects and data from 2.5 million micro-PK trials. The attempted replication, with 227 participants and over 2 million trials, failed to confirm the original results ( Jahn et al., 2000 ). Another is the Maier and Dechamps (in press) micro-PK research that reported on two micro-PK studies using Bayesian methods. The authors reported strong evidence supporting micro-PK in Study 1 (BF 10 = 66.7). However, in Study 2, a pre-registered, high-quality replication of Study 1, they found strong evidence for the null effect (BF 01 = 11.07). Failure of these high-powered studies to replicate earlier results also raised doubts about the existence of micro-PK. To date, robust and convincing evidence for micro-PK is missing. One potential factor explaining some of the replication failures might lie in the way intentional observation was manipulated. So far, to the greater part explicit goal manipulation procedures have been used in micro-PK research. To our view, this might be less fruitful than a more subtle implicit manipulation. We will outline this in the following paragraphs.

In parallel with empirical efforts, theoretical models have been proposed to explain and predict the effect of the human mind on quantum-based outcomes. Orthodox quantum physics regards the randomness that occurs during the act of measuring a quantum system as ontic and inherent in nature (see Greenstein and Zajonc, 2006 ). For example, the location of one electron is not classically defined before measurement but needs to be described as a superposition of several simultaneous locations. This phenomenon is captured by the mathematical concept of a “wave function” ( Schrödinger, 1935 ). After measurement, an electron is found in one specific location with a probability given by the square of the amplitude of the wave function ( Born, 1926 ). Thus, the results of a quantum measurement are only predictable with likelihoods, never with certainty. In addition, no measurement or observation can influence the probabilities to produce deviations from randomness. Although this is the standard interpretation of quantum mechanics, some revised versions of quantum theory have recently been developed that allow for mental processes during observation to slightly influence the likelihood of an outcome of a quantum process.

Atmanspacher et al. (2002) presented the Generalized Quantum Theory (GQT) (see also Römer, 2004 ; Filk and Römer, 2011 ; Atmanspacher and Filk, 2012 ). Here, a measurement in a quantum experiment is considered an observation when knowledge transfer takes place (epistemic split). This epistemic split occurs when unknown potential quantum alternatives are transferred into conscious knowledge. The knowledge transfer can be shaped by the observer’s mind set, for example, his or her intentions. Observer effects are thus described as entanglement correlations between the intentional observer and the observed system ( von Lucadou and Römer, 2007 ). Consequently, non-random deviations from quantum probabilities are allowed. This theory also proposes that such deviations should decline shortly after their first detection. The reason for this is that deviations from randomness constitute a severe violation of the “no signal theorem” in quantum mechanics. To utilize this as evidence-based documentation is forbidden. This leads to the disappearance of the micro-PK effect in later replication attempts. Thus, on the macroscopic level, the no signal theorem is saved ( von Lucadou, 2006 , 2015 ); in other words, if a signal transfer occurs, it cannot be used intentionally because its appearance and disappearance vary unsystematically. Maier and Dechamps (in press) also suggested that a micro-PK effect changes over time and argued by referring to the entropy principle that such effects might behave like dampened harmonic oscillations, reflecting the interplay between the quantum-PK effect and its counter-mechanism ‘entropy.’

Another theory is the OrchOR model proposed by Penrose and Hameroff (2011 ; see also Penrose, 1989 , 1994 ; Hameroff and Penrose, 1996 ; Hameroff, 2012 ). Similar to Atmanspacher et al. (2002) , they view the act of measurement as a transfer from unconscious knowledge about the nature of a quantum state into a conscious experience of its exact existence. This transfer occurs through quantum gravitation and can be affected by Platonic values directly related to gravity. These values amongst others include mental concepts ( Hameroff and Chopra, 2012 ). Thus, intentional observers might be able to non-randomly influence the transition of potential quantum states into one specific classical state. Both approaches have in common that the mental effect on quantum randomness takes place before the transition from the unconscious quantum to the conscious classical state. Micro-PK should thus be mainly affected by unconscious-related states of mind of the observer ( Maier and Dechamps, in press ).

Given this premise, in our study we put participants who observed the outcomes of a quantum experiment in a mindset that was related to specific unconscious inner states. Participants were presented with a brief relaxation and optimism inducing meditative episode before they participated in the experiment. This was designed to stimulate an inner, deeply rooted belief that everything was good and will remain good. On an unconscious level, this should attract more positive outcomes than negative ones during randomly chosen stimulus presentations in a micro-PK experiment with a tRNG. The stimuli selected by a quantum based RNG at each trial consisted of positive and negative pictures and auditory stimuli. Overall, we expected that the average mean score for positive stimuli presentations should be above chance (50%). No decline or harmonic oscillation effects were expected at the beginning of the study and were thus not the focus of our predictions. They are explored in the additional analyses of the result section. The main goal of our research was to provide a decisive, high-quality test for micro-PK. We therefore decided to apply a Bayesian testing approach. This method allows for data accumulation until a stopping criterion (i.e., a pre-specified amount of evidence) has been reached. As effects expected from micro-PK would be small, it was clear we would need a sample size in the 1000s.

Materials and Methods

Participant recruitment and data collection were organized by Norstat, Germany 1 , a professional data collection company specializing in online polling and testing with access to 650,000 potential participants in 18 European countries. The participant pool consists of pre-profiled volunteer adults and undergoes constant quality control.

Participants

We decided to work with participant pools from three countries, resulting in a sample highly representative of the European population (see Table 1 ).

Overview of the sample pools in Norstat.

An invitation to participate in the study was sent from Norstat to a random selection of participants daily, aiming for a completion rate of about 100 per day. About 20–25% of invited individuals completed the study.

Ethics Considerations

The experiment was approved by the ethics boards of Norstat and the Department of Psychology (LMU). Norstat obtained written consent from participants electronically by having them press an “accept” button 2 . Within the consent form, participants were informed in general terms about the study and advised that participation was voluntary. Participants could also withdraw at any point during the study. All data were coded, stored, and analyzed anonymously.

Data Collection

The final sample size was not predefined. Instead an accumulative data collection and analysis strategy using Bayesian inference techniques for hypotheses testing was used (see Wagenmakers et al., 2011 ). This approach allows for data accumulation (i.e., additional subjects can be tested and results added into the data set) until a specified Bayes factor (BF) for H 1 (or H 0 ) has been reached. It also allows an option to stop data collection at a predetermined BF, so it is a more effective way of hypothesis testing than frequentist inference methods. We used BF = 10 as a stopping point for evidence collection of both an effect and a null effect.

The Bayesian approach provides information on how to update our beliefs given new incoming data. Bayesian methods accumulate data concerning the effect in question and repeatedly update the likelihood for an effect given additional data. The strength of evidence for the effect is considered dependent on both the likelihood of the data given that H 0 is true as well as the likelihood of the data given H 1 is true. Those two likelihoods are pit against each other leading to the so-called BF. The BF describes the relative amount of evidence that the data provide for or against a postulated effect. In this way, the existence (H 1 ) and the non-existence (H 0 ) of an effect can be tested. A BF of 10 or higher is considered to indicate strong evidence for H 1 or H 0 , respectively. For instance, a BF 10 = 10 means that the H 1 is 10-times more likely to be true than the H 0 .

To calculate the BF, a probability distribution for effect size that is centered around zero with scale parameter r needs to be specified a priori . This Cauchy distribution (δ ∼ Cauchy [0, r]) identifies the prior, i.e., the likelihood of the data given there is an effect, p(data|H 1 ). Wagenmakers et al. (2011) recommend an r equal to 1. The statistic software JASP designed to perform basic Bayesian analyses uses a default r of 0.707. Other authors recommend a lower r of 0.5 ( Bem et al., 2011 ) or of 0.1 ( Maier et al., 2014 ) knowing that PSI effect sizes are usually very small (i.e., mostly in the range of 0.1 to 0.2). The choice of the prior provides a degree of freedom within the Bayesian approach. For data analysis in the studies presented here we decided to use a r of 0.1, i.e., δ ∼ Cauchy (0, 0.1). This parameter was selected before data collection had been started.

We also decided in advance to analyze the data with a Bayesian one sample t -test using a one-tailed approach given our directed prediction. On a regular basis, almost every week, a one-sample t -test was performed testing the actual sample’s mean score of positive stimuli presentations against chance (50%). For all Bayesian analyses, the statistical software tool JASP (Version 0.8.2, JASP Team, 2017 ) was used. This was repeated over several months from November 2016 to July 2017 until the stopping criterion was met.

Final Sample

When the criterion to stop was satisfied (BF > 10), a total of 12,571 participants had been tested from three different countries. Due to a technical difficulty and some participants quitting the survey immediately after completing the study, demographic data was only available for 11,158 of the participants. Mean age of the final sample was 48.73 ( SD = 13.60; range from 16 to 90) with 5,617 females (50.3%) and 5,541 males (49.7%). Table 2 provides more demographic details of the participant sample.

Overview of the participant sample.

A survey was created that included a link to the study materials on our webserver.

Experimental Program

The study was constructed as an online experiment. This means participants were not tested in a laboratory but could participate from any computer with internet access and audio output. The experiment was displayed in the computer’s browser in full-screen mode. It was implemented with jsPsych ( de Leeuw, 2015 3 ), a JavasScript library for creating and running behavioral experiments in a web browser and ran on a dedicated webserver in the university’s computer center. The Quantis tRNG, used as random source for stimulus selection, was located in the same room and connected to the designated server via USB.

Visual stimulus material was obtained from Shutterstock 4 , a provider of royalty-free stock photos. Out of the library of around 125 million photographs, 100 pictures reflecting a positive prevailing mood and 100 pictures reflecting a negative one were selected. Positive picture material consisted of photos showing aspects of social belonging and affiliation, landscape shots, and pictures of cute animals. The negative material was selected to evoke displeasure within the participants; this was accomplished through pictures depicting imminent danger (e.g., attacking predators or weapons directed at the viewer, imagery provoking distress or misery, or pitiful and nauseating images). Picture selection was performed by the first and the second author based on their experience in emotion induction.

In order to intensify the mediated affect, a multisensory approach was used and audio stimuli were presented in addition to the images. The positive and negative impacts were conveyed by consonant and dissonant chords respectively. These piano chords consisted of tones that either harmonize well (i.e., produce a harmonious and melodious experience) or tones that harmonize poorly (i.e., form sharp dissonances, which are usually perceived as unpleasant). A total of eight consonant and eight dissonant chords were generated out of which two positive and two negative ones were selected by the experimenters for the study.

Generation of Quantum Randomness

For the purpose of random number generation, a quantum number generator (Quantis-v10.10.08) developed by the company idquantique from Geneva, Switzerland was used on the webserver 5 . This apparatus produces quantum states by using photons that are sent through a semi-conductive mirror-like prism. Each photon has an equal chance of being deflected in one of two directions, producing a superposition of both states, until a measurement is performed. Upon measurement, the photon is found on either route with 50% probability which is then transformed into a numerical score such as 0 or 1, depending on the track it was found (technically Quantis transforms 8 such bits into 1 Byte). This procedure is thus a reenactment of the famous double-slit experiment known in quantum physics testing the wave-particle duality. The hardware passed validation tests of randomness, such as the DIEHARD and the NIST tests (see certificates from various independent agencies on the website), and is regarded as one of the most effective tRNG worldwide ( Turiel, 2007 ). The tRNG was connected to the server via USB. Since it operates without a buffer, it was ensured that the bit responsible for the selection of the stimuli was created directly before the presentation. A user monitoring code made sure that different participants did not access the tRNG at the exact same time but that everybody receives an individual bit.

Participants received an email from the data collection company inviting them to take part in a survey. They were asked to relocate, if necessary, to an undisturbed environment. The participants’ audio was tested by playing a short audio clip and asking them for its content. If they answered correctly, they were forwarded to the university’s webserver, where the experiment was displayed in full-screen mode. Participants were asked to close their eyes and listen to a pre-recorded relaxation exercise designed to put them in a relaxed and optimistic mood. The exercise was repeated once with a total playing time of about 2 min 6 . It was available in German, Italian, and Spanish and spoken by a native speaker. The text of the relaxation exercise was:

Leave all your thoughts and worries behind you. Breath slowly and calmly. Focus only on your breathing. Slowly and calmly… Slowly and calmly… slowly and calmly… slowly and calmly. You are feeling completely peaceful and relaxed and fully in the present. Release all your tension. Relax your muscles. You are feeling comfortable and safe! Completely comfortable and safe.

Following the relaxation exercise, participants were advised about the study. They were told that they would be presented with pleasant and unpleasant images and sounds and that they could abort the experiment at any time by closing the window. Presentation of stimuli began after these instructions.

During each trial, the tRNG chose a random number between 1 and 100 to decide which visual and auditory stimuli would be displayed, then a random number between 0 and 1 to determine whether the stimuli would be positive or negative. During this process, a fixation cross was shown to the participant for 700 ms. The stimuli (picture and sound) were presented for 400 ms. Before the next trial, a black screen was shown for 1100 ms. This process is illustrated in Figure 1 . A total of 100 trials were performed on each participant, which took approximately 6 mins.

Stimulus selections and presentation times during one trial.

After completing the task, participants were asked to fill out a short questionnaire. With regards to our relaxation induction, we asked participants to indicate their belief toward a general contentedness and hopeful confidence by asking them to rate the following statement: “I am strongly convinced that everything is going to be fine” on a seven-point scale from Not true to Very True .

Subsequently stimulus seeking was assessed with a scale constructed by Bem et al. (2011) that contained two statements: “I am easily bored” and “I often enjoy seeing movies I’ve seen before” (reverse scored). Responses were recorded on five-point scales that ranged from Very Untrue to Very True and averaged into a single score ranging from 1 to 5 (Cronbach’s α = 0.59).

Furthermore, we constructed a self-efficacy attitude measure related to general life outcome expectancies. This scale comprised the following six statements: “In life, you don’t get anything for free,” “You have to fight for everything,” “Life generally doesn’t mean well for me,” “You have to take stick a lot if you want to succeed,” “Nothing is going to change,” and “When it rains, it pours.” Responses were recorded on a seven-point scale from Not true to Very true and averaged into a single score. The scale provides a good reliability (Cronbach’s α = 0.80).

Lastly we asked participants to fill out the Life Orientation Test-Revised (LOT-R; Scheier et al., 1994 ). This questionnaire assesses generalized optimism (Cronbach’s α = 0.76) and pessimism (Cronbach’s α = 0.73) with three items each.

To explore the effectiveness of our relaxation manipulation, we first analyzed the single item measure “I am strongly convinced that everything is going to be fine.” Our hypothesis was that the sample’s mean score of this conviction was above the average. A one sample t -test (two-tailed) testing the item’s mean score against 4, which was the exact midpoint of a seven-point scale (ranging from not at all to very much), yielded a significant effect, t (11157) = 67.05; p < 0.001. On average the mean rating was significantly above the midpoint of the scale, M = 4.96 ( SD = 1.51).

Next the observer effects on picture selection will be reported. The data were analyzed on average every week by the experimenter, the second author, depending on the number of participants tested during the preceding days (see Bayesian approach described above).

The study tested the hypothesis that after being exposed to a relaxing and optimism inducing intervention, participants will, on average, observe more positive stimuli than expected by chance. The dependent variable was the percentage of positive stimuli achieved by each participant across 100 trials. The average percentage of positive stimuli for all participants was then tested against the 50% score. The final Bayesian one sample t -test (one-tailed) with 12,571 participants revealed a BF 01 of 10.07 for H 0 . The mean score for positive stimuli for all participants was M = 50.02%, SD = 5.06, providing very strong evidence for a null effect, with no deviation from chance. Figure 2 represents a sequential analysis of the BF across all participants in the order of testing.

Sequential Bayesian one-sample t -test analysis of the percentage of positive pictures across all 12,529 participants.

A standard practice in micro-PK research is to display the effect and its change over the time of data collection as a cumulative z-score. This data sequence is shown in Figure 3 .

Sequential analysis of data by computing a cumulative z-score from the percentage of positive stimuli obtained after each participant.

As depicted in the graph, the effect went in the predicted direction almost entirely throughout the experiment and several times hit the 1.96 z-score line but then finally dropped to zero. An identical variation in effect can be seen in the BF sequential analysis.

In an additional set of analyses we also explored the relationship between the personality variables assessed from 11158 participants and the mean number of positive stimuli obtained from each individual. No significant correlations between number of positive stimuli and general life outcome expectancies (GLOE6), generalized optimism (LOT_Opt) and pessimism (LOT_Pess) or Stimulus Seeking was found (see Table 3 ).

Bayesian Pearson correlations between number of positive stimuli and personality variables.

The results of our study provide strong evidence for H 0 , indicating no deviation of the mean number of positive stimuli from chance in our sample. Relaxed and optimistically induced participants who passively observed the pictures and auditory stimuli, chosen at each trial by a highly sophisticated and effectively working quantum RNG, seemed not to unconsciously affect the quantum process toward non-randomness. The data support the null hypothesis that predicted no mental effects on quantum randomness. A BF higher than 10 also underlines the robustness of this effect. In sum, the evidence speaks against the revised quantum models of Atmanspacher et al. (2002) and Penrose and Hameroff (2011) which postulate non-random deviations of quantum outcomes by deeply rooted mental activity. The data rather support original quantum theoretical interpretations from Bohr, Bohm, Wigner, and von Neumann that claim the observer has no active influence on the probabilities of quantum experimental outcomes (see Greenstein and Zajonc, 2006 ; Byrne, 2010 ). Although there are many possibilities as to why an effect may not have occurred in this experiment (e.g., failure of the induction method, distractions on behalf of the subjects during performance, or low quality of stimuli used), we assume that these and other factors would only increase error variance. Even then, the power of several 1000 subjects should be sufficient to detect an effect given a high error variance. The study was designed to overcome these limitations with an enormous power and to provide a sincere test of the proposed micro-psychokinetic mind-matter interaction. We thus believe that the null effect documented here might very well-constitute a real absence of a mental influence on quantum randomness at least at the level of the average mean score for positive stimuli. This would fit with earlier skeptical arguments raised by Bösch et al. (2006) against micro-psychokinesis reported in meta-analyses who suggest that those effects might be due to publication biases. In addition, no moderating effects of personality traits were found.

Additional Analysis

The sequential Bayesian analysis and the z-score accumulation across participants also allowed for a closer inspection of statistical trends in the data. Interestingly, there seems to be a pattern of repeated change. The micro-PK effect appears to be alternatingly increasing and decreasing several times during the data collection period. This fact is noteworthy, since a similar observation has recently been made in comparable micro-psychokinesis studies investigating uninstructed goal-dependent observation effects on tRNG’s outputs ( Maier and Dechamps, in press ). In this research, two studies have been performed testing the hypothesis that smokers would cause a substantial deviation from chance on the mean number of cigarette-related pictures being presented. Without going into substantial detail, the general outcome of the studies was that the effect increased to an overall BF of above 100 until the beginning of Study 2; subsequently, there was a reversion of the effect back to a BF of 1 at the end of Study 2. This appearance-disappearance-pattern was not present in the non-smokers data nor in a simulation where no observers were present. In the latter two cases, no effect was there at any time. In addition, other research groups who extensively studied micro-psychokinesis also report such a decline of the effect during replication attempts ( Jahn et al., 2000 ). Reports of similar decline effects in other studies complete this picture (see Radin, 2006 ).

Given these unexpected, yet broadly manifesting appearance-disappearance-patterns, theoretical efforts have been undertaken to understand such decline effects in micro-PK. One model that tried to understand the nature of this empirical phenomenon was proposed by von Lucadou (2006 , 2015 ). His theory refers to the idea of Pragmatic Information and applies it to observer-related quantum effects. According to this theory, the novelty of a finding is complementary related to its likelihood of confirmation (i.e., the more novel a quantum effect, the lower the likelihood of a successful replication). The supposed principle behind this mutual relation is that quantum effects, such as micro-PK, violate the “no-signal theorem.” To cure this violation, the later confirmation of this effect needs to be prevented such that the macroscopic evidence vanishes when additional data are collected. Empirically, this should lead during proceeding observation to a decline of the effect after initial appearance. According to von Lucadou (2015) , rather, the effect might unsystematically re-appear on other indicators that were not initially studied. Any effects are thus unsystematically hidden within the additional data acquisition.

As Maier and Dechamps (in press) emphasize, “ the theoretical problem with this approach however is that real null effects documented by replication failures of spurious findings cannot be distinguished from decline effects. The consequence is that with the standard scientific replication approach micro-psychokinesis effects cannot be scientifically studied. Either way, this would mean we should abandon PSI research from science (for a similar argument see Etzold, 2004 ) (p. 32).” To solve this dilemma, Maier and Dechamps (in press) adjusted von Lucadou’s model arguing that a violation of the no-signal theorem in quantum physics constitutes a severe violation of the Second Law of Thermodynamics which states that entropy needs to increase over time. The consequence would be that a mentally induced deviation from quantum randomness causes entropy to set in and to counteract this trend. The weaker the quantum effect becomes by this intervention, the quicker the entropic counter-process decreases. This would allow the deviation effect to re-establish itself although with a lowered effect size than initially shown. The authors propose that this interplay continues until the quantum effect has completely vanished. The decline is thus proposed not to be unsystematically drifting toward other indicators but rather follows a systematic pattern of alternations within the same indicator best described as dampened harmonic oscillation of this type:

With y indicating the effect (e.g., the accumulative z-score) and t representing the additional data collected. The meaning of the parameters for the proposed function can be found in Table 4 .

Parameter description of the dampened harmonic oscillation.

We propose that the data presented in this study here also follow a similar systematic pattern of decline matching a dampened harmonic oscillation function as suggested by Maier and Dechamps (in press) . In the following, we estimated the parameters of the mathematical function shown above for the human data reported here and compared the estimation found with a function derived from simulated data. The simulation was performed with the same experimental design, apparati, and procedures but without human observation. It contains data from 12,571 simulated participants.

Parameter Estimation for Human Data

Parameter estimation was performed with curve-fitting algorithms provided by the mathematical software tool Wolfram Mathematica Version 11.1.1.0 7 . The mathematical equation mentioned above, reflecting the dampened harmonic oscillation, was provided to the software. The program went through several reiterations, until according to the Maximum Likelihood principle, the group of estimated parameters best fit the empirical data pattern. The approximated function found for the data reported in this study here was:

The minimal mean error variance obtained was 0.14 and constitutes the best fit to the data. Any other solution produced a higher error variance. A graphical display of this function together with the empirical effects described as cumulative z-score is displayed in Figure 4 .

Approximated harmonic oscillation function for the data from all 12,529 participants.

As can be seen, the course of the effect across time approximately follows an alternating pattern, similar to a dampened harmonic oscillation (see red line).

Parameter Estimation for the Simulation

A simulation was performed to create a control data set for experimental trials without any form of observation. 12,571 data sets were created by running the experimental sessions without any observer being present. Procedure, apparatus, and experimental design were the same as in the human observation condition.

The simulated data were submitted to a Bayesian analysis testing the difference of the average mean score of positive stimuli against chance. A one-sample Bayesian t -test was performed revealing a mean score of positive stimuli of M = 50.00% ( SD = 4.99) with a BF 01 of 13.78, indicating strong evidence for the H 0 . The sequential analysis can be seen in Figure 5 .

Sequential Bayesian one-sample t -test analysis of the percentage of positive pictures obtained by the simulation.

In addition, the same parameter estimation for the dampened harmonic oscillation equation reported above was also performed with the simulated data. The z-transformed accumulated data were submitted to the parameter estimation again with curve-fitting algorithms provided by the mathematical software tool Wolfram Mathematica Version 11.1.1.0 8 . The approximated function found for the simulation was:

The minimal mean error variance obtained was 0.09. A graphical display of this function together with the cumulative empirical z-scores can be seen in Figure 6 .

As can be seen from the graph, simulated data can be approximated by a dampened harmonic oscillation function. This is no surprise, since real random effects should initially alternate and with further accumulation asymptotically drift to the zero line. However, in this case, the likelihood for substantial further alternations strongly decreases with additional data generation.

Comparing Human and Simulated Data

The human and the simulated data should – if the harmonic oscillation assumption is true – differ mainly in the frequency parameter ω. Real effects should produce more pronounced oscillations than artificial data. To explore this, we compared the 95%-confidence intervals for both frequency scores and found indeed that they did not overlap. The frequency score ω obtained with the human data was ω = 0.0018058 with a 95%-confidence interval ranging from [0.00179767; 0.00181392] and the frequency for the simulated data was ω = 0.000763727 with a 95%-confidence interval ranging from [0.000755895; 0.000771559]. Thus, oscillations are much more frequent in the human data than in the simulated control data. This difference is also illustrated in Figures 7A,B . This graph only reflects the frequency score when all the other parameters are held constant.

Frequency of the oscillation for real (A) and simulated (B) participants.

The raw data of the results presented in this manuscript (excluding the personality variables) can be found under: https://open-data.spr.ac.uk/dataset/role-conscious-observation-quantum-randomness-dataset .

In the additional analyses, specific trends in the micro-PK data of our study were investigated. Based on the original explanation for decline effects in PSI, data first presented by von Lucadou (2006 , 2015 ) who assumed a complementary relation between novelty of an effect and its confirmation, Maier and Dechamps (in press) in a re-analysis of their original micro-PK effects proposed that such effects should decline in a systematic way through the interplay between PK-effect and entropy. Specifically, the time course of micro-PK effects should closely match a dampened harmonic oscillation. The pattern of human-related micro-PK oscillations should be different from data obtained without observation. This proposition was tested with the data obtained in the study here. Interestingly, as predicted, the oscillating pattern was different for human as compared to simulated data. The frequency score thus appears to be a good indicator for micro-PK and – assuming that the postulated systematic decline mechanism is true – might be a much better indicator for non-random deviations than the overall mean score obtained in any micro-PK experiment. Future research should focus on systematic decline effects of this nature rather than on normative deviations from chance. Admittedly, at the beginning of this study, this hypothesis and the theoretical background did not exist. It was developed at the end of the data collection from Maier and Dechamps (in press) and applied to the data collected and described here on a post hoc basis only. However, we think that it provides a good basis for future research not only on micro-PK but on PSI in general. For now, it is not considered as evidence for micro-PK in the present data. Rather, the goal here was to inform the community about this promising development.

An alternative explanation for this null effect or for the oscillating pattern might also be found in experimenter effects on micro-PK that are specifically tied to the Bayesian approach. The Bayesian sequential analysis demands a continuous monitoring of effect changes due to its stopping rule. The experimenter who is repeatedly watching the updated mean score might develop drifts in his beliefs and by doing so incidentally causes effect changes across time. Varvoglis and Bancel (2015) discuss such experimenter effects in PK research in which the experimenters are considered hidden participants. We are not sure whether this would fully explain the non-existence of the effect or its oscillation, but in future research an “experimentally and theoretically blind” data analyst or an automatic analysis procedure that simply indicates when the stopping criterion is met could be used.

This study was introduced as a high quality and decisive test for micro-PK. Although several meta-analyses found evidence for micro-PK ( Radin and Nelson, 1989 , 2003 ; Bösch et al., 2006 ), the bulk of the scientific community was not convinced by this form of data aggregation. Rather, they took Carl Sagan’s position arguing that ‘extraordinary claims require extraordinary evidence.’ Brugha et al. (2012) identified a “single high-quality, well-reported study” as one such potential form of extraordinary evidence. We have tried to deliver such a study. The quality features we aimed for were: high power (using a very large sample size), representative sample, high-quality randomization, sophisticated stimuli, objective presentation procedures and high standards on participants’ compliance. All these requirements were met from our team and with the aid of Norstat, a professional polling agency. The results obtained were indeed decisive. Clear and strong evidence for a null effect was found. Thus, micro-PK was not existent in the data. This supports the arguments raised against micro-PK by many skeptics in the field (e.g., Alcock, 2011 ). It has to be noted that in our study we focused on unconsciously affected intentional states of the observers by using a rather indirect manipulation of our participants’ goals during the picture presentations. The majority of micro-PK studies however have been performed with explicitly induced intentional states. It is unclear how such a manipulation would have affected our participants’ behavior in our study design. Having no such comparison condition is a limitation of our study in terms of generalizability and should be addressed in future micro-PK research.

We would like to emphasize, that the conclusion of “evidence for no effect” is only true when referring to the average mean score of positive stimuli. No deviation from randomness was indeed found with this score. A closer inspection of the temporal change of the effect on the other side revealed some potentially systematic regularity that was not present in the simulated data and can thus hardly be explained by random fluctuations alone. It seemed that the effect in its temporal development across participants behaved like a dampened harmonic oscillation and the amount of oscillations found with human compared to simulated data clearly differed. Maier and Dechamps (in press) explained the existence of such a data pattern through the occurrence of a mechanism called entropy that counteracts the original micro-PK effect. Their mutual interplay most likely produces a dampened harmonic oscillation. If this, admittedly speculative, assumption is true, future PSI research involving quantum RNGs should not focus on significant deviations from chance, but rather should explore oscillating patterns across time and compare these with simulated data. This would be a more fruitful approach than fighting a basic premise in quantum mechanics and it would fit the law of conservation of energy and therefore avoid theoretical paradoxes within science.

In addition, such an oscillating pattern could also be true for standard psychological experiments that involve unconscious processing. Also, for these studies, from a physical point of view, effects are produced effortless and automatic and should therefore also violate the laws of energy conservation and entropy. This should also lead to entropic declines during replication attempts and might also result in an oscillating pattern of effect change. The replication crisis could thus to some extent be also influenced by these mechanisms. We encourage researchers who were working on unconscious processing to analyze their original data and replication attempts accordingly.

Descartes, a famous and well-respected 17th century mathematician and philosopher, was convinced of the existence of micro-PK when he stated that a gambler’s optimistic attitude can bias the outcome of a gambling game toward success. In light of our empirical finding, we would say that he was right only to a certain extent. Indeed, the optimistic gambler might initially achieve higher gains, but then he also has to pay the price of higher losses. Gains and losses during the game will then alternate and approach the chance line at some point. The net earnings will be zero despite the optimistic attitude, but the wins and losses during the game will be more pronounced than in a neutral mood.

Author Contributions

MM developed theory and hypotheses, designed the study, ran the study, analyzed the data, wrote the first draft; MD designed the study, ran the study, analyzed the data, revised the first draft; MP designed the study, ran the study, revised the first draft.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer JJ and handling Editor declared their shared affiliation.

http://www.norstat.de

http://opinion-people.com/dataprotection

www.jspsych.org

www.shutterstock.com

http://www.idquantique.com/random-number-generation/quantis-random-number-generator/

The playing times varied slightly between different languages.

https://www.wolfram.com/mathematica/

http://www.wolfram.com/mathematica/

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(1) PK on random number generators

The advent of electronic and computer technologies has allowed researchers to develop highly automated experiments studying the interaction between mind and matter. In one such experiment, a Random Number Generator (RNG) based on electronic or radioactive noise produces a data stream that is recorded and analyzed by computer software.

In the typical RNG experiment, a subject attempts to mentally change the distribution of the random numbers, usually in an experimental design that is functionally equivalent to getting more "heads" than "tails" while flipping a coin. Of course the electronic, computerized experiment has many advantages over earlier research using, e.g., tossed coins or dice. In the RNG experiment, great flexibility is combined with careful scientific control and a high rate of data acquisition.

A meta-analysis of the database, published in 1989, examined 800 experiments by more than 60 researchers over the preceding 30 years. The effect size was found to be very small, but remarkably consistent, resulting in an overall statistical deviation of approximately 15 standard errors from a chance effect. The probability that the observed effect was actually zero (i.e., no psi) was less than one part in a trillion, verifying that human consciousness can indeed affect the behavior of a random physical system. Furthermore, while experimental quality had significantly increased over time, this was uncorrelated with the effect size, in contradiction to a frequent, but unfounded skeptical criticism. Some parapsychologists believe that these results can be accounted for by ESP if the experimenter (or their participants) intuitively know the right moment to start their studies to get significant results. This is known as Decision Augmentation Theory. However, the apparent effect of focused mass consciousness on a world-wide network of RNGs (see the Global Consciousness Project ) suggest that at least some of the time, there is an element of mind-matter interaction. 

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• Published: 12 October 2021

A cognitive fingerprint in human random number generation

• Marc-André Schulz 1 ,
• Sebastian Baier 2 ,
• Benjamin Timmermann 2 ,
• Danilo Bzdok 3 , 4 &
• Karsten Witt 5  

Scientific Reports volume  11 , Article number:  20217 ( 2021 ) Cite this article

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Is the cognitive process of random number generation implemented via person-specific strategies corresponding to highly individual random generation behaviour? We examined random number sequences of 115 healthy participants and developed a method to quantify the similarity between two number sequences on the basis of Damerau and Levenshtein’s edit distance. “Same-author” and “different author” sequence pairs could be distinguished (96.5% AUC) based on 300 pseudo-random digits alone. We show that this phenomenon is driven by individual preference and inhibition of patterns and stays constant over a period of 1 week, forming a cognitive fingerprint .

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Introduction.

Most work in the neurosciences collapses data from multiple subjects to obtain robust statistical results, which ignores that even in healthy subjects brain structure and function are known to be highly variable 1 . In this study we explore the question of whether cognitive processes may bear highly individual strategies—i.e. “Can we identify single individuals based on how they think?”. We address the general question of interindividual differences in the specific context of human random number generation and search for a cognitive fingerprint in random sequences.

Variations in executive function are reflected in the characteristic ways in which humans deviate from mathematical randomness 2 . Based on this premise, random number generation has previously been used to investigate cognitive changes in brain disorders, such as in Parkinson's disease and schizophrenia 3 , 4 .

Human-generated pseudorandom sequences have been repeatedly found to exhibit strong regularities: cycling of response alternatives, a tendency to count in increments of one, suppression of response repetitions, and others 5 , 6 , 7 , 8 . A general way to characterize these sequences is by reference to their n-gram frequencies (i.e., n-tuples are henceforth called “patterns”). In our previous work 9 we demonstrated that this characterization can be improved by taking into account variations of patterns: humans apparently have a tendency to reuse certain predominant patterns and to obscure these recurring themes by implementing minor changes (i.e., variations) within the respective pattern (Fig.  1 a).

Reproduced from 9 .

Demonstration of the pattern based approach. ( A ) In this sequence, the pattern (2, 1, 9, 6), marked in red, is predominant. Variations of this pattern are marked in orange. ( B ) Demonstrates the concept of the edit distance according to Damerau–Levenshtein. The edit distance indicates the number of edit operations necessary to convert the humanly generated random number sequence at any position into a given pattern. A distance of 0 marks a perfect match (d). At a distance of 1, one edit operation is needed to convert the sequence string into the pattern (a: deletion, c: insertion). If the patterns do not match to the given string of the sequence, up to 4 edit operations are needed. Therefore the score is 4 (b). The inverse numbers of the edit operations are added up and this score represents the mathematical “affinity” of a given pattern to the humanly generated random sequence with a lower score for patterns with diminished “affinity” to the original sequence.

To measure the prevalence of these variations, we borrowed a technique from computer science called “edit-distance”. The edit-distance quantifies the “distance” between two strings: the minimum number of operations needed to transform one string into the other. For our use case we chose the Damerau–Levenshtein distance 10 . This metric considers the number of insertions, deletions, substitutions, and transposition of adjacent characters (Fig.  1 b). In particular, we introduced the “score” (Damerau–Levenshtein Score, DLS) function s(m, z), which takes a sequence (z) and a pattern (m) as input and returns a real number. The score of a pattern is calculated by computing the Damerau–Levenshtein distance of the pattern to the corresponding part of the random sequence on every position of the human generated random number sequence. The sum of the inverses of the distances (each incremented by one to avoid dividing by zero), divided by the sequence length, returns the score of the pattern on the random sequence. The efficacy of this approach was demonstrated by predicting the pseudorandom sequences based on the sequence's immediate history. When predicting one subject’s sequence, predictions based on statistical information from that same subject yielded a higher success rate than predictions based on statistical information from a different subject 9 . This effect motivates the present study.

To summarize, the random generation process can be characterized by certain predominant patterns (n-tuples, e.g. “4-3-2-1” is a length 4 pattern). These occur in different variations (“3-4-2-1” is a variation of the former) repeatedly throughout the sequence 9 . Variation-tolerant search based on the Damerau–Levenshtein distance measures the prevalence of a pattern in a sequence and identifies predominant patterns. Knowledge of the predominant patterns allows predicting individual upcoming digits in the running sequence.

130 healthy adults (53 male, aged 35.3 years (SD = 15.6)) participated in the study. Subjects were recruited from the university, as well as friends and family of students, of the Christian Albrecht University Kiel, Germany. All participants reported that they were in good health and without any history of neurologic or psychiatric disease. They had not taken any medication or consumed any illicit drugs in the 3 months before the study. Cognitive disturbances (Mini Mental Status Examination < 26 points 11 and depressive mood (Beck Depression Inventory > 15) served as exclusion criteria. The study procedure was approved by the local ethics committee of the Christian Albrecht University and subjects gave written informed consent before participating in the study. All methods were performed in accordance with the relevant guidelines and regulations.

The Random Generation Task was administered in a standardized procedure based on Towse and Cheshire 12 : participants were asked to vocally produce a random series of numbers from the range 1–9 (inclusive) at a pace of 1 Hz (metronome). The concept of randomness was explained using both the analogy of a fair dice and the analogy of repeatedly drawing digits from a hat (see supplement material 1 ). The criteria (a) equal distribution of numbers, (b) independence of responses (c) unpredictability and absence of patterns and algorithms, were explicitly mentioned. A practice run ensured that participants understood the instructions. Deviations from Towse and Cheshire's 12 instructions and their underlying reasoning are also detailed in the supplementary material 1 . Subjects generated two sequences of 300 digits with a 40 min pause in between, during which neuropsychological testing (MMSE, BDI) took place. 20 subjects generated an additional sequence after 1 week. Of the 130 subjects, 3 were excluded from further analysis for not maintaining a uniform digit distribution (> 3.5 \(\sigma\) outlier). 12 were excluded for taking excessively long (> 15 s) pauses during testing. In sum, the data set analyzed includes 115 healthy non-depressed cognitive intact participants.

For each sequence we computed the Damerau–Levenshtein scores for all patterns of length 1–6, as previous research indicated that effects are limited to that range 9 . The score-function computes the prevalence of a pattern in a sequence. With this function, sequences can be characterized by reference to their pattern-scores. Any random sequence can be considered a point in a 9 n dimensional (9 n length-n variations if 9 digits) vector space. The euclidean distance of these points gives a measure of their similarity. To avoid that a few strategy changes could dominate the measure we standardize the scores and map the pattern-wise differences with the tanh-function. This effectively down-weights the (both positive and negative) outliers. Intuitively, the DLS distance represents the aggregate difference in pattern frequencies between two sequences, allowing for insertions, deletions, and transpositions.

We excluded digit repeats (“11”, “555”) from the pattern-vector space because subjects have trouble judging the probabilities of rare patterns and tend to use them inconsistently 13 . Based on this approach, we computed the distances for all pairs of sequences, separately for each pattern length. Additionally, we computed RNGT statistics Redundancy (quantifying equality of response usage) and Run-Ups (quantifying seriation, i.e. the tendency to “count”) as detailed by Towse and Neil 7 .

Sanity check: RNGT statistics, practice and fatigue effects

The collected pseudorandom sequences corresponded to expectations in key RNGT statistics Redundancy and Run-Ups (Table 1 ). For both indices the data were distributed approximately log-normal. In contrast to earlier studies 14 , 15 , both indices exhibited statistically significant differences between timepoints, possibly fatigue or practice effects 16 . The occurrence of fatigue or practice effects is not a problem for the present study: as we attempt to show invariance in the human random number generator, such effects can only incur underestimation of the true effect size.

Identification

The introduced DLS distance measure can be used to classify sequence pairs as a “match” (both sequences originate from the same subject) or “non-match” (sequences originate from different subjects) by introducing a discrimination threshold. The receiver operating characteristic (Fig.  2 A) illustrates the performance of this simple classifier when its discrimination threshold is varied. Classifier performance (area under the curve, AUC) indicates a good classification performance based on single digit preferences ( \(91.79\%\pm 1.\) 25). Classifier performance increased with pattern-length (jackknife resampled linear model, z = 4.67, p < 0.001). Performance reached \((96.48\pm 0.69)\mathrm{\%}\) for length-6 patterns, a significant improvement over single-digit based prediction (p < 0.01). Note that this approach has no free parameters, which would have to be chosen subjectively by the investigators.

Long patterns drive identification performance. The Damerau-Levenshtein approach to quantify the similarity between two sequences was used to distinguish “same-author” and “different author” sequence pairs. ( A ) Receiver operating characteristic for the RNG-based classifier. ( B ) Identification performance (AUC) in relation to pattern-length for sequences that were generated 40 min or 1 week apart. Error bars indicate jackknife-estimated standard error of the mean.

Non-DLS baseline

To characterize the role of edit operations (insertion, substitutions or deletions) for our results, we additionally calculated identification performance (AUC) based on exact matches only, that is without tolerance to edit operations (non-DLS). For a pattern length of one, identification performance is identical by design. For a pattern length of two, DLS and non-DLS approaches achieved comparable results (DLS 94.50% vs non-DLS 94.43%). However, for all longer patterns, the non-DLS performance degraded, down to 84.30% at length 3, and down to the level of a random guess at length 6. In a comparison between the maximum DLS-based performance (at length 6) with the maximum baseline performance (at length 2), DLS showed significant improvements over the baseline (z = 2.47, p < 0.01, jackknife resampled, one-tailed test).

Long term stability

20 additional subjects were tested again 1 week later to evaluate the temporal stability of individual random generation behaviour. Again, we used jackknife resampling to estimate the cross pattern-length grand average identification performance statistics and compared the 40 min interval (128 subjects) and the 1 week interval (subset of 20 subjects). Identification performance after 1 week was qualitatively similar to the Identification rate after 40 min results, albeit marginally lower for all pattern lengths (see Fig.  2 B). In our statistical analysis, this decrease in identification performance over time was not statistically significant (jackknife resampled linear model, z = 0.87, p = 0.62,), though our analysis may have been underpowered to detect such effects.

Preferences and inhibitions

Finally, we investigated the mechanisms behind the intrinsic subject specificity of the random number sequences. Is the effect driven by individual preferences, individual pattern inhibitions, or perhaps both? Given the high identification performance, it is reasonable to assume that different subjects preferred (inhibited) different patterns. It thus does not help to examine specific patterns on the group level. Instead, for each subject, we found the most preferred (most prevalent) pattern in her first sequence, then calculated the difference in scores (prevalence) between (a) this pattern in her first sequence and this pattern in her second sequence (within-subject difference) and (b) this pattern in her first sequence and this pattern in all other subjects' sequences (between-subject difference). We repeated this process for the next most preferred patterns, then the next, etc., until we got to the most inhibited (most rare) patterns. In the end, we have a list of within-subject and between-subject differences for each subject's respective most inhibited and most preferred patterns. The difference between between-subject and within-subject difference represents the degree of individuality of a pattern (Fig.  3 ). This information was (implicitly) used by the algorithm in the last analysis to identify subjects based on their random sequences.

Subject specificity of rare vs. common patterns. ( A ) Inter- and intra-subject difference in usage of all 9 3 length-3 patterns. The ordinal numbers represent each subject’s respective most rare to most common patterns. ( B ) The difference between inter- and intra-subject differences represents the degree of individuality for the respective rare and common patterns. The marked areas represent (a) universal exceptions, (b) rare patterns, (c) individual inhibitions, (d) individual preferences. All error bars represent SEM.

Five areas of interest can be identified: (a) universal exceptions are patterns no one expected in an RNG series (triplets of the same digit); (b) rare patterns are duplets and x–y–x patterns; their degree of individuality is dominated by individual pattern preferences; (c) individual inhibitions are subjectively perceived as non-random (these might potentially be birthdates, anniversaries, phone numbers—numbers we are too familiar with for them to feel random); (d) individual preferences that are person specific and that drive RNG identification.

To verify this interpretation we split the ordinal scale of length-3 patterns into 4 bands (Table 2 , column 1) based on the local minima in Fig.  3 B and extract the most common patterns in the respective areas (Table 2 , column 2).

Cognitive capacity and environmental vs. procedural fingerprints

Globally, there are three possible explanations for individual pattern preferences and inhibitions. Firstly, the individuality might be due to differences in cognitive capacity: this is unlikely, as the few relevant components of cognitive capacity (e.g. working memory) should only affect correspondingly few dimensions of randomness. It cannot explain the entire effect of cognitive individuality. Secondly, preferences and inhibitions could be familiar patterns from the subjects personal environment: based on the degree of familiarity, subjects might either perceive these patterns as non-random and suppress them, or habitually produce them preferentially (“ environmental cognitive fingerprint ”). To investigate this, we tested whether each individual's familiar patterns (birth dates, postal codes, telephone numbers) were over- (or under-) represented in her sequence in comparison to this pattern’s prevalence in sequences from other subjects. Neither birth dates (t(114) = 0.22; p = 0.83/K–S for normality D = 0.05; p = 0.90), telephone numbers (t(19) = − 0.68; p = 0.50/D = 0.14; p = 0.89), nor postal codes (t(19) = − 1.19; p = 0.25/D = 0.13; p = 0.90) yielded significant results. Birth dates were collected ab inito for all subjects; 20 sets of telephone numbers and postal codes were collected post hoc for this experiment. Based on these results, we conclude that individuality in RNG is most likely the direct result of an individual generative algorithm (“ procedural cognitive fingerprint ”).

In the present study we address interindividual differences in the cognitive process of random number generation. We conclude that the mechanism by which humans generate random sequences is (a) highly unique and that (b) this uniqueness is driven by both individual preferences and individual inhibitions. This insight begs further questions: (a) do we really see unique generative algorithms or can our results be explained by individual differences in cognitive capacity? The latter is unlikely, as the few relevant components of cognitive capacity (e.g. working memory) should only affect correspondingly few dimensions of randomness. It cannot explain the entire effect of cognitive individuality. (b) Where do the individual preferences and inhibitions originate? They could be familiar patterns from the subjects personal environment: based on the degree of familiarity, subjects might either perceive these patterns as non-random and suppress them, or habitually produce them preferentially (“ environmental cognitive fingerprint ”). But tests for the individual telephone numbers, postal codes, and birthdates revealed no significant anomalies, leading us to conclude that individuality in RNG is most likely the direct result of an individual generative algorithm (“ procedural cognitive fingerprint ”).

Remarkably, single digit preferences alone allowed for identification of participants based on their random sequences. Individual digit preferences shine through in the random sequences, even though participants were instructed to generate an equal distribution of digits. Still, increasing the analyzed pattern length significantly improved the identification performance. Quantifying and delineating the contributions of different pattern lengths is beyond the scope of this manuscript and will require further research.

We were able to demonstrate that individual idiosyncrasies within the number sequence lead to robust identification performance and that individuality of the cognitive processes responsible for random number generation remains stable over at least a week. Further research is needed to explore the stability of patterns in individual random number generation behaviour on a larger number of participants over a longer test–retest interval.

Our results suggest that a tolerance to edit operations is necessary to leverage information for patterns longer than two digits. This reliance on tolerance to edit operations can be explained in two ways.

First, in the context of the generative processes for random sequences. In Baddeley’s model of random number generation 2 , 17 participants use generative schemata to produce new responses, while continuously monitoring each new response for perceived randomness. In case of an unsatisfactory response, the response is inhibited and another schema is chosen. Consider that tolerance to edit operations as implemented by the DLS helps to extract predictive 9 and identifying information. The implication for Baddeley’s model would be an extra step in which participants avoid switching between generative schemata by obscuring the unsatisfactory response via insertions, deletions, or transpositions.

The second possible explanation for the effectiveness of the DLS is its versatility. The DLS can be thought of as a generalized approach, incorporating many aspects of the established measures of randomness. For instance, the length-one DLS reflects the redundancy R, the length-two DLS reflects Evan’s RNG score 13 with some tolerance for edit operations, and length > 2 DLS reflects, amongst others, runs (numerically increasing patterns, with tolerance for insertions). As such, there is no competition between traditional measures of randomness which aggregate information about the sequence into one scalar and the DLS which primarily serves as to represent a sequence in a high dimensional vector space.

Our experimental and statistical techniques enable simultaneous tracking of inhibitory (individual pattern inhibitions) and generative (pattern preferences) performance. This may be a useful tool for investigating attention-deficit/hyperactivity disorder, pharmacological interventions, or consequences of sleep deprivation. Moreover, our findings underline the notion that human cognition may not only vary cross-culturally 18 , but that the cognitive strategies and approaches people take to solve specific problems may be different from individual to individual.

Code availability

All statistical analyses were performed in Python. Scripts and custom software modules that were used to generate the results will be made accessible online for reproducibility and reuse at http://github.com/maschulz/rnglib .

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Marc-André Schulz

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M.A.S. and K.W. designed the study. M.A.S., D.B. and K.W. analysed the data and wrote the paper. S.B. and B.T. collected the data and revised the manuscript.

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Schulz, MA., Baier, S., Timmermann, B. et al. A cognitive fingerprint in human random number generation. Sci Rep 11 , 20217 (2021). https://doi.org/10.1038/s41598-021-98315-y

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The Global Consciousness Project

Three devices.

The Global Consciousness Project uses three different random event generators (REG or RNG). These are the PEAR portable REG, the Mindsong Microreg, and the Orion RNG. All three use quantum-indeterminate electronic noise. They are designed for research applications and are widely used in laboratory experiments. They are subjected to calibration procedures based on large samples, typically a million or more trials, each the sum of 200 bits. In the GCP application, an unbiased mean is guaranteed by XOR logic. Although they have different fundamental noise sources, they all provide high-quality random sequences that are functionally equivalent.

PEAR type REG

We begin with the PEAR type REG, which is one of the devices developed for experimental research at the Princeton Engineering Anomalies Research (PEAR) laboratory. The PEAR program has used three generations of random event generators, with different primary sources of white noise, but important common features of design. The original benchmark experiment used a commercial random source developed by Elgenco, Inc., the core of which is proprietary. Elgenco’s engineering staff describe the proprietary module as solid state junctions with precision pre-amplifiers, implying processes that rely on quantum tunneling to produce an unpredictable, broad-spectrum white noise in the form of low-amplitude voltage fluctuations. The PEAR Portable REG, which was designed by John Bradish of the PEAR team, uses Johnson noise in resistors, which is so-called thermal noise and is also a quantum level phenomenon that produces a well-behaved broad-spectrum voltage fluctuation. Three of these have been used in the EGG network. The other random sources use quantum tunneling in diodes or transistors.

Mindsong MicroREG

The Mindsong MicroREG is a miniaturized REG device that was developed and prototyped at PEAR, is used in about half the nodes in the GCP network. The MicroREG also was designed by John Bradish, with a little help from Roger Nelson and others of the PEAR team. The design was provided to an independent company, Mindsong, Inc. for production and marketing. It uses a field effect transistor (FET) for the primary noise source, again relying on quantum tunneling, which provides completely uncorrelated fundamental events that compound to an unpredictable voltage fluctuation. The third device used by the GCP is the Orion, which is described below.

The third device in the GCP network is the Orion RNG, designed by Dick Bierman and Joop Houtkooper in the Netherlands, as a project of their collaborative Foundation for Fundamental Research on Man and Matter (FREMM). The company that made the Orion is defunct as of 2010, so the following is historical documentation. The Orion is manufactured in Amsterdam and distributed by ICATT interactive media.

The following excerpt is drawn from the ICATT website’s full description of the device (now at archive.org):

Orion’s Random Number Generator consists of two independent analogue Zener diode based noise sources. Both signals are converted into random bitstreams, combined [using an XOR] and subsequently transmitted in the form of bytes to the RS-232 port of your computer. Special timing circuits ensure that crucial logical operations occur at moments that the device has stable signals. The baud rate is 9600. So the device is capable of supplying you with about 960random bytes or 7600 random bits per second Power is drawn from the RTS and TXD signal. (pins 4 and 2 of the D-25connector). In order to work properly the RTS signal should be high (5 volts or higher) and one should not send bytes to the device!

In all cases, the design begins with white noise, for example in the PEAR Portable REG, a flat spectrum +/- 1 dB from 1100 Hz to 30 kHz. A low end cutoff at 1000 Hz eliminates frequencies at and below the data-sampling rate. This filtering, together with appropriate amplification and clipping, produces an approximate square wave with unpredictable temporal variation. Sampling at a constant 1 kHz rate is typical, although special sources have been constructed allowing higher rates (up to 2 MHz). Analog and digital processes are completely isolated by alternating these operations to exclude contamination of the analog noise train by digital pulses.

To eliminate biases of the mean that might arise from such environmental stresses as temperature change, electromagnetic fields, or component aging, an exclusive or (XOR) mask is applied to the digital data stream. This is either an alternating 1/0 pattern (this is applied by the GCP software for the Orion devices, and in hardware for the PEAR devices) or a more complex mask comprising an array of all bytes with equal occurrence of 1/0 (this is in firmware in the Mindsong devices). Both types of XOR exclude bias of the mean, in principle, and the latter also excludes all short-lag bit-to-bit and byte-to-byte autocorrelations. Finally, data for the GCP are recorded as trials that are the sum of 200 bits drawn from the primary bit sequence. This sum across bits further mitigates any residual short-lag autocorrelations or other time-series predictability. The result is a data sequence of random numbers that conform to the appropriate theoretical binomial distribution and to its normal approximation.

The final output of the physical REG unit is a sequence of bytes presented to the computer’s serial port, which are then formed by the acquisition software into a sequence of trials (sums of 200 bits), generated at 1 per second. This composite trial value, based on an N of 200 bits has an expected mean of 100 and standard deviation of Sqrt(50). Calibrations on all of the devices show behavior that closely models theoretical expectations for mean, variance, skew and kurtosis. The calibration suite includes tests for runs, autocorrelation at raw and 50-trial block levels, conformance to the Arcsine distribution, and a number of other statistical criteria. See the full device description from Orion's website (via archive.org.)

REG Experiment Design

Given a sequence of trials with a well-defined expectation for the mean and standard deviation (100, 7.071), participants try to change the output according to pre-stated intentions. The situation is analogous to trying to get more heads or more tails while flipping an unbiased coin. The REG is in this sense a very sophisticated, high speed electronic coin-flipper, connected to a computer for reliable data collection in controlled experiments. The computer also allows immediate computation of statistics, and feedback of various kinds including graphic displays of the accumulating deviations from what is expected for an undisturbed random process.

The basic design for laboratory experiments using the REG technology constitutes a final level of protection against artifactual sources of apparent effect. It is a tripolar design, where participants generate data under three conditions of pre-specified intention, namely to achieve high (HI) or low (LO) means, or to generate baseline (BL) data. In addition to this primary variable, a number of secondary parameters are represented as options that can be explored. These include the identity of the individual operators (participants), including robust comparisons that are possible among a subset of prolific operators who do many replications of the experiment. A related, simpler variable is the operator’s gender, including operator pairs who may be of the same or opposite sex, and who may be bonded pairs. Different sources for the data include the true random sources described earlier, and both hardware and algorithmic pseudorandom generators. Other parameters include the distance the operator is from the machine, up to thousands of miles, and analogous separations in time, up to several hours or a few days. The information density (bits per second) and the number of trials in runs have been varied, as have the instruction mode, feedback type, and the replication number or serial position. A number of publications giving details are available.

Use of the REG/RNG devices in the GCP network

In the EGG project, any of the three devices, PEAR, Orion, MicroREG, may be used. In the application, they are functionally indistinguishable, although the EGG software configuration must specify which device is in use. In addition to the technical details of the device construction and operation, an adequate picture of the overall project requires a description of the physical data-acquisition system, and definition of the terms used for the specialized equipment.

At each of a growing number (about 40 in late 2001; over 60 in 2005) of host sites around the world, one of these well-qualified sources of random bits (REG or RNG) is attached to a computer running custom software to collect data continuously at the rate of one 200-bit trial per second. This local system is referred to as an egg , and the whole network has been dubbed the EGG, standing for electrogaiagram, because its design is reminiscent of an EEG for the earth. (Of course this is just an evocative name; we are recording statistical parameters, not electrical measures.) The egg software regularly sends time-stamped, checksum-qualified data packets (each containing 5 min of data) to a server in Princeton. We access official timeservers to synchronize all the eggs to the second, to optimize the detection of inter-egg correlations. Occasional drifts occur, but any mis-synchronization is expected to have a conservative influence in our standard analyses. The server runs a program called the basket to manage the archival storage of the data.

Other programs on the server monitor the status of the network and do automatic analytical processing of the data. These programs and processing scripts are used to create up-to-date pages on the GCP Web site, providing public access to the complete history of the project’s results. The raw data are also made available for download by those interested in checking our analyses or conducting their own assessments of the data. Each day’s data are stored in a single file with a header that provides complete identifying information, followed by the trial outcomes (sums of 200 bits) for each egg and each second. With 40 eggs running, there are well over 3 million trials generated each day, and the complete database at the end of 2001 occupies approximately 3 gigabytes of storage in a highly compressed form.

A thorough analysis of the data has been done for the Analysis 2004 project. This includes detailed assessment of the performance of the individual devices and a description of the normalized random data used in our rigorous formal analysis.

If you need more information you can find good general references on theory and practice of random number generation on the web. (The reference I previously had went away.)

A version of this page with some links to background material is available at REG experimental design . A question that may be relevant for use of these well-designed devices is whether they might be affected by power line influences .

• Introduction
• Brief overview
• For the Media
• The egg metaphor
• The egg story
• Original plan
• Organization
• Orange arrows indicate offsite links to related sites.
• Main results
• Network status
• Network map
• Daily tables
• Daily videos
• Realtime display
• GCP DOT variance
• Long-term data display
• About the data
• Data analysis
• Data access
• Data extract
• Advanced data access
• Data errors
• Eggshell analysis
• EggAnalysis app
• The science
• Professional
• Linked ideas
• Applications
• Poetic history
• Speculations
• Musical interlude
• Random tapestry
• Global Brain paint
• How to help
• Contributions
• TV, web, print, radio
• Documentaries
• YouTube spots
• Google group
• Network of eggs

Princeton Engineering Anomalies Research (PEAR)

One of the most prominent psi research groups in recent decades was the Princeton Engineering Anomalies Research (PEAR) laboratory. It was founded in 1979 by Robert G Jahn of Princeton University to conduct research on the extended capacities of human consciousness. The program continued for nearly three decades, ending in 2007. As one of the highest ranking officers at an elite Ivy League university, Jahn was in a position to muster financial support and a rich academic context for potentially ground-breaking science. PEAR was a well-supported, technologically sophisticated effort to establish and explore anomalous effects of intention on physical systems, and anomalous communication of information across barriers of distance and time.

Experiments

Random event generator (reg), other machines, secondary parameters, objective analysis, time and distance, theory and models, documentary.

The PEAR lab was founded in 1979 by Robert G Jahn , then Dean of the School of Engineering and Applied Sciences at Princeton University. Jahn was a physicist and a professor in the Department of Mechanical and Aerospace Engineering, and also headed a NASA funded program researching electric propulsion systems for spacecraft. He was asked by an undergraduate to sponsor an independent study to try to replicate findings by Helmut Schmidt 1 that suggested that the behaviour of sensitive equipment based on a physical random source could be influenced by human intention. The results of this study persuaded Jahn that the questions deserved a serious examination, using the best available protocols and technologies. He sought funding from private donors, and began building a staff and laboratory space. The first major financial support came from James MacDonnell of MacDonnell Douglas Aircraft Corporation, who thought it important to study possible effects of consciousness on sensitive instruments such as might be found in the emotionally intense environment of an aircraft cockpit.

Jahn attended professional conferences of specialists in psi research such as the Annual Convention of the Parapsychological Association , and at one of these he met developmental psychologist Brenda Dunne, who had presented her research on remote viewing. He recruited Dunne to help create a dedicated laboratory at Princeton University, and she accepted the position of Laboratory Manager for what became PEAR. Over the next few years the multi-disciplinary staff increased to include experimental psychologist Roger Nelson , electrical engineer John Bradish, astrophysicist York Dobyns, philosopher Arnold Lettieri and other researchers, as well as interns and students.

The PEAR lab produced a variety of  publications documenting the research, including technical reports, peer-reviewed journal articles, and books. Jahn also worked with other mainstream scientists to create a broad-spectrum forum for science at the borders, called the Society for Scientific Exploration (SSE).

The PEAR lab had from the beginning three areas of primary interest. Most of the experiments which were developed addressed mind-machine interaction (MMI) with a focus on the broad range of potential modulators of anomalous effects. 2 The goal was to first establish that an anomalous effect of human intention could be demonstrated, and then to study what factors facilitated or hindered such effects. These factors specifically included both psychological and physical parameters.

The second experimental thrust was on remote viewing , which PEAR called ‘remote perception’ to reflect interest in multiple sensory modalities. The majority of trials used a precognitive protocol, where the perception was recorded before the target was selected, so the experiment was called Precognitive Remote Perception or PRP.

The third focus at PEAR was on modeling and theory building in an effort to develop an explanatory framework for empirical results that were difficult to integrate into standard scientific models.

Mind-Machine Interaction

The largest and longest running MMI experiment used a technologically sophisticated random event generator (REG) based on electron tunneling in solid state junctions. This quantum process is exploited to produce fundamentally unpredictable bit sequences. The design uses a ‘back-volted’ circuit where voltage pressure against the closed switch of a diode or transistor results in some electrons ‘tunneling’ through the forbidden gap. Because of quantum tunneling, a tiny, fundamentally unpredictable fluctuating voltage occurs after the junction; this is sampled and the high and low voltages are converted into a sequence of 1 and 0 binary digits or bits. Conceptually, such an REG (which is also called an RNG or random number generator) is equivalent to a high-speed electronic coin flipper. Modern technology is applied to ensure an output that meets statistical criteria for true randomness, which is tested in repeated calibration trials.

These true random number generators are to be distinguished from algorithmic pseudorandom number generators using computer software to create long sequences which look random, but are deterministic. A subset of PEAR REG experiments asked whether both types of random source would show anomalous consciousness-related effects, and tentatively concluded that the deterministic sources were not susceptible to the effects of intention. True random sources are labile, so the future state of a data sequence is in principle changeable. Other researchers have reported some MMI effects using pseudorandom sources so the question is not settled.

The REG experiment at PEAR typically used trials which were the sum of 200 bits drawn from the sequence at a rate of one trial per second. The bit generation rate was several thousand bits per second, so the experiment could explore the effect of larger trials (for instance 2,000 bits) as well as other physical parameters including the rate of bit or trial generation. The original REG device had switches to set such parameters to allow exploration. Later generations of the experiment used simpler, miniaturized random sources, with software to control the conditions which the PEAR program assessed.

Over many years, the REG experiment accumulated substantial evidence for an effect of human intention using a tri-polar protocol where operators (PEAR’s preferred name for subjects) succeeded overall in achieving high trial values under that instruction, low numbers when so instructed, and no significant deviation from expectation for baseline trials. In the original ‘benchmark’ REG experiment, the difference between high and low conditions in 2.5 million trials over twelve years of efforts by 91 operators is small but highly significant with a Z-score of 3.99 (~ 4 sigma) and a corresponding probability of 7 x 10 -5 or odds of about 15,000 to 1 against chance as an explanation. Various parameters, such as the speed of data generation, the size of the trials, random vs volitional choice of intention, pairs of operators vs individuals were examined. Most had small effects, but of special interest was the effect size for bonded pairs – their scoring levels were significantly higher than for either of the individuals.

When all variations on the basic experiment are combined, producing a much larger database, the Z-score is 6.06 (6 sigma), and the probability is 6 x 10 -10 . Though the possibility these results are due to chance is virtually excluded, this represents a very small effect size of only a few parts in 10,000. But the finding has outsize implications given the highly controlled experiments because it does not fit easily into standard models of physics or psychology. A valid demonstration of mind-machine effects at any level requires serious consideration by physicists and, because human consciousness is involved, also by psychologists. 3

The PEAR REG experiments were themselves conceptual replications of work by Helmut Schmidt and others, and over the past few decades many other laboratories did similar experiments, sometimes successfully replicating the anomalous results. A notable exception was a replication attempt by a consortium of three labs including PEAR and research groups in Giessen and Freiburg, Germany. This was a strict replication in the sense that PEAR software and REGs were used, and all three labs followed the same protocols. The results showed positive but non-significant trends at all three labs, despite what seemed to be suitable conditions and sufficient statistical power. Analysis showed significant deviations in various parameters, but in the primary replication measure, this large experiment failed to replicate. 4  Nevertheless, meta-analyses over all known REG experiments including this one provide clear and highly significant evidence that human consciousness and intention can affect the behavior of random number generators. 5

After more than a decade of laboratory research with REGs, the PEAR lab took advantage of advances in electronics and computer technology to miniaturize the random sources to take the experiments into the field. A new experiment called ‘FieldREG’ assessed data produced in environments which were conducive to subjective resonance or coherence such as small intimate groups, group rituals, sacred sites, and musical and theatrical performances. The expectation or hypothesis was that the coherent attention, even without explicit intention, would affect the behavior of the REG, producing anomalous deviations of the mean. Since this was not a directional prediction (no intention) the measure chosen was variance of the mean; strong deviations in either direction would indicate an anomalous effect.

Several exploratory datasets were collected under strict protocols in a replication paradigm where the same procedures were used in a variety of venues. The data collected in resonant/coherent venues conducive to group consciousness showed larger deviations than those generated in more mundane venues such as shopping centers, academic conferences, and business meetings. The composite results of the formal replications strongly confirm the general hypothesis, yielding a composite probability against chance for the resonant subset of p = 2.2 x10 -6 compared to p = 0.91 for the mundane subset. 6

Several other experiments asked the same basic question: Can human intention affect the behavior of well-calibrated physical devices and instruments? These included several devices that looked at micro-PK, that is, mental influences on a microscopic level, engaging quantum level variations in physical systems like the electronic RNG. Another example of this micro-PK category was the PEAR CHIP experiment. It was quite directly representative of the motivating questions about human intentions or emotions in a sensitive electronic environment. The experimental device was built around a widely used electronic component, a shift-register, which was supplied with voltage lower than its normally specified level, set and maintained precisely at the threshold where it might begin to make errors. The psi task was to increase or decrease the error rate or let it be, in the standard tripolar protocol, and as in the REG experiment, operators succeeded in generating a small effect in the direction of intention. 7

At the other end of the dimensional scale, PEAR created a large machine which was formally called the Random Mechanical Cascade (RMC). The device had various nicknames and it was familiarly called Murphy, a name which arose from its complexity which ensured that it followed Murphy's Law: ‘If anything can go wrong, it will.’ It was nearly ten feet (~ three meters) tall and six feet (~ two meters) wide, with a Plexiglas front enclosing a matrix of 336 equally spaced nylon pins about ¾ inch in diameter (~ two cm). A conveyor belt brought 9,000 ¾ inch diameter polystyrene balls to the top where they dropped from a central funnel into the pin matrix where they bounced left and right through the pins.

The machine was modeled after a statistical demonstration display in a technology museum, where the bouncing balls gradually built up a Gaussian or bell curve distribution. The PEAR machine was instrumented to count the balls as they fell into nineteen collecting bins, with the data registered in computer files for analysis. Operators sitting across the room attempted to shift the distribution to the left or right or let the machine do a baseline run. The analysis controlled for physical changes such as component wear or effects of humidity by calculating differences between conditions within a tripolar set which would be completed typically within about 45 minutes.

The overall results showed a statistically significant difference between the right vs left conditions, with t = 3.89 and p < 10 -4 , or odds of about 10,000 to 1 against chance fluctuation. 8 It is worth noting that although this experiment at first glance appears to be macroscopic, involving large forces, careful study shows it to actually involve only non-energetic decisions – does the ball bounce left or right?

PEAR also built a Linear Pendulum experiment, in which operators attempted to change the damping rate of a pendulum inside a Plexiglas box. The pendulum bob was a 2.5 inch quartz crystal ball suspended from precision bearings on a thirty-inch rod made of fused silica, which has a zero coefficient of thermal expansion. The speed of the swinging pendulum was measured by a fifty-nanosecond clock interrupted by a diode beam cut by a razor edge mounted on the pendulum rod. The high-quality bearings ensured that most of the damping forces were from passage through the air, and the change in damping rate was too small to see visually, but the precision instrumentation allowed measurement of changes with five-digit accuracy.

Again, data collection in the tripolar protocol allowed experimental control over changing environmental conditions including variations in barometric pressure. The overall result was a significant positive effect aligned with the operators’ intentions. 9

Other experiments covered a wide range of physical phenomena. These included an exquisitely sensitive Fabry-Perot interferomenter (which created optical interference patterns), and a controlled miniature fountain displaying the delicate transition from laminar (smooth) to turbulent flow.

A dual-thermistor experiment asked whether operators could differentially change the readings of two sensitive heat detectors in the same controlled environment.

A small mobile robot whose direction and excursion distance was controlled by an REG traveled in a random path on a round table surface. The operator task was to mentally attract the robot or push it away. It was a playful experiment which operators enjoyed, and which showed modest but variable success corresponding to the assigned intentions. As a side note, the PEAR lab occasionally hosted visits from school classes. The students, about ten years old, were delighted by all the experiments they tried, but the mobile robot was their favorite. While PEAR did not conduct formal experiments with minor children, they appeared to be more successful than adults.

Several more physical experiments were built, some of which could be brought to completion and used in formal tests. Some did not make it to that stage because their sensitivity to environmental conditions made a completely clean, controlled experiment impractical. What is of special note is that all of these widely differing physical systems appeared to be susceptible to modulation correlated with human intention. The MMI experiments provide clear evidence that there is correlation of mental states with unexpected structure in data from a variety of physical systems characterized by a fundamental random aspect. To compare effect sizes in experiments with varied basic trial units an analogue to the ordinary calculation (Z/N -2 ) was developed which normalizes the Z-score by the square root of the number hours spent generating the effects. Using this metric, PEAR found the effect size was similar in all the MMI experiments. 10

The primary correlate in all the REG experiments was intention, but there are several secondary correlates or modulators. 11 Individuals exhibited different levels and styles of performance (which PEAR called signatures) and there was suggestive evidence that the signature was similar in different experiments. A comprehensive analysis indicated that about 15% of the unselected operators were successful in the experimental tasks. Both random assignment and volitional selection of intentions had similar levels of achievement overall, but for some individuals, this modulator produced significant differences. Increasing the size of trials from twenty to 200 to 2,000 bits showed a modest increase of scoring level, but going to a two million bit trial size gave confounding results with a highly significant backwards effect – the high and low outcomes were reversed relative to operator intention. 12

PEAR also assessed manual vs automatic trial sequencing in groups of fifty or 1,000 trials. Performance in this parameter depended on the individual operators. Most preferred the automatic sequencing, but interestingly, the largest effect size in the PEAR REG database was achieved by an operator who chose to use only the manual mode, explaining this allowed a meditative approach to the task.

Most experiments targeted the distribution mean, but a small subset used distribution variance as the target, and these also showed successful performance. One of the most important parameter variations PEAR tested was spatial and temporal separation of the operator from the REG device. Programs to collect data continuously enabled trials to be identified in an index, with intention periods of fifteen minutes (1,000 trials) marked, while the operator was at a distant location. The order and timing of High, Low, and Baseline intentions were communicated to the PEAR staff, who entered the information in the computer index, after which analysis could be completed. These remote trials not only showed successful performance, but slightly higher effect sizes, on average, compared with on-time, local trials. 13

Remote Perception

The second experimental program at PEAR was initially a conceptual replication of so-called remote viewing (RV) experiments originated in the Stanford Research Institute (SRI) laboratory and described by Charles Tart , Russell Targ and Harold Puthoff . 14 At PEAR, the focus of what were called Remote Perception, and later Precognitive Remote Perception (PRP) experiments, quickly shifted from simple demonstration to sophisticated measurement. Originally, the efficacy of the remote perception was determined by human judging – rating a set of targets including the actual target and several decoys. PEAR replaced this with a protocol where the ‘agent’ at the scene filled out a binary descriptor list indicating whether each of thirty elements were present or absent from the scene. The ‘percipient’ encoded his or her experience in a narrative and sketches, but also using the same descriptor list, and the subsequent analysis compared the two lists yielding a score which reflected the relative accuracy of the perception.

The analysis proceeded by constructing a square matrix of scores calculated by comparing each perception against all targets in the given dataset. The properly matched trials (on the main diagonal of the matrix) were assigned a statistical merit (Z-score) by comparison with the distribution of off-diagonal, mismatched scores, which had sufficiently Gaussian characteristics to allow robust parametric tests. As the database accumulated, this procedure created an empirical background distribution of scores against which the actual performance could be compared, and ultimately allowed a precise score to be assigned to each trial in the PRP experiment. 15

Over the life of the program, more than two decades, a large database of 650 independent trials was generated. These included some trials where the agent and percipient worked at the same time. A somewhat larger number of trials were done with the percipient producing the binary descriptor list, sketches and descriptions some hours or days after the agent visited the scene. The largest portion of the trials were done in the precognitive mode – the percipient made a report with sketches and the binary list as much as two or three days before the target was selected and visited by the agent. This protocol provided an additional level of control in the experiment, including a strong defense against any reasonable criticisms arguing possible normal communication.

More important, the scores in precognitive trials turned out to be statistically indistinguishable from those in the other conditions. The regression of score level on the time difference between the agent and percipient activity is non-significant. Similarly, the PRP experiment explored the distance separating agent and percipient and determined that it also does not affect scoring. Trials over five or 5,000 kilometers yielded similar results. 16

Other parameters were explored, including e x post facto vs. participant-encoded descriptions, agent-chosen versus randomly-assigned targets, and single vs. multiple percipients. Most of these factors were not strong modulators of the scoring. These findings help delimit the kinds of models which have potential to explain how remote perception works. While the empirical status seems quite clear, explanations remain speculative in large part. The time and distance independence of the results are helpful qualities in a search for appropriate descriptions. They suggest something akin to nonlocality in quantum physics.

The remote perception program explored various scoring procedures in addition to the binary descriptors. One alternative added a third category allowing the participants somewhat more subtle distinctions. This was carried further to employ a nine-point scale providing still more apparent precision. Over time, the level of scoring decreased, suggesting that the efforts to give participants greater choice and more latitude for accurate descriptions were actually counterproductive. Brenda Dunne, the lead investigator for PRP, concluded that these explorations tended to encourage the intellectual over the aesthetic and free flowing style of thinking which characterized the early work in the paradigm. Even with the moderating effects of such explorations, the bottom line of the PEAR Remote Perception research program is strong. The composite probability across roughly 650 trials is about 3 x 10 -10 corresponding to a six-sigma deviation. 17

PEAR's empirical results in technically sophisticated, controlled experiments over many years showed a need for some form of expansion or modification of the scientific models which have served well as descriptions of the world. A major exception in these otherwise competent models is that mind or consciousness have had no place, even though there is no question about their presence and importance in the world. Only in the past two or three decades have mainstream scientists undertaken to explain the sources and nature of consciousness.

The PEAR research program, together with similar research endeavors around the world, adds a further dimension to the challenge of describing consciousness. Nearly three decades of experiments demonstrated and documented anomalous physical phenomena that were significantly correlated with such subjective variables as intention, meaning, and resonance. The results were in stark contrast with established physical and psychological principles, but there were no well-developed theories in the field. Based on the PEAR lab experience, Jahn and Dunne sought to develop competent theoretical models or valid extensions of accepted theories. Several factors provided both justification and some guidance for extended models.

The anomalous results depend on operator intention and emotional resonance with the task, and they also show suggestive operator-specific structure in the data. PEAR looked for but did not find evidence of traditional learning or improvement with practice. Instead, operator debriefing interviews indicated that successful performance was more dependent on ‘getting out of the way’ and accepting that mind-machine interactions are possible. The finding that results had no explicit space or time dependence made explanations based on existing physical or psychological frameworks insufficient, leading to theoretical efforts at PEAR giving consciousness a proactive role in the establishment of its experience of the physical world.

Jahn and Dunne produced a series of progressively refined efforts beginning with a paper called ‘On the Quantum Mechanics of Consciousness, With Application to Anomalous Phenomena,’ 18 and later published with supporting materials in a book documenting the PEAR research, Margins of Reality . 19

This and subsequent variants attempt a functional application of metaphors from physics to phenomena of mind. The major premise is that ‘the basic processes by which consciousness exchanges information with its environment, orders that information, and interprets it, also enable it to bias probabilistic systems and thereby to avail itself of some control over its reality. This model regards the concepts that underlie all physical models of reality, particularly those of observational quantum mechanics such as the principles of uncertainty, complementarity, exclusion, indistinguishability, and wave mechanical resonance, as fundamental characteristics of consciousness rather than as intrinsic features of an objective physical environment.’ 20

The next step toward a practical model recognized that both mental and physical qualities exist on a continuum from tangible to intangible or ephemeral, and suggested that there is a deep unconscious and intangible area where the mental and physical may intermix. A conscious thought or intention rests on an unconscious foundation, and similarly, a tangible material system has a corresponding intangible aspect. This recognition undergirds a proposal that the inherently probabilistic nature of unconscious mind and intangible physical mechanisms could be invoked to achieve anomalous acquisition of information about, or anomalous influence upon, otherwise inaccessible material processes. The model's viability depends on a workable representation of the ‘merging of mental and material dimensions into indistinguishability at their deepest levels’. 21  

Although it was a well-supported research program at a major university, the PEAR lab was not insulated from criticisms and attacks by sceptics. Many of these were uninformed expressions of bias, but some were by serious scholars, and the PEAR group welcomed informed critiques which could be addressed and incorporated to improve the quality of research.

As is the case for virtually all controversial scientific topics, the Wikipedia entry for PEAR has little information about the research, but is comprised almost exclusively of sceptical statements and references. These are typically not supported by direct experience or knowledge of the experiments, but rely mainly on magazine articles and sceptics’ blog posts as sources.

Some professional sceptics did comment on PEAR. For example James Alcock mentioned various problems with the PEAR experiments such as poor controls and documentation with the possibility of fraud, data selection and optional stopping not being ruled out. He provided no documentation for any of these suspicions, but nevertheless concluded there was no reason to believe the results were from paranormal origin. 22

A sceptic who built his career criticizing JB Rhine , the psychologist CEM Hansel, evaluated Jahn's early psychokinesis experiments at the PEAR laboratory, and, according to the Wikipedia article, wrote that a satisfactory control series had not been employed, that they had not been independently replicated, and that the reports lacked detail. Hansel noted that ‘very little information is provided about the design of the experiment, the subjects, or the procedure adopted. Details are not given about the subjects, the times they were tested, or the precise conditions under which they were tested.’ All of this is belied by the prolific output of publications and technical reports from PEAR.

Unfortunately, most of the critical views expressed about PEAR tend to be simple expressions of bias and an unquestioned belief in the standard models of science. For example, physics professor Milton Rothman is quoted as saying that Jahn's experiments at PEAR started from an idealistic assumption, ignored the laws of physics and had no basis in reality, 23 but he provided no evidence for his belief nor indications that he had read any PEAR reports. A telling anecdote illustrates the point. The editor of a prominent scientific journal once told the lab’s founder and senior scientist, Robert Jahn, that he might consider publishing one of Jahn’s recent papers, provided the author would transmit it telepathically.

In a few cases, critical scientists put in the time and effort to replicate or emulate PEAR experiments and failed to confirm their results. A good example is Stanley Jeffers, who visited and consulted PEAR researchers while developing an optical interference experiment in which he obtained null results. He donated the equipment to the PEAR lab, and interestingly, in that context, the experiment showed a nominally significant effect. 24

PEAR's speculative explanation for the difference is that Jeffers approached the experiment as a physics question without consideration of human factors such as motivation and comfort. His laboratory environment was sterile and the procedures were those customary in physics research. When the PEAR lab took over the experiment, it became a combination of 'the white turban and the white lab coat' -- in other words, the human participants and the the dual-slit technology were treated as equally important. More recently, a series of experiments by Dean Radin has yielded robust evidence (4.36 sigma) that the optical interference experiment is replicable. 25

In 2007, the PEAR lab formally closed its doors, having pursued a scientific assessment of mind-machine interaction and anomalous information transfer for some 27 years. One of the ongoing efforts over that period was communication of information and support for independent replications. The lab had an ongoing program of internships, including students from Princeton University and from many other schools. A number of formal research programs in other institutions such as the University of Giessen, Germany, and the Institute für Grenzgebiete der Psychologie und Parapsychologie in Freiburg, Germany were direct offshoots from PEAR.

The independent Global Consciousness Project (GCP) founded by PEAR's research coordinator, Roger Nelson, employed REG technology and an extended version of protocols developed for the FieldREG research program. PEAR technology formed the basis for a company called Psyleron, Inc . which is producing line of state-of-the-art REG equipment for both research and personal applications. Another developing venture arising out of the PEAR and GCP technology is ‘ Entangled, a Conciousness App ’ which promises a hardware-based random source and software for personal and global network monitoring of consciousness effects for mobile devices.

Most directly, the PEAR legacy includes the International Consciousness Research Laboratories (ICRL), which hosts a communication network and continuing research program as well as a publication house focused on the issues that were central to the PEAR laboratory.

StripMindMedia (2006). The PEAR Proposition: A Quarter Century of Princeton Engineering Anomalies Research . Directed by Aaron Michels (520 minutes, 2 DVD discs, 1 Audio CD)

Roger D Nelson

Alcock, J. (2003). Give the null hypothesis a chance: Reasons to remain doubtful about the existence of psi.  Journal of Consciousness Studies  10, 29-50.

Dobyns, Y.H., Dunne, B.J., Jahn, R.G., & Nelson, R.D. (2004). The MegaREG Experiment: Replication and interpretation . Journal of Scientific Exploration  18/3, 369-97.

Dunne, B.J., Dobyns, Y.H., & Intner, S.M. (1989). Precognitive remote perception, III: Complete binary database with analytical refinements. PEAR Technical Report 89002 , Princeton Engineering Anomalies Research, Princeton Univ. School of Engineering/Applied Science.

Dunne, B.J., Nelson, R.D., & Jahn, R.G. (1988). Operator-related  anomalies in a random  mechanical cascade . Journal of Scientific Exploration 2/2, 155-79.

Dunne, B.J. & Jahn, R.G. (1992). Experiments in remote human/machine interaction .  Journal of Scientific Exploration  6/4, 311-32.

Dunne, B.J. & Jahn, R.G. (2003). Information and uncertainty in remote perception research .  Journal of Scientific Exploration  17/2, 207-41.

Ibison, M. & Jeffers, S. (1998). A double-slit diffraction experiment to investigate claims of consciousness-related anomalies .  Journal of Scientific Exploration  12/4, 543-50.

Jahn, R.G. (1982). The persistent paradox of psychic phenomena: An engineering perspective. Proceedings IEEE 70/2, 136-70.

Jahn, R.G., Dunne, B.J., & Jahn, E.G. (1980). Analytical judging procedure for remote perception experiments.  The Journal of Parapsychology  44/3, 207.

Jahn, R.G. & Dunne, B.J. (1986). On the quantum mechanics of consciousness, with application to anomalous phenomena.  Found ations of Physics  16/8, 721-72.

Jahn, R.G. & Dunne, B.J. (1988). Margins of Reality: The Role of Consciousness in the Physical World .   New York: Harcourt Brace; Harvest Books (1989); ICRL Press, (2009).

Jahn, R.G. et al (2000). Mind/Machine Interaction Consortium: PortREG Replication Experiments .  Journal of Scientific Exploration  14/4, 499-555.

Jahn, R.G. & Dunne, B.J. (2001). A modular model of mind/matter manifestations (M5) . Journal of Scientific Exploration  15/3, 299-329.

Jahn, R.G., Dunne, B.J., Nelson, R.D., Dobyns, Y.H. & Bradish, G.J. (2007). Correlations of random binary sequences with pre-stated operator intention: A review of a 12-year program. Explore: The Journal of Science and Healing 3/3, 244-53.

Jahn, Robert G. & Dunne, Brenda J. (2011). Consciousness and the Source of Reality. ICRL Press.

Nelson, R.D., Ziemelis, U.O., & Cook, I.A. (1992). A microelectronic chip experiment: Effects of operator intention on error rates. Technical Note PEAR 92003 , Princeton Engineering Anomalies Research, Princeton Univ. School of Engineering/Applied Science.

Nelson, R.D., Bradish, G.J., Jahn, R.G., & Dunne, B.J. (1994). A linear pendulum experiment: Effects of operator intention on damping rate . Journal of Scientific Exploration  8/4, 471-89.

Nelson, R.D., Bradish, G.J., Dobyns, Y.H., Dunne, B.J., & Jahn, R.G. (1998). FieldREG II: Consciousness field effects: Replications and explorations . Journal of Scientific Exploration  12/3, 425-54.

Nelson, R.D., Jahn, R.G., Dobyns, Y.H., & Dunne, B.J. (2000). Contributions to variance in REG experiments: ANOVA models and specialized subsidiary analyses . Journal of Scientific Exploration  14/1, 473-89.

Nelson, R.D. (2006). Time-normalized yield: A natural unit for effect size in anomalies experiments . Journal of Scientific Exploration  20/2, 177-99.

Radin, D.I., & Nelson, R.D. (1989). Evidence for consciousness-related anomalies in random physical systems. Foundations of Physics  19/12, 1,499-1,514.

Radin, D., Michel, L., Galdamez, K., Wendland, P., Rickenbach, R., & Delorme, A. (2012). Consciouness and the douple-slit interference pattern: Six experiments. Physics Essays  25/2.

Rothman, M.A. (1992). The Science Gap: Dispelling the Myths and Understanding the Reality of Science.  Prometheus Books. 

Schmidt, H. (1971). Mental influence on random events. New Scientist and Science Journal (June), 757-58.

Tart, C., Puthoff, H., & Targ, R. (1980). Information transmission in remote viewing experiments.  Nature 284, 191.

• 1. Schmidt (1971).
• 2. Jahn (1982).
• 3. Jahn et al (2007).
• 4. Jahn et al (2000).
• 5. Radin & Nelson (1989).
• 6. Nelson et al (1998).
• 7. Nelson et al (1992).
• 8. Dunne et al (1988).
• 9. Nelson et al (1994).
• 10. Nelson (2006).
• 11. Nelson et al (2000).
• 12. Dobyns et al (2004).
• 13. Dunne & Jahn (1992).
• 14. Tart, Puthoff & Targ (1980).
• 15. Jahn, Dunne & Jahn (1980).
• 16. Dunne, Dobyns & Intner (1989).
• 17. Dunne & Jahn (2003).
• 18. Jahn & Dunne (1986).
• 19. Jahn & Dunne (1988) .
• 20. Jahn & Dunne (2001).
• 21. Jahn & Dunne (2001).
• 22. Alcock (2003).
• 23. Rothman (1992).
• 24. Ibison & Jeffers (1998).
• 25. Radin et al (2012).

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Exploratory study: the random number generator and group meditation

2007, Journal of Scientific Exploration

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Psyleron is a technology company and research organization that explores the connection between the mind and the physical world. Discoveries made at the Princeton Engineering Anomalies Research (PEAR) laboratory have shown that consciousness and intention can influence the behavior of quantum electronic devices known as "Random Event Generators" (REGs) or "Random Number Generators." Psyleron was founded by PEAR scientists and associates for the purpose of providing tools that enable ongoing research and personal exploration of mind-matter effects.

The Mind Lamp is a color-changing ambient device that can respond to human intention and group consciousness. The lamp combines a Psyleron true random event generator with algorithms and visual feedback designed to elicit a response from human consciousness. Whether used as a decorative centerpiece, a meditation tool, or a group game, the lamp is an engaging yet relaxing way to explore mind-matter interaction.

SyncTXT is a new service from Psyleron that allows subscribers to explore the world of synchronicity and meaningful coincidence with a mobile phone. SyncTXT uses Random Event Generators to send customizable SMS (text) messages to the user. Because the user's subconscious can influence REGs at a distance, these SMS messages can arrive at sychronistic (useful or meaningful) times.

The Psyleron REG-1 exploration kit was designed to allow individuals and researchers to conduct their own experiments in direct mind-matter interaction. The package includes a USB-based true random event generator and software, documentation, and data analysis tools. Some versions of the package also include a software development kit for creating custom applications. All packages make it possible to conduct basic PEAR type experiments out of the box.

This video is sampled from The PEAR Proposition DVD set, an eight-hour overview of the research conducted at the PEAR lab that provides a more detailed understanding of the twenty-eight year history of this work.