I will indeed, I got the Geiger one running on windows, this is very commendable and useful, I wonder how hard it would be to run on scotts PCQNG or similar
Congratulations! You have influenced the RNG.
what a fun feeling. hook this up to a leaderboard and you have a popular game on your hands.
Hey Joshua,
Great to hear you are enjoying this. I also have been using it for quite a few months and am hoping to run some simple analyses once I get enough data (and time to analyze it 
). At the moment, it feels more like a training ground for me to build my PK skills. I play it for a few minutes every day. Are you able to get consistent results when you play?
I’ll let you know, I think it is amazing you added the database, many interesting things could come from that. This is the sort of thing we would chat dreaming about and here you are doing the work! Well done.
After trying to learn a bunch of Bayesian statistics etc that Scott talks about, I realized it didn’t really matter if I was able to get the math right if I didn’t have the skill to influence the RNG. So, I moved my efforts to creating simple games (I like to call them games because if you tell someone its a PK skill trainer sometimes they will look at you crazy and you can get away with a whole lot more calling something a game. Plus there is some proof that games were created because of divination) like this one, except my coding skills are not as good as yours so I can’t guarantee any of it was working and my scientific method was not good because I was just kind of doing it for the hell of it) which is why I am grateful I am able to look at your code to kind of compare notes.
I tried for a bit earlier and seemed to come up with mostly hits, but I am very interested in what the historic data will look like. So far results with your software are modestly successful, with a decent hit rate, however the z score and p value may suggest that the results are in the realm of chancel I’ll update after I play consistently for a while.
I will say I have seen with my own eyes a few people who played games like this with consistency, and ended up being able to influence it in a very obvious way.
This is very interesting, is there any reason you want to train your MMI skills? And do you have any other related projects you are working on? I am impressed by how well organized and how much thought you put into this.
I appreciate you saying so. I would say I want to train MMI skills because I am personally intrigued by the experience, and it fits with my experiential approach to “spirituality”, or getting to know what is beyond our 5 senses.
In line with this, I work as the Principal Data Scientist at HeartMath, with a main focus on the Global Consciousness Project 2.0, a large global network of RNG’s in which we are essentially looking for MMI signals based on the evidence of the original Global Consciousness Project from Dr. Roger Nelson at PEAR Labs in Princeton.
ah awesome, i am familiar, nice to meet you and welcome and thanks again i have been training with your software
Woops, well thank YOU it has been very helpful
Thanks for this - I have been playing around with it, however, I am wondering how to tell if I’m successfully influencing the results.
From Scott’s paper, it seems that the hit rate / surprisal value is the number to pay attention to?
I wonder if you could break down these results for someone who isn’t too versed in probability haha.
Last window z-value: 1.4847085110790545
Last window p-value: 0.9311894402287253
Last window surprisal value: 0.10285339683879327
Last window was hit?: No
Running overall window bound tracker: 5
Window group z-value: 1.1535206473472759
Window group p-value: 0.9453125
Window group surprisal value: 0.08113676272540553
I would also be confused by this if I had not attempted to make one myself before, and I have trouble getting results out of the database because I’ve never used it. when i asked @fluidfcs1 had no plans on changing the code. I dont remember seeing a license, would you be willing to liberally license it so that it can be expanded on? Reading your code really helped me, thank you for making it open source.
Hi Joshua,
To be clear, @fluidfcs1 did a good job of implementing one of my advanced processing methods, which involved a lot of effort, but he does not own either the method or the code. One of the conditions for supplying an MMI generator and carefully explaining most steps in its implementation is that the results will be shared on the forum. The only exception is if someone wants to use it, directly or indirectly, to make money. Then we could discuss a license if necessary.
Any specific proposal for a more expanded application, please private mail me. Otherwise, ask specific, detailed questions on this forum. I am super busy with the new tech, but I will find some time to respond.
Hi Jon,
Some details with the user interface were decided by Dan (@fluidfcs1) so I am not totally familiar with them. The meaning of the statistics you provided depends mostly on whether you were intending to produce more ones or more zeros.
If you were intending higher numbers, the results suggest a negative outcome. z-score and p-value are transformable either way, so one is as good as the other. p-vlues should be less than 0.5 to suggest successful results, but should be 0.05 to indicate statistically significant results. Surprisal value is an indicator of success, the higher the better, but if it is less than 1.0, there was no noticeable effect. If these results were for producing more zeros when the tester was set to measure increased ones, the statistics would be all wrong. The probability of the test results, in surprisal value, is 2^-SV: SV of 5 means probability of 2^-5 = 1/32 = 0.03125 (which would be statistically significant).
Ahh that makes a lot of sense. So contributions can be made as long as the results are shared back to the forum. The only thing I would personally change is how the results are reported, if I get around to it then I will be sure to loop back to the forum.
@nplonka @JoshuaLengfelder @Jonf fyi
Thanks @ScottWilber for responding. To add a bit more detail, I’ll first note the following from the GitHub readme:
- It uses Scott Wilber’s Random Walk Bias Amplification (RWBA) method with advanced processing methods for increased effect size in anomalous cognition details. By default we use a bound of 31, which is the distance to the upper or lower bound. Basically every 1 that’s generated is a step to the right, every 0 generated is a step to the left. This runs until either the upper or lower bound of 31 / -31 is hit. We track how many steps it took to hit the bound and which bound was hit.
- Results are then grouped into subtrials, trials, windows, window groups, and supertrials. A subtrial consists of one attempt to hit an upper or lower bound. By default there are 21 subtrials per trial, 5 trials per window, 10 windows per window group. A window lasts roughly 1 second. A supertrial equals one overall program execution, which can be configured either to execute indefinitely or based on a configured amount of seconds.
- For each trial, window, and window group we calculate p and z values to determine probability of randomness. Both one-tailed (targeting either just more 1s or just more 0s) and two-tailed (targeting both more 1s and more 0s) are supported in a given supertrial.
- Calculated probability values are used to control a graphic window that displays a spinning cube, a bar-chart, and if two-tailed is selected then an on/off light. Behavior of objects and displayed values is controlled by window group probabilities. The goal is to influence the output of the random number generator in such a way so as to reach targets in the graphical window.
As Scott noted, z-value, p-value, and surprisal value are all just different ways of viewing the probability of the selected outcome occurring randomly. You can convert between them with algorithms. In the context of probability theory, z-value and p-value are very common.
The thing you’ll see used most in other studies, and perhaps the simplest to understand, is the p-value. A commonly used threshold to denote statistical significance is p < 0.05 (5% probability of being random). p < 0.005 (0.5% chance of being random) is considered very significant. It’s important to note that in the code I’ve shared above I made a choice to invert the p-value (1-p). So, instead of targeting p < 0.05 we instead target p > 0.95. I found this to be more intuitive since I wanted the cube to spin faster when the results were less probable, and it felt more natural to have the faster spinning speed associated with a higher numeric value.
It may seem confusing that we are using subtrials, trials, windows, and window groups. In effect, each of these is an aggregation of the thing before it (a group of subtrials is a trial, a group of trials is a window, a group of windows is a window group). Studies have shown the effects of psi to be small, but statistically significant over large data-sets. It takes a lot of data to ensure that an anomalous effect is really occurring (e.g. if I flip a coin 4 times, and get heads 3 times, the probability of this happening randomly is quite high).
As such, tests require a lot of data, and subsequent statistical analysis on that date. The general purpose and intent of Scott’s approach here is to amplify and identify the impact of any anomalous (aka psychokinetic, or psi) effects. If we get a positive result in a single subtrial, or even trial, in isolation it doesn’t tell us much, but looking at large volumes of subtrial outcomes over time (via windows or window groups) gives us much more data and thus we can evaluate the probability of anomalous effect (psi) occurring with much greater confidence.
Lastly, to reference the two other values returned in the console during program execution…
Hope this is all helpful and understandable. I’ve also gone ahead and added this description under a new heading of Understanding console output in the GitHub readme for both the database and no-database versions of the code. Happy to answer questions.
Thank you! Your explanation for windows and sub trials was very enlightening for me, in fact the entire repo is so awesome for introducing people to the topic. It would make a cool web app
@fluidfcs1 @ScottWilber thank you! That helps clarify the results for me.
One observation I noted when using the game is that the act of “forcing” the cube to spin does little to influence the results. I’ve noticed that having a feeling of anticipation of a positive result somehow “blocks” the influence on the number generator.
About a year ago, I spoke with a researcher from JHU who told me that during the PEAR experiments, the best results came from participants who had no expectations coming into the experiments and that when the participants were attempting to “force” a result, they ended up having no influence on the RNGs.
Is this true in all of your observations as well?
Yes. In magical circles this is called a lust for results. I find my best scores are when I just go with the flow
How one uses intention is very important. I would say one of the more important ways to get better results is to always maintain focus on the desired outcome. To be sure, gritting your teeth and trying to “force” something to happen is more likely to cause a headache than the intended result. The part of mind that causes results is apparently not the outer layer of the conscious mind, which on the contrary, seems to stand in the way if that is the primary way of focusing. However, nothing will also happen if focus is not maintained.
The problem with comparing with PEAR results is that their system was so insensitive, good user feedback was not possible. That makes it difficult for a subject to learn what state of mind gives best results. Besides the necessity to constantly focus, it is also important to realize it’s not my surface layer of consciousness, what might be called the outer self or ego, that causes the desired shift in probability. Then I can release that outer effort to control, and let a deeper level of mind cause the effect. This takes some practice, which will be facilitated with a good training tool providing good user feedback.
To answer your question, my experience in many thousands of trials is, it’s not black or white. Good results can be gotten with a wide range of concentration approaches. The more the focus is from or in the outer mind, the harder and more tiring. However, I have not reached the point in learning where I can get significant results 100% of the time. At least that is true with the previous systems. My goal is to provide systems (hardware and processing) that allow most people with some practice to get good results.
I think there’s also good reason to believe that successful influence is a result of more than just the right type of focus, but which variables have input and to what degree is not well understood. The version of the code that uses a database stores information for many different types of variables, which I deemed to have the best potential based on review of the admittedly limited research that’s available. These include:
Participant age - Entered manually
Participant overall feeling - Subjective evaluation manually entered
Participant energy level - Subjective evaluation manually entered
Participant focus level - Subjective evaluation manually entered
Whether participant ate in the last 90 minutes - Manually entered
Whether participant meditated recently - Manually entered
Current environment temperature - Manually entered
Current environment humidity - Manually entered
Solar DST Index - A measure of the disturbance level in the Earth’s magnetosphere caused by solar wind variations. It is used to quantify the intensity of geomagnetic storms and their effects on Earth’s magnetic field. This is pulled automatically in real-time from from the World Data Center for Geomagnetism.
The Kp geomagnetic index - A measurement of the global geomagnetic activity level. It quantifies the disturbances in the Earth’s magnetic field caused by solar activity, specifically related to coronal mass ejections (CMEs) and solar flares. This is pulled automatically in real-time from the German Research Centre for Geosciences.
Galvanic Skin Response - Galvanic skin response (GSR) is a method of measuring the electrical conductance of the skin. Strong emotion can cause stimulus to your sympathetic nervous system, resulting more sweat being secreted by the sweat glands. Code is written to use the Grove GSR Sensor connected to a Raspberry Pi. (Note: In current code values are captured and printed but not stored to the database, that needs to be done still.)
Lots of EEG Data - Captures real-time brainwave data from the user. Code is written to read data from an Emotiv Epoch EEG using a node-red server and Mosquitto (in theory would work the same with any Emotiv device). It’s all working, and includes not just the ability to measure raw brainwave signals (alpha, beta-high, beta-low, gamma, theta) but, interestingly, also uses Emotiv’s algorithms to assign objective values to level of excitement, focus, interest, engagement, stress, relaxation, longterm excitement, attention, and cognitive stress. Similar to the GSR portion, the current code is capturing data but not yet storing it to the database. This produces an immense amount of data and I never quite decided if I wanted to store it all in raw form or average/aggregate it somehow before storage.
My initial intent, which I never fully realized, was to do a large number of experiments while measuring all of this data. Since it’s being stored to the database it can then be analyzed later, likely using machine learning or even “AI”, to find correlations between positive psi outcomes and values of specific variables (e.g. perhaps we find a certain value for the Solar DST index that is conducive to psi). In theory, after identifying the variables most conducive to psi, these could be fed back into algorithms to further enhance our ability to measure psi influence (e.g. perhaps we only “count” a measurement when variables are most conducive to successful outcomes). If someone ever wanted to carry this effort forward I still think it could be really useful.
Have you considered ephemeris data? There is a fascinating book called Cosmic Patterns by John Nelson. Nelson worked as a radio analyst for RCA, and devised a method to forecast radio blackouts by observing the relationship between planetary alignments and sunspot activity. During high sunspot activity, solar flares and CMEs are more prevalent, which is what causes radio blackouts. Nelson found that sunspot activity on the Sun, and consequently solar flares and CMES, increased when the planets aligned at specific angles. Activity was highest when the planets formed either a Conjunction (0-degree angle), a Square (90-degree), or an Opposition, (180-degree angle) to the Sun.
By knowing when planets would form these angles, he was able to accurately forecast radio blackouts - with a success rate of something like 90% if I remember correctly.
The correlation between planets and sunspot activity is pretty interesting and really makes you think of the validity of the claimed influence that planets supposedly have on human consciousness.
I wonder if ephemeris data would be a useful variable to consider in this program.
I hadn’t considered this. It sounds interesting and potentially worth including, assuming that 1) there is a data-source that I could pull from, and 2) someone expresses interest in actually doing something with this code (otherwise I would conserve my time).