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Time Traveler Fatigue


In classical associative remote viewing experiments, or even more standard remote viewing experiments like Ganzfeld, researchers typically attach one outcome to one trial rather than how we do it at time-machine by attaching 10 trials to one outcome. There are some advantages when considering the consensus of 10 trials to predict one outcome vs. relying on a single remove viewing session - even if that single RV session is a much more in-depth session.


Aside from the power of using consensus, the other major advantage to linking 10 trials to one event prediction, is that we can view all 10 trials as simply one big remote viewing trial, but broken up into 10 smaller parts which can each be analyzed separately.


So really, if you look at it that way, we are doing it the same way as the classic experiment, but we are breaking our single target into 10 smaller parts and tasking our remove viewers to describe each "part" separately. Of course, our "parts" are actually 10 unique and separate images, but other than that, it's pretty much the same protocol.


Because our "single target" is split up into 10 smaller targets, this allows us to analyze performance for the first few trials, vs the last few trials to see if we measure any sort of performance drop-off due to fatigue. Perhaps you have noticed that you don't feel as motivated by your 10th trial compared to your first trial. This is fatigue. But does this fatigue result in a drop in performance?


YES it does! We actually do measure a drop-off from your first trial to your 10th! And this drop off in performance is significant. Check out the plot below. The vertical axis is average percentage correct trials from a low of 48% to a high of 54%. The horizontal axis is the trial number starting with trials 1 to 3, then trials 4 to 10 at the far right. The purple line is the average percentage win rate, with the blue and green lines signifying 2 standard deviations of error (error bars).



You can clearly see that the percentage of correct trials for trials 1, 2 & 3 is around 52.8%, then the percentage drops to a low of 49% for all remaining trials after your third. Considering the error bars, the difference is over 2 standard deviation spread between the lowest error bar and the highest error bar which is significant.


So does this mean that you should just ignore trials 4 to 10? NO of course not! We still consider the scores for ALL 10 trials, so if anything, this study should encourage you to continue to FOCUS on your remote viewing AFTER your first few trials, ALL the way to the 10th. Let's see if we can reduce this fatigue effect!



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