Roger Nelson discovered patterns in GCP data that resemble patterns of evoked potentials in human brains. (2020) In both cases there is a stimulating event that is linked to a characteristic response. A flash of light evokes a response in noisy data from the occipital cortex: a time-locked signal average across many flash repetitions shows a large voltage peak preceded and followed by a smaller peak of opposite sign. In a similar manner, powerfully engaging events on the world stage apparently evoke deviations in GCP data. Signal averaging and smoothing reveal the same pattern as the brain evoked response: a large peak surrounded by smaller peaks of opposite sign.
Several analyses, independent of the originally specified procedures, yield interesting and robust correlation or covariance measures linked to variations in GCP data. There are potentially predictive relationships between GCP data and stock market valuation shifts, and we also can see significant correlation of GCP data to high-usage Google search terms. We find evidence that data from the GCP network varies in its calculated entropy: the data are not fully entropic, and instead show structure (negentropy) in multi-scaled entropy measures. Finally, the network appears to respond to stimulating events (disasters, celebrations) as if they were stimuli to the senses of a global mind (or at least, stimuli modulating the coherent attention of huge numbers of people). Visualized using standard brain wave processing tools, the GCP network response typically shows a characteristic pattern that parallels human brain evoked responses, albeit on a vastly different temporal scale.
Discussion: The desire to explain data like those from the GCP leads to suggestive theories or models. One candidate is a “consciousness field” that can be modeled as an information field sourced in the mass of attentive human beings and focused by world events. Another candidate theory is a kind of observational model in which the experimenter(s) are the source of the psi effect, using precognition of future states of the data to enable opportune specification of test events. The former is a more capacious model, flexible enough to accommodate structure not predicted in the original event analysis. The latter can plausibly explain the original hypothesis test results, but not the other structure that deeper analysis reveals, nor the linkage to external correlates.
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Published on November 17, 2023
