Count Population Profiles in Engineering Anomalies Experiments

Abstract 

Four technically and conceptually distinct experiments — a random binary generator driven by a microelectronic noise diode; a deterministic pseudorandom generator; a large-scale random mechanical cascade; and a digitized remote perception protocol — display strikingly similar patterns of count deviations from their corresponding chance distributions. Specifically, each conforms to a statistical linear regression of the form Δn/n = δ(x − μ), where Δn/n is the deviation from change expectation of the population frequency of the score value x divided by its chance frequency, μ is the mean of the chance distribution, and δ is the slope of the regression line, constant for a given data subset, but parametrically dependent on the experimental device, the particular operator or data concatenation, and the prevailing secondary conditions. In each case, the result is tantamount to a simple marginal transposition of the appropriate chance Gaussian distribution to a new mean value μ′ = μ + Nε, where N is sample size, or equivalently to a change in the elemental probability of the basic binary process to p′ = p + ε, where p is the chance value and ε = δ/4. Proposition of a common psychophysical mechanism by which the consciousness of the operator may achieve these elemental probability shifts is thwarted by the complexity and disparity of the several technical and logical tasks that would be involved. More parsimonious, albeit more radical, explication may be posed via a holistic information-theoretic approach, wherein the consciousness adds some increment of information, in the technical sense, into the particular experimental system, which then deploys it in the most efficient fashion to achieve the experimental goal, i.e., the volition-correlated mean shift. The relationship of this technical information transfer to the subjective teleological processes of the consciousness remains to be understood.

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