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knowing the physiological limitations of the human systems, we further concluded that the observed
effects could not have arisen due to a human-mediated force (i .e.,AP).
Figure 7.
Observed and Theoretical Correlation Distributions.
Mathematical Model of the Noise Diode
Because of the unique construction parameters of Texas Instrument’s MD-20 noise diode, May et al.
were able to construct a quantum mechanical model of the detailed workings of the device. This model
contained all known properties of the material and it’s construction parameters. For example, the band
gap energy in Si, the effective mass of an electron or hole in the semiconductor, and the impurity con-
centration were among the parameters for the model.
The model was successful at calculating the
diode’s known and measured behavior as a function of temperature. May et al. were able to simulate
their RNG experiment down to the quantum mechanical details of the noise source. They hoped that
by adjusting the model’s parameters so that the computed output agreed with the experimental one,
that they could gain insight as to where the causal influence “entered” the device.
May et al. were not able to find a set of model parameters that mimicked their RNG data. For example,
changing the band gap energy for short periods of time; increasing or reducing the electron’s effect
mass; or redistributing or changing the impurity content produced no unexpected changes in the device
output. The only device behavior that could be effected was its known function of temperature.
Because of the construction details of the physical RNG, this result could have been anticipated. The
changes that could be simulated in the model were all in the microsecond domain because of the details
of the device.
Both with the RNG and
in
its model, the diode’s multi-MHz output was filtered by a
100-KHz wide bandwidth filter. Thus, any microsecond changes would not pass through the filter.
In
short, because of this filtering, the RNG was particularly insensitive to potential changes of the physical
parameters of the diode.
Yet solid statistical evidence for an anomaly was seen by May et al.
Since the diode device was shown
mathematically and empirically to be insensitive to environmental and physical changes, these results
must have been as a result of/I C rather than any causal /IF. In fact, this observation coupled with the bit
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timing argument, which we have described above, lead May et al. to question causality in RNG studies
in general.
Summary of Circumstantial Evidence Against AP
We have identified six circumstantial arguments that, when taken together, provide increasingly diffi-
cult requirements that must be met by a putative AP force.
In summary, the RNG database demon-
strates that:
(1) Data are independent of internal complexity of the hardware RNG device.
(2) Data are independent of the physical mechanism producing the randomness (i.e., weak nuclear or
electromagnetic).
(3)
Effects with pseudorandom generators are
statistically equivalent to those observed with true
hardware generators.
(4) Reasonable AP models of mechanism do not fit the data.
(5)
In one study, bits which are ±
l ms from a “perturbed” target bit are themselves unperturbed.
(6) A detailed model of a diode noise source, which includes all known physics of the device, could not
simulate the observed data streams.
In addition, AC, which is a mechanism to describe the data, has been confirmed in non-RNG experi-
ments, We conclude, therefore, anAP force that is consistent with the database must
• Be equally coupled to the electromagnetic and weak nuclear forces.
• Be mentally mediated in times shorter than one millisecond.
• Follow a 1 fy/H law.
For these to be true, an AP force would be at odds with an extensive amount of verified physics and
common behavioral observables. We are not saying, therefore, that/lF cannot exist; rather, we are sug-
gesting that instead of having to force ourselves to invent a whole new science, we should look for ways
in which AP might fit into the present world view.
In addition, as DAT tries to accomplish, we should
invent non-causal and testable alternate mechanisms for the experimental observables.
Conclusions
We have shown that DAT can determine whether a causal or informational explanation is more consis-
tent with a given set of anomalous statistical data.
In applying DAT to the substantial physical RNG
database, we showed that an informational mechanism is strongly favored over a large class of perturba-
tional ones. Given that one possible information mechanism (i.e., precognitive/lC) can, and has been,
independently confirmed in the laboratory, and given the weight of the empirical, yet circumstantial,
arguments taken together against AP, we conclude that the anomalous results from the RNG studies
arise not because of a mentally mediated force, but rather because of a human ability to be a mental
opportunist by making/lC-mediated decisions to capitalize on the locally deviant circumstances.
Our recent results in the study of anomalous cognition (May, Spottiswoode, and James, 1994) suggest
the the quality of AC
is proportional to the change in Shannon entropy.
Following Vassy (1990), we
compute the change in Shannon entropy for an extra-chance, binary sequence of length n. The total
change of entropy is given by:
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AS —
tS q
iS ,
where for an unbiased binary sequence of length n , SQ =
rc, and S is given by:
5 =
- np^logzpi - n(l ~ p x )log2 {1 -£,)•
Let/?; = 0.5 (7 + e) and assume that e, the effect size, is small compared to one (i.e., typical RNG effect
sizes are of the order of 3 x 10
Using the approximation:
ln( 1 +
£ ) = e -
we find that S is given by:
or that the total change of entropy for a biased binary sequence is given by;
AS
S Q - S = n
E 2
2ln2
*
Since our analysis of the historical RNG database shows that the effect size is proportional to 1 / y/n
(i.e., Z-score is independent of n), the total change of Shannon entropy becomes a constant that is inde-
pendent of the sequence length. Thus, if entropy is related to what is being sensed by anomalous cogni-
tion, then this argument suggests that informational processes are responsible for the RNG anomaly.
The one exception to this conclusion is Brand’s study ofAP on red blood cells.
It may be that there is
something unique about living systems that can account for this observation. On the other hand, it is the
only biological /l/
3 study we could find that could be analyzed by DA T, and the perturbation hypothesis is
only weakly favored over the selection one (i.e., p < 0.023). Before we would be willing to declare that
AP is a valid mechanism, more than a single, albeit well designed and executed, study is needed.
Generally, we suggest that future RNG, PRNG, and biological AP studies be designed in such away that
the criteria, as outlined in this paper and in May, Utts, Spottiswoode (1994), are adhered to for a valid
DAT analysis. Our discipline has evolved to the point where we can no longer be satisfied with yet one
more piece of evidence of a statistical anomaly. We must identify the sources of variance as suggested
by May, Spottiswoode, and James (1994); limit them as much as possible; and apply models, such as
DAT, which can begin
to shed light on the physical, physiological, and psychological mechanisms of
anomalous mental phenomena.
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