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Theoretical Considerations

At this time, there is no comprehensive theory of anomalous cognition; yet, the Cognitive Sciences Laboratory has developed the first successful steps toward that goal.

Currently we have two, perhaps related, models. The first of these assumes that the phenomena are mediated through an additional sensorial system, and, therefore, will most likely share some of the properties of the other five--a source of energy, a transmission mechanism, and finally a specialized set of neurons that are capable of being receptors.

We have recently published a paper that outlines our progress toward identifying a potential source of anomalous cognition. It appears that the gradient of Shannon entropy is related to the "brightness" of an AC target. The experiment leading to this assertion is also suggestive of a sensorial system in that it was the gradient of, rather than the entropy itself that correlated with the quality of the AC perception. All other senses are known to be more sensitive to changing inputs than they are to static ones.

Our second model is called Decision Augmentation Theory. It holds that decisions (of any kind) may be statistically augmented toward a desired outcome by anomalous cognition. We have applied the model to the data from a class of random number generator experiments in which human operators are asked to initiate the collection of a sequence of binary bits from a suitably designed hardware random number generator. Known as RNG experiments, the competing explanations for the results are that the operator "forces" the data stream to be different than it otherwise would be, or the operator is a statistical opportunist by initiating the experimental run to capture a locally deviant subsequence of binary bits from an otherwise undisturbed truly random sequence. The Decision Augmentation Model overwhelmingly supports the latter.

Our two theoretical models may be related in that the gradient of Shannon entropy of a locally deviant subsequence of binary bits is independent of the length of the sequence--a fact that is observed in the experimental RNG data. Thus it appears that successful human operators in RNG experiments are "sensing" the gradient and statistically augmenting their decision when to initiate the run.

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