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ABSTRACT
from the
Journal of Parapsychology


APPLICATIONS OF DECISION AUGMENTATION THEORY

By Edwin C. May, S. James P. Spottiswoode, Jessica M. Utts, and Christine L. James

Cognitive Sciences Laboratory
330 Cowper Street, Suite 200
Palo Alto CA 94301 (May, Spottiswoode, and James)
and: Division of Statistics
University of California, Davis
Davis CA 95616­8705 (Utts).

Decision augmentation theory (DAT) provides an informational mechanism for a class of anomalous mental phenomena that have hitherto been viewed as being caused by a force­like mechanism. Under specifiable conditions, DAT's predictions for statistical anomalous perturbation databases are different from those of all force­like mechanisms. For large random number generator databases, DAT predicts a zero slope for a least squares fit to the (z2, n) scatter diagram, where n is the number of bits resulting from a single run and z is the resulting z-score. We find a slope of (1.73plus/minus3.19) 10-6 (t = 0.543, df = 126, p = .295) for the historical binary RNG database, which strongly suggests that some informational mechanism is responsible for the anomaly. In a two sequence length analysis of a limited set of RNG data from the Princeton Engineering Anomalies Research laboratory, we find that a force­like explanation misses the observed data by 8.6 sigma; however, the observed data are within 1.1 sigma of the DAT prediction. We also apply DAT to one pseudo­RNG study and find that its predicted slope is not significantly different from the expected value for an informational mechanism. We review and comment on six published articles that discussed DAT's earlier formalism (i.e., intuitive data sorting). We found two studies that support a force­like mechanism. Our analysis of Braud's 1990 hemolysis study confirms his finding in favor of an influence model over a selection one (p = .023), and Braud and Schlitz (1989) demonstrated a force­like interaction in their remote staring experiment (p = .020). We provide six circumstantial arguments against an influence hypothesis. Our anomalous cognition research suggests that the quality of the data is proportional to the total change of Shannon entropy. We demonstrate that the change of Shannon entropy of a binary sequence from chance is independent of sequence length; thus, we suggest that a fundamental argument supports DAT over influence models. In our conclusion, we suggest that, except for one special case, the physical RNG database cannot be explained by any influence model, and that contradicting evidence from two experiments on biological systems should inspire more investigations in a way that would allow valid DAT analyses.

 

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