Roger D. Nelson

Evidence for Consciousness-Related Anomalies in Random Physical Systems

Speculations about the role of consciousness in physical systems are frequently observed in the literature concerned with the interpretation of quantum mechanics. While only three experimental investigations can be found on this topic in physics journals, more than 800 relevant experiments have been reported in the literature of parapsychology. A well-defined body of empirical evidence from this domain was reviewed using meta-analytic techniques to assess methodological quality and overall effect size. Results showed effects conforming to chance expectation in control conditions and unequivocal non-chance effects in experimental conditions. This quantitative literature review agrees with the findings of two earlier reviews, suggesting the existence of some form of consciousness-related anomaly in random physical systems.

CORRELATIONS OF CONTINUOUS RANDOM DATA WITH MAJOR WORLD EVENTS

The interaction of consciousness and physical systems is most often discussed in theoretical terms, usually with reference to the epistemo-logical and ontological challenges of quantum theory. Less well known is a growing literature reporting experiments that examine the mind-matter relationship empirically. Here we describe data from a global network of physical random number generators that shows unexpected structure apparently associated with major world events. Arbitrary samples from the continuous, four-year data archive meet rigorous criteria for randomness, but pre-specified samples corresponding to events of broad regional or global importance show significant departures of distribution parameters from expectation. These deviations also correlate with a quantitative index of daily news intensity. Focused analyses of data recorded on September 11, 2001, show departures from random expectation in several statistics. Contextual analyses indicate that these cannot be attributed to identifiable physical interactions and may be attributable to some unidentified interaction associated with human consciousness.

Anomalous Anticipatory Responses in Networked Random Data

We examine an 8‐year archive of synchronized, parallel time series of random data from a world spanning network of physical random event generators (REGs). The archive is a publicly accessible matrix of normally distributed 200‐bit sums recorded at 1 Hz which extends from August 1998 to the present. The primary question is whether these data show non‐random structure associated with major events such as natural or man‐made disasters, terrible accidents, or grand celebrations. Secondarily, we examine the time course of apparently correlated responses. Statistical analyses of the data reveal consistent evidence that events which strongly affect people engender small but significant effects. These include suggestions of anticipatory responses in some cases, leading to a series of specialized analyses to assess possible non‐random structure preceding precisely timed events. A focused examination of data collected around the time of earthquakes with Richter magnitude 6 and greater reveals non‐random structure ...

Weighting the Parameters, a Response to Bancel׳s "Searching for Global Consciousness: A Seventeen Year Exploration".

&NA; This brief report is a response to the article by Peter Bancel entitled “Searching for Global Consciousness: A Seventeen Year Exploration” in which he compares a goal orientation (GO) model with a field‐like model he refers to as global consciousness (GC). He first attempts to exclude the latter, and then presents selected tests that compare the models. While the article appears to provide support for Bancels conclusion that GC cannot explain the data and must be supplanted by GO, there are good reasons to believe this conclusion is premature at best. I address the vulnerable assumptions underlying Bancels rejection of GC, and then provide multiple examples of parametric structure in the data, which cannot be attributed to GO, but are amenable to explanation by field‐like models.