Abstract
Schmidt notes that these attention-focusing facilitation experiments (AFFEs) are part of a larger series of distant intentionality experiments, sometimes known as direct mental interaction in living systems (DMILS). Two other types of DMILS occur in the experimental setup whereby a participant attempts to change the electrodermal activity (EDA) of a subject from a distance (EDA-DMILS). The second type of DMILS is remote staring, whereby the dependent variable is the EDA physiologic arousal of the person being stared at.
Importantly, subsequent to his meta-analysis of the AFFE experiments, Schmidt presents two previously performed meta-analyses on DMILS and remote staring that produced almost the same small, but significant effect size. Based on this, he noted that, whether the dependent variable in an experiment was physiologic or behavioral seems to be of little importance. Nor was the effect size related to the specific task.
Schmidt suggests the possibility that, because it is not the specificity of task (i.e., helping, activating, or staring) that is important, we might be left with only the intentional component toward the remote person. This, he speculates, might be related to a number of areas in which distant intention seems to be at work, including healing and meditation. This is wonderful stuff, indeed.
At the same time that we can acknowledge and appreciate the quality of Schmidt's meticulous analysis, and the creative comparison of his meta-analysis to previous meta-analyses, we might also stop to consider and compare applications of intention beyond those directed at remote persons. He is exactly “on the money” in trying to go beyond his present meta-analysis to seek other comparisons. Perhaps the exercise needs to be extended beyond the person-to-person experimental model to include targets that are not persons.
For example, the Princeton Engineering Anomalies Research (PEAR) laboratory worked on a variety of permutations of the issues that Schmidt raises; only that laboratory also included questions approached from an engineering perspective (the “E” in PEAR). In almost 30 years of work, the principal investigators Robert G. Jahn and Brenda J. Dunne looked into (1) whether human operators could consciously or unconsciously influence the output of random physical systems of various types; (2) whether human operators could consciously extract information from the physical environment in ways that would be considered anomalous; and (3) how to construct useful theoretical models that make sense of the experimental data. 1 These researchers' early work with random-event generators (REGs) looked for statistical shifts in the output based on the prestated intentions of the operators. Later so-called “field-REGs” took their portable machines into a wide variety of locations that were thought to be emotionally “coherent,” such as sporting events or musical concerts. These scientists asked such questions as: “Did the results depend on whether the operator was male or female?” “Do multiple operators add to the effect size?” “If one operator intends to create ‘high’ deviations and another operator intends to create ‘low,’ ones do these deviations cancel each other out?” “Does immediate feedback enhance performance?” “Does practice improve performance?” “Does it matter whether the generated random events are ‘true’ or ‘pseudo’ events?” “Does it matter whether the randomness is generated electronically, mechanically, or through fluid dynamics?” “Does distance matter?” “Time?” You get the idea. Their data and theoretical musings are astonishing by any measure.
The point here is not to have a contest with either research agenda. Rather, it is to suggest that, just as Schmidt rightly compared his meta-analysis to previous ones, works such as Jahn and Dunne's similarly expand the comparison horizon to a potentially even wider net. Could it be that many of us are saying the same things but are each coming from a different disciplinary perspective? And, if so, how can the different studies complement each other?
The PEAR laboratory—which also got small effect sizes (much smaller than those reported by Schmidt), but with their large databases extremely significant results—found the following: Prestated intentions of the human operators correlated well with the deviations in random outputs, regardless of the method of the generation of random numbers. The character of the effects varied from operator to operator, but individually distinctive patterns of effects were evident. Volitional versus instructed assignments of intention, manual versus automatic trial generation, modes of feedback, etc., showed little overall correlation. There was no attenuation of effect size by distance or time. There was a predictable series-position pattern of effects with an immediate decline followed by a rebound. Multiple operators produced significantly stronger effects than did individuals. And this list only began to scratch the surface.
It should be obvious that there is much that overlaps with the DMILS experiments, and much researchers using each mode of experimentation might learn from each other. Could it be that the larger effect sizes seen by Schmidt reflect the fact that there were always multiple human operators in the DMILS experiments? When Jahn and Dunne used multiple operators in the REG experiments, their combined effects exceeded the sum of their individual effect sizes. Could effect sizes be a function of the number of consciousnesses?
At this point in the development of interesting research questions, it is no longer of interest to question whether intention produces measurable changes. If a careful reading of the work of people such as Schmidt does not persuade us; and a careful reading of Jahn and Dunne does not persuade us, there is probably not much that can be done. What we would be left with is the interesting phenomenon of the irrelevance of data, which is more a subject for the sociology of science. But for those of us interested in moving on to next-generation questions, let us try to discover the commonalities in output by different experimental protocols. Let us get to the patterns. That is what real science should be about.