Overview

When May entered remote viewing research in the late 1970s, the field faced a fundamental methodological challenge: how to objectively evaluate subjective descriptions of distant locations. Remote viewing experiments generated lengthy transcripts containing imagery, impressions, and spatial descriptions that resisted straightforward quantification. May recognized that advancing the field required moving beyond anecdotal success stories toward rigorous analytical methods that could withstand scientific scrutiny.

May’s approach combined his background in physics with insights from information theory and statistical analysis. Rather than dismissing qualitative data as unsuitable for scientific analysis, he developed methods to extract quantifiable information from viewer descriptions. This methodological innovation proved crucial during the 1980s, when remote viewing research transitioned from exploratory studies to operational programs requiring measurable performance metrics.

His work on methodology occurred within the broader context of the SRI International remote viewing program, where he collaborated with Harold Puthoff, Russell Targ, and others on government-sponsored anomalous cognition research. May’s analytical contributions helped establish remote viewing as a subject amenable to systematic investigation, even as debates continued about the underlying mechanisms.

Analytical frameworks and fuzzy set technology

One of May’s most significant methodological contributions was the application of fuzzy set theory to remote viewing analysis. Traditional analytical approaches required binary judgments, either a description matched a target or it did not. This created problems when viewer descriptions were partially accurate or contained elements that matched multiple possible targets.

Fuzzy set technology, developed in information theory, allowed for graduated membership values rather than strict binary categories. May and his colleagues applied this framework to remote viewing transcripts, enabling analysts to assign partial credit to descriptions that were directionally correct but imprecise, or that contained accurate elements mixed with inaccurate ones.¹ This approach preserved information that binary scoring would have discarded.

The fuzzy set methodology proved particularly valuable for analyzing complex targets such as geographical locations, where viewers might accurately perceive certain features (a body of water, architectural elements, terrain characteristics) while misidentifying others. Rather than treating such mixed results as failures, fuzzy set analysis quantified the degree of correspondence between description and target. This allowed researchers to detect patterns in viewer accuracy that might otherwise remain hidden in the noise of binary scoring.

May’s application of fuzzy set technology represented a broader principle: that parapsychological data, though inherently noisy and probabilistic, could be subjected to sophisticated mathematical analysis. The approach demonstrated that qualitative phenomena need not remain qualitative in their analysis, appropriate mathematical frameworks could extract quantitative information without distorting the underlying data.

Evaluation protocols and standardization

Beyond analytical mathematics, May recognized that remote viewing research required standardized protocols for conducting and evaluating experiments. Inconsistent procedures across different laboratories or experimenters could introduce confounding variables that obscured genuine effects or created false positives.

May contributed to the development of remote viewing evaluation techniques that specified how transcripts should be prepared, how targets should be selected and described, and how matching between descriptions and targets should be assessed.² These protocols addressed practical questions that had previously been handled ad hoc: Should viewers be given feedback about their accuracy? How should target pools be constructed to avoid obvious patterns? What information should be available to judges conducting blind matching?

Standardization proved essential when remote viewing research moved into operational contexts. Government programs required reproducible procedures that could be implemented consistently across different sites and with different personnel. May’s evaluation techniques provided the framework for such consistency, ensuring that results from different sessions and different viewers could be meaningfully compared.

The emphasis on standardized protocols also addressed a persistent criticism of parapsychological research: that positive results often disappeared when procedures were tightened or when skeptical observers were present. By establishing explicit protocols in advance, May’s approach reduced the opportunity for unconscious bias in data collection or analysis. Researchers could not adjust their procedures after seeing results if the procedures were specified beforehand.

Target search and location techniques

One practical application of May’s analytical methods was the development of target search techniques. In operational remote viewing, the goal was often to locate specific geographical targets (a building, a facility, a geographical feature) based on viewer descriptions. This required methods for systematically comparing viewer descriptions against candidate locations.

May and Puthoff developed target search techniques that used viewer descriptions to narrow the search space.³ Rather than requiring viewers to identify targets with perfect accuracy, the search methodology extracted useful information from partial or ambiguous descriptions. Viewers might describe a location as having water, mountains, and industrial structures without precisely identifying which specific location they were perceiving. The search technique would use these elements to identify candidate locations that matched the description, then use additional viewer information to further narrow the possibilities.

This approach proved valuable in operational contexts where the goal was not to demonstrate perfect accuracy but to extract actionable intelligence from anomalous cognition. Even descriptions that were only partially accurate could contribute to target location if the search methodology was sophisticated enough to extract the useful information while discounting the errors.

The target search methodology also illustrated a key principle of May’s approach: that remote viewing analysis need not depend on perfect accuracy to be scientifically meaningful. Statistical analysis of imperfect data could still reveal genuine patterns. A viewer who was correct 60 percent of the time, rather than the 50 percent expected by chance, demonstrated anomalous cognition even though the majority of descriptions were inaccurate.

Advances in remote viewing analysis

Throughout the 1980s and early 1990s, May continued to develop and refine analytical methods for remote viewing research. His work built on earlier protocols while incorporating new statistical techniques and computational approaches. The cumulative effect was a transformation of remote viewing from a field dominated by case studies and anecdotal reports into one capable of generating quantifiable, reproducible data.

In 1990, May, Utts, and colleagues published a comprehensive review of advances in remote viewing analysis that synthesized methodological developments from the preceding decade.⁴ This work documented how analytical techniques had evolved to handle increasingly complex questions: not just whether remote viewing occurred, but under what conditions it was most reliable, which viewer characteristics predicted success, and how target properties influenced performance.

May’s analytical innovations enabled researchers to move beyond simple yes-or-no questions about the reality of remote viewing toward more nuanced investigations of the phenomenon’s properties. Analysis could now address questions such as whether certain types of targets were perceived more accurately than others, whether viewer experience improved performance, and whether different analytical methods yielded consistent results.

The development of these analytical methods also had implications for how remote viewing research was presented to skeptical audiences. By demonstrating that results were reproducible under standardized conditions and that statistical analysis revealed consistent patterns, May’s work provided a more rigorous foundation for claims about anomalous cognition. Rather than relying on dramatic individual successes, researchers could point to systematic patterns in large datasets analyzed according to explicit protocols.

Integration with theoretical frameworks

May’s methodological contributions did not exist in isolation from theoretical concerns. The analytical methods he developed were designed to test specific hypotheses about how remote viewing might work. Early remote viewing research had proposed various mechanisms, direct perception, electromagnetic sensitivity, unconscious inference from subtle cues, but lacked the analytical tools to distinguish between these possibilities.

May’s analytical approaches enabled researchers to examine whether remote viewing performance varied in ways that would be predicted by different theoretical models. For instance, if remote viewing depended on electromagnetic sensitivity, performance should vary with electromagnetic shielding. If it depended on unconscious inference from subtle sensory cues, performance should degrade when such cues were eliminated. Systematic analysis of performance across different conditions could test these predictions.

The special orientation techniques that May helped develop represented another integration of methodology with theoretical investigation.⁵ These techniques modified the standard remote viewing procedure in ways designed to enhance performance or to test hypotheses about the mechanisms underlying anomalous cognition. By systematically varying the procedure and analyzing how performance changed, researchers could gain insight into what factors facilitated or hindered remote viewing.