Abstract
This preregistered study analyzed more than 25 million trials from a web-based forced-choice remote viewing task to examine patterns of clairvoyant performance across all participants and among a subset of top performers. At the aggregate level, runs ending at the four planned lengths (5, 10, 25, or 100 trials) conformed to chance expectations. In contrast, optionally stopped runs displayed systematic fluctuations: short runs (1–3 trials) were above chance before declining, runs of 11–19 fell below chance, and runs beginning at 20 showed recurring above-chance spikes at every fifth run length (e. g., 30, 35, 40, 45, 50), which diminished beyond 80 trials. A Monte Carlo simulation matched to the empirical stopping distribution clarified the extent to which these patterns could be reproduced by optional-stopping behavior alone, with much of the run-length pattern, including the 11–19 trough and round-number variability, falling within the simulated null envelope. Exploratory analyses of top performers, defined post-hoc as the 1,235 users (2.64%) who exceeded chance uncorrected, after no users survived the preregistered FDR-corrected criterion, examined belief in psi, prior precognitive experience, meditation, total trials, and optional stopping as predictors. Optional stopping was the predictor most consistently associated with hits, both on first trials and across all trials, where it also interacted with belief, prior precognitive experience, and meditation jointly with cumulative task experience. Effect sizes were small (Δp and Cohen’s d near zero for most predictors), and findings are interpreted as exploratory. The results suggest that group-level outcomes primarily reflect optional-stopping and related behavioral dynamics, whereas top-performer analyses surface more nuanced, but small, context-dependent associations between belief, experience, and behavior. These findings highlight the methodological challenges of large-scale, open online testing and the value of preregistered, participant-level approaches, combined with benchmark simulations, for distinguishing behavioral artifacts from potential psi signals.
