Noetic Adventure

Abstract

Preregistration for Noetic Adventure online experiment Study Overview The Noetic Adventure web and mobile app resembles an adventure game front-end to a “does consciousness collapse the wavefunction experiment” involving a diffraction grating interferometer. Users are asked to view an animated candle flame while intending that it becomes dimmer. The height and brightness of the flame are linked by association to a first-order dot of light in the interferometer. No feedback is provided about the other first-order dot of light as a control Which dot of light is observed in each trial is randomly decided by the web app. For ease of exposition, we will refer to these two dots of light as observed and unobserved sensors to distinguish the source of the light from the “focus” vs. “relax” epochs, where feedback is provided vs. not provided only from the observed sensor. The analysis of interest has two components: First, the slope of illumination at the observed sensor is analyzed separately for the focus periods and the relax periods, and the difference in those two slopes is the statistic of interest. Second, the same comparison is performed for the unobserved sensor. These tests were based on the results of a previously published experiment that also used a diffraction grating interferometer, and which found that a difference in slopes was the most sensitive way to detect the hypothesized collapse effect. That is, there does not seem to be a sudden collapse but rather a slow degradation as intentional focus continues over time. (Radin, D. (2025). Observer influence on quantum interference: Testing the von Neumann-Wigner consciousness-collapse theory. Physics Essays 38 (1)). Data collection in this study started in February 2026. Formal data collection will continue until 1000 sessions are completed, where a session will be considered complete if all 10 focus and relax epochs are contributed, and where at least 9 of 10 “secret words” are correctly identified in the session. This latter criterion was a feature of the test to assess if the user was paying attention during each focus epoch, because the identify of that word – which was randomly selected for each focus epoch -- had to be identified during the following relax epoch. If the user was not paying attention, they were less likely to correctly identify the word (or provide any answer at all), the time it took them to select a word was also recorded to assess if they were paying attention. Staring in the month of April 2026, AI agents will be used to simulate human users. The idea is to see if AI can be considered to be conscious, and as such could they perform similarly to humans on this task. The same slope analyses will be performed on AI agent results, leading to the following predictions: 1) Humans will result in a significant one-tailed differential comparison in focus vs. relax slopes from the observed sensor. 2) Humans will result in a nonsignificant one-tailed differential comparison in focus vs. relax slopes from the observed sensor 3) AI agents will result in nonsignificant one-tailed differential comparisons in focus vs. relax slopes from both the observed sensor and the unobserved sensors. Results so far Before proceeding to the AI phase of the experiment, it was first necessary to establish that a human effect was detectable within this experimental design. Three complementary analyses were used. Cumulative z-Score The per-epoch slope difference (Focus minus Relax) was accumulated across sessions in randomized order (200 random orderings). The cumulative one-tailed z-score crossed the p = 0.05 threshold (z = -1.645) at approximately 150-200 sessions and continued to build steadily, reaching z ≈ -3.2 by the full sample of 646 sessions recorded to date (end of March 2026). The 90% envelope of random orderings confirmed that this trajectory was consistent regardless of session order, ruling out dependence on a small subset of outlier sessions. Ensemble slope permutation tests (time-shuffled split-half method) Using an ensemble time-shuffled split half test, both halves show a significant negative Intention slope (p = 0.0002 and p = 0.018), while Relax slopes remained non-significant. This was the expected pattern for a genuine Intention-specific effect. Bootstrap Split Replicability 500 bootstrap iterations drew two independent samples of 307 sessions each (with replacement): - Each sample reached p < 0.05: 67.5% of iterations - Both samples significant: 44.2% - At least one significant: 90.8% - Both samples show negative effect (sign-consistent): 96.6% - Correlation between bootstrap z-scores: r = -0.04 (confirming independence) The 96.6% sign consistency demonstrates that the effect direction was robust. Nearly each random subsample showed a negative Intention slope, even when significance was not always reached at half power. Conclusion At 646 of 1000 planned sessions, the human effect was statistically significant (z = -3.2, p = 0.0004 one-tailed), accumulating steadily across sessions, and replicated across independent data splits. Intention slopes on the ensemble time-shuffle split half test resulted in (p = 0.0002 and p = 0.018), with 96.6% sign consistency across bootstrap resamples. These outcomes supported proceeding to finishing the human data collection and to start the AI phase of the experiment. Effect Size Basis Effect sizes derived from the human dataset, based on N = 646 qualifying sessions as of end of March 2026, produced a Cohen's d = 0.035. For detecting a result of the same magnitude, using a one-tailed test at α = 0.05, we determined that 500 sessions were required for 80% power. We will thus run 500 AI agent sessions. Primary Analysis Script All analyses will use a Matlab script “twelve_regression.m” with identical parameters: lagSeconds = 5; % To account for attention switching delay minCorrect = 9; % High engagement with 9 out of 10 “secret words” answered correctly totalRequired = 10; % All focus epochs required per session nPerm = 5000; % Permutation iterations Regression-Residual Method For each session: 1. Regress observed sensor on unobserved sensor: `obs = b0 + b1*unobs + residual` 2. The residual isolates observation-specific signal with shared laser variance removed 3. Extract Intention and Relax half-epochs (after 5-second cognitive delay) 4. Compute linear slope of each half-epoch Statistical Tests There are three preregistered tests, all one-tailed at α = 0.05: 1. Residual Intention slope ≠ 0 (permutation test, 5,000 iterations): This shuffles the time order of ensemble mean curve to test if the Intention slope is significantly negative. 2. Residual Relax slope ≠ 0 (permutation test, 5,000 iterations): Same test on Relax epochs, expected null in both human and AI data. 3. Int-Rel slope difference (permutation test, 5,000 iterations): This randomly swaps Intention/Relax labels within blocks to test whether the slope difference is significantly negative. Predictions If (1) human observed sensor test is significant, (2) human unobserved sensor test is not, and (3) AI observed and observed sensor tests are not significant, that would support the idea that consciousness as enjoyed by humans is involved in collapse of the wavefunction, and that AI agents (as of April 2026) are not conscious. If 1 is not significant that would not support the consciousness collapse hypothesis (as far as this experimental test was concerned), or if 2 or 3 were significant, that would suggest that the underlying analytical methods were biased in some way. AI Data Preprocessing Identical to human data pipeline: - Raw sensor data: 2 Hz sampling - Segments: 1,200 samples (10 minutes), first 50 samples skipped for stabilization - Raw data normalization: zscore(detrend(data)) - Sessions with fewer than 9/10 correct “secret word” epoch responses Data Collection Stopping Rule Data collection for human sessions will stop at 1000 completed qualifying sessions,. Data collection for AI agent sessions will stop at 500 completed qualifying sessions.