Micro-stabilisation of Sensor S2 during Conscious Observation:
A Monte Carlo Analysis in the Quantum
Contact Double-Slit Setup
Microestabilización del sensor S2 durante la observación consciente:
Un análisis Monte Carlo en el montaje de doble rendija Quantum Contact
Author / Autor: Juan Sebastián Baena Cock
Affiliation / Afiliación: QuantumContact 👉 JSBC Labs – Independent Researcher (Ojén, Málaga, Spain)
1.Abstract
This exploratory report analyses the behaviour of sensor S2 in a homemade double-slit optical setup within the Quantum Contact framework. Using data from a single session of Hypothesis 1 (conscious observation vs. rest), we compared the coefficient of variation (CV) of S2 between ON phases (s4 = 1, focused attention) and OFF phases (s4 = 0, rest). All ON samples (n = 223) were contrasted with an equal-sized random subset of OFF samples (n = 223). We obtained CV_ON ≈ 0.0062 and CV_OFF ≈ 0.0506, yielding ΔCV_global(S2) = CV_ON – CV_OFF ≈ -0.0444. A Monte Carlo permutation test with 5000 iterations produced both two-sided and one-sided p-values close to 0.0000, indicating that such a strong reduction of S2 variability during ON phases is highly unlikely to arise under the null hypothesis of no association between S4 and S2. While statistically striking, these results are presented as exploratory evidence that must be replicated and challenged under stricter controls before drawing firm conclusions about any putative observer–system interaction.
2.Resumen
Este informe exploratorio analiza el comportamiento del sensor S2 en un montaje óptico casero de doble rendija dentro del marco de Quantum Contact. Utilizando datos de una única sesión de la Hipótesis 1 (observación consciente frente a reposo), comparamos el coeficiente de variación (CV) de S2 entre las fases ON (s4 = 1, atención focalizada) y las fases OFF (s4 = 0, reposo). Todas las muestras ON (n = 223) se compararon con un subconjunto aleatorio de igual tamaño de muestras OFF (n = 223). Obtuvimos CV_ON ≈ 0,0062 y CV_OFF ≈ 0,0506, lo que da lugar a un ΔCV_global(S2) = CV_ON – CV_OFF ≈ -0,0444. Un test de permutación tipo Monte Carlo con 5000 iteraciones arrojó p-valores bilateral y unidireccional cercanos a 0,0000, lo que indica que una reducción tan marcada de la variabilidad de S2 durante las fases ON es muy poco probable bajo la hipótesis nula de ausencia de asociación entre S4 y S2. Aunque estadísticamente llamativos, estos resultados se presentan como evidencia exploratoria que debe replicarse y someterse a controles más estrictos antes de extraer conclusiones firmes sobre una posible interacción observador–sistema.
3.1. Introduction
The Quantum Contact project investigates whether subtle changes in the statistical behaviour of an optical interference pattern can be detected when a human observer intentionally focuses attention on the system. The experimental setup combines a simple double-slit or slit-like optical arrangement, a laser source, and a set of photodiode-like sensors (S1, S2, S3) whose intensity readings are recorded together with a digital marker channel S4. The S4 channel indicates ON phases (s4 = 1), when the observer deliberately concentrates on the system, and OFF phases (s4 = 0), when the observer relaxes or looks away.
In Hypothesis 1, the central question is whether the micro-variability of S2 differs between ON and OFF phases. Rather than focusing on mean intensity, the present analysis targets the coefficient of variation (CV), a dimensionless measure of relative variability. A reduction of CV during ON phases (CV_ON < CV_OFF, i.e., ΔCV = CV_ON – CV_OFF < 0) is interpreted as micro-stabilisation of the sensor signal when the observer is actively engaged.
4.2. Methods
2.1. Data acquisition
Data were recorded in a home-made Quantum Contact session on 30 November 2025 using a Raspberry Pi–based acquisition system. Sensor S2 captures light intensity from the optical path, while S4 encodes the state of the protocol: OFF (s4 = 0) during rest phases and ON (s4 = 1) during focused observation. The resulting CSV file (quantum_contact_data.csv) contains timestamped rows with S1, S2, S3, S4 and additional channels such as temperature and inertial sensors.
2.2. Pre-processing and phase definition
For the present report we use only the S2 and S4 channels. Rows with s4 = 1 are labelled as ON, and rows with s4 = 0 as OFF. All available ON samples were kept (n_ON = 223). Because the OFF phase was longer, we randomly selected an equal number of OFF samples (n_OFF = 223) to match the sample sizes between conditions. This subsampling was controlled by a fixed random number generator seed (RNG seed = 12345) for reproducibility.
2.3. Coefficient of variation
For a given set of S2 values x, the coefficient of variation is defined as CV(x) = sd(x) / mean(x), using the unbiased sample standard deviation. We computed CV_ON for the ON samples and CV_OFF for the matched OFF samples, and then defined ΔCV_global(S2) = CV_ON – CV_OFF as the primary effect size.
2.4. Monte Carlo permutation test
To assess how extreme the observed ΔCV is under the null hypothesis of no association between S4 and S2, we performed a permutation-based Monte Carlo test. In each of 5000 iterations, the ON/OFF labels were randomly shuffled (with balanced counts preserved), CV_ON and CV_OFF were recomputed, and a simulated ΔCV_sim was stored. The two-sided p-value was estimated as the proportion of |ΔCV_sim| greater than or equal to the observed |ΔCV_obs|. The one-sided p-value (expecting ΔCV_obs < 0) was estimated as the proportion of ΔCV_sim ≤ ΔCV_obs.
5.3. Results
3.1. Descriptive behaviour of S2
Figure 1 shows the time series of S2 during the session, with OFF (s4 = 0) and ON (s4 = 1) phases indicated. Visual inspection suggests that S2 remains within a relatively narrow dynamic range throughout the recording, but subtle differences in variability between phases are not obvious by eye.
Figure 2 compares the distribution of S2 values for all ON samples (n = 223) against an equal-sized random subset of OFF samples (n = 223). The ON distribution appears much more concentrated, while the OFF distribution is broader, qualitatively anticipating a smaller coefficient of variation during ON.
3.2. Coefficient of variation and ΔCV
Quantitatively, we obtained CV_ON ≈ 0.0062 and CV_OFF ≈ 0.0506, yielding a global difference ΔCV_global(S2) = CV_ON – CV_OFF ≈ -0.0444. In other words, the relative variability of S2 in the OFF phase was roughly eight times larger than in the ON phase, suggesting marked micro-stabilisation when the observer was focused on the system.
3.3. Monte Carlo significance
Figure 3 displays the Monte Carlo null distribution of ΔCV(S2) under random relabelling of ON/OFF across 5000 permutations. The observed ΔCV lies far in the negative tail of the distribution. The estimated two-sided p-value was approximately 0.0000, and the one-sided p-value (testing ΔCV < 0) was also approximately 0.0000. Under the null model, such a strong reduction of S2 variability during ON phases appears highly unlikely to occur by chance.
6.4. Discussion
The present exploratory analysis suggests that, in this specific Quantum Contact session, sensor S2 exhibited a pronounced reduction in relative variability during ON phases, when the observer intentionally focused attention on the system. From a purely statistical standpoint, the Monte Carlo permutation test indicates that the observed ΔCV is highly unlikely under the null hypothesis of no association between S4 and S2.
However, several caveats must be emphasised. First, the dataset analysed here comes from a single session with a single participant (the experimenter). Second, the optical and electronic setup is homemade and may be susceptible to environmental factors (micro-vibrations, temperature drift, laser fluctuations) that could, in principle, correlate with the ON/OFF protocol. Third, multiple metrics and analysis strategies are being explored within the broader Quantum Contact project, so any isolated significant result must be interpreted cautiously.
For these reasons, the current findings should not be taken as conclusive evidence of an observer–system interaction but rather as a strong candidate effect that merits replication. Future work will systematically repeat Hypothesis 1 sessions, include control conditions (e.g., sham ON phases without real attention), and extend the analyses to other sensors and independent participants. Only under sustained replication and tighter controls would it be appropriate to discuss deeper physical or neurocognitive interpretations.
7.5. Limitations and future work
This report has several limitations. It is based on a single recording session; it does not yet incorporate EEG measures, which are reserved for Hypothesis 2 analyses; and it focuses on a single sensor (S2) and a single metric (CV). Future work will:
(i) replicate the protocol across multiple days and sessions;
(ii) integrate EEG-derived indices of attention and meditation;
(iii) explore whether similar micro-stabilisation patterns appear in S1 and S3; and
(iv) pre-register the primary metrics and analysis pipeline to reduce flexibility and potential biases.
Despite these limitations, the strong Monte Carlo result reported here provides a concrete, well-defined starting point for the next phases of the Quantum Contact project.
8.6. Data availability
The CSV file used in this report (quantum_contact_data.csv), together with the analysis scripts and generated figures, will be made publicly available via a Zenodo repository under a Creative Commons Attribution (CC BY 4.0) license. The DOI will be provided in a future, more comprehensive release of the dataset and associated reports.
9.7. Acknowledgements
The author thanks the broader scientific community for the open-source tools and inspiration that make projects like Quantum Contact possible. Any errors or over-interpretations remain the sole responsibility of the author.
10.8. Introducción (versión en español)
El proyecto Quantum Contact investiga si es posible detectar cambios sutiles en el comportamiento estadístico de un patrón de interferencia óptica cuando un observador humano focaliza de forma intencional su atención sobre el sistema. El montaje combina un arreglo óptico tipo doble rendija, una fuente láser y un conjunto de sensores de luz (S1, S2, S3), cuyas lecturas se registran junto con un canal digital S4 que marca las fases del protocolo: OFF (s4 = 0) durante el reposo y ON (s4 = 1) durante la observación consciente.
En la Hipótesis 1, la pregunta central es si la microvariabilidad de S2 difiere entre las fases ON y OFF. En lugar de centrarnos en la intensidad media, este análisis utiliza el coeficiente de variación (CV) como medida de variabilidad relativa. Una reducción del CV en la fase ON (CV_ON < CV_OFF, es decir, ΔCV < 0) se interpreta como microestabilización de la señal cuando el observador está activamente involucrado.
11.9. Métodos (versión en español)
Los datos se registraron el 30 de noviembre de 2025 mediante un sistema de adquisición basado en Raspberry Pi. El sensor S2 mide la intensidad de luz, mientras que S4 codifica el estado del protocolo. Para este informe solo se utilizaron los canales S2 y S4. Todas las muestras con s4 = 1 se asignaron a la fase ON (n = 223), y se seleccionó aleatoriamente un subconjunto de igual tamaño de muestras OFF (s4 = 0, n = 223) usando una semilla aleatoria fija.
El coeficiente de variación se definió como CV(x) = sd(x) / mean(x). Se calcularon CV_ON y CV_OFF y se definió ΔCV_global(S2) = CV_ON – CV_OFF como medida principal del efecto. Para estimar la significación estadística se empleó un test de permutación tipo Monte Carlo con 5000 iteraciones, barajando las etiquetas ON/OFF y recalculando ΔCV en cada permutación.
12.10. Resultados (versión en español)
Las distribuciones de S2 mostraron una diferencia clara entre fases: la fase ON presentó una distribución mucho más concentrada que la fase OFF. Cuantitativamente, se obtuvo CV_ON ≈ 0,0062 y CV_OFF ≈ 0,0506, de modo que ΔCV_global(S2) ≈ -0,0444. Es decir, la variabilidad relativa en OFF fue aproximadamente ocho veces mayor que en ON.
El test de permutación Monte Carlo generó una distribución nula de ΔCV en la que el valor observado se situó claramente en la cola negativa. Los p-valores bilateral y unidireccional resultaron cercanos a 0,0000, lo que indica que, bajo la hipótesis nula, es muy poco probable obtener una reducción tan grande de la variabilidad de S2 en la fase ON por puro azar.
13.11. Discusión y conclusiones (versión en español)
Este análisis exploratorio sugiere que, en esta sesión concreta de Quantum Contact, el sensor S2 experimentó una marcada microestabilización durante la fase de observación consciente (ON). Desde el punto de vista estadístico, el resultado del test Monte Carlo es muy llamativo. Sin embargo, debe interpretarse con cautela: se trata de un único registro, con un solo participante y un montaje casero susceptible a factores ambientales.
El objetivo de este informe no es presentar una prueba definitiva de interacción observador–sistema, sino identificar un efecto concreto, bien definido y cuantificable que sirva como punto de partida para estudios posteriores. La prioridad ahora es replicar el resultado en múltiples sesiones, introducir condiciones de control y, una vez que exista un patrón estable, discutir con mayor profundidad sus posibles implicaciones físicas y neurocientíficas.
14.Figures / Figuras
Figure 1 / Figura 1
Figure 1. Time series of sensor S2 during Hypothesis 1, showing OFF (s4 = 0) and ON (s4 = 1) phases.
Figura 1. Serie temporal del sensor S2 durante la Hipótesis 1, mostrando las fases OFF (s4 = 0) y ON (s4 = 1).
Figure 2 / Figura 2
Figure 2. Distributions of S2 values during ON (s4 = 1) and OFF (s4 = 0, matched sample) phases.
Figura 2. Distribuciones de los valores de S2 durante las fases ON (s4 = 1) y OFF (s4 = 0, muestra igualada).
Figure 3 / Figura 3
Figure 3. Monte Carlo null distribution of ΔCV(S2) with the observed value marked in the negative tail.
Figura 3. Distribución nula Monte Carlo de ΔCV(S2) con el valor observado marcado en la cola negativa.
