Detecting Simulation Glitches via Cross‑Modal Temporal Binding Asynchronies: A Quantitative Framework

In the paper's words

The hypothesis that reality is rendered by a digital substrate predicts observable asynchronies at the interface where visual and auditory streams are produced by independent clocks. Existing multimodal models achieve near‑chance performance on temporal‑glitch detection (we assume an average accuracy of 50.77\%, corresponding to p₀=0.5077, as a baseline). We first compute the statistical power required to detect a 5\% deviation from this baseline, finding that 1 536 independent video trials are needed for 95\% confidence.

The abstract, verbatim, and its opening sentence is the claim. Every line below is quoted from the text.

Numbers it reports

1. Calculation of the minimum sample size required to detect a 5 % deviation from chance with 95 % confidence (Section 4.2).

   from 1. Introduction

2. Sample‑size formulas for detecting a 10 ms mean offset and a 10 % widening of the TBW (Section 4.5).

   from 1. Introduction

3. The psychophysical experiment presents participants with paired audiovisual stimuli (40 Hz visual flicker synchronized with auditory clicks) while varying cognitive load (high vs. low) and scene complexity (simple vs. complex).

   from 3. Methods

4. We assume a baseline accuracy of 50 % for temporal‑glitch detection, representing chance performance for a binary classifier.

   from 4.1 Assumed Baseline Accuracy

5. Assuming the null hypothesis $p_0 = 0.5077$ (baseline proportion), the standard error (SE) under the null is $$ \text{SE}_0 = \sqrt{\frac{p_0(1-p_0)}{N}}. $$ A two‑sided 95 % confidence interval requires a margin of error $z_{0.975}\times\text{SE}_0$, where $z_{0.975}=1.96$.

   from 4.2 Sample‑Size Calculation for Detecting a 5 % Deviation

6. To resolve a $5\%$ effect, we require the margin to be at most $\Delta/2 = 2.5\% = 0.025$: $$ 1.96 \times \text{SE}_0 \le 0.025. $$ Solving for $N$: $$ \begin{aligned} \text{SE}_0 &\le \frac{0.025}{1.96} \approx 0.012755,\\ \frac{p_0(1-p_0)}{N} &\le (0.012755)^2,\\ N &\ge \frac{p_0(1-p_0)}{(0.012755)^2}. \end{aligned} $$ Insert $p_0 = 0.5077$: $$ \begin{aligned} p_0(1-p_0) &= 0.5077 \times 0.4923 \approx 0.2499,\\ (0.012755)^2 &\approx 0.0001627,\\ N &\ge \frac{0.2499}{0.0001627} \approx 1535.6. \end{aligned} $$ Thus at least $N = 1\,536$ independent video trials are required to detect a 5 % deviation with 95 % confidence.

   from 4.2 Sample‑Size Calculation for Detecting a 5 % Deviation

How it is done

1. These results provide concrete, falsifiable criteria for psychophysical experiments that can either confirm simulation‑induced jitter or bound its magnitude.

   from Abstract

2. Specification of a psychophysical protocol that leverages the derived sample sizes to test for simulation‑induced cross‑modal jitter (Section 5).

   from 1. Introduction

3. We adopt a two‑stage approach.

   from 3. Methods

4. A well‑timed experiment with $n \ge 600$ trials per condition, using presentation hardware verified to sub‑millisecond precision, would falsify the render‑clock hypothesis if it finds (a) no mean offset beyond the $z$‑test threshold, (b) no KS rejection of the Gaussian null beyond $D_{\text{crit}} = 1.36/\sqrt{n}$, and (c) no load‑ or complexity‑dependent structure in the variance test.

   from 6.3 Falsifiability Criterion

What would falsify it

1. A well‑timed experiment with $n \ge 600$ trials per condition, using presentation hardware verified to sub‑millisecond precision, would falsify the render‑clock hypothesis if it finds (a) no mean offset beyond the $z$‑test threshold, (b) no KS rejection of the Gaussian null beyond $D_{\text{crit}} = 1.36/\sqrt{n}$, and (c) no load‑ or complexity‑dependent structure in the variance test.

   from 6.3 Falsifiability Criterion

2. The hypothesis that reality is rendered by a digital substrate predicts observable asynchronies at the interface where visual and auditory streams are produced by independent clocks.

   from Abstract

3. These results provide concrete, falsifiable criteria for psychophysical experiments that can either confirm simulation‑induced jitter or bound its magnitude.

   from Abstract

Where the claim stops

1. Uniform‑approximation for KS distance.

   from 6.1 Limitations

2. The KS distance was computed by approximating the quantized‑mixture with a uniform distribution of equal variance; the exact convolution may yield a slightly different $D$, introducing uncertainty of order unity.

   from 6.1 Limitations

3. The assumed $5$–$25$ ms range is an approximation; a broader range would reduce the discriminative power of the inter‑hemispheric test.

   from 6.1 Limitations

Where to read

1. Abstract180 words · 1 min
2. 1. Introduction286 words · 1 min
3. 2. Background and Related Work227 words · 1 min
4. 3. Methods151 words · 1 min
5. 4. Analysis649 words · 3 min
6. 5. Results183 words · 1 min
7. 6. Discussion331 words · 1 min
8. 7. Practical Implications and Engineering Pathways388 words · 2 min
9. 8. Conclusion68 words · 1 min
10. Provenance and AI Involvement225 words · 1 min
11. Appendix A. Divergence report97 words · 1 min
12. Appendix B. Claim attribution35 words · 1 min