A Brain Filter Model for Changed Consciousness

In the paper's words

Clinicians, meditation instructors, and psychedelic‑assisted‑therapy researchers confront a persistent gap: measurable neural changes such as thalamocortical gating reduction and default‑mode‑network (DMN) suppression are documented, yet no quantitative framework links these changes to the invariant phenomenology of altered states—timelessness, unity, and presence. We model the brain as an information‑theoretic filter that limits the flow of a pre‑existing informational substrate (L3) into an embodied perspective (L2). Using a Shannon‑capacity channel with bandwidth B and signal‑to‑noise ratio SNR, we derive a baseline capacity of 3.46×10² bits s^-1 and an altered‑state capacity of 1.81×10² bits s^-1 under empirically motivated reductions (30 % bandwidth, 50 % SNR).

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

Numbers it reports

1. Using a Shannon‑capacity channel with bandwidth $B$ and signal‑to‑noise ratio SNR, we derive a baseline capacity of $3.46\times10^{2}\,\text{bits s}^{-1}$ and an altered‑state capacity of $1.81\times10^{2}\,\text{bits s}^{-1}$ under empirically motivated reductions (30 % bandwidth, 50 % SNR).

   from Abstract

2. Altered‑state parameters are assumed based on reported reductions: Bandwidth reduction of 30 % → $B_{1}=0.7\,B_{0}=70\,$Hz.

   from 3.3 Parameter Selection

3. The relative reduction is $$ \frac{C_{1}}{C_{0}}=\frac{180.95}{345.94}\approx0.523. $$ Hence the filter capacity is reduced to 52.3 % of its baseline value.

   from 4.3 Capacity Ratio

4. The 48 % reduction reported in Section 4.3 corresponds to Scenario B, which matches empirical reports of roughly 30 % bandwidth loss and 50 % SNR loss during deep meditation.

   from 2. Sensitivity of Capacity to Bandwidth and SNR Reductions

5. For example, a meditation protocol could be calibrated to target at least a 30 % bandwidth reduction and a 50 % SNR reduction, which, under the model, would produce the derived 52.3 % capacity ratio.

   from 8.1 Potential Users and Decision Impact

6. The derived capacity reduction of roughly 48 % under plausible altered‑state parameters aligns with the emergence of timelessness, unity, and presence as robust phenomenological invariants.

   from 9. Conclusion

How it is done

1. Using a Shannon‑capacity channel with bandwidth $B$ and signal‑to‑noise ratio SNR, we derive a baseline capacity of $3.46\times10^{2}\,\text{bits s}^{-1}$ and an altered‑state capacity of $1.81\times10^{2}\,\text{bits s}^{-1}$ under empirically motivated reductions (30 % bandwidth, 50 % SNR).

   from Abstract

2. C4 : Specification of a falsifiable experimental protocol linking thalamic gating measurements to predicted capacity reductions (Section 7).

   from 1. Introduction

3. We treat the brain as a communication channel that transmits information from a non‑spatiotemporal substrate L3 (intrinsic information rate $I_{0}$, bits s$^{-1}$) to an embodied perspective L2 .

   from 3.1 Conceptual Model

4. The Bekenstein bound states that the maximum amount of information $I_{\max }$ that can be stored in a physical system of radius $R$ and total energy $E$ is $$ I_{\max }=\frac{2\pi R E}{\hbar c\,\ln 2}\quad\text{bits}, $$ where $\hbar$ is the reduced Planck constant and $c$ the speed of light $[9]$.

   from 1. Relating Channel Capacity to the Bekenstein Information Bound

What would falsify it

1. A direct falsification would occur if simultaneous MEG‑derived capacity estimates $C_{1}$ show no correlation (Pearson $|r|\lt 0.1$) with reported timelessness, unity, or presence across a cohort of meditators.

   from 6.3 Falsification Criterion

2. Such a null correlation would invalidate the model’s core claim that capacity reduction predicts invariant phenomenology.

   from 6.3 Falsification Criterion

3. The model yields a concrete, falsifiable prediction linking neural gating measures to capacity reductions, providing a quantitative bridge between neurophysiology and subjective experience.

   from Abstract

Where the claim stops

1. L1 : Bandwidth $B$ is approximated as a scalar aggregate of frequency bands; real neural spectra are non‑uniform.

   from 6.1 Limitations

2. L3 : The linear filter $F$ assumes stationarity; ASCs may involve non‑linear dynamics.

   from 6.1 Limitations

3. L4 : Mapping eigenvectors to phenomenological features assumes a one‑to‑one correspondence that may be oversimplified.

   from 6.1 Limitations

Where to read

1. Abstract152 words · 1 min
2. 1. Introduction200 words · 1 min
3. 2. Background and Related Work226 words · 1 min
4. 3. Methods277 words · 1 min
5. 4. Analysis143 words · 1 min
6. 5. Results57 words · 1 min
7. Appendix A. Extended Derivations and Worked Examples1,339 words · 6 min
8. 6. Discussion183 words · 1 min
9. 8. Practical Implications and Engineering Pathways285 words · 1 min
10. 9. Conclusion79 words · 1 min
11. Provenance and AI Involvement225 words · 1 min
12. Appendix A. Divergence report13 words · 1 min