Psionics Feature Planning Research Guide

Version: March 6, 2026

Mission

Build a session-only psionics training program that improves repeatability through:

• CRV structure discipline,
• explicit AOL management,
• blind cueing and immediate feedback,
• and measured state-conditioning experiments (including ear-split frequency audio).

Core rule: no mechanism claims without repeatable blinded performance gains.

1) What the Local Corpus Supports

1.1 CRV/Controlled-RV manuals (core doctrine)

Across the two core manuals in your corpus, the stable training pattern is:

1. stage progression from general to specific,
2. structure enforcement by monitor/process,
3. externalized AOL instead of suppression,
4. explicit break types for contamination/reset,
5. immediate feedback for calibration.

Project implication:

• Any feature that bypasses stage discipline or weakens feedback loops should be considered low-priority.
• Any feature that strengthens blinding, transcript lock, or AOL handling is high-priority.

Local sources:

• [1] /workspace/reference-material/Remote Viewing/Coordinate Remote Viewing Manual.pdf
• [2] /workspace/reference-material/Remote Viewing/Intelligence - Remote Viewing Manual.pdf

1.2 Buchanan / applied training context

The Seventh Sense emphasizes practical training reality: protocol discipline, frontloading risk, and scoring rigor are the difference between anecdotes and usable performance tracking.

Project implication:

• Keep debrief and scoring first-class.
• Track misses with the same rigor as hits.

Local source:

• [3] /workspace/reference-material/The Seventh Sense, Secrets of Remote Viewing - Lyn Buchanan.epub

1.3 Silva material (state-prep framing)

The Silva workbook in your corpus strongly centers:

• centering routines,
• mental rehearsal/imagery,
• repeated exercise journaling,
• and outcome logging.

Project implication:

• A structured pre-session state routine is justified as a training control variable (not as proof of psi mechanism).

Local source:

• [4] /workspace/reference-material/Remote Viewing/The Silva Method - Ultramind'S Remote Viewing & Influencing.pdf

1.4 Men Who Stare at Goats (operational caution)

This source is useful as cultural/operational narrative, especially around hype, chain-of-command distortion, and myth formation. It is not controlled experimental evidence.

Project implication:

• Use it to generate hypotheses and risk checks, not truth claims.

Local source:

• [5] /workspace/reference-material/The-Men-Who-Stare-at-Goats.pdf

1.5 Tesla/frequency corpus (hypothesis generation only)

Your Tesla corpus is useful for resonance-oriented experimentation ideas and disciplined trial framing, but not as direct evidence for remote-viewing causality.

Two relevant source lines from your Tesla corpus:

"I took up the experimental study of mechanical and electrical resonance."

"The Earth is responsive to electrical vibrations of definite pitch."

Project implication:

• Frequency and resonance concepts belong in the intervention layer.
• Keep or drop them based on blinded outcome deltas only.

Local sources:

• [6] /workspace/reference-material/Nikola Tesla/The Autobiography of Nikola Tesla and Other Works by Nikola Tesla.epub
• [7] /workspace/reference-material/Nikola Tesla/(ebook - english) John J. O'Neill - Biography of Nikola Tesla (1944).pdf

2) External Evidence Snapshot (Primary Sources)

2.1 Remote viewing evidence posture

The 1995 AIR/CIA-era evaluation reports above-chance statistical signals in some controlled work, while also concluding operational reliability/utility constraints and methodological concerns.

Planning consequence:

• This app should optimize for long-run calibration and protocol quality, not certainty rhetoric.

Sources:

• [8] CIA/AIR evaluation report: https://www.cia.gov/readingroom/document/cia-rdp96-00791r000200180005-5
• [9] AIR report copy in workspace: /workspace/research-sources/air1995.pdf

2.2 Binaural and auditory-beat evidence

Current evidence is mixed and condition-dependent:

• 2019 meta-analysis found small effects in some outcomes with major heterogeneity [10].
• A broad review of auditory beat stimulation literature emphasized contradictory findings and strong dependence on protocol parameters [11].
• 2017 EEG work reported little support for strong entrainment claims in some settings [12].
• Newer RCTs and perioperative syntheses report anxiety/pain benefits in specific medical contexts [13][14][15].
• Intracranial/physiology work supports that binaural stimuli are neurally processed, but this is not equivalent to proving hemispheric synchronization or enlightenment outcomes [16].

Planning consequence:

• Treat ear-split audio as an assistive intervention to test, not a guaranteed state-control mechanism.

2.3 Breathing and autonomic regulation

Slow breathing and HRV-oriented protocols have stronger mainstream support for autonomic regulation, stress reduction, and attention stability than binaural claims alone [17][18][19].

Planning consequence:

• Breath tools should be default adjuncts in state-conditioning experiments.

2.4 Meditation and “enlightenment” framing

Mainstream evidence supports benefits for stress/anxiety and some well-being measures, but not deterministic “enlightenment by frequency” claims [20].

Planning consequence:

• The product language should avoid deterministic spiritual-state guarantees.

2.5 Listening safety

Noise exposure risk is non-negotiable:

• NIOSH REL remains 85 dBA over 8 hours with 3 dB exchange [21][22].
• WHO/ITU safe-listening standards reinforce safe exposure management in personal audio contexts [23].

Planning consequence:

• Keep low volume defaults, stop controls, and discomfort logging mandatory.

3) Evidence Grading for Feature Decisions

Use this grading model in backlog grooming:

1. Tier A (strong support)
   • Protocol structure, blinding, immediate feedback, logging, breath pacing, calibration analytics.
2. Tier B (plausible but mixed)
   • Binaural presets, frequency sweeps, session timing variants.
3. Tier C (exploratory/high uncertainty)
   • Strong hemispheric-sync claims, “energy” framing, enlightenment scoring.
4. Tier D (exclude unless new evidence)
   • Guaranteed outcome claims, mechanism certainty language, operational certainty without sample thresholds.

4) Feature Planning Matrix

4.1 High-confidence (build/keep)

1. Blind cue + transcript lock enforcement.
2. CRV stage-flow guardrails + AOL logging.
3. Immediate feedback and descriptor scoring.
4. Pre/post state measures (focus/calm/clarity).
5. Breath pacing and reset timers.
6. Rolling calibration dashboards and miss analysis.

4.2 Medium-confidence (test with strict metrics)

1. Ear-split preset library (control/delta/theta/alpha/beta/gamma).
2. Frequency sweep mode.
3. Randomized A/B condition assignment.
4. Masked-condition mode to reduce expectancy.

4.3 Low-confidence (sandbox only)

1. Any “enlightenment score.”
2. Hard claims of hemispheric synchronization as causal driver.
3. Tesla-derived mechanism assertions without blinded delta support.

5) Assistive Tools: Current Build + Expansion

5.1 Implemented in app now

1. Frequency Lab with randomized A/B and session-backed history.
2. Ear-split playback engine with low default volume and stop control.
3. Beat-sweep playback option.
4. Masked-condition mode for expectancy reduction.
5. Pre/post delta scoring + composite outcome metric.
6. A/B pair comparison analytics (mean deltas + effect-size estimate).
7. Breath pacer.
8. CRV break timers (AOL break and reset break).
9. Hypothesis + stop-rule capture for trial preregistration discipline.

5.2 Next tools to add

1. Condition Block Scheduler
   • Pre-generate balanced A/B sequence for 10/20/30 trial blocks.
2. Blind Judge Panel
   • Split viewer transcript scoring from condition identity.
3. Protocol Drift Alerts
   • Flag skipped stages, missing AOL logs, or repeated narrative lock-in.
4. Carryover Control
   • Enforce washout gaps between condition changes.
5. Cross-Method Dashboard
   • Compare CRV/ERV/ARV performance by state-conditioning mode.

6) Experimental Protocols to Use Immediately

6.1 Ear-split A/B protocol (N-of-1)

1. Define one hypothesis before trial block start.
2. Define stop rule (example: evaluate after 20 randomized paired trials).
3. Keep all constants fixed except intervention condition:
   • same task type,
   • same time window,
   • same feedback timing,
   • same scoring rubric.
4. Randomize condition per trial.
5. Keep condition masked during trial when possible.
6. Capture side effects every trial.
7. Evaluate by rolling-window deltas and calibration, not peak anecdotal sessions.

6.2 CRV-integrated state-conditioning protocol

1. Preflight: choose conditioning mode (none/breath/frequency).
2. Run full CRV stage flow without skipping.
3. Use AOL break timers when overlay surges.
4. Lock transcript before reveal.
5. Score immediately.
6. Aggregate by condition and protocol mode weekly.

6.3 Keep/Kill decision threshold

Keep a condition only if it shows:

1. positive net delta over adequate sample size,
2. acceptable side-effect profile,
3. no reduction in blind-integrity metrics,
4. stable or improved confidence calibration.

7) Theory-to-Application Map

1. CRV structure theory -> stage panels, AOL fields, break tools.
2. Learning-loop theory -> immediate reveal + debrief scoring.
3. State-regulation theory -> breath pacer and conditioning logs.
4. Auditory entrainment hypothesis -> ear-split A/B lab with masked mode.
5. Operational caution theory -> anti-hype language, sample thresholds, and failure-mode review.

8) Risks and Mitigations

1. Expectation/placebo bias
   • Mitigation: masked trials, randomized assignment, preregistered stop rules.
2. Protocol drift
   • Mitigation: stage completion checks and weekly transcript audits.
3. Cherry-picking
   • Mitigation: full-session accounting and rolling-window charts.
4. Audio safety risk
   • Mitigation: conservative defaults, explicit stop conditions, discomfort logging.
5. Mechanism overclaiming
   • Mitigation: strict wording policy and evidence-tier labels in UI.

9) Required Definitions to Standardize in UI/Docs

These glossary terms from the manuals should remain canonical in your app copy:

• Analytic Overlay (AOL)
• AOL Drive
• AI (Aesthetic Impact)
• Stage I–VI
• Signal Line
• Monitor
• Break types (AOL break, confusion break, reset)
• Tangibles / Intangibles
• Gestalt / Sub-gestalt

Full expanded glossary is maintained in the main guide page:

• /workspace/rv-trainer/docs/remote-viewing-effectiveness-guide.md

10) Practical Bottom Line

For this project, the best path is:

1. treat psionics/frequency ideas as testable interventions,
2. preserve CRV structure and blind controls,
3. run repeated session blocks,
4. and only retain methods that improve blinded metrics.

This keeps the program ambitious while still evidence-disciplined.

Source Index

Local corpus

[1] /workspace/reference-material/Remote Viewing/Coordinate Remote Viewing Manual.pdf

[2] /workspace/reference-material/Remote Viewing/Intelligence - Remote Viewing Manual.pdf

[3] /workspace/reference-material/The Seventh Sense, Secrets of Remote Viewing - Lyn Buchanan.epub

[4] /workspace/reference-material/Remote Viewing/The Silva Method - Ultramind'S Remote Viewing & Influencing.pdf

[5] /workspace/reference-material/The-Men-Who-Stare-at-Goats.pdf

[6] /workspace/reference-material/Nikola Tesla/The Autobiography of Nikola Tesla and Other Works by Nikola Tesla.epub

[7] /workspace/reference-material/Nikola Tesla/(ebook - english) John J. O'Neill - Biography of Nikola Tesla (1944).pdf

[9] /workspace/research-sources/air1995.pdf

External primary sources

[8] CIA Reading Room, AIR remote viewing evaluation: https://www.cia.gov/readingroom/document/cia-rdp96-00791r000200180005-5

[10] García-Argibay et al. (2019), binaural beats meta-analysis (PubMed): https://pubmed.ncbi.nlm.nih.gov/30073406/

[11] Chaieb et al. (2015), auditory beat stimulation review (PubMed): https://pubmed.ncbi.nlm.nih.gov/26029120/

[12] López-Caballero & Escera (2017), EEG/entrainment limits (PubMed): https://pubmed.ncbi.nlm.nih.gov/29187819/

[13] Isik et al. (2020), placebo-controlled binaural-beat RCT (PubMed): https://pubmed.ncbi.nlm.nih.gov/33107329/

[14] Esen et al. (2024), binaural-beat endoscopy RCT (PubMed): https://pubmed.ncbi.nlm.nih.gov/39088370/

[15] Xiong et al. (2025), perioperative binaural-beat systematic review/meta-analysis (PubMed): https://pubmed.ncbi.nlm.nih.gov/41176178/

[16] Gao et al. (2014), intracranial binaural response study (PubMed): https://pubmed.ncbi.nlm.nih.gov/25345689/

[17] Zaccaro et al. (2018), slow breathing systematic review: https://www.frontiersin.org/articles/10.3389/fnhum.2018.00353/full

[18] Lehrer et al. (2020), HRV biofeedback review/meta-analysis (PubMed): https://pubmed.ncbi.nlm.nih.gov/32385728/

[19] Laborde et al. (2023), HRV biofeedback methods review (PubMed): https://pubmed.ncbi.nlm.nih.gov/36917418/

[20] NCCIH meditation/mindfulness overview: https://www.nccih.nih.gov/health/meditation-and-mindfulness-what-you-need-to-know

[21] CDC/NIOSH noise topic page: https://www.cdc.gov/niosh/topics/noise/

[22] NIOSH recommended exposure limits page: https://www.cdc.gov/niosh/noise/about/noise.html

[23] WHO/ITU safe listening standard note: https://www.who.int/news/item/31-10-2023-who-itu-issues-new-standard-to-prevent-hearing-loss-among-video-gamers-and-esports-players