Double-Blind Protocols - Why They Matter for AI Remote Viewing Research

Why doesn't mainstream science take most anomalous cognition claims seriously?

The answer isn't bias. It's methodology.

The difference between research that gets published in peer-reviewed journals and claims that get dismissed comes down to one concept: double-blind experimental design.

For AI testing, proper controls are essential. Without them, we can't distinguish genuine effects from artifacts.

What "Double-Blind" Actually Means

In a double-blind experiment, neither the subject nor the evaluator knows critical information that could influence the results.

For AI remote viewing testing, this means:

• The viewer (AI or human) receives no descriptive target information during capture—only a coordinate (or even less, depending on blinding settings)
• Output is locked before reveal: impressions are recorded before any ground truth is shown or entered
• Analysis is post-hoc: scoring runs only after capture is complete to avoid contaminating the session

This matters because without controls, subtle artifacts can masquerade as effects. Training data leakage, prompt artifacts, and pattern matching can all produce apparent "hits" that aren't genuine target correlation.

The same principle applies to AI testing. Without proper controls, we might measure prompt artifacts, training data patterns, or methodology errors—not target correlation.

The Three Levels of Blindness

Single-Blind

The AI doesn't receive target information directly. But prompt structure or coordinate format might encode information.

Problem: Potential information leakage through experimental design.

Double-Blind

Neither the AI NOR the analysis system has access to target information during the session. Target is revealed only after output is locked.

Improvement: Eliminates most methodological artifacts. Standard for rigorous research.

Triple-Blind

The AI, analysis system, AND the researcher reviewing results are all blind to target assignment until analysis is complete.

Gold standard: Used in the most rigorous research designs.

How Early RV Research Got It Wrong (And Right)

The history of remote viewing experiments is also a history of methodological refinement—lessons that apply directly to AI testing.

The SRI Problems

Early experiments at Stanford Research Institute had flaws that skeptics rightfully criticized:

1. Embedded cues in transcripts: Researchers sometimes wrote notes like "yesterday's target" that revealed session order to judges
2. Non-randomized target selection: Targets weren't always randomly assigned, creating potential patterns
3. Feedback contamination: Viewers sometimes received feedback while still in session

Skeptic David Marks documented these issues and failed to replicate results when the cues were removed.

The SAIC Improvements

By the time the STARGATE program moved to Science Applications International Corporation (SAIC) in the 1990s, protocols had tightened significantly:

• Random number generators selected targets
• Sessions were encrypted before judging
• Independent statisticians analyzed results
• Targets came from pre-established pools with no experimenter input

The 1995 AIR review, led by statistician Jessica Utts, found the improved studies still showed significant effects—effect sizes around 0.2.

Why This Matters For AI Testing

"Why do we need laboratory-grade controls for AI experiments?"

Because without controls, we can't trust results. Here's why:

1. Model Artifacts Are Everywhere

Large language models have complex patterns from training data. Without proper blindness, we can't distinguish target correlation from model priors, prompt effects, or subtle information leakage.

2. Confirmation Bias Affects Analysis

Researchers unconsciously find patterns they expect. Double-blind design ensures analysis isn't influenced by knowledge of targets.

3. Reproducibility Requires Controls

For results to mean anything, experiments must be reproducible. That requires documented, controlled protocols.

4. Statistical Validity Depends on Clean Data

A correlation rate above chance is meaningful only if the methodology was sound. Hundreds of sessions with flawed controls are worthless.

How RVLab Implements Blinded Capture

Our platform is designed to keep capture blind and auditable:

Mode matters:

• In AI-as-Viewer sessions, the AI is blind to the target, but the experimenter (you) knows it.
• In AI-as-Tasker sessions, the target can be generated and withheld so no human sees it until reveal.

In both cases, the key safeguard is that capture happens before reveal.

The Research Standard

If you're conducting AI remote viewing experiments anywhere—not just on RVLab—here's the minimum for meaningful results:

Beyond the Double-Blind

Advanced experimental designs implement additional controls:

• Target pools: Pre-selected sets of distinct targets, preventing drift
• Prompt variation: Testing multiple prompt structures to identify artifacts
• Cross-model comparison: Same targets across different AI systems
• Repeated sessions: Same coordinate multiple times to measure consistency
• Control conditions: Null targets to establish baseline output patterns

These aren't necessary for every session—but they matter for generating publishable research data.

The Bottom Line

Double-blind design isn't skepticism. It's intellectual honesty.

The entire history of anomalous cognition research is littered with claims that collapsed under proper controls. The few results that survived rigorous testing are the ones worth investigating.

If we want to know whether AI systems exhibit measurable correlation with hidden targets, we need clean experimental conditions. Not because we're skeptics—but because we want data we can trust.

And trustworthy data requires methodology.

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Run properly controlled experiments. The platform handles the blinded capture workflow. Your results will generate usable records—whether they show correlation or not.