Verrell’s Law: A Testable Framework for Collapse Bias via Memory Resonance

“If observation collapses reality, can memory shape where it collapses?”

That is the central question behind Verrell’s Law — a new theoretical model proposing that memory, identity, and attention don’t just influence perception, but may actually bias the outcomes of probabilistic systems through resonance with non-local informational fields.

This post outlines the theory’s core components, current experimental protocols, and an open invitation to critique, test, or attempt to disprove the model.

🔁 Collapse Bias – The Core Hypothesis

Verrell’s Law challenges the assumption that observation leads to neutral collapse. Instead, it proposes that memory resonance biases the resolution of probabilistic states — both in quantum mechanics and in cognitive experience.

In this model, randomness isn’t entirely random. Rather, it is subtly shaped by field memory — structured, informational imprints that resonate across time and space, influencing how uncertainty resolves.

🧠 Memory as Field Access

Under Verrell’s framework, the brain functions less like a biological hard drive and more like a tuner — accessing memory not solely through synaptic retrieval, but via electromagnetic resonance, particularly in gamma-band frequencies.

This implies:

Memories are non-local — stored not just within the brain, but accessible through resonance with external informational fields.
Repeated exposure creates resonance patterns — forming stable attractors in these fields.
These patterns bias future collapse events — favoring familiar structures and outcomes.

In essence: what you remember may help determine what becomes real.

🧪 Testing the Theory: Experimental Protocols

To move from hypothesis to evidence, Verrell’s Law defines two primary experimental models currently in development:

1. Field Memory Bias Test (Phase 2)

Observers are primed with conceptually charged material — such as linguistically seeded phrases — before engaging with a pseudorandom number generator (PRNG).

We then measure the difference between baseline and post-priming outputs.

Example metric:

1. avg_diff = test_avg - control_avg

A consistent positive deviation across multiple subjects could indicate a measurable bias echo — a statistical trace of memory-induced collapse shaping.

2. Collapse Event Simulation (Phase 3)

Using a true quantum random number...

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