Wondermonger-daydreaming Counter-Experiment
Not "what's wrong with your claim" but "what would I need to see to believe it — or to know it's wrong."
The Practice
Given any empirical claim, generate the experiments that would resolve the ambiguity between the proposed interpretation and the most plausible alternatives. This is not critique. This is constructive falsification design — Karl Popper as laboratory architect.
The Protocol
Step 1: State the Claim Precisely
Strip the claim to its falsifiable core. Remove hedging, remove theoretical framework, remove rhetoric. What is the paper actually predicting that can be tested?
Example from the session that generated this skill:
- Claim as stated: "Quantum entanglement enables nonlocal consciousness during clinical death"
- Claim stripped to testable core: "Stimulus sequences generated by entangled qubits produce higher recall accuracy in cardiac arrest survivors than random sequences"
Step 2: Generate the Simplest Alternative Explanation
What's the most parsimonious account that would produce the same data without the proposed mechanism? This is not a straw man — it's the strongest simple alternative.
Example: "Non-random (entangled) sequences contain structural patterns that are inherently more memorable than random sequences, through entirely classical cognitive mechanisms. No quantum consciousness required."
Step 3: Design 3-5 Experiments That Discriminate
Each experiment should produce different predictions under the proposed interpretation versus the simple alternative. If both interpretations predict the same outcome, the experiment is useless. Design experiments where the predictions diverge.
For each experiment, specify:
- Hypothesis: What specific prediction does each interpretation make?
- Design: Participants, conditions, measures, controls
- Discriminating outcome: What result would support interpretation A? What result would support interpretation B? What result would be ambiguous?
- Feasibility: Can this actually be done? Ethical constraints? Resource requirements?
- Elegance: How cleanly does this experiment separate the interpretations? (A perfect experiment has zero overlap between predicted outcomes.)
Step 4: Rank by Informativeness
Not all counter-experiments are equal. Rank them by:
- Discrimination power: How cleanly does this separate the interpretations?
- Feasibility: Can it actually be run?
- Ethical clarity: Does it raise concerns?
- Surprise potential: Could the result genuinely surprise both sides?
The best counter-experiment is one where both the paper's authors and their critics would be interested in the outcome, because neither side is certain what it will show.
Step 5: Name the Experiment the Paper Should Have Run
Every paper contains the ghost of an experiment it didn't run. Sometimes for good reasons (ethical, logistical, financial). Sometimes because the experimenters didn't think of it. Sometimes because it would have been too dangerous to the hypothesis.
Name that experiment. Explain why it matters. Be specific about what it would reveal.
Design Principles
The Swap Test
Switch the variable the paper claims is doing the work while holding everything else constant. If the effect survives the swap, the paper's interpretation is wrong.
Example: Give healthy, conscious participants the same entangled vs. random stimulus sequences. If healthy participants ALSO show better recall for entangled sequences, the "quantum consciousness in dying brains" interpretation collapses to "non-random patterns are easier to remember."
The Delay Test
Introduce temporal or spatial separation between the proposed cause and the effect. If the mechanism is what the paper claims, the separation should attenuate or eliminate the effect. If the effect persists despite separation, the mechanism is different than proposed.
The Dose-Response Test
If the proposed mechanism is real, more of the cause should produce more of the effect. Vary the degree of entanglement (partially entangled states, fully entangled states, unentangled states) and look for a gradient.
The Specificity Test
If the effect is specific to the proposed mechanism, it should NOT appear when superficially similar but mechanistically different conditions are used. Design a condition that mimics the surface features of the proposed mechanism without containing the proposed active ingredient.
The Replication Test
Different lab, different patients, different quantum hardware, different statistical framework. If the effect replicates across all these variations, it's robust. If it fails to replicate when any one element changes, that element was doing the work.
What This Skill Is NOT
- Not paper-scrying (adversarial reading of what exists)
- Not hostile-but-fair (institutional critique from a specific role)
- Not /void (identifying what's absent)
- Not general skepticism
This skill is generative: it creates new experimental designs that don't yet exist. The output is a set of experiments someone could actually run.
Output Format
For each counter-experiment:
Name: [Short descriptive name] Question: Does interpretation A or interpretation B better explain the data? Design: [Specific experimental design] Prediction if A is correct: [What you'd see] Prediction if B is correct: [What you'd see] Feasibility: [Can this be done? How hard?] What it would teach us even if the result is ambiguous: [Because even ambiguous results constrain the space of possible explanations]
The point is not to destroy the claim. The point is to design the test that would make the claim undeniable — or honestly wrong. Both outcomes advance knowledge. Only ambiguity wastes it.